{"pageNumber":"15","pageRowStart":"350","pageSize":"25","recordCount":1869,"records":[{"id":70215389,"text":"70215389 - 2019 - Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine","interactions":[],"lastModifiedDate":"2024-05-16T13:57:09.889555","indexId":"70215389","displayToPublicDate":"2019-07-19T08:44:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab005\" class=\"abstract author\" lang=\"en\"><div id=\"as005\"><p id=\"sp0005\">Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2019.07.005","usgsCitation":"Xie, Y., Lark, T.J., Brown, J.F., and Gibbs, H., 2019, Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine: ISPRS Journal of Photogrammetry and Remote Sensing, v. 155, p. 136-149, https://doi.org/10.1016/j.isprsjprs.2019.07.005.","productDescription":"14 p.","startPage":"136","endPage":"149","ipdsId":"IP-109078","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467440,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2019.07.005","text":"Publisher Index 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]\n}","volume":"155","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xie, Yanhua 0000-0001-9814-5395","orcid":"https://orcid.org/0000-0001-9814-5395","contributorId":243290,"corporation":false,"usgs":false,"family":"Xie","given":"Yanhua","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":801959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lark, Tyler J.","contributorId":211637,"corporation":false,"usgs":false,"family":"Lark","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":38289,"text":"Center for Sustainability and the Global Environment, University of Wisconsin-Madison, Madison, WI 53726, USA","active":true,"usgs":false}],"preferred":false,"id":801960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":801961,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibbs, Holly","contributorId":243291,"corporation":false,"usgs":false,"family":"Gibbs","given":"Holly","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":801962,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70204191,"text":"70204191 - 2019 - Bundle adjustment using space based triangulation method for improving the Landsat global ground reference","interactions":[],"lastModifiedDate":"2019-07-10T12:01:42","indexId":"70204191","displayToPublicDate":"2019-07-10T11:58:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Bundle adjustment using space based triangulation method for improving the Landsat global ground reference","docAbstract":"There is an ever-increasing interest and need for accurate geo-registration of remotely sensed data products to a common global geometric reference. Although the geo-registration has improved significantly in the last decade, the lack of an accurate global ground reference dataset\nposes serious issues for data providers seeking to make geometrically stackable analysis ready data. The existing Global Land Survey 2000 (GLS2000) dataset derived from Landsat 7 images provide global coverage and can be used as a reference dataset, but its accuracy is much lower than what can be attained using the agile and precise pointing capability of the new spacecrafts. The improved position and pointing knowledge of the new spacecrafts such as Landsat 8 can be used to improve the accuracy of the existing global ground control points using a space based triangulation method. This paper discusses the theoretical basis, formulation, and application of the space based triangulation method at a continental scale to improve the accuracy of the GLS-derived ground control points.Our triangulation method involves adjusting the spacecraft position, velocity, attitude, attitude rate, and ground control point locations, iteratively, by linearizing the non-linear viewing geometry, such that the residual errors in the measured image points are minimized. The complexity of the numerical inversion and processing is dealt with in our approach by processing and eliminating the ground points one at a time. This helps to reduce the size of the normal matrix significantly, thereby making the triangulation of a continent-wide scale block feasible and efficient. One of the unique characteristics of our method is the use of a correlation model linking the attitude corrections between images of the same pass, which promotes consistency in the attitude corrections. We evaluated the performance of our triangulation method over the Australian continent using the Australian Geographic Reference Image (AGRI) dataset as a reference. Both a free adjustment, using only the pointing information of the Landsat 8 spacecraft, and a constrained adjustment, using the AGRI as external control were performed and the results compared. The Australian block’s horizontal accuracy improved from 15.4 m to 3.6 m with the use of AGRI controls, and from 15.4 m to 8.8 m without the use of AGRI controls.","language":"English","publisher":"MDPI","doi":"10.3390/rs11141640","usgsCitation":"Storey, J.C., Rengarajan, R., and Choate, M., 2019, Bundle adjustment using space based triangulation method for improving the Landsat global ground reference: Remote Sensing, v. 11, no. 14, 1640; 25 p., https://doi.org/10.3390/rs11141640.","productDescription":"1640; 25 p.","ipdsId":"IP-108480","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11141640","text":"Publisher Index Page"},{"id":365464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"14","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-10","publicationStatus":"PW","contributors":{"editors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765937,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, R. 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":56036,"corporation":false,"usgs":true,"family":"Rengarajan","given":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":765940,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70204229,"text":"70204229 - 2019 - Long-term (1986–2015) crop water use characterization over the Upper Rio Grande Basin of United States and Mexico using Landsat-based evapotranspiration","interactions":[],"lastModifiedDate":"2019-07-15T10:50:05","indexId":"70204229","displayToPublicDate":"2019-07-04T10:36:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Long-term (1986–2015) crop water use characterization over the Upper Rio Grande Basin of United States and Mexico using Landsat-based evapotranspiration","docAbstract":"The evaluation of historical water use in the Upper Rio Grande Basin (URGB), United States and Mexico, using Landsat-derived actual evapotranspiration (ETa) from 1986 to 2015 is presented here as the first study of its kind to apply satellite observations to quantify long-term, basin-wide crop consumptive use in a large basin. The rich archive of Landsat imagery combined with the Operational Simplified Surface Energy Balance (SSEBop) model was used to estimate and map ETa across the basin and over irrigated fields for historical characterization of water-use dynamics. Monthly ETa estimates were evaluated using six eddy-covariance (EC) flux towers showing strong correspondence (r2 > 0.80) with reasonable error rates (root mean square error between 6 and 19 mm/month). Detailed spatiotemporal analysis using peak growing season (June–August) ETa over irrigated areas revealed declining regional crop water-use patterns throughout the basin, a trend reinforced through comparisons with gridded ETa from the Max Planck Institute (MPI). The interrelationships among seven agro-hydroclimatic variables (ETa, Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), maximum air temperature (Ta), potential ET (ETo), precipitation, and runoff) are all summarized to support the assessment and context of historical water-use dynamics over 30 years in the URGB.","language":"English","publisher":"MDPI","doi":"10.3390/rs11131587","usgsCitation":"Senay, G., Schauer, M., Velpuri, N., Singh, R., Kagone, S., Friedrichs, M., Litvak, M., and Douglas-Mankin, K., 2019, Long-term (1986–2015) crop water use characterization over the Upper Rio Grande Basin of United States and Mexico using Landsat-based evapotranspiration: Remote Sensing, v. 11, no. 13, 1587, 25 p., https://doi.org/10.3390/rs11131587.","productDescription":"1587, 25 p.","ipdsId":"IP-106097","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":467480,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11131587","text":"Publisher Index Page"},{"id":437397,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KOPFQ9","text":"USGS data release","linkHelpText":"Long-term (1986 -2015) Crop Water Use Characterization over the Upper Rio Grande Basin using Landsat-based Evapotranspiration"},{"id":365541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Colorado, New Mexico, Texas","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.32568359375,\n              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0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":216909,"corporation":false,"usgs":true,"family":"Schauer","given":"Matthew","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":766089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Velpuri, Naga Manohar  0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":766091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Singh, Ramesh  0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":216912,"corporation":false,"usgs":false,"family":"Singh","given":"Ramesh ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":766092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":216913,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":766093,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":216914,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":766094,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Litvak, Marcy","contributorId":216915,"corporation":false,"usgs":false,"family":"Litvak","given":"Marcy","affiliations":[{"id":39549,"text":"University of New Mexico: Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":766095,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":216916,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[{"id":39550,"text":"U.S. Department of Agriculture, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":766096,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70203909,"text":"70203909 - 2019 - Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada","interactions":[],"lastModifiedDate":"2019-10-28T09:55:47","indexId":"70203909","displayToPublicDate":"2019-06-17T15:37:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada","docAbstract":"Conservation of montane meadows is a high priority for land and water managers given their critical role in buffering the effects of climate variability and their vulnerability to increasing temperatures and evaporative demands. Recent advances in cloud computing have provided new opportunities to examine ecological responses to climate variability over the past few decades, and at large spatial scales.  In this study we characterized the sensitivities (magnitude and direction of the slope) of meadow vegetation responses to interannual variations in climate. We calculated sensitivity as the regression slope between a 35-year (1985-2016) time series of Landsat-derived vegetation indices characterizing late-season vegetation vigor and water balance variables from the Basin Characterization Model. We identified April 1 snowpack as the climate variable the majority of meadows were most sensitive to. We assessed how vegetation sensitivities to snowpack varied with hydrogeomorphic context (e.g., climate, geology, soils, watershed geometry and land cover) across the Sierra Nevada mountain range using factor analysis to reduce the dimensionality of the hydrogeomorphic data, and multiple linear regression to model sensitivity responses. We found that meadow sensitivities to snowpack varied with long-term average meadow climate, indicators of watershed subsurface water storage capacity, and indicators of meadow vegetation composition. Alpine and sub-alpine meadows with high average annual precipitation, but limited catchment subsurface storage exhibited the largest sensitivities. Our results provide a novel regional perspective on spatial patterns of meadow sensitivities to climate variability and the landscape-scale hydrogeomorphic factors that influence late-season water availability in meadow ecosystems in the Sierra Nevada.","language":"English","publisher":"Wiley","doi":"10.1002/eco.2128","usgsCitation":"Albano, C.M., McClure, M.L., Gross, S.E., Kitlasten, W., Soulard, C., Charles Morton, and Huntington, J., 2019, Spatial patterns of meadow sensitivities to interannual climate variability in the Sierra Nevada: Ecohydrology, v. 12, no. 7, e2128, 20 p., https://doi.org/10.1002/eco.2128.","productDescription":"e2128, 20 p.","ipdsId":"IP-103662","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":467526,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2128","text":"Publisher Index Page"},{"id":364849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364848,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/abs/10.1002/eco.2128"}],"country":"United States","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122,\n              37.5\n            ],\n            [\n              -119.5,\n              37.5\n            ],\n            [\n              -119.5,\n              40\n            ],\n            [\n              -122,\n              40\n            ],\n            [\n              -122,\n              37.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Albano, Christine M.","contributorId":182427,"corporation":false,"usgs":false,"family":"Albano","given":"Christine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":764705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClure, Meredith L.","contributorId":216395,"corporation":false,"usgs":false,"family":"McClure","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":764706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gross, Shana E.","contributorId":216396,"corporation":false,"usgs":false,"family":"Gross","given":"Shana","email":"","middleInitial":"E.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":764707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kitlasten, Wesley","contributorId":216397,"corporation":false,"usgs":true,"family":"Kitlasten","given":"Wesley","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764708,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soulard, Christopher 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":216394,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":764704,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Charles Morton","contributorId":169457,"corporation":false,"usgs":false,"family":"Charles Morton","affiliations":[{"id":25514,"text":"UNR Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":764709,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huntington, Justin","contributorId":169456,"corporation":false,"usgs":false,"family":"Huntington","given":"Justin","email":"","affiliations":[{"id":25514,"text":"UNR Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":764710,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70203935,"text":"70203935 - 2019 - Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert","interactions":[],"lastModifiedDate":"2019-06-24T15:50:40","indexId":"70203935","displayToPublicDate":"2019-06-13T15:47:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3380,"text":"Sensors","active":true,"publicationSubtype":{"id":10}},"title":"Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert","docAbstract":"Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution.","language":"English","publisher":"MDPI","doi":"10.3390/s19122669","usgsCitation":"Nowicki, S.A., Inman, R.D., Esque, T., Nussear, K., and Edwards, C., 2019, Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert: Sensors, v. 19, no. 12, 2669; 17 p., https://doi.org/10.3390/s19122669.","productDescription":"2669; 17 p.","ipdsId":"IP-093332","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467532,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/s19122669","text":"Publisher Index Page"},{"id":364965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"ARizone, California, Nevada, Utah","otherGeospatial":"Mojave Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119,\n              34\n            ],\n            [\n              -113,\n              34\n            ],\n            [\n              -113,\n              37\n            ],\n            [\n              -119,\n              37\n            ],\n            [\n              -119,\n              34\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"19","issue":"12","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nowicki, Scott A","contributorId":216483,"corporation":false,"usgs":false,"family":"Nowicki","given":"Scott","email":"","middleInitial":"A","affiliations":[{"id":13339,"text":"University of New Mexico, Albuquerque","active":true,"usgs":false}],"preferred":false,"id":764840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Inman, Richard D. 0000-0002-1982-7791 rdinman@usgs.gov","orcid":"https://orcid.org/0000-0002-1982-7791","contributorId":187754,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":764841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":764842,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nussear, Kenneth","contributorId":194538,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":764843,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, Christopher S.","contributorId":206168,"corporation":false,"usgs":false,"family":"Edwards","given":"Christopher S.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":764844,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70228750,"text":"70228750 - 2019 - Long-term trajectories of fractional component change in the Northern Great Basin, USA","interactions":[],"lastModifiedDate":"2022-03-31T14:01:26.798217","indexId":"70228750","displayToPublicDate":"2019-06-03T11:22:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Long-term trajectories of fractional component change in the Northern Great Basin, USA","docAbstract":"The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the northern Great Basin from 1986 to 2016 using a dense Landsat imagery time-series. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30-year period shrub cover declined and bare ground increased. While few pixels had > 10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and negatively to warming WYTMAX since the largest increases in WYTMAX are in the warmest and driest areas. The trade-off of lowering temporal density against removing cloud-contaminated years is justified as temporal density appears to have only a modest impact on trends and climate relationships until n ≤ 6, but multi-year gaps are proportionally more influential. Gradual change analysis is likely to be less sensitive to n than abrupt change. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2762","usgsCitation":"Rigge, M.B., Shi, H., Homer, C., Danielson, P., and Granneman, B.J., 2019, Long-term trajectories of fractional component change in the Northern Great Basin, USA: Ecosphere, v. 10, no. 6, e02762, 24 p., https://doi.org/10.1002/ecs2.2762.","productDescription":"e02762, 24 p.","ipdsId":"IP-102771","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":460369,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2762","text":"Publisher Index Page"},{"id":396119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396132,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C9O66W","text":"USGS data release","description":"USGS data release","linkHelpText":"Remote Sensing Shrub/Grass National Land Cover Database (NLCD) Back-in-Time (BIT) Products for the Western U.S., 1985 - 2018"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Northern Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.56347656249999,\n              42.032974332441405\n            ],\n            [\n              -118.16894531249999,\n              35.35321610123823\n            ],\n            [\n              -112.2802734375,\n              34.59704151614417\n            ],\n            [\n              -109.248046875,\n              38.37611542403604\n            ],\n            [\n              -110.0830078125,\n              43.13306116240612\n            ],\n            [\n              -112.8955078125,\n              44.02442151965934\n            ],\n            [\n              -115.6201171875,\n              43.58039085560784\n            ],\n            [\n              -119.35546875000001,\n              44.15068115978094\n            ],\n            [\n              -121.025390625,\n              44.08758502824516\n            ],\n            [\n              -122.56347656249999,\n              42.032974332441405\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-06-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":835302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":835303,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":238918,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":835304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":835305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Granneman, Brian J. 0000-0002-1910-0955","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":273180,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":835306,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70250178,"text":"70250178 - 2019 - Method for observing breach geomorphic evolution: Satellite observation of the Fire Island Wilderness breach","interactions":[],"lastModifiedDate":"2023-11-27T16:45:44.805703","indexId":"70250178","displayToPublicDate":"2019-05-31T10:38:33","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Method for observing breach geomorphic evolution: Satellite observation of the Fire Island Wilderness breach","docAbstract":"<p><span>Satellite derived shorelines are extracted using the Google Earth Engine API for Landsat and Sentinel satellites from 1984 through 2018. These shorelines are evaluated against existing surveys and show satellite-derived breach shorelines are in good agreement with directly-observed shorelines and capture the trend of the Fire Island wilderness breach evolution. Results of this study show the wilderness breach resulted in down drift shoreline erosion at a rate greater than the historical average within 4 km of the breach. This study demonstrates the benefits of utilizing satellite observations for monitoring the full extent of breach development and resulting shoreline changes.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Coastal Sediments 2019","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2019","conferenceDate":"May 27-31, 2019","conferenceLocation":"Tampa/St. Petersburg, FL","language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0007","usgsCitation":"Nelson, T., and Miselis, J.L., 2019, Method for observing breach geomorphic evolution: Satellite observation of the Fire Island Wilderness breach, <i>in</i> Coastal Sediments 2019, Tampa/St. Petersburg, FL, May 27-31, 2019, p. 71-84, https://doi.org/10.1142/9789811204487_0007.","productDescription":"14 p.","startPage":"71","endPage":"84","ipdsId":"IP-105818","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":422971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island Wilderness breach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -72.88045704857345,\n              40.7386289733175\n            ],\n            [\n              -72.9229595443374,\n              40.7386289733175\n            ],\n            [\n              -72.9229595443374,\n              40.70933604608808\n            ],\n            [\n              -72.88045704857345,\n              40.70933604608808\n            ],\n            [\n              -72.88045704857345,\n              40.7386289733175\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Timothy 0000-0002-5005-7617 trnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-5005-7617","contributorId":191933,"corporation":false,"usgs":true,"family":"Nelson","given":"Timothy","email":"trnelson@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":888680,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70203561,"text":"70203561 - 2019 - Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine","interactions":[],"lastModifiedDate":"2019-05-22T16:12:58","indexId":"70203561","displayToPublicDate":"2019-05-22T16:11:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\"><div id=\"abst0010\"><p id=\"spar0185\">Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\\or small farms with mixed signatures from different crop types and\\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (&lt;1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R<sup>2</sup><span>&nbsp;</span>value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R<sup>2</sup> = 0.95 for South Korea and R<sup>2</sup> = 0.94 for Thailand. The cropland products are made available on an interactive viewer at<span>&nbsp;</span><a rel=\"noreferrer noopener\" href=\"http://www.croplands.org/\" target=\"_blank\" data-mce-href=\"http://www.croplands.org/\">www.croplands.org</a><span>&nbsp;</span>and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC):<span>&nbsp;</span><a rel=\"noreferrer noopener\" href=\"https://lpdaac.usgs.gov/node/1281\" target=\"_blank\" data-mce-href=\"https://lpdaac.usgs.gov/node/1281\">https://lpdaac.usgs.gov/node/1281</a>.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2018.11.014","usgsCitation":"Oliphant, A., Thenkabail, P.S., Teluguntla, P., Xiong, J., Gumma, M.K., Congalton, R.G., and Kamini Yadav, 2019, Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine: International Journal of Applied Earth Observation and Geoinformation, v. 81, p. 110-124, https://doi.org/10.1016/j.jag.2018.11.014.","productDescription":"15 p.","startPage":"110","endPage":"124","ipdsId":"IP-099863","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":460381,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2018.11.014","text":"Publisher Index Page"},{"id":364099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364095,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0303243418307414"}],"volume":"81","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":763159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":763160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teluguntla, Pardhasaradhi 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":211780,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":763161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xiong, Jun 0000-0002-2320-0780","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":211781,"corporation":false,"usgs":false,"family":"Xiong","given":"Jun","affiliations":[{"id":38318,"text":"BAERI","active":true,"usgs":false}],"preferred":false,"id":763162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gumma, Murali Krishna 0000-0002-3760-3935","orcid":"https://orcid.org/0000-0002-3760-3935","contributorId":192327,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[],"preferred":false,"id":763163,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Congalton, Russell G.","contributorId":211782,"corporation":false,"usgs":false,"family":"Congalton","given":"Russell","email":"","middleInitial":"G.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":763164,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kamini Yadav","contributorId":211783,"corporation":false,"usgs":false,"family":"Kamini Yadav","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":763165,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70202978,"text":"ofr20191037 - 2019 - Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya","interactions":[],"lastModifiedDate":"2019-05-14T11:37:50","indexId":"ofr20191037","displayToPublicDate":"2019-05-13T11:49:01","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1037","displayTitle":"Monitoring Live Vegetation in Semiarid and Arid Rangeland Environments with Satellite Remote Sensing in Northern Kenya","title":"Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya","docAbstract":"<p>As part of the U.S. Department of the Interior’s (DOI) commitment to provide technical assistance to the Kenyan Northern Rangelands Trust (NRT), the U.S. Geological Survey, in collaboration with the DOI International Technical Assistance Program and the U.S. Agency for International Development’s regional mission in East Africa, created a high spatial and time-sensitive live vegetation monitoring system for NRT. The system built with advanced field and sensor technologies produced directly calibrated and highly accurate satellite mapping that is extendable both forward and backward in time. The maps are produced in a simple 0–100-percent representation of live vegetation status and change over time. The backbone of the mapping is the Sentinel satellite remote sensing systems with 5-day collection frequencies and ground spatial resolutions of 10 meters. The European Space Agency (ESA) offers free Sentinel satellite image data through conveniently accessed websites and free user-friendly image processing software downloadable directly onto a personal workstation. ESA provides free online software support. The mapping capability was extended from the forward mapping of Sentinel back in time with the Landsat satellite remote sensing system that has an available and free data archive back to 1983. Although Landsat has coarser spatial resolution, the Landsat to Sentinel live vegetation mapping comparison supports the use of Landsat to provide NRT the historical recreation of prominent live vegetation changes.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191037","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Rangoonwala, Amina, and Ramsey, E.W., III, 2019, Monitoring live vegetation in semiarid and arid rangeland environments with satellite remote sensing in northern Kenya: U.S. Geological Survey Open-File Report 2019–1037, 83 p., https://doi.org/10.3133/ofr20191037.","productDescription":"Report: vii, 83 p.; 15 Figures","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105119","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":363609,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037.pdf","text":"Report","size":"22.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1037"},{"id":363608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1037/coverthb.jpg"},{"id":363610,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig15a.tif","text":"Figure 15A—high resolution—","description":"OFR 2019–1037 Figure 15A","linkHelpText":"June 2018 live vegetation map"},{"id":363617,"rank":10,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig18b.tif","text":"Figure 18B—high resolution—","description":"OFR 2019–1037 Figure 18B","linkHelpText":"Live cover map with tree mask overlay (dark 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map"},{"id":363619,"rank":12,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig19b.tif","text":"Figure 19B—high resolution—","description":"OFR 2019–1037 Figure 19B","linkHelpText":"Live cover maps with tree mask overlay (dark green)"},{"id":363620,"rank":13,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig20a.tif","text":"Figure 20A—high resolution—","description":"OFR 2019–1037 Figure 20A","linkHelpText":"June 2017 to September 2017 live vegetation cover proportion change map"},{"id":363621,"rank":14,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig20b.tif","text":"Figure 20B—high resolution—","description":"OFR 2019–1037 Figure 20B","linkHelpText":"Live vegetation change map with tree mask overlay (dark green)"},{"id":363622,"rank":15,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig40a.tif","text":"Figure 40A—high resolution—","description":"OFR 2019–1037 Figure 40A","linkHelpText":"Live vegetation cover proportion for the core-Kenyan Northern Rangelands Trust conservancies in June 2017 "},{"id":363623,"rank":16,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig40b.tif","text":"Figure 40B—high resolution—","description":"OFR 2019–1037 Figure 40B","linkHelpText":"Live vegetation cover proportion for the core-Kenyan Northern Rangelands Trust conservancies in June 2018 "},{"id":363624,"rank":17,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2019/1037/ofr20191037_fig41.jpg","text":"Figure 41—high resolution—","description":"OFR 2019–1037 Figure 41","linkHelpText":"June 2018 synthetic aperture radar (SAR) vertical send and vertical receive (VV) and vertical send and horizontal receive (VH) images"}],"country":"Kenya","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              33.8818359375,\n              -0.9667509997666298\n            ],\n            [\n              37.72705078125,\n              -3.601142320158722\n            ],\n            [\n              39.26513671875,\n              -4.8282597468669755\n            ],\n            [\n              40.166015625,\n              -3.3160183381615123\n            ],\n            [\n              41.72607421875,\n              -1.7794990011582128\n            ],\n            [\n              41.02294921875,\n              1.0765967983064109\n            ],\n            [\n              42.22045898437501,\n              4.313546364068527\n            ],\n            [\n              40.78125,\n              4.280680030820496\n            ],\n            [\n              38.97949218749999,\n              3.71078200434872\n            ],\n            [\n              35.244140625,\n              4.872047700241915\n            ],\n            [\n              33.42041015625,\n              4.313546364068527\n            ],\n            [\n              33.8818359375,\n              -0.9667509997666298\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc\" href=\"https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc\">Wetland and Aquatic Research Center</a><br>U.S. Geological Survey<br>700 Cajundome&nbsp;Blvd.<br>Lafayette, LA 70506</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2019-05-13","noUsgsAuthors":false,"publicationDate":"2019-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rangoonwala, Amina 0000-0002-0556-0598","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":214747,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":760676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramsey III, Elijah W. 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":214746,"corporation":false,"usgs":true,"family":"Ramsey III","given":"Elijah W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":760675,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70203366,"text":"70203366 - 2019 - Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica","interactions":[],"lastModifiedDate":"2019-05-09T08:56:24","indexId":"70203366","displayToPublicDate":"2019-04-26T09:52:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2328,"text":"Journal of Glaciology","active":true,"publicationSubtype":{"id":10}},"title":"Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica","docAbstract":"<div class=\"row\"><div class=\"large-10 medium-10 small-12 columns\"><div class=\"description\"><div class=\"abstract\" data-abstract-type=\"normal\"><p>Surface debris covers much of the western portion of the McMurdo Ice Shelf and has a strong influence on the local surface albedo and energy balance. Differential ablation between debris-covered and debris-free areas creates an unusual heterogeneous surface of topographically low, high-ablation, and topographically raised (‘pedestalled’), low-ablation areas. Analysis of Landsat and MODIS satellite imagery from 1999 to 2018, alongside field observations from the 2016/2017 austral summer, shows that pedestalled relict lakes (‘pedestals’) form when an active surface meltwater lake that develops in the summer, freezes-over in winter, resulting in the lake-bottom debris being masked by a high-albedo, superimposed, ice surface. If this ice surface fails to melt during a subsequent melt season, it experiences reduced surface ablation relative to the surrounding debris-covered areas of the ice shelf. We propose that this differential ablation, and resultant hydrostatic and flexural readjustments of the ice shelf, causes the former supraglacial lake surface to become increasingly pedestalled above the lower topography of the surrounding ice shelf. Consequently, meltwater streams cannot flow onto these pedestalled features, and instead divert around them. We suggest that the development of pedestals has a significant influence on the surface-energy balance, hydrology and flexure of the ice shelf.</p></div></div></div></div>","language":"English","publisher":"Cambridge University Press","doi":"10.1017/jog.2019.17","usgsCitation":"MacDonald, G.J., Banwell, A.F., Willis, I.C., Mayer, D., Goodsell, B., and MacAyeal, D.R., 2019, Formation of pedestalled, relict lakes on the McMurdo Ice Shelf, Antarctica: Journal of Glaciology, p. 1-7, https://doi.org/10.1017/jog.2019.17.","productDescription":"7 p.","startPage":"1","endPage":"7","ipdsId":"IP-104102","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":467669,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/jog.2019.17","text":"Publisher Index Page"},{"id":363580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -210.9375,\n              -80.70399666821143\n            ],\n            [\n              -38.3203125,\n              -80.70399666821143\n            ],\n            [\n              -38.3203125,\n              -65.21989393613208\n            ],\n            [\n              -210.9375,\n              -65.21989393613208\n            ],\n            [\n              -210.9375,\n              -80.70399666821143\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"MacDonald, Grant J 0000-0002-9295-085X","orcid":"https://orcid.org/0000-0002-9295-085X","contributorId":215430,"corporation":false,"usgs":false,"family":"MacDonald","given":"Grant","email":"","middleInitial":"J","affiliations":[{"id":39244,"text":"Department of the Geophysical Science, The University of Chicago","active":true,"usgs":false}],"preferred":false,"id":762336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banwell, Alison F 0000-0001-9545-829X","orcid":"https://orcid.org/0000-0001-9545-829X","contributorId":215431,"corporation":false,"usgs":false,"family":"Banwell","given":"Alison","email":"","middleInitial":"F","affiliations":[{"id":39245,"text":"Scott Polar Research Institute, and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":762337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willis, Ian C","contributorId":215432,"corporation":false,"usgs":false,"family":"Willis","given":"Ian","email":"","middleInitial":"C","affiliations":[{"id":39246,"text":"Scott Polar Research Institute, The University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":762338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mayer, David 0000-0001-8351-1807","orcid":"https://orcid.org/0000-0001-8351-1807","contributorId":215429,"corporation":false,"usgs":true,"family":"Mayer","given":"David","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":762335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goodsell, Becky","contributorId":215433,"corporation":false,"usgs":false,"family":"Goodsell","given":"Becky","email":"","affiliations":[{"id":39244,"text":"Department of the Geophysical Science, The University of Chicago","active":true,"usgs":false}],"preferred":false,"id":762339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"MacAyeal, Douglas R 0000-0003-0647-6176","orcid":"https://orcid.org/0000-0003-0647-6176","contributorId":215434,"corporation":false,"usgs":false,"family":"MacAyeal","given":"Douglas","email":"","middleInitial":"R","affiliations":[{"id":39244,"text":"Department of the Geophysical Science, The University of Chicago","active":true,"usgs":false}],"preferred":false,"id":762340,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70202394,"text":"fs20193008 - 2019 - Landsat 9","interactions":[],"lastModifiedDate":"2022-08-03T22:06:00.386184","indexId":"fs20193008","displayToPublicDate":"2019-04-18T14:47:27","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3008","displayTitle":"Landsat 9","title":"Landsat 9","docAbstract":"<p>Landsat 9 is a partnership between the National Aeronautics and Space Administration and the U.S. Geological Survey that will continue the Landsat program’s critical role of repeat global observations for monitoring, understanding, and managing Earth’s natural resources. Since 1972, Landsat data have provided a unique resource for those who work in agriculture, geology, forestry, regional planning, education, mapping, and global-change research. Landsat images have also proved invaluable to the International Charter: Space and Major Disasters, supporting emergency response and disaster relief to save lives. With the addition of Landsat 9, the Landsat program’s record of land imaging will be extended to over half a century.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193008","usgsCitation":"U.S. Geological Survey, 2019, Landsat 9 (ver. 1.3, August 2022): U.S. Geological Survey Fact Sheet 2019–3008, 2 p., https://doi.org/10.3133/fs20193008.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-102185","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":363027,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3008/coverthb4.jpg"},{"id":404567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3008/fs20193008.pdf","text":"Report","size":"2.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3008"},{"id":404568,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2019/3008/versionHist.txt","text":"Version History","size":"8.43 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"edition":"Version 1.0: April 18, 2019; Version 1.1: May 1, 2019; Version 1.2: April 8, 2020; Version 1.3: August 3, 2022","contact":"<p><a data-mce-href=\"https://www.usgs.gov/centers/eros\" href=\"https://www.usgs.gov/centers/eros\">Earth Resources Observation and Science (EROS) Center</a><br>U.S. Geological Survey<br>47914 252nd Street <br>Sioux Falls, SD 57198</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Landsat 9 Spacecraft and Launch Components</li><li>Landsat 9 Instruments</li><li>Landsat 9 Data Products</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-04-18","revisedDate":"2022-08-03","noUsgsAuthors":false,"publicationDate":"2019-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":202815,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":758168,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70202846,"text":"70202846 - 2019 - Radiometric calibration of a non-imaging airborne spectrometer to measure the Greenland ice sheet surface","interactions":[],"lastModifiedDate":"2019-03-29T11:27:32","indexId":"70202846","displayToPublicDate":"2019-03-26T10:42:54","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":926,"text":"Atmospheric Measurement Techniques","active":true,"publicationSubtype":{"id":10}},"title":"Radiometric calibration of a non-imaging airborne spectrometer to measure the Greenland ice sheet surface","docAbstract":"<p><span>Methods to radiometrically calibrate a non-imaging airborne visible-to-shortwave infrared (VSWIR) spectrometer to measure the Greenland ice sheet surface are presented. Airborne VSWIR measurement performance for bright Greenland ice and dark bare rock/soil targets is compared against the MODerate resolution atmospheric TRANsmission (MODTRAN</span><sup>®</sup><span>) radiative transfer code (version 6.0), and a coincident Landsat 8 Operational Land Imager (OLI) acquisition on 29&nbsp;July&nbsp;2015 during an in-flight radiometric calibration experiment. Airborne remote sensing flights were carried out in northwestern Greenland in preparation for the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) laser altimeter mission. A total of nine science flights were conducted over the Greenland ice sheet, sea ice, and open-ocean water. The campaign's primary purpose was to correlate green laser pulse penetration into snow and ice with spectroscopic-derived surface properties. An experimental airborne instrument configuration that included a nadir-viewing (looking downward at the surface) non-imaging Analytical Spectral Devices (ASD) Inc. spectrometer that measured upwelling VSWIR (0.35 to 2.5 </span><span class=\"inline-formula\">µ</span><span>m) spectral radiance (</span><span class=\"inline-formula\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot; id=&quot;M2&quot; display=&quot;inline&quot; overflow=&quot;scroll&quot; dspmath=&quot;mathml&quot;><mrow class=&quot;unit&quot;><mi mathvariant=&quot;normal&quot;>W</mi><mspace width=&quot;0.125em&quot; linebreak=&quot;nobreak&quot; /><msup><mi mathvariant=&quot;normal&quot;>m</mi><mrow><mo>-</mo><mn mathvariant=&quot;normal&quot;>2</mn></mrow></msup><mspace width=&quot;0.125em&quot; linebreak=&quot;nobreak&quot; /><msup><mi mathvariant=&quot;normal&quot;>sr</mi><mrow><mo>-</mo><mn mathvariant=&quot;normal&quot;>1</mn></mrow></msup><mspace linebreak=&quot;nobreak&quot; width=&quot;0.125em&quot; /><mi mathvariant=&quot;normal&quot;>&amp;#xB5;</mi><msup><mi mathvariant=&quot;normal&quot;>m</mi><mrow><mo>-</mo><mn mathvariant=&quot;normal&quot;>1</mn></mrow></msup></mrow></math>\"><span id=\"M2\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"mrow unit\"><span id=\"MathJax-Span-4\" class=\"mi\">W</span><span id=\"MathJax-Span-5\" class=\"mspace\"></span><span id=\"MathJax-Span-6\" class=\"msup\"><span id=\"MathJax-Span-7\" class=\"mi\">m</span><span id=\"MathJax-Span-8\" class=\"mrow\"><span id=\"MathJax-Span-9\" class=\"mo\">−</span><span id=\"MathJax-Span-10\" class=\"mn\">2</span></span></span><span id=\"MathJax-Span-11\" class=\"mspace\"></span><span id=\"MathJax-Span-12\" class=\"msup\"><span id=\"MathJax-Span-13\" class=\"mi\">sr</span><span id=\"MathJax-Span-14\" class=\"mrow\"><span id=\"MathJax-Span-15\" class=\"mo\">−</span><span id=\"MathJax-Span-16\" class=\"mn\">1</span></span></span><span id=\"MathJax-Span-17\" class=\"mspace\"></span><span id=\"MathJax-Span-18\" class=\"mi\">µ</span><span id=\"MathJax-Span-19\" class=\"msup\"><span id=\"MathJax-Span-20\" class=\"mi\">m</span><span id=\"MathJax-Span-21\" class=\"mrow\"><span id=\"MathJax-Span-22\" class=\"mo\">−</span><span id=\"MathJax-Span-23\" class=\"mn\">1</span></span></span></span></span></span></span></span></span><span>) in the two-color Slope Imaging Multi-polarization Photon-Counting Lidar's (SIMPL) ground instantaneous field of view, and a zenith-viewing (looking upward at the sky) ASD spectrometer that measured VSWIR spectral irradiance (W m</span><span class=\"inline-formula\"><sup>−2</sup></span><span> nm</span><span class=\"inline-formula\"><sup>−1</sup></span><span>) was flown. National Institute of Standards and Technology (NIST) traceable radiometric calibration procedures for laboratory, in-flight, and field</span><span id=\"page1914\"></span><span>&nbsp;environments are described in detail to achieve a targeted VSWIR measurement requirement of within 5 % to support calibration/validation efforts and remote sensing algorithm development. Our MODTRAN predictions for the 29&nbsp;July flight line over dark and bright targets indicate that the airborne nadir-viewing spectrometer spectral radiance measurement uncertainty was between 0.6 % and 4.7 % for VSWIR wavelengths (0.4 to 2.0 </span><span class=\"inline-formula\">µ</span><span>m) with atmospheric transmittance greater than 80 %. MODTRAN predictions for Landsat 8 OLI relative spectral response functions suggest that OLI is measuring 6 % to 16 % more top-of-atmosphere (TOA) spectral radiance from the Greenland ice sheet surface than was predicted using apparent reflectance spectra from the nadir-viewing spectrometer. While more investigation is required to convert airborne VSWIR spectral radiance into atmospherically corrected airborne surface reflectance, it is expected that airborne science flight data products will contribute to spectroscopic determination of Greenland ice sheet surface optical properties to improve understanding of their potential influence on ICESat-2 measurements.</span></p>","language":"English","publisher":"Atmospheric Measurement Techniques","doi":"10.5194/amt-12-1913-2019","usgsCitation":"Crawford, C., van den Bosch, J., Brunt, K.M., Hom, M.G., Cooper, J.W., Harding, D.J., Butler, J., Dabney, P.W., Neumann, T.A., Cleckner, C.S., and Markus, T., 2019, Radiometric calibration of a non-imaging airborne spectrometer to measure the Greenland ice sheet surface: Atmospheric Measurement Techniques, v. 12, p. 1913-1933, https://doi.org/10.5194/amt-12-1913-2019.","productDescription":"21 p.","startPage":"1913","endPage":"1933","ipdsId":"IP-105345","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467777,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/amt-12-1913-2019","text":"Publisher Index Page"},{"id":362531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -68.5546875,\n              58.63121664342478\n            ],\n            [\n              -9.84375,\n              58.63121664342478\n            ],\n            [\n              -9.84375,\n              83.82994542398042\n            ],\n            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Bosch","given":"Jeannette","email":"","affiliations":[{"id":39073,"text":"US Air Force Research Lab","active":true,"usgs":false}],"preferred":false,"id":760242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brunt, Kelly M. 0000-0002-6462-6112","orcid":"https://orcid.org/0000-0002-6462-6112","contributorId":214567,"corporation":false,"usgs":false,"family":"Brunt","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":39074,"text":"University of Maryland / NASA","active":true,"usgs":false}],"preferred":true,"id":760243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hom, Milton G.","contributorId":214568,"corporation":false,"usgs":false,"family":"Hom","given":"Milton","email":"","middleInitial":"G.","affiliations":[{"id":39075,"text":"Science Systems and Applications / NASA","active":true,"usgs":false}],"preferred":false,"id":760244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, John W.","contributorId":214569,"corporation":false,"usgs":false,"family":"Cooper","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":39076,"text":"Science Systems and Applications  / NASA","active":true,"usgs":false}],"preferred":false,"id":760245,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harding, David J.","contributorId":214570,"corporation":false,"usgs":false,"family":"Harding","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":760246,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, James J.","contributorId":214571,"corporation":false,"usgs":false,"family":"Butler","given":"James J.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":760247,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dabney, Philip W.","contributorId":214572,"corporation":false,"usgs":false,"family":"Dabney","given":"Philip","email":"","middleInitial":"W.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":760248,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Neumann, Thomas A.","contributorId":214573,"corporation":false,"usgs":false,"family":"Neumann","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":760249,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cleckner, Craig S.","contributorId":214574,"corporation":false,"usgs":false,"family":"Cleckner","given":"Craig","email":"","middleInitial":"S.","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":760250,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Markus, Thorsten","contributorId":214575,"corporation":false,"usgs":false,"family":"Markus","given":"Thorsten","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":760251,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70202390,"text":"sir20195007 - 2019 - Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota","interactions":[],"lastModifiedDate":"2019-03-12T14:56:28","indexId":"sir20195007","displayToPublicDate":"2019-03-11T13:37:33","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5007","displayTitle":"Water-Balance Modeling of Selected Lakes for Evaluating Viability as Long-Term Fisheries in Kidder, Logan, and Stutsman Counties, North Dakota","title":"Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota","docAbstract":"<p>Water levels in lakes and wetlands in the central North Dakota Missouri Coteau region that were either dry or only sporadically held water since before the 1930s have been rising since the early 1990s in response to an extended wet period. The lakes have remained full since the mid-1990s, which has provided benefits to migratory waterfowl, fisheries, and wildlife. A small shift in climate conditions, either to drier or wetter conditions, can have a large effect on the lake levels of these water bodies. The North Dakota Game and Fish Department identified five lakes as candidates for sustaining long-term fisheries. The lakes are in Kidder, Stutsman, and Logan Counties, and some lakes might receive inflow from mostly freshwater aquifers, such as the Central Dakota and Streeter aquifers, and were mostly dry during the early 1990s. After about 1995, the lakes had filled up and were deep enough to sustain populations of game fish such as walleye, perch, and northern pike. Before investing in development of permanent fisheries and associated infrastructure, such as campgrounds and boat ramps, fisheries biologists needed to know if the lake levels are likely to remain high in coming decades.</p><p>The U.S. Geological Survey, in cooperation with the North Dakota Game and Fish Department, developed a water-balance model to determine the effects of precipitation, evapotranspiration, and groundwater interaction on lake volumes. The model was developed using climate input data and lake volumes for the calibration period 1992 through 2016, during which historical lake volumes could be estimated using land surface elevation data and Landsat images. Long-term (1940–2018) climate input data were used with the water-balance model to reconstruct historical lake volumes prior to the calibration period, and block-bootstrapping was used to simulate potential future climate input data and lake volumes for 2017 through 2067. The simulated future lake volumes were used to estimate the likelihood of annual lake volumes remaining consistent, increasing, or decreasing through the year 2067.</p><p>Of the five lakes, Sibley Lake was the most likely to sustain a long-term fishery for a period longer than 50 years. The simulated lake volumes for Alkaline Lake, Big Mallard Marsh, and Remmick Lake indicated the lakes have a 50-percent chance to fall below 75 percent of their 2016 volume by about 2030, 2067, and 2025, respectively. Simulation results for Marvin Miller Lake were substantially different compared to the other four lakes and indicated the lake has a 50-percent chance to fall below 75 percent of its 2016 volume prior to 2025.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195007","collaboration":"Prepared in cooperation with the North Dakota Game and Fish Department","usgsCitation":"Lundgren, R.F., York, B.C., Stroh, N.A., and Vecchia, A.V., 2019, Water-balance modeling of selected lakes for evaluating viability as long-term fisheries in Kidder, Logan, and Stutsman Counties, North Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5007, 22 p., https://doi.org/10.3133/sir20195007.","productDescription":"Report: v, 22 p.; Downloads","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102504","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":361913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5007/coverthb.jpg"},{"id":361914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5007/sir20195007.pdf","text":"Report","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5007"},{"id":361915,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2019/5007/downloads/","text":"Water-Balance Model R code scripts","linkFileType":{"id":6,"text":"zip"},"description":"Water-Balance Model R code scripts"}],"country":"United States","state":"North Dakota","county":"Kidder County, Logan County, Stutsman County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -100.1667,\n              46.5\n            ],\n            [\n              -99.1667,\n              46.5\n            ],\n            [\n              -99.1667,\n              47.5\n            ],\n            [\n              -100.1667,\n              47.5\n            ],\n            [\n              -100.1667,\n              46.5\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a><br>U.S. Geological Survey<br>821 East Interstate Avenue, Bismarck, ND 58503<br>1608 Mountain View Road, Rapid City, SD 57702</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Data Resources</li><li>Water-Balance Model Development</li><li>Water-Balance Model Simulations</li><li>Simulated Future Lake Volumes</li><li>Summary</li><li>References Cited</li><li>Appendix 1. Water-Balance Modeling R Documentation and Supporting Dataset</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2019-03-11","noUsgsAuthors":false,"publicationDate":"2019-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Lundgren, Robert F. 0000-0001-7669-0552 rflundgr@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-0552","contributorId":1657,"corporation":false,"usgs":true,"family":"Lundgren","given":"Robert","email":"rflundgr@usgs.gov","middleInitial":"F.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"York, Benjamin C. 0000-0002-3449-3574 byork@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-3574","contributorId":213613,"corporation":false,"usgs":true,"family":"York","given":"Benjamin","email":"byork@usgs.gov","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stroh, Nathan A. nstroh@usgs.gov","contributorId":214077,"corporation":false,"usgs":true,"family":"Stroh","given":"Nathan","email":"nstroh@usgs.gov","middleInitial":"A.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vecchia, Aldo V. 0000-0002-2661-4401 avecchia@usgs.gov","orcid":"https://orcid.org/0000-0002-2661-4401","contributorId":1173,"corporation":false,"usgs":true,"family":"Vecchia","given":"Aldo","email":"avecchia@usgs.gov","middleInitial":"V.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758156,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70201001,"text":"70201001 - 2019 - Spatiotemporal remote sensing of ecosystem change and causation across Alaska","interactions":[],"lastModifiedDate":"2024-05-17T15:00:39.48988","indexId":"70201001","displayToPublicDate":"2019-03-01T10:33:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal remote sensing of ecosystem change and causation across Alaska","docAbstract":"<p><span>Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000&nbsp;±&nbsp;8700&nbsp;km</span><sup>2</sup><span>) of Alaska has experienced directional change in the last 32&nbsp;years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14279","usgsCitation":"Pastick, N.J., Jorgenson, M., Goetz, S., Jones, B.M., Wylie, B.K., Minsley, B.J., Genet, H., Knight, J.F., Swanson, D.K., and Jorgenson, J.C., 2019, Spatiotemporal remote sensing of ecosystem change and causation across Alaska: Global Change Biology, v. 25, no. 3, p. 1171-1189, https://doi.org/10.1111/gcb.14279.","productDescription":"18 p.","startPage":"1171","endPage":"1189","ipdsId":"IP-096342","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":437552,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DV1J6N","text":"USGS data release","linkHelpText":"Probabilistic estimates of landscape change in Alaska (1984 to 2015)"},{"id":359597,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Torre","affiliations":[],"preferred":false,"id":751666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goetz, Scott J.","contributorId":22232,"corporation":false,"usgs":true,"family":"Goetz","given":"Scott J.","affiliations":[],"preferred":false,"id":751667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":751668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":751669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":751670,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Genet, Hélène","contributorId":195179,"corporation":false,"usgs":false,"family":"Genet","given":"Hélène","affiliations":[],"preferred":false,"id":751671,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knight, Joseph F.","contributorId":55311,"corporation":false,"usgs":true,"family":"Knight","given":"Joseph","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":751672,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Swanson, David K.","contributorId":178902,"corporation":false,"usgs":false,"family":"Swanson","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":751673,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jorgenson, Janet C.","contributorId":191903,"corporation":false,"usgs":false,"family":"Jorgenson","given":"Janet","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":751674,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70202401,"text":"70202401 - 2019 - A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units","interactions":[],"lastModifiedDate":"2019-10-28T09:44:47","indexId":"70202401","displayToPublicDate":"2019-02-27T15:48:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5621,"text":"Journal of Operational Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units","docAbstract":"<p><span>A new 30-m spatial resolution global shoreline vector (GSV) was developed from annual composites of 2014 Landsat satellite imagery. The semi-automated classification of the imagery was accomplished by manual selection of training points representing water and non-water classes along the entire global coastline. Polygon topology was applied to the GSV, resulting in a new characterisation of the number and size of global islands. Three size classes of islands were mapped: continental mainlands (5), islands greater than 1 km</span><sup>2</sup><span>&nbsp;(21,818), and islands smaller than 1 km</span><sup>2</sup><span>&nbsp;(318,868). The GSV represents the shore zone land and water interface boundary, and is a spatially explicit ecological domain separator between terrestrial and marine environments. The development and characteristics of the GSV are presented herein. An approach is also proposed for delineating standardised, high spatial resolution global ecological coastal units (ECUs). For this coastal ecosystem mapping effort, the GSV will be used to separate the nearshore coastal waters from the onshore coastal lands. The work to produce the GSV and the ECUs is commissioned by the Group on Earth Observations (GEO), and is associated with several GEO initiatives including GEO Ecosystems, GEO Marine Biodiversity Observation Network (MBON) and GEO Blue Planet.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/1755876X.2018.1529714","usgsCitation":"Sayre, R., Noble, S., Hamann, S.L., Smith, R.A., Wright, D.J., Breyer, S.P., Butler, K., Van Graafeiland, K., Frye, C., Karagulle, D., Hopkins, D., Stephens, D., Kelly, K., Basher, Z., Burton, D., Cress, J., Atkins, K., Van Sistine, D.P., Friesen, B., Allee, R., Allen, T., Aniello, P., Asaad, I., Costello, M.J., Goodin, K., Harrison, P., Kavanaugh, M.T., Lillis, H., Manca, E., Muller-Karger, F.E., Nyberg, B., Parsons, R., Saarinen, J., Steiner, J., and Reed, A., 2019, A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units: Journal of Operational Oceanography, v. 12, no. suppl 2, p. s47-s56, https://doi.org/10.1080/1755876X.2018.1529714.","productDescription":"10 p.","startPage":"s47","endPage":"s56","ipdsId":"IP-097896","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":460457,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/1755876x.2018.1529714","text":"Publisher Index Page"},{"id":437558,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ZCSGM","text":"USGS data release","linkHelpText":"Global Islands"},{"id":361594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"suppl 2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":213640,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":758208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noble, Suzanne","contributorId":213641,"corporation":false,"usgs":false,"family":"Noble","given":"Suzanne","affiliations":[],"preferred":false,"id":758209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hamann, Sharon L. shamann@usgs.gov","contributorId":4059,"corporation":false,"usgs":true,"family":"Hamann","given":"Sharon","email":"shamann@usgs.gov","middleInitial":"L.","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":758220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Rebecca A. 0000-0002-9823-706X rsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9823-706X","contributorId":201349,"corporation":false,"usgs":true,"family":"Smith","given":"Rebecca","email":"rsmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":758366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Dawn J.","contributorId":191639,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","email":"","middleInitial":"J.","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. 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,{"id":70218702,"text":"70218702 - 2019 - Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains","interactions":[],"lastModifiedDate":"2021-03-05T21:56:58.666116","indexId":"70218702","displayToPublicDate":"2019-02-26T15:51:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Mapping a keystone shrub species, huckleberry (<i>Vaccinium membranaceum</i>), using seasonal colour change in the Rocky Mountains","title":"Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains","docAbstract":"<p><span>Black huckleberries (</span><i>Vaccinium membranaceum</i><span>) provide a critical food resource to many wildlife species, including apex omnivores such as the grizzly bear (</span><i>Ursus arctos</i><span>), and play an important socioeconomic role for many communities in western North America, especially indigenous peoples. Remote sensing imagery offers the potential for accurate landscape-level mapping of huckleberries because the shrub changes colour seasonally. We developed two methods, for local and regional scales, to map a shrub species using leaf seasonal colour change from remote sensing imagery. We assessed accuracy with ground-based vegetation surveys. The high-resolution supervised random forest classification from one-meter resolution National Agricultural Imagery Program (NAIP) imagery achieved an overall accuracy of 75.31% (kappa&nbsp;=&nbsp;0.26). The approach using multi-temporal 30-meter Landsat imagery similarly had an overall accuracy of 79.19% (kappa&nbsp;=&nbsp;.31). We found underprediction error was related to higher forest cover and a lack of visible colour change on the ground in some plots. Where forest cover was low, both models performed better. In areas with &lt;10% forest cover, the high-resolution classification achieved an accuracy of 80.73% (kappa&nbsp;=&nbsp;0.48), while the Landsat model had an accuracy of 82.55% (kappa&nbsp;=&nbsp;0.47). Based on the fine-scale predictions, we found that 94% of huckleberry shrubs identified in our study area of Glacier National Park, Montana, USA are over 100 meters from human recreation trails. This information could be combined with productivity and phenology information to estimate the timing and availability of food resources for wildlife and to provide managers with a tool to identify and manage huckleberries. The development of the multi-temporal Landsat models sets the stage for assessment of impacts of disturbance at regional scales on this ecologically, culturally, and economically important shrub species. Our approach to map huckleberries is straightforward, efficient and accessible to wildlife and environmental managers and researchers in diverse fields.</span></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2019.1580819","usgsCitation":"Shores, C.R., Mikle, N., and Graves, T.A., 2019, Mapping a keystone shrub species, huckleberry (Vaccinium membranaceum), using seasonal colour change in the Rocky Mountains: International Journal of Remote Sensing, v. 40, no. 15, p. 5695-5715, https://doi.org/10.1080/01431161.2019.1580819.","productDescription":"21 p.","startPage":"5695","endPage":"5715","ipdsId":"IP-095407","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":384206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.961181640625,\n              48.31060120649363\n            ],\n            [\n              -112.939453125,\n              48.242967421301366\n            ],\n            [\n              -113.65631103515625,\n              48.99103162515999\n            ],\n            [\n              -114.81536865234374,\n              49.005447494058096\n            ],\n            [\n              -114.5050048828125,\n              48.545705491847464\n            ],\n            [\n              -114.15618896484375,\n              48.35989909002194\n            ],\n            [\n              -113.961181640625,\n              48.31060120649363\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"40","issue":"15","noUsgsAuthors":false,"publicationDate":"2019-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Shores, Carolyn R.","contributorId":254828,"corporation":false,"usgs":false,"family":"Shores","given":"Carolyn","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":811433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mikle, Nathaniel 0000-0002-6529-8210 nmikle@usgs.gov","orcid":"https://orcid.org/0000-0002-6529-8210","contributorId":177026,"corporation":false,"usgs":true,"family":"Mikle","given":"Nathaniel","email":"nmikle@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":811434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graves, Tabitha A. 0000-0001-5145-2400 tgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":5898,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha","email":"tgraves@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":811435,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70202363,"text":"70202363 - 2019 - Landsat: The cornerstone of global land imaging","interactions":[],"lastModifiedDate":"2019-02-26T14:23:49","indexId":"70202363","displayToPublicDate":"2019-02-26T14:23:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1720,"text":"GIM International","active":true,"publicationSubtype":{"id":10}},"title":"Landsat: The cornerstone of global land imaging","docAbstract":"Since 1972, the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites have provided an uninterrupted space-based data record of the Earth’s land surface to help advance scientific research towards the understanding of our planet and the environmental impact of its inhabitants. Early Landsat satellites offered a wealth of new data that improved mapping of remote areas and geologic features along with digital analysis of vegetation. The utility of Landsat’s spatial and spectral resolution has advanced its use for applications that benefit society such as global crop forecasting, forest monitoring, water use, carbon assessments, and the base for Google Maps. Landsat’s long-term data record provides an unrivaled resource for observing land cover and land-use change over a timescale of decades. The free and open Landsat data policy in 2008 was a paradigm shift for the world. Today, due to improved analytical and computing capabilities, the Landsat archive is poised to shift into a more real-time monitoring and understanding of the Earth.","language":"English","publisher":"GIM International Magazine","usgsCitation":"Butcher, G., Barnes, C., and Owen, L., 2019, Landsat: The cornerstone of global land imaging: GIM International, v. January/February 2019, p. 31-35.","productDescription":"5 p.","startPage":"31","endPage":"35","ipdsId":"IP-104692","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":361528,"type":{"id":15,"text":"Index Page"},"url":"https://www.gim-international.com/magazine/january-february-2019"},{"id":361555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"January/February 2019","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Butcher, Ginger","contributorId":213551,"corporation":false,"usgs":false,"family":"Butcher","given":"Ginger","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":758010,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnes, Christopher 0000-0002-4608-4364 christopher.barnes.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":198908,"corporation":false,"usgs":true,"family":"Barnes","given":"Christopher","email":"christopher.barnes.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Owen, Linda 0000-0002-1734-5406 jonescheit@usgs.gov","orcid":"https://orcid.org/0000-0002-1734-5406","contributorId":478,"corporation":false,"usgs":true,"family":"Owen","given":"Linda","email":"jonescheit@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758011,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70202237,"text":"70202237 - 2019 - Improved automated detection of subpixel-scale inundation – Revised Dynamic Surface Water Extent (DSWE) partial surface water tests","interactions":[],"lastModifiedDate":"2019-02-19T11:45:14","indexId":"70202237","displayToPublicDate":"2019-02-19T11:45:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Improved automated detection of subpixel-scale inundation – Revised Dynamic Surface Water Extent (DSWE) partial surface water tests","docAbstract":"<p><span>In order to produce useful hydrologic and aquatic habitat data from the Landsat system, the U.S. Geological Survey has developed the “Dynamic Surface Water Extent” (DSWE) Landsat Science Product. DSWE will provide long-term, high-temporal resolution data on variations in inundation extent. The model used to generate DSWE is composed of five decision-rule based tests that do not require scene-based training. To allow its general application, required inputs are limited to the Landsat at-surface reflectance product and a digital elevation model. Unlike other Landsat-based water products, DSWE includes pixels that are only partially covered by water to increase inundation dynamics information content. Previously published DSWE model development included one wetland-focused test developed through visual inspection of field-collected Everglades spectra. A comparison of that test’s output against Everglades Depth Estimation Network (EDEN) in situ data confirmed the expectation that omission errors were a prime source of inaccuracy in vegetated environments. Further evaluation exposed a tendency toward commission error in coniferous forests. Improvements to the subpixel level “partial surface water” (PSW) component of DSWE was the focus of this research. Spectral mixture models were created from a variety of laboratory and image-derived endmembers. Based on the mixture modeling, a more “aggressive” PSW rule improved accuracy in herbaceous wetlands and reduced errors of commission elsewhere, while a second “conservative” test provides an alternative when commission errors must be minimized. Replication of the EDEN-based experiments using the revised PSW tests yielded a statistically significant increase in mean overall agreement (4%, p = 0.01, n = 50) and a statistically significant decrease (11%, p = 0.009, n = 50) in mean errors of omission. Because the developed spectral mixture models included image-derived vegetation endmembers and laboratory spectra for soil groups found across the US, simulations suggest where the revised DSWE PSW tests perform as they do in the Everglades and where they may prove problematic. Visual comparison of DSWE outputs with an unusual variety of coincidently collected images for locations spread throughout the US support conclusions drawn from Everglades quantitative analyses and highlight DSWE PSW component strengths and weaknesses.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs11040374","usgsCitation":"Jones, J., 2019, Improved automated detection of subpixel-scale inundation – Revised Dynamic Surface Water Extent (DSWE) partial surface water tests: Remote Sensing, v. 11, no. 4, p. 1-26, https://doi.org/10.3390/rs11040374.","productDescription":"Article 374; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-102379","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467892,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11040374","text":"Publisher Index Page"},{"id":361339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":757437,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70202288,"text":"70202288 - 2019 - The potential role of very high-resolution imagery to characterise lake, wetland and stream systems across the Prairie Pothole Region, United States","interactions":[],"lastModifiedDate":"2019-06-13T14:18:43","indexId":"70202288","displayToPublicDate":"2019-02-18T10:47:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The potential role of very high-resolution imagery to characterise lake, wetland and stream systems across the Prairie Pothole Region, United States","docAbstract":"<div class=\"hlFld-Abstract\"><div class=\"abstractSection abstractInFull\"><p>Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify ~60–90% more surface water interactions between waterbodies, relative to the GSW Landsat product. However, regardless of Landsat source, by documenting many smaller (&lt;0.2&nbsp;ha), inundated wetlands, the PSHR outputs modified our interpretation of wetland size distribution across the Prairie Pothole Region.</p></div></div>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2019.1582112","usgsCitation":"Vanderhoof, M.K., and Lane, C., 2019, The potential role of very high-resolution imagery to characterise lake, wetland and stream systems across the Prairie Pothole Region, United States: International Journal of Remote Sensing, v. 40, no. 15, p. 5768-5798, https://doi.org/10.1080/01431161.2019.1582112.","productDescription":"31 p.","startPage":"5768","endPage":"5798","ipdsId":"IP-094052","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467898,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7784670","text":"External Repository"},{"id":437570,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BVAURT","text":"USGS data release","linkHelpText":"Data release for the potential role of very high-resolution imagery to characterise lake, wetland and stream systems across the Prairie Pothole Region, United States"},{"id":361377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Prairie Pothole Region","volume":"40","issue":"15","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-02-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":757657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lane, Charles R.","contributorId":138991,"corporation":false,"usgs":false,"family":"Lane","given":"Charles R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":757658,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70202165,"text":"70202165 - 2019 - A bibliometric profile of the Remote Sensing Open Access Journal published by MDPI between 2009 and 2018","interactions":[],"lastModifiedDate":"2019-02-12T13:10:39","indexId":"70202165","displayToPublicDate":"2019-02-12T13:10:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A bibliometric profile of the Remote Sensing Open Access Journal published by MDPI between 2009 and 2018","docAbstract":"<p><span>Remote Sensing Open Access Journal (RS OAJ) is an international leading journal in the field of remote sensing science and technology. It was first published in the year 2009 and is currently celebrating tenth year of publications. In this research, a bibliometric analysis of RS OAJ was conducted based on 5588 articles published during the 10-year (2009–2018) time-period. The bibliometric analysis includes a comprehensive set of indicators such as dynamics and trends of publications, journal impact factor, total cites, eigenfactor score, normalized eigenfactor, CiteScore, h-index, h-classic publications, most productive countries (or territories) and institutions, co-authorship collaboration about countries (territories), research themes, citation impact of co-occurrences keywords, intellectual structure, and knowledge commutation. We found that publications of RS OAJ presented an exponential growth in the past ten years. From 2010 to 2017 (for which complete years data were available), the h-index of RS OAJ is 67. From 2009–2018, RS OAJ includes publications from 129 countries (or territories) and 3826 institutions. The leading nations contributing articles, based on 2009–2018 data, and listed based on ranking were: China, United States, Germany, Italy, France, Spain, Canada, England, Australia, Netherlands, Japan, Switzerland and Austria. The leading institutions, also for the same period and listed based on ranking were: Chinese Academy of Sciences, Wuhan University, University of Chinese Academy of Sciences, Beijing Normal University, The university of Maryland, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, China University of Geosciences, United States Geological Survey, German Aerospace Centre, University of Twente, and California Institute of Technology. For the year 2017, RS OAJ had an impressive journal impact factor of 3.4060, a CiteScore of 4.03, eigenfactor score of 0.0342, and normalized eigenfactor score of 3.99. In addition, based on 2009–2018, data co-word analysis determined that “remote sensing”, “MODIS”, “Landsat”, “LiDAR” and “NDVI” are the high-frequency of author keywords co-occurrence in RS OAJ. The main themes of RS OAJ are multi-spectral and hyperspectral remote sensing, LiDAR scanning and forestry remote sensing monitoring, MODIS and LAI data applications, Remote sensing applications and Synthetic Aperture Radar (SAR). Through author keywords citation impact analysis, we find the most influential keyword is Unmanned Aerial Vehicle (UAV), followed, forestry, Normalized Difference Vegetation Index (NDVI), terrestrial laser scanning, airborne laser scanning, forestry inventory, urban heat island, monitoring, agriculture, and laser scanning. By analyzing the intellectual structure of RS OAJ, we identify the main reference publications and find that the themes are about Random Forests, MODIS vegetation indices and image analysis, etc. RS OAJ ranks first in cited journals and third in citing, this indicates that RS OAJ has the internal knowledge flow. Our results will bring more benefits to scholars, researchers and graduate students, who hopes to get a quick overview of the RS OAJ. And this article will also be the starting point for communication between scholars and practitioners. Finally, this paper proposed a nuanced h-index (nh-index) to measure productivity and intellectual contribution of authors by considering h-index based on whether the one is first, second, third, or nth author. This nuanced approach to determining h-index of authors is powerful indicator of an academician’s productivity and intellectual contribution.</span></p>","language":"English","publisher":"MPDI","doi":"10.3390/rs11010091","usgsCitation":"Zhang, Y., Thenkabail, P.S., and Wang, P., 2019, A bibliometric profile of the Remote Sensing Open Access Journal published by MDPI between 2009 and 2018: Remote Sensing, v. 11, no. 1, p. 1-34, https://doi.org/10.3390/rs11010091.","productDescription":"Article 91; 34 p.","startPage":"1","endPage":"34","ipdsId":"IP-103309","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":467911,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11010091","text":"Publisher Index Page"},{"id":361176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, YuYing","contributorId":213186,"corporation":false,"usgs":false,"family":"Zhang","given":"YuYing","email":"","affiliations":[{"id":38712,"text":"Faculty of Education, Dalian University, Dalian 116622, China","active":true,"usgs":false}],"preferred":false,"id":757060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":757059,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Peng","contributorId":213187,"corporation":false,"usgs":false,"family":"Wang","given":"Peng","email":"","affiliations":[{"id":38713,"text":"Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, China","active":true,"usgs":false}],"preferred":false,"id":757061,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70218277,"text":"70218277 - 2019 - Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data","interactions":[],"lastModifiedDate":"2021-02-24T13:13:25.614595","indexId":"70218277","displayToPublicDate":"2019-02-07T07:08:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data","docAbstract":"<p><span>Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a &gt;50% probability of obtaining weekly clear observations.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs11030328","usgsCitation":"Zhou, Q., Rover, J., Brown, J.F., Worstell, B.B., Howard, D., Wu, Z., Gallant, A.L., Rundquist, B., and Burke, M., 2019, Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data: Remote Sensing, v. 11, no. 3, 328, 23 p., https://doi.org/10.3390/rs11030328.","productDescription":"328, 23 p.","ipdsId":"IP-104526","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11030328","text":"Publisher Index Page"},{"id":383590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.2509765625,\n              47.90161354142077\n            ],\n            [\n              -96.3720703125,\n              47.90161354142077\n            ],\n            [\n              -96.3720703125,\n              49.009050809382046\n            ],\n            [\n              -97.2509765625,\n              49.009050809382046\n            ],\n            [\n              -97.2509765625,\n              47.90161354142077\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":810803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":810877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":810876,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Worstell, Bruce B. 0000-0001-8927-3336 worstell@usgs.gov","orcid":"https://orcid.org/0000-0001-8927-3336","contributorId":1815,"corporation":false,"usgs":true,"family":"Worstell","given":"Bruce","email":"worstell@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":810804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176973,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":810878,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":810880,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":810881,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rundquist, Bradley 0000-0002-2572-9792","orcid":"https://orcid.org/0000-0002-2572-9792","contributorId":251983,"corporation":false,"usgs":false,"family":"Rundquist","given":"Bradley","email":"","affiliations":[],"preferred":false,"id":810888,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Burke, Morgan","contributorId":251990,"corporation":false,"usgs":false,"family":"Burke","given":"Morgan","email":"","affiliations":[],"preferred":false,"id":810889,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70201728,"text":"70201728 - 2019 - Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska","interactions":[],"lastModifiedDate":"2022-04-14T19:31:06.616565","indexId":"70201728","displayToPublicDate":"2019-01-28T13:57:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska","docAbstract":"<p><span>Lakes in boreal lowlands cycle carbon and supply an important source of freshwater for wildlife and migratory waterfowl. The abundance and distribution of these lakes are supported, in part, by permafrost distribution, which is subject to change. Relationships between permafrost thaw and lake dynamics remain poorly known in most boreal regions. Here, new airborne electromagnetic (AEM) data collected during June 2010 and February 2016 were used to constrain deep permafrost distribution. AEM data were coupled with Landsat-derived lake surface-area data from 1979 through 2011 to inform temporal lake behavior changes in the 35 500- km</span><sup>2</sup><span>&nbsp;Yukon Flats ecoregion of Alaska. Together, over 1500 km of AEM data, and roughly 30 years of Landsat data were used to explore processes that drive lake dynamics across a variety of permafrost thaw states not possible in studies conducted with satellite imagery or field measurements alone. Clustered time-series data identified lakes with similar temporal dynamics. Clusters possessed similarities in lake permanence (i.e. ephemeral versus perennial), subsurface permafrost distribution, and proximity to rivers and streams. Of the clustered lakes, ~66% are inferred to have at least intermittent connectivity with other surface-water features, ~19% are inferred to have shallow subsurface connectivity to other surface water features that served as a low-pass filter for hydroclimatic fluctuations, and ~15% appear to be isolated by surrounding permafrost (i.e. no connectivity). Integrated analysis of AEM and Landsat data reveals a progression from relatively synchronous lake dynamics among disconnected lakes in the most spatially continuous, thick permafrost to quite high spatiotemporal heterogeneity in lake behavior among variably-connected lakes in regions with notably less continuous permafrost. Variability can be explained by the preferential development of thawed permeable gravel pathways for lateral water redistribution in this area. The general spatial progression in permafrost thaw state and lake area behavior may be extended to the temporal dimension. However, extensive permafrost thaw, beyond what is currently observed, is expected to promote ubiquitous subsurface connectivity, eventually evolving to a state of increased lake synchronicity.</span></p>","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/aaf06f","usgsCitation":"Rey, D., Walvoord, M., Minsley, B., Rover, J., and Singha, K., 2019, Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska: Environmental Research Letters, v. 14, no. 2, p. 1-13, https://doi.org/10.1088/1748-9326/aaf06f.","productDescription":"Article 025001; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-098493","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aaf06f","text":"Publisher Index Page"},{"id":360756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats","volume":"14","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-21","publicationStatus":"PW","scienceBaseUri":"5c5022c1e4b0708288f7e7cc","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211847,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle Ann","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":211849,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":755037,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":755038,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":755039,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70200539,"text":"70200539 - 2019 - Using remote sensing to quantify ecosystem site potential community structure and deviation in the Great Basin, United States","interactions":[],"lastModifiedDate":"2024-05-17T15:01:47.161889","indexId":"70200539","displayToPublicDate":"2019-01-01T11:35:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Using remote sensing to quantify ecosystem site potential community structure and deviation in the Great Basin, United States","docAbstract":"<p><span>The semi-arid Great Basin region in the Northwest U.S. is impacted by a suite of change agents including fire, grazing, and climate variability to which native vegetation can have low resilience and resistance. Assessing ecosystem condition in relation to these change agents is difficult due to a lack of a consistent and objective Site Potential (SP) information of the conditions biophysically possible at each site. Our objectives were to assess and quantify patterns in ecosystem condition, based on actual fractional component cover and a SP map and to evaluate drivers of change. We used long-term 90th percentile&nbsp;Landsat&nbsp;NDVI(Normalized Difference Vegetation Index) and biophysical variables to produce a map of SP. Ecosystem condition was assessed using two methods, first we integrated fractional components into an index which was regressed against SP. Regression confidence intervals were used to segment the study area into normal, over-, and under-performing relative to SP. Next, the relationships between SP and fractional component cover produced SP expected component cover, from which we mapped the actual cover deviation. Much of the study area is within the range of conditions expected by the SP model, but degraded conditions are more common than those above SP expectations. We found that shrub cover deviation is more positive at higher elevation, while&nbsp;</span>herbaceous<span>&nbsp;cover deviation has the opposite pattern, supporting the hypothesis that more resistant and resilient sites are less likely to change from the shrub dominated legacy. Another key finding was that regions with significant annual herbaceous invasions tend to have lower than expected bare ground and shrub cover.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2018.09.037","usgsCitation":"Rigge, M.B., Homer, C.G., Wylie, B.K., Gu, Y., Shi, H., Xian, G.Z., Meyer, D.K., and Bunde, B., 2019, Using remote sensing to quantify ecosystem site potential community structure and deviation in the Great Basin, United States: Ecological Indicators, v. 96, no. 1, p. 516-531, https://doi.org/10.1016/j.ecolind.2018.09.037.","productDescription":"16 p.","startPage":"516","endPage":"531","ipdsId":"IP-101049","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468010,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2018.09.037","text":"Publisher Index Page"},{"id":358741,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","volume":"96","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10a8dde4b034bf6a7e4d89","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":749407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":749408,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":749409,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gu, Yingxin 0000-0002-3544-1856","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":209983,"corporation":false,"usgs":false,"family":"Gu","given":"Yingxin","affiliations":[],"preferred":false,"id":749410,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":749411,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":749412,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":749414,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bunde, Brett 0000-0003-0228-779X brett.bunde.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":198821,"corporation":false,"usgs":true,"family":"Bunde","given":"Brett","email":"brett.bunde.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":749413,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70204439,"text":"70204439 - 2019 - Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data","interactions":[],"lastModifiedDate":"2019-07-23T14:41:00","indexId":"70204439","displayToPublicDate":"2018-10-01T14:38:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":836,"text":"Applied Geography","active":true,"publicationSubtype":{"id":10}},"title":"Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\"><span>Increasing tree&nbsp;canopy&nbsp;cover has led to increasing&nbsp;wildfire&nbsp;activity in conifer dominated areas of the southwestern United States. Estimating historical changes in the spatial distribution of tree canopy cover can provide further insights into the dynamics of forest and fuel conditions in these landscapes and help prioritize areas for restoration to mitigate wildfire risks and restore biological functioning. In this study, we explored the relationship between LiDAR derived canopy cover data and&nbsp;Landsat&nbsp;reflectance&nbsp;values, and derived a model to estimate percent canopy cover (PCC) on historical Landsat data from 1987 to 2015 for the Valles&nbsp;Caldera&nbsp;National Preserve (VCNP), located in the southwest Jemez Mountains of New Mexico. We developed a&nbsp;regression model&nbsp;between LiDAR generated canopy cover collected in June 2010 and Landsat Thematic Mapper (TM) reflectance values (bands 1–7 except band 6) and&nbsp;vegetation indices&nbsp;collected for the same date. About 5% (17,000) of the total LiDAR points (329,102) were used as training points and a separate, non-overlapping set of 17,000 points as test points to validate the regression model. A simple linear model with the red band (band 3;&nbsp;</span><i>R</i><sup><i>2</i></sup><span> = 0.70) was selected as the best model to predict PCC in the rest of the images for 1987–2015. In general, we found a strong consistency between the spatial dynamics of modelled tree canopy cover based on historical Landsat data, wildfire events and forest&nbsp;management practicesthat occurred during the same period. Results showed that about 11% of the&nbsp;study area&nbsp;experienced an increase in PCC for the period of 1987–2015 while 41% of the study area experienced a reduction in PCC during the same time period, mostly in the areas which were affected by stand replacing wildfires in 2011 and 2013. The results indicate an overall increase in medium and high canopy cover classes in specific&nbsp;regions&nbsp;of the study area, which could lead to hazardous wildfires such as those in 2011 and 2013. In the context of ongoing&nbsp;ecological restoration&nbsp;of these&nbsp;montane forests, predicted PCC of contemporary forests could help local managers to identify the areas in the need of immediate restoration efforts by focusing management practices on the areas with closed canopy.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeog.2018.07.024","usgsCitation":"Cain, J.W., Humagain1, K., Portillo-Quintero1, C., and Cox1, R.D., 2019, Estimating forest canopy cover dynamics in Valles Caldera National Preserve, New Mexico, using LiDAR and Landsat data: Applied Geography, v. 99, p. 120-132, https://doi.org/10.1016/j.apgeog.2018.07.024.","productDescription":"13 p.","startPage":"120","endPage":"132","ipdsId":"IP-083749","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":365870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Valles Caldera National Preserve","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.0562744140625,\n              35.51881428123057\n            ],\n            [\n              -105.9466552734375,\n              35.51881428123057\n            ],\n            [\n              -105.9466552734375,\n              36.328402729422656\n            ],\n            [\n              -107.0562744140625,\n              36.328402729422656\n            ],\n            [\n              -107.0562744140625,\n              35.51881428123057\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"99","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":766911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Humagain1, Kamal","contributorId":217501,"corporation":false,"usgs":false,"family":"Humagain1","given":"Kamal","email":"","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":766912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Portillo-Quintero1, Carlos","contributorId":217502,"corporation":false,"usgs":false,"family":"Portillo-Quintero1","given":"Carlos","email":"","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":766913,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cox1, Robert D.","contributorId":217503,"corporation":false,"usgs":false,"family":"Cox1","given":"Robert","email":"","middleInitial":"D.","affiliations":[{"id":37463,"text":"TTU","active":true,"usgs":false}],"preferred":false,"id":766914,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70202775,"text":"70202775 - 2019 - Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine","interactions":[],"lastModifiedDate":"2019-03-26T11:36:43","indexId":"70202775","displayToPublicDate":"2017-10-26T10:54:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine","docAbstract":"<p>A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016.</p>","language":"English","publisher":"MDPI","doi":"10.3390/rs9101065","usgsCitation":"Xiong, J., Thenkabail, P.S., James C. Tilton, Gumma, M.K., Teluguntla, P.G., Oliphant, A., Congalton, R., Yadav, K., and Gorelick, N., 2019, Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine: Remote Sensing, v. 9, no. 10, Article 1065: 27 p., https://doi.org/10.3390/rs9101065.","productDescription":"Article 1065: 27 p.","ipdsId":"IP-088538","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9101065","text":"Publisher Index Page"},{"id":362333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Africa","volume":"9","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Xiong, Jun 0000-0002-2320-0780 jxiong@usgs.gov","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":5276,"corporation":false,"usgs":true,"family":"Xiong","given":"Jun","email":"jxiong@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James C. 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