{"pageNumber":"24","pageRowStart":"575","pageSize":"25","recordCount":1869,"records":[{"id":70133366,"text":"70133366 - 2014 - Landsat 8 thermal infrared sensor geometric characterization and calibration","interactions":[],"lastModifiedDate":"2017-01-18T11:24:53","indexId":"70133366","displayToPublicDate":"2014-11-14T15:15:00","publicationYear":"2014","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":"Landsat 8 thermal infrared sensor geometric characterization and calibration","docAbstract":"<p>The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization challenging. TIRS geometric calibration thus relied heavily on on-orbit measurements. Since the two Landsat 8 payloads are complementary and generate combined Level 1 data products, the TIRS geometric performance requirements emphasize the co-alignment of the OLI and TIRS instrument fields of view and the registration of the OLI reflective bands to the TIRS long-wave infrared emissive bands. The TIRS on-orbit calibration procedures include measuring the TIRS-to-OLI alignment, refining the alignment of the three TIRS sensor chips, and ensuring the alignment of the two TIRS spectral bands. The two key TIRS performance metrics are the OLI reflective to TIRS emissive band registration accuracy, and the registration accuracy between the TIRS thermal bands. The on-orbit calibration campaign conducted during the commissioning period provided an accurate TIRS geometric model that enabled TIRS Level 1 data to meet all geometric accuracy requirements. Seasonal variations in TIRS-to-OLI alignment have led to several small calibration parameter adjustments since commissioning.</p>","language":"English","publisher":"MDPI","doi":"10.3390/rs61111153","usgsCitation":"Storey, J.C., Choate, M., and Moe, D., 2014, Landsat 8 thermal infrared sensor geometric characterization and calibration: Remote Sensing, v. 6, no. 11, p. 11153-11181, https://doi.org/10.3390/rs61111153.","productDescription":"29 p.","startPage":"11153","endPage":"11181","numberOfPages":"29","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057919","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472637,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61111153","text":"Publisher Index Page"},{"id":296114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"11","noUsgsAuthors":false,"publicationDate":"2014-11-11","publicationStatus":"PW","scienceBaseUri":"5467199ce4b04d4b7dbde52a","contributors":{"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":525033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":525034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moe, Donald dpmoe@usgs.gov","contributorId":127405,"corporation":false,"usgs":true,"family":"Moe","given":"Donald","email":"dpmoe@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":525035,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70188058,"text":"70188058 - 2014 - Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia","interactions":[],"lastModifiedDate":"2017-05-31T16:10:33","indexId":"70188058","displayToPublicDate":"2014-11-14T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia","docAbstract":"<p><span>Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2014WR015634","usgsCitation":"Midekisa, A., Senay, G., and Wimberly, M.C., 2014, Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia: Water Resources Research, v. 50, no. 11, p. 8791-8806, https://doi.org/10.1002/2014WR015634.","productDescription":"16 p.","startPage":"8791","endPage":"8806","ipdsId":"IP-060676","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472639,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2014wr015634","text":"Publisher Index 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senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":166812,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","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":696342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wimberly, Michael C.","contributorId":167855,"corporation":false,"usgs":false,"family":"Wimberly","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":696469,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70131483,"text":"70131483 - 2014 - On the downscaling of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates","interactions":[],"lastModifiedDate":"2017-01-18T11:27:04","indexId":"70131483","displayToPublicDate":"2014-11-07T17:00:00","publicationYear":"2014","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":"On the downscaling of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates","docAbstract":"<p>&nbsp;Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from &minus;16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.</p>","language":"English","publisher":"MDPI","doi":"10.3390/rs61110483","usgsCitation":"Singh, R.K., Senay, G.B., Velpuri, N.M., Bohms, S., and Verdin, J.P., 2014, On the downscaling of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates: Remote Sensing, v. 6, no. 11, p. 10483-10509, https://doi.org/10.3390/rs61110483.","productDescription":"27 p.","startPage":"10483","endPage":"10509","numberOfPages":"27","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057248","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472650,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61110483","text":"Publisher Index Page"},{"id":295950,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":295953,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.mdpi.com/2072-4292/6/11/10483"}],"volume":"6","issue":"11","noUsgsAuthors":false,"publicationDate":"2014-10-30","publicationStatus":"PW","scienceBaseUri":"545ddf17e4b0ba8303f8b625","contributors":{"authors":[{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":521245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":521246,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926 nvelpuri@usgs.gov","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":4441,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga","email":"nvelpuri@usgs.gov","middleInitial":"Manohar","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":521247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohms, Stefanie 0000-0002-2979-4655 sbohms@usgs.gov","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":3148,"corporation":false,"usgs":true,"family":"Bohms","given":"Stefanie","email":"sbohms@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":521248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":521249,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70135808,"text":"70135808 - 2014 - On-orbit performance of the Landsat 8 Operational Land Imager","interactions":[],"lastModifiedDate":"2017-04-21T15:57:22","indexId":"70135808","displayToPublicDate":"2014-11-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"On-orbit performance of the Landsat 8 Operational Land Imager","docAbstract":"<p><span>The Landsat 8 satellite was launched on February 11, 2013, to systematically collect multispectral images for detection and quantitative analysis of changes on the Earth’s surface. The collected data are stored at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and continue the longest archive of medium resolution Earth images. There are two imaging instruments onboard the satellite: the Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS). This paper summarizes radiometric performance of the OLI including the bias stability, the system noise, saturation and other artifacts observed in its data during the first 1.5 years on orbit. Detector noise levels remain low and Signal-To-Noise Ratio high, largely exceeding the requirements. Impulse noise and saturation are present in imagery, but have negligible effect on Landsat 8 products. Oversaturation happens occasionally, but the affected detectors quickly restore their nominal responsivity. Overall, the OLI performs very well on orbit and provides high quality products to the user community. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proc. SPIE 9218, Earth Observing Systems XIX","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Earth Observing Systems XIX","conferenceDate":"August 17, 2014","conferenceLocation":"San Diego, CA","language":"English","publisher":"SPIE","doi":"10.1117/12.2063338","usgsCitation":"Micijevic, E., Vanderwerff, K., Scaramuzza, P., Morfitt, R., Barsi, J.A., and Levy, R., 2014, On-orbit performance of the Landsat 8 Operational Land Imager, <i>in</i> Proc. SPIE 9218, Earth Observing Systems XIX, v. 9218, San Diego, CA, August 17, 2014, https://doi.org/10.1117/12.2063338.","ipdsId":"IP-059265","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9218","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4fe4b0c3010a8087d3","contributors":{"authors":[{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":536881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderwerff, Kelly kvanderwerff@usgs.gov","contributorId":4617,"corporation":false,"usgs":true,"family":"Vanderwerff","given":"Kelly","email":"kvanderwerff@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":536882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scaramuzza, Pat 0000-0002-2616-8456 pscar@usgs.gov","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":3970,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","email":"pscar@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":536883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morfitt, Ron 0000-0002-4777-4877 rmorfitt@usgs.gov","orcid":"https://orcid.org/0000-0002-4777-4877","contributorId":4097,"corporation":false,"usgs":true,"family":"Morfitt","given":"Ron","email":"rmorfitt@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":536884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":536885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Levy, Raviv","contributorId":131008,"corporation":false,"usgs":false,"family":"Levy","given":"Raviv","email":"","affiliations":[{"id":7209,"text":"SSAI / NASA / GSFC","active":true,"usgs":false}],"preferred":false,"id":536886,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70191714,"text":"70191714 - 2014 - A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest","interactions":[],"lastModifiedDate":"2017-11-08T17:06:40","indexId":"70191714","displayToPublicDate":"2014-11-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest","docAbstract":"<p><span>Cross comparison of satellite-derived land surface phenology (LSP) and ground measurements is useful to ensure the relevance of detected seasonal vegetation change to the underlying biophysical processes. While standard 16-day and 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index (VI)-based springtime LSP has been evaluated in previous studies, it remains unclear whether LSP with enhanced temporal and spatial resolutions can capture additional details of ground phenology. In this paper, we compared LSP derived from 500-m daily MODIS and 30-m MODIS-Landsat fused VI data with landscape phenology (LP) in a northern U.S. mixed forest. LP was previously developed from intensively observed deciduous and coniferous tree phenology using an upscaling approach. Results showed that daily MODIS-based LSP consistently estimated greenup onset dates at the study area (625 m × 625 m) level with 4.48 days of mean absolute error (MAE), slightly better than that of using 16-day standard VI (4.63 days MAE). For the observed study areas, the time series with increased number of observations confirmed that post-bud burst deciduous tree phenology contributes the most to vegetation reflectance change. Moreover, fused VI time series demonstrated closer correspondences with LP at the community level (0.1-20 ha) than using MODIS alone at the study area level (390 ha). The fused LSP captured greenup onset dates for respective forest communities of varied sizes and compositions with four days of the overall MAE. This study supports further use of spatiotemporally enhanced LSP for more precise phenological monitoring.</span></p>","language":"English","doi":"10.1109/TGRS.2014.2313558","usgsCitation":"Li, L., Schwartz, M., Wang, Z., Gao, F., Schaaf, C.B., Bin Tan, Morisette, J.T., and Zhang, X., 2014, A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest: IEEE Transactions on Geoscience and Remote Sensing, v. 52, no. 12, p. 7513-7526, https://doi.org/10.1109/TGRS.2014.2313558.","productDescription":"14 p.","startPage":"7513","endPage":"7526","ipdsId":"IP-053376","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":348521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Chequamegon–Nicolet National Forest","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.35362243652344,\n              45.83501885571072\n            ],\n            [\n              -90.10711669921875,\n              45.83501885571072\n            ],\n            [\n              -90.10711669921875,\n              45.98217232489232\n            ],\n            [\n              -90.35362243652344,\n              45.98217232489232\n            ],\n            [\n              -90.35362243652344,\n              45.83501885571072\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"52","issue":"12","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425c6e4b0dc0b45b4541e","contributors":{"authors":[{"text":"Li, Li 0000-0002-1641-3710","orcid":"https://orcid.org/0000-0002-1641-3710","contributorId":197290,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":713151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwartz, Mark D.","contributorId":11092,"corporation":false,"usgs":true,"family":"Schwartz","given":"Mark D.","affiliations":[],"preferred":false,"id":713152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Zhuosen","contributorId":197296,"corporation":false,"usgs":false,"family":"Wang","given":"Zhuosen","email":"","affiliations":[],"preferred":false,"id":713153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":713154,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schaaf, Crystal B.","contributorId":149538,"corporation":false,"usgs":false,"family":"Schaaf","given":"Crystal","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":713155,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bin Tan","contributorId":197299,"corporation":false,"usgs":false,"family":"Bin Tan","affiliations":[],"preferred":false,"id":713156,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":713150,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhang, Xiaoyang","contributorId":197726,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaoyang","email":"","affiliations":[],"preferred":false,"id":713157,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70188056,"text":"70188056 - 2014 - Examining change detection approaches for tropical mangrove monitoring","interactions":[],"lastModifiedDate":"2017-05-31T16:10:54","indexId":"70188056","displayToPublicDate":"2014-10-21T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Examining change detection approaches for tropical mangrove monitoring","docAbstract":"<p>This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.</p>","language":"English","publisher":"American Society of Photogrammetry and Remote Sensing","doi":"10.14358/PERS.80.10.983","usgsCitation":"Myint, S.W., Franklin, J., Buenemann, M., Kim, W., and Giri, C., 2014, Examining change detection approaches for tropical mangrove monitoring: Photogrammetric Engineering and Remote Sensing, v. 10, p. 983-993, https://doi.org/10.14358/PERS.80.10.983.","productDescription":"11 p.","startPage":"983","endPage":"993","ipdsId":"IP-059199","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472687,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.80.10.983","text":"Publisher Index Page"},{"id":341870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84c2e4b092b266f10d79","contributors":{"authors":[{"text":"Myint, Soe W.","contributorId":192372,"corporation":false,"usgs":false,"family":"Myint","given":"Soe","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":696474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franklin, Janet","contributorId":90833,"corporation":false,"usgs":true,"family":"Franklin","given":"Janet","affiliations":[],"preferred":false,"id":696475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buenemann, Michaela","contributorId":192374,"corporation":false,"usgs":false,"family":"Buenemann","given":"Michaela","email":"","affiliations":[],"preferred":false,"id":696476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Won","contributorId":192375,"corporation":false,"usgs":false,"family":"Kim","given":"Won","email":"","affiliations":[],"preferred":false,"id":696477,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696335,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70129102,"text":"70129102 - 2014 - Spatio-temporal analysis of gyres in oriented lakes on the Arctic Coastal Plain of northern Alaska based on remotely sensed images","interactions":[],"lastModifiedDate":"2014-10-17T10:33:42","indexId":"70129102","displayToPublicDate":"2014-10-17T10:23:00","publicationYear":"2014","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":"Spatio-temporal analysis of gyres in oriented lakes on the Arctic Coastal Plain of northern Alaska based on remotely sensed images","docAbstract":"The formation of oriented thermokarst lakes on the Arctic Coastal Plain of northern Alaska has been the subject of debate for more than half a century. The striking elongation of the lakes perpendicular to the prevailing wind direction has led to the development of a preferred wind-generated gyre hypothesis, while other hypotheses include a combination of sun angle, topographic aspect, and/or antecedent conditions. A spatio-temporal analysis of oriented thermokarst lake gyres with recent (Landsat 8) and historical (Landsat 4, 5, 7 and ASTER) satellite imagery of the Arctic Coastal Plain of northern Alaska indicates that wind-generated gyres are both frequent and regionally extensive. Gyres are most common in lakes located near the Arctic coast after several days of sustained winds from a single direction, typically the northeast, and decrease in number landward with decreasing wind energy. This analysis indicates that the conditions necessary for the Carson and Hussey (1962) wind-generated gyre for oriented thermokarst lake formation are common temporally and regionally and correspond spatially with the geographic distribution of oriented lakes on the Arctic Coastal Plain. Given an increase in the ice-free season for lakes as well as strengthening of the wind regime, the frequency and distribution of lake gyres may increase. This increase has implications for changes in northern high latitude aquatic ecosystems, particularly if wind-generated gyres promote permafrost degradation and thermokarst lake expansion.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"MDPI AG","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs6109170","usgsCitation":"Zhan, S., Beck, R.A., Hinkel, K., Liu, H., and Jones, B.M., 2014, Spatio-temporal analysis of gyres in oriented lakes on the Arctic Coastal Plain of northern Alaska based on remotely sensed images: Remote Sensing, v. 6, no. 10, p. 9170-9193, https://doi.org/10.3390/rs6109170.","productDescription":"24 p.","startPage":"9170","endPage":"9193","numberOfPages":"24","ipdsId":"IP-058095","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":472693,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6109170","text":"Publisher Index Page"},{"id":295445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":295441,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/rs6109170"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","volume":"6","issue":"10","noUsgsAuthors":false,"publicationDate":"2014-09-26","publicationStatus":"PW","scienceBaseUri":"5442218ce4b0192a5a42f3c5","contributors":{"authors":[{"text":"Zhan, Shengan","contributorId":83855,"corporation":false,"usgs":true,"family":"Zhan","given":"Shengan","email":"","affiliations":[],"preferred":false,"id":503432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Richard A.","contributorId":70316,"corporation":false,"usgs":true,"family":"Beck","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":503431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hinkel, Kenneth M.","contributorId":64170,"corporation":false,"usgs":true,"family":"Hinkel","given":"Kenneth M.","affiliations":[],"preferred":false,"id":503430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liu, Hongxing","contributorId":38075,"corporation":false,"usgs":true,"family":"Liu","given":"Hongxing","email":"","affiliations":[],"preferred":false,"id":503429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":503428,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70121281,"text":"sir20145163 - 2014 - Assessment of the spatial extent and height of flooding in Lake Champlain during May 2011, using satellite remote sensing and ground-based information","interactions":[],"lastModifiedDate":"2014-10-02T09:02:28","indexId":"sir20145163","displayToPublicDate":"2014-10-02T08:56:00","publicationYear":"2014","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":"2014-5163","title":"Assessment of the spatial extent and height of flooding in Lake Champlain during May 2011, using satellite remote sensing and ground-based information","docAbstract":"Landsat 5 and moderate resolution imaging spectro-radiometer satellite imagery were used to map the area of inundation of Lake Champlain, which forms part of the border between New York and Vermont, during May 2011. During this month, the lake’s water levels were record high values not observed in the previous 150 years. Lake inundation area determined from the satellite imagery is correlated with lake stage measured at three U.S. Geological Survey lake level gages to provide estimates of lake area at different lake levels (stage/area rating) and also compared with the levels of the high-water marks (HWMs) located on the Vermont side of the lake. The rating developed from the imagery shows a somewhat different relation than a similar stage/area rating developed from a medium-resolution digital elevation model (DEM) of the region. According to the rating derived from the imagery, the lake surface area during the peak lake level increased by about 17 percent above the average or “normal” lake level. By using a comparable rating developed from the DEM, the increase above average is estimated to be about 12 percent. The northern part of the lake (north of Burlington) showed the largest amount of flooding. Based on intersecting the inundation maps with the medium-resolution DEM, lake levels were not uniform around the lake. This is also evident from the lake level gage measurements and HWMs. The gage data indicate differences up to 0.5 feet between the northern and southern end of the lake. Additionally, the gage data show day-to-day and intradaily variation of the same range (0.5 foot). The high-water mark observations show differences up to 2 feet around the lake, with the highest level generally along the south- and west-facing shorelines. The data suggest that during most of May 2011, water levels were slightly higher and less variable in the northern part of the lake. These phenomena may be caused by wind effects as well as proximity to major river inputs to the lake. The inundation areas generated from the imagery generally coincide with flood mapping as estimated by the Federal Emergency Management Agency (FEMA) and shown on its digital flood insurance rate maps. Where areas in the flood inundation map derived from the imagery and the FEMA estimated flooded areas differ substantially, this difference may be due to differences between the flood magnitude at the time of the image and the assumed flood condition used for the FEMA modeling and mapping, wind/storage effects not accounted for by the FEMA modeling, and the resolution of the image compared to the DEM used in the FEMA mapping.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20145163","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Bjerklie, D.M., Trombley, T.J., and Olson, S.A., 2014, Assessment of the spatial extent and height of flooding in Lake Champlain during May 2011, using satellite remote sensing and ground-based information: U.S. Geological Survey Scientific Investigations Report 2014-5163, Report: vii, 18 p.; 1 Plate: 24 x 27 inches, https://doi.org/10.3133/sir20145163.","productDescription":"Report: vii, 18 p.; 1 Plate: 24 x 27 inches","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-051120","costCenters":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"links":[{"id":294753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20145163.jpg"},{"id":294750,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2014/5163/pdf/sir2014-5163.pdf"},{"id":294751,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2014/5163/"},{"id":294752,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2014/5163/figure/sir2014-5163_fig08.pdf"}],"country":"Canada, United States","otherGeospatial":"Lake Champlain","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"542e5b06e4b092f17df5a6a5","contributors":{"authors":[{"text":"Bjerklie, David M. 0000-0002-9890-4125 dmbjerkl@usgs.gov","orcid":"https://orcid.org/0000-0002-9890-4125","contributorId":3589,"corporation":false,"usgs":true,"family":"Bjerklie","given":"David","email":"dmbjerkl@usgs.gov","middleInitial":"M.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":498914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trombley, Thomas J. trombley@usgs.gov","contributorId":1803,"corporation":false,"usgs":true,"family":"Trombley","given":"Thomas","email":"trombley@usgs.gov","middleInitial":"J.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true}],"preferred":true,"id":498912,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olson, Scott A. 0000-0002-1064-2125 solson@usgs.gov","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":2059,"corporation":false,"usgs":true,"family":"Olson","given":"Scott","email":"solson@usgs.gov","middleInitial":"A.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":498913,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70127549,"text":"70127549 - 2014 - Characterizing recent and projecting future potential patterns of mountain pine beetle outbreaks in the Southern Rocky Mountains","interactions":[],"lastModifiedDate":"2014-10-02T09:50:27","indexId":"70127549","displayToPublicDate":"2014-09-30T09:43:00","publicationYear":"2014","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":"Characterizing recent and projecting future potential patterns of mountain pine beetle outbreaks in the Southern Rocky Mountains","docAbstract":"The recent widespread mountain pine beetle (MPB) outbreak in the Southern Rocky Mountains presents an opportunity to investigate the relative influence of anthropogenic, biologic, and physical drivers that have shaped the spatiotemporal patterns of the outbreak. The aim of this study was to quantify the landscape-level drivers that explained the dynamic patterns of MPB mortality, and simulate areas with future potential MPB mortality under projected climate-change scenarios in Grand County, Colorado, USA. The outbreak patterns of MPB were characterized by analysis of a decade-long Landsat time-series stack, aided by automatic attribution of change detected by the Landsat-based Detection of Trends in Disturbance and Recovery algorithm (LandTrendr). The annual area of new MPB mortality was then related to a suite of anthropogenic, biologic, and physical predictor variables under a general linear model (GLM) framework. Data from years 2001–2005 were used to train the model and data from years 2006–2011 were retained for validation. After stepwise removal of non-significant predictors, the remaining predictors in the GLM indicated that neighborhood mortality, winter mean temperature anomaly, and residential housing density were positively associated with MPB mortality, whereas summer precipitation was negatively related. The final model had an average area under the curve (AUC) of a receiver operating characteristic plot value of 0.72 in predicting the annual area of new mortality for the independent validation years, and the mean deviation from the base maps in the MPB mortality areal estimates was around 5%. The extent of MPB mortality will likely expand under two climate-change scenarios (RCP 4.5 and 8.5) in Grand County, which implies that the impacts of MPB outbreaks on vegetation composition and structure, and ecosystem functioning are likely to increase in the future.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Applied Geography","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeog.2014.09.012","usgsCitation":"Liang, L., Hawbaker, T., Chen, Y., Zhu, Z., and Gong, P., 2014, Characterizing recent and projecting future potential patterns of mountain pine beetle outbreaks in the Southern Rocky Mountains: Applied Geography, v. 55, p. 165-175, https://doi.org/10.1016/j.apgeog.2014.09.012.","productDescription":"11 p.","startPage":"165","endPage":"175","numberOfPages":"11","ipdsId":"IP-055165","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":472739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeog.2014.09.012","text":"Publisher Index Page"},{"id":294606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294594,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.apgeog.2014.09.012"}],"country":"United States","state":"Colorado","county":"Grand County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.653,39.6841 ], [ -106.653,40.4863 ], [ -105.6261,40.4863 ], [ -105.6261,39.6841 ], [ -106.653,39.6841 ] ] ] } } ] }","volume":"55","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"542bb80ae4b0abfb4c809669","chorus":{"doi":"10.1016/j.apgeog.2014.09.012","url":"http://dx.doi.org/10.1016/j.apgeog.2014.09.012","publisher":"Elsevier BV","authors":"Liang Lu, Hawbaker Todd J., Chen Yanlei, Zhu Zhiliang, Gong Peng","journalName":"Applied Geography","publicationDate":"12/2014","auditedOn":"3/22/2016","publiclyAccessibleDate":"9/19/2014"},"contributors":{"authors":[{"text":"Liang, Lu","contributorId":72714,"corporation":false,"usgs":true,"family":"Liang","given":"Lu","affiliations":[],"preferred":false,"id":502387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":502384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Yanlei","contributorId":18276,"corporation":false,"usgs":true,"family":"Chen","given":"Yanlei","email":"","affiliations":[],"preferred":false,"id":502385,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhi-Liang","contributorId":70726,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhi-Liang","affiliations":[],"preferred":false,"id":502386,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gong, Peng","contributorId":102393,"corporation":false,"usgs":true,"family":"Gong","given":"Peng","affiliations":[],"preferred":false,"id":502388,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70123861,"text":"70123861 - 2014 - Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park","interactions":[],"lastModifiedDate":"2014-09-10T11:13:48","indexId":"70123861","displayToPublicDate":"2014-09-10T11:07:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park","docAbstract":"Mosaics of tree clumps and openings are characteristic of forests dominated by frequent, low- and moderate-severity fires. When restoring these fire-suppressed forests, managers often try to reproduce these structures to increase ecosystem resilience. We examined unburned and burned forest structures for 1937 0.81 ha sample areas in Yosemite National Park, USA. We estimated severity for fires from 1984 to 2010 using the Landsat-derived Relativized differenced Normalized Burn Ratio (RdNBR) and measured openings and canopy clumps in five height strata using airborne LiDAR data. Because our study area lacked concurrent field data, we identified methods to allow structural analysis using LiDAR data alone. We found three spatial structures, canopy-gap, clump-open, and open, that differed in spatial arrangement and proportion of canopy and openings. As fire severity increased, the total area in canopy decreased while the number of clumps increased, creating a patchwork of openings and multistory tree clumps. The presence of openings > 0.3 ha, an approximate minimum gap size needed to favor shade-intolerant pine regeneration, increased rapidly with loss of canopy area. The range and variation of structures for a given fire severity were specific to each forest type. Low- to moderate-severity fires best replicated the historic clump-opening patterns that were common in forests with frequent fire regimes. Our results suggest that managers consider the following goals for their forest restoration: 1) reduce total canopy cover by breaking up large contiguous areas into variable-sized tree clumps and scattered large individual trees; 2) create a range of opening sizes and shapes, including ~ 50% of the open area in gaps > 0.3 ha; 3) create multistory clumps in addition to single story clumps; 4) retain historic densities of large trees; and 5) vary treatments to include canopy-gap, clump-open, and open mosaics across project areas to mimic the range of patterns found for each forest type in our study.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.07.041","usgsCitation":"Kane, V., North, M.P., Lutz, J.A., Churchill, D.J., Roberts, S.L., Smith, D.F., McGaughey, R.J., Kane, J.T., and Brooks, M.L., 2014, Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park: Remote Sensing of Environment, v. 151, p. 89-101, https://doi.org/10.1016/j.rse.2013.07.041.","productDescription":"13 p.","startPage":"89","endPage":"101","ipdsId":"IP-045760","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":293600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293599,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2013.07.041"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.886496,37.494762 ], [ -119.886496,38.185228 ], [ -119.195416,38.185228 ], [ -119.195416,37.494762 ], [ -119.886496,37.494762 ] ] ] } } ] }","volume":"151","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541157b1e4b0fe7e184a5531","contributors":{"authors":[{"text":"Kane, Van R.","contributorId":25873,"corporation":false,"usgs":true,"family":"Kane","given":"Van R.","affiliations":[],"preferred":false,"id":500417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"North, Malcolm P.","contributorId":9975,"corporation":false,"usgs":true,"family":"North","given":"Malcolm","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":500414,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lutz, James A.","contributorId":61350,"corporation":false,"usgs":true,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":500419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Churchill, Derek J.","contributorId":16763,"corporation":false,"usgs":true,"family":"Churchill","given":"Derek","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":500416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Susan L.","contributorId":85312,"corporation":false,"usgs":true,"family":"Roberts","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":500421,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Douglas F.","contributorId":76235,"corporation":false,"usgs":true,"family":"Smith","given":"Douglas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":500420,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGaughey, Robert J.","contributorId":36865,"corporation":false,"usgs":true,"family":"McGaughey","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":500418,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kane, Jonathan T.","contributorId":16329,"corporation":false,"usgs":true,"family":"Kane","given":"Jonathan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":500415,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":500413,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70120135,"text":"70120135 - 2014 - Marsh dieback, loss, and recovery mapped with satellite optical, airborne polarimetric radar, and field data","interactions":[],"lastModifiedDate":"2014-08-12T15:50:47","indexId":"70120135","displayToPublicDate":"2014-08-12T15:46:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Marsh dieback, loss, and recovery mapped with satellite optical, airborne polarimetric radar, and field data","docAbstract":"Landsat Thematic Mapper and Satellite Pour l'Observation de la Terre (SPOT) satellite based optical sensors, NASA Uninhabited Aerial Vehicle synthetic aperture radar (UAVSAR) polarimetric SAR (PolSAR), and field data captured the occurrence and the recovery of an undetected dieback that occurred between the summers of 2010, 2011, and 2012 in the <i>Spartina alterniflora</i> marshes of coastal Louisiana. Field measurements recorded the dramatic biomass decrease from 2010 to 2011 and a biomass recovery in 2012 dominated by a decrease of live biomass, and the loss of marsh as part of the dieback event. Based on an established relationship, the near-infrared/red vegetation index (VI) and site-specific measurements delineated a contiguous expanse of marsh dieback encompassing 6649.9 ha of 18,292.3 ha of <i>S. alterniflora</i> marshes within the study region. PolSAR data were transformed to variables used in biophysical mapping, and of this variable suite, the cross-polarization HV (horizontal send and vertical receive) backscatter was the best single indicator of marsh dieback and recovery. HV backscatter exhibited substantial and significant changes over the dieback and recovery period, tracked measured biomass changes, and significantly correlated with the live/dead biomass ratio. Within the context of regional trends, both HV and VI indicators started higher in pre-dieback marshes and exhibited substantially and statistically higher variability from year to year than that exhibited in the non-dieback marshes. That distinct difference allowed the capturing of the S. alterniflora marsh dieback and recovery; however, these changes were incorporated in a regional trend exhibiting similar but more subtle biomass composition changes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.07.002","usgsCitation":"Ramsey, E., Rangoonwala, A., Chi, Z., Jones, C.E., and Bannister, T., 2014, Marsh dieback, loss, and recovery mapped with satellite optical, airborne polarimetric radar, and field data: Remote Sensing of Environment, v. 152, p. 364-374, https://doi.org/10.1016/j.rse.2014.07.002.","productDescription":"11 p.","startPage":"364","endPage":"374","numberOfPages":"11","ipdsId":"IP-045076","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":292046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":292038,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2014.07.002"}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90.497591,29.22702 ], [ -90.497591,29.402539 ], [ -90.190336,29.402539 ], [ -90.190336,29.22702 ], [ -90.497591,29.22702 ] ] ] } } ] }","volume":"152","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53eb1c2ee4b0461e4475c429","contributors":{"authors":[{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":72769,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah W.","suffix":"III","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":497949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":497946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chi, Zhaohui","contributorId":8003,"corporation":false,"usgs":true,"family":"Chi","given":"Zhaohui","affiliations":[],"preferred":false,"id":497947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Cathleen E.","contributorId":11890,"corporation":false,"usgs":true,"family":"Jones","given":"Cathleen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":497948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bannister, Terri","contributorId":82836,"corporation":false,"usgs":true,"family":"Bannister","given":"Terri","email":"","affiliations":[],"preferred":false,"id":497950,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70134860,"text":"70134860 - 2014 - The Multi-Resolution Land Characteristics (MRLC) Consortium: 20 years of development and integration of USA national land cover data","interactions":[],"lastModifiedDate":"2018-12-21T13:03:39","indexId":"70134860","displayToPublicDate":"2014-08-11T00:00:00","publicationYear":"2014","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":"The Multi-Resolution Land Characteristics (MRLC) Consortium: 20 years of development and integration of USA national land cover data","docAbstract":"<p>The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Project (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.</p>","language":"English","publisher":"MDPI","doi":"10.3390/rs6087424","usgsCitation":"Wickham, J.D., Homer, C.G., Vogelmann, J., McKerrow, A., Mueller, R., Herold, N., and Coluston, J., 2014, The Multi-Resolution Land Characteristics (MRLC) Consortium: 20 years of development and integration of USA national land cover data: Remote Sensing, v. 6, no. 8, p. 7424-7441, https://doi.org/10.3390/rs6087424.","productDescription":"18 p.","startPage":"7424","endPage":"7441","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050924","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":38315,"text":"GAP Analysis Project","active":true,"usgs":true}],"links":[{"id":472824,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6087424","text":"Publisher Index Page"},{"id":296471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"8","noUsgsAuthors":false,"publicationDate":"2014-08-11","publicationStatus":"PW","scienceBaseUri":"5482e54ae4b0aa6d7785300e","contributors":{"authors":[{"text":"Wickham, James D.","contributorId":72278,"corporation":false,"usgs":false,"family":"Wickham","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":526624,"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":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":526623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":526626,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":4542,"corporation":false,"usgs":false,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":526627,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Rick","contributorId":101182,"corporation":false,"usgs":false,"family":"Mueller","given":"Rick","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":526628,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herold, Nate","contributorId":127749,"corporation":false,"usgs":false,"family":"Herold","given":"Nate","email":"","affiliations":[{"id":7054,"text":"NOAA/NMFS, Silver Spring, MD","active":true,"usgs":false}],"preferred":false,"id":526629,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Coluston, John","contributorId":127750,"corporation":false,"usgs":false,"family":"Coluston","given":"John","email":"","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":526630,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70148178,"text":"70148178 - 2014 - Linking multi-temporal satellite imagery to coastal wetland dynamics and bird distribution","interactions":[],"lastModifiedDate":"2015-05-26T11:01:51","indexId":"70148178","displayToPublicDate":"2014-08-10T12:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Linking multi-temporal satellite imagery to coastal wetland dynamics and bird distribution","docAbstract":"<p>Ecosystems are characterized by dynamic ecological processes, such as flooding and fires, but spatial models are often limited to a single measurement in time. The characterization of direct, fine-scale processes affecting animals is potentially valuable for management applications, but these are difficult to quantify over broad extents. Direct predictors are also expected to improve transferability of models beyond the area of study. Here, we investigated the ability of non-static and multi-temporal habitat characteristics to predict marsh bird distributions, while testing model generality and transferability between two coastal habitats. Distribution models were developed for king rail (<i>Rallus elegans</i>), common gallinule (<i>Gallinula galeata</i>), least bittern (<i>Ixobrychus exilis</i>), and purple gallinule (<i>Porphyrio martinica</i>) in fresh and intermediate marsh types in the northern Gulf Coast of Louisiana and Texas, USA. For model development, repeated point count surveys of marsh birds were conducted from 2009 to 2011. Landsat satellite imagery was used to quantify both annual conditions and cumulative, multi-temporal habitat characteristics. We used multivariate adaptive regression splines to quantify bird-habitat relationships for fresh, intermediate, and combined marsh habitats. Multi-temporal habitat characteristics ranked as more important than single-date characteristics, as temporary water was most influential in six of eight models. Predictive power was greater for marsh type-specific models compared to general models and model transferability was poor. Birds in fresh marsh selected for annual habitat characterizations, while birds in intermediate marsh selected for cumulative wetness and heterogeneity. Our findings emphasize that dynamic ecological processes can affect species distribution and species-habitat relationships may differ with dominant landscape characteristics.</p>","language":"English","publisher":"Elsevier Science B.V.","publisherLocation":"Amsterdam","doi":"10.1016/j.ecolmodel.2014.04.013","collaboration":"U.S. Geological Survey; U.S. Fish and Wildlife Service; Gulf Coast Joint Venture","usgsCitation":"Pickens, B.A., and King, S.L., 2014, Linking multi-temporal satellite imagery to coastal wetland dynamics and bird distribution: Ecological Modelling, v. 285, p. 1-12, https://doi.org/10.1016/j.ecolmodel.2014.04.013.","productDescription":"12 p.","startPage":"1","endPage":"12","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-040505","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":300782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5565994ce4b0d9246a9eb62f","contributors":{"authors":[{"text":"Pickens, Bradley A.","contributorId":140926,"corporation":false,"usgs":false,"family":"Pickens","given":"Bradley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":547606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":547536,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70103394,"text":"ds844 - 2014 - Land cover trends dataset, 1973-2000","interactions":[],"lastModifiedDate":"2017-03-29T12:37:51","indexId":"ds844","displayToPublicDate":"2014-08-06T08:44:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"844","title":"Land cover trends dataset, 1973-2000","docAbstract":"<p>The U.S. Geological Survey Land Cover Trends Project is releasing a 1973&ndash;2000 time-series land-use/land-cover dataset for the conterminous United States. The dataset contains 5 dates of land-use/land-cover data for 2,688 sample blocks randomly selected within 84 ecological regions. The nominal dates of the land-use/land-cover maps are 1973, 1980, 1986, 1992, and 2000. The land-use/land-cover maps were classified manually from Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus imagery using a modified Anderson Level I classification scheme. The resulting land-use/land-cover data has a 60-meter resolution and the projection is set to Albers Equal-Area Conic, North American Datum of 1983. The files are labeled using a standard file naming convention that contains the number of the ecoregion, sample block, and Landsat year. The downloadable files are organized by ecoregion, and are available in the ERDAS IMAGINE<sup>TM</sup> (.img) raster file format.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds844","usgsCitation":"Soulard, C.E., Acevedo, W., Auch, R.F., Sohl, T.L., Drummond, M.A., Sleeter, B.M., Sorenson, D.G., Kambly, S., Wilson, T.S., Taylor, J., Sayler, K., Stier, M.P., Barnes, C., Methven, S.C., Loveland, T., Headley, R., and Brooks, M.S., 2014, Land cover trends dataset, 1973-2000: U.S. Geological Survey Data Series 844, Report: v, 10 p.; National data, https://doi.org/10.3133/ds844.","productDescription":"Report: v, 10 p.; National data","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-049832","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science 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0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":493299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sorenson, Daniel G. 0000-0003-0365-9444 dsorenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0365-9444","contributorId":2898,"corporation":false,"usgs":true,"family":"Sorenson","given":"Daniel","email":"dsorenson@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":493294,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kambly, Steven 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0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":493295,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stier, Michael P. 0000-0002-8518-9855 mpstier@usgs.gov","orcid":"https://orcid.org/0000-0002-8518-9855","contributorId":3121,"corporation":false,"usgs":true,"family":"Stier","given":"Michael","email":"mpstier@usgs.gov","middleInitial":"P.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":493298,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Barnes, Christopher A. 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(Geography)","active":false,"usgs":true}],"preferred":false,"id":493296,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Headley, Rachel rheadley@usgs.gov","contributorId":1744,"corporation":false,"usgs":true,"family":"Headley","given":"Rachel","email":"rheadley@usgs.gov","affiliations":[],"preferred":true,"id":493290,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Brooks, Mark S. mbrooks@usgs.gov","contributorId":5296,"corporation":false,"usgs":true,"family":"Brooks","given":"Mark","email":"mbrooks@usgs.gov","middleInitial":"S.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":493301,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}}
,{"id":70119003,"text":"70119003 - 2014 - Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery","interactions":[],"lastModifiedDate":"2016-04-26T10:02:52","indexId":"70119003","displayToPublicDate":"2014-08-04T09:27:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery","docAbstract":"<p>The Mount Graham red squirrel (<i>Tamiasciurus hudsonicus grahamensis</i>) is an endemic subspecies located in the Pinale&ntilde;o Mountains of southeast Arizona. Living in a conifer forest on a sky-island surrounded by desert, the Mount Graham red squirrel is one of the rarest mammals in North America. Over the last two decades, drought, insect infestations, and fire destroyed much of its habitat. A federal recovery team is working on a plan to recover the squirrel and detailed information is necessary on its habitat requirements and population dynamics. Toward that goal I developed and compared three probabilistic models of Mount Graham red squirrel habitat with a geographic information system and logistic regression. Each model contained the same topographic variables (slope, aspect, elevation), but the Landsat model contained a greenness variable (Normalized Difference Vegetation Index) extracted from Landsat, the Lidar model contained three forest-inventory variables extracted from lidar, while the Hybrid model contained Landsat and lidar variables. The Hybrid model produced the best habitat classification accuracy, followed by the Landsat and Lidar models, respectively. Landsat-derived forest greenness was the best predictor of habitat, followed by topographic (elevation, slope, aspect) and lidar (tree height, canopy bulk density, and live basal area) variables, respectively. The Landsat model's probabilities were significantly correlated with all 12 lidar variables, indicating its utility for habitat mapping. While the Hybrid model produced the best classification results, only the Landsat model was suitable for creating a habitat time series or habitat&ndash;population function between 1986 and 2013. The techniques I highlight should prove valuable in the development of Landsat- or lidar-based habitat models range wide.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2014.07.004","usgsCitation":"Hatten, J.R., 2014, Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery: Ecological Modelling, v. 289, p. 106-123, https://doi.org/10.1016/j.ecolmodel.2014.07.004.","productDescription":"18 p.","startPage":"106","endPage":"123","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053195","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":291561,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":291556,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolmodel.2014.07.004"}],"country":"United States","state":"Arizona","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.859696,32.631505 ], [ -109.859696,32.650297 ], [ -109.827681,32.650297 ], [ -109.827681,32.631505 ], [ -109.859696,32.631505 ] ] ] } } ] }","volume":"289","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53e09030e4b0beb42bdc040c","contributors":{"authors":[{"text":"Hatten, James R. 0000-0003-4676-8093 jhatten@usgs.gov","orcid":"https://orcid.org/0000-0003-4676-8093","contributorId":3431,"corporation":false,"usgs":true,"family":"Hatten","given":"James","email":"jhatten@usgs.gov","middleInitial":"R.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":497568,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70132440,"text":"70132440 - 2014 - Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland","interactions":[],"lastModifiedDate":"2020-12-31T16:51:48.214552","indexId":"70132440","displayToPublicDate":"2014-08-01T11:00:00","publicationYear":"2014","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":"Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland","docAbstract":"<p>Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically &le;10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were &le;10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM&rsquo;s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.</p>","language":"English","publisher":"Elsevier, Inc.","publisherLocation":"Amsterdam, Holland","doi":"10.1016/j.jag.2014.01.008","usgsCitation":"Olexa, E.M., and Lawrence, R.L., 2014, Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland: International Journal of Applied Earth Observation and Geoinformation, v. 30, p. 30-41, https://doi.org/10.1016/j.jag.2014.01.008.","productDescription":"12 p.","startPage":"30","endPage":"41","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-050870","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":296058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Utah, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -112.87353515625,\n              41.4509614012039\n            ],\n            [\n              -110.379638671875,\n              40.455307212131494\n            ],\n            [\n              -109.302978515625,\n              42.439674178149424\n            ],\n            [\n              -111.90673828125,\n              43.5326204268101\n            ],\n            [\n              -112.87353515625,\n              41.4509614012039\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5465d635e4b04d4b7dbd662b","contributors":{"authors":[{"text":"Olexa, Edward M. 0000-0002-2000-6798 eolexa@usgs.gov","orcid":"https://orcid.org/0000-0002-2000-6798","contributorId":4448,"corporation":false,"usgs":true,"family":"Olexa","given":"Edward","email":"eolexa@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":522880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Rick L","contributorId":127018,"corporation":false,"usgs":false,"family":"Lawrence","given":"Rick","email":"","middleInitial":"L","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":522881,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70188034,"text":"70188034 - 2014 - Bringing an ecological view of change to Landsat-based remote sensing","interactions":[],"lastModifiedDate":"2017-05-31T15:11:30","indexId":"70188034","displayToPublicDate":"2014-08-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Bringing an ecological view of change to Landsat-based remote sensing","docAbstract":"<p><span>When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1890/130066","usgsCitation":"Kennedy, R., Andrefouet, S., Cohen, W., Gomez, C., Griffiths, P., Hais, M., Healey, S., Helmer, E.H., Hostert, P., Lyons, M., Meigs, G., Pflugmacher, D., Phinn, S., Powell, S., Scarth, P., Susmita, S., Schroeder, T.A., Schneider, A., Sonnenschein, R., Vogelmann, J., Wulder, M.A., and Zhu, Z., 2014, Bringing an ecological view of change to Landsat-based remote sensing: Frontiers in Ecology and the Environment, v. 12, no. 6, p. 339-346, https://doi.org/10.1890/130066.","productDescription":"8 p.","startPage":"339","endPage":"346","ipdsId":"IP-053956","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341956,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-06-18","publicationStatus":"PW","scienceBaseUri":"592fd63fe4b0e9bd0ea89701","contributors":{"authors":[{"text":"Kennedy, Robert E.","contributorId":41916,"corporation":false,"usgs":true,"family":"Kennedy","given":"Robert E.","affiliations":[],"preferred":false,"id":696261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andrefouet, Serge","contributorId":192335,"corporation":false,"usgs":false,"family":"Andrefouet","given":"Serge","email":"","affiliations":[],"preferred":false,"id":696262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cohen, Warren","contributorId":192336,"corporation":false,"usgs":false,"family":"Cohen","given":"Warren","affiliations":[],"preferred":false,"id":696263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomez, Cristina","contributorId":192337,"corporation":false,"usgs":false,"family":"Gomez","given":"Cristina","email":"","affiliations":[],"preferred":false,"id":696264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Griffiths, Patrick","contributorId":192338,"corporation":false,"usgs":false,"family":"Griffiths","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":696265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hais, Martin","contributorId":192339,"corporation":false,"usgs":false,"family":"Hais","given":"Martin","email":"","affiliations":[],"preferred":false,"id":696266,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Healey, Sean","contributorId":192340,"corporation":false,"usgs":false,"family":"Healey","given":"Sean","affiliations":[],"preferred":false,"id":696267,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Helmer, Eileen H.","contributorId":192341,"corporation":false,"usgs":false,"family":"Helmer","given":"Eileen","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":696268,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hostert, Patrick","contributorId":192342,"corporation":false,"usgs":false,"family":"Hostert","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":696269,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lyons, Mitchell","contributorId":192343,"corporation":false,"usgs":false,"family":"Lyons","given":"Mitchell","email":"","affiliations":[],"preferred":false,"id":696270,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Meigs, Garrett","contributorId":192344,"corporation":false,"usgs":false,"family":"Meigs","given":"Garrett","affiliations":[],"preferred":false,"id":696271,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pflugmacher, Dirk","contributorId":192345,"corporation":false,"usgs":false,"family":"Pflugmacher","given":"Dirk","email":"","affiliations":[],"preferred":false,"id":696272,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Phinn, Stuart","contributorId":192346,"corporation":false,"usgs":false,"family":"Phinn","given":"Stuart","affiliations":[],"preferred":false,"id":696273,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Powell, Scott","contributorId":192347,"corporation":false,"usgs":false,"family":"Powell","given":"Scott","affiliations":[],"preferred":false,"id":696274,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Scarth, Peter","contributorId":192348,"corporation":false,"usgs":false,"family":"Scarth","given":"Peter","email":"","affiliations":[],"preferred":false,"id":696275,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Susmita, Sen","contributorId":192349,"corporation":false,"usgs":false,"family":"Susmita","given":"Sen","email":"","affiliations":[],"preferred":false,"id":696276,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Schroeder, Todd A. taschroeder@fs.fed.us","contributorId":190802,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","email":"taschroeder@fs.fed.us","middleInitial":"A.","affiliations":[],"preferred":false,"id":696277,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Schneider, Annemarie","contributorId":192350,"corporation":false,"usgs":false,"family":"Schneider","given":"Annemarie","email":"","affiliations":[],"preferred":false,"id":696278,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Sonnenschein, Ruth","contributorId":192351,"corporation":false,"usgs":false,"family":"Sonnenschein","given":"Ruth","email":"","affiliations":[],"preferred":false,"id":696279,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":192352,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":696280,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wulder, Michael A.","contributorId":189990,"corporation":false,"usgs":false,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":696281,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":696282,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}}
,{"id":70101943,"text":"ofr20141071 - 2014 - Characterization of potential mineralization in Afghanistan: four permissive areas identified using imaging spectroscopy data","interactions":[],"lastModifiedDate":"2014-07-22T08:30:07","indexId":"ofr20141071","displayToPublicDate":"2014-07-18T11:26:00","publicationYear":"2014","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":"2014-1071","title":"Characterization of potential mineralization in Afghanistan: four permissive areas identified using imaging spectroscopy data","docAbstract":"As part of the U.S. Geological Survey and Department of Defense Task Force for Business and Stability Operations natural resources revitalization activities in Afghanistan, four permissive areas for mineralization, Bamyan 1, Farah 1, Ghazni 1, and Ghazni 2, have been identified using imaging spectroscopy data. To support economic development, the areas of potential mineralization were selected on the occurrence of selected mineral assemblages mapped using the HyMap™ data (kaolinite, jarosite, hydrated silica, chlorite, epidote, iron-bearing carbonate, buddingtonite, dickite, and alunite) that may be indicative of past mineralization processes in areas with limited or no previous mineral resource studies. Approximately 30 sites were initially determined to be candidates for areas of potential mineralization. Additional criteria and material used to refine the selection and prioritization process included existing geologic maps, Landsat Thematic Mapper data, and published literature. The HyMapTM data were interpreted in the context of the regional geologic and tectonic setting and used the presence of alteration mineral assemblages to identify areas with the potential for undiscovered mineral resources. Further field-sampling, mapping, and supporting geochemical analyses are necessary to fully substantiate and verify the specific deposit types in the four areas of potential mineralization.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141071","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"King, T., Berger, B.R., and Johnson, M., 2014, Characterization of potential mineralization in Afghanistan: four permissive areas identified using imaging spectroscopy data: U.S. Geological Survey Open-File Report 2014-1071, vi, 67 p., https://doi.org/10.3133/ofr20141071.","productDescription":"vi, 67 p.","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-053227","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":290456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141071.jpg"},{"id":290454,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1071/"},{"id":290455,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1071/pdf/ofr2014-1071.pdf"}],"scale":"100000","projection":"Transverse Mercator projection","datum":"World Geodetic System 1984","country":"Afghanistan","state":"Bamyan;Farah;Ghazni","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 62.0,30.0 ], [ 62.0,38.0 ], [ 74.0,38.0 ], [ 74.0,30.0 ], [ 62.0,30.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd50bbe4b0b290850f3827","contributors":{"authors":[{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":492809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berger, Byron R. bberger@usgs.gov","contributorId":1490,"corporation":false,"usgs":true,"family":"Berger","given":"Byron","email":"bberger@usgs.gov","middleInitial":"R.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":492808,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Michaela R. 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":1013,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela R.","email":"mrjohns@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":492807,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70188040,"text":"70188040 - 2014 - Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR","interactions":[],"lastModifiedDate":"2017-05-31T16:11:15","indexId":"70188040","displayToPublicDate":"2014-07-17T00:00:00","publicationYear":"2014","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":"Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR","docAbstract":"<p><span>Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs61212409","usgsCitation":"Peterson, B., and Nelson, K., 2014, Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR: Remote Sensing, v. 6, no. 125, p. 12409-12426, https://doi.org/10.3390/rs61212409.","productDescription":"18 p.","startPage":"12409","endPage":"12426","ipdsId":"IP-057857","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472873,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61212409","text":"Publisher Index 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,{"id":70112161,"text":"ofr20141108 - 2014 - Landsat and water: case studies of the uses and benefits of landsat imagery in water resources","interactions":[],"lastModifiedDate":"2014-06-26T10:16:32","indexId":"ofr20141108","displayToPublicDate":"2014-06-26T10:05:00","publicationYear":"2014","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":"2014-1108","title":"Landsat and water: case studies of the uses and benefits of landsat imagery in water resources","docAbstract":"<p>The Landsat program has been collecting and archiving moderate resolution earth imagery since 1972. The number of Landsat users and uses has increased exponentially since the enactment of a free and open data policy in 2008, which made data available free of charge to all users. Benefits from the information Landsat data provides vary from improving environmental quality to protecting public health and safety and informing decision makers such as consumers and producers, government officials and the public at large. Although some studies have been conducted, little is known about the total benefit provided by open access Landsat imagery.</p>\n<br/>\n<p>This report contains a set of case studies focused on the uses and benefits of Landsat imagery. The purpose of these is to shed more light on the benefits accrued from Landsat imagery and to gain a better understanding of the program’s value. The case studies tell a story of how Landsat imagery is used and what its value is to different private and public entities. Most of the case studies focus on the use of Landsat in water resource management, although some other content areas are included.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20141108","usgsCitation":"Serbina, L.O., and Miller, H.M., 2014, Landsat and water: case studies of the uses and benefits of landsat imagery in water resources: U.S. Geological Survey Open-File Report 2014-1108, xii, 61 p., https://doi.org/10.3133/ofr20141108.","productDescription":"xii, 61 p.","numberOfPages":"73","onlineOnly":"Y","ipdsId":"IP-052473","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":289072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20141108.jpg"},{"id":289070,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2014/1108/"},{"id":289071,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2014/1108/pdf/ofr2014-1108.pdf"}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -180.0,-90.0 ], [ -180.0,90.0 ], [ 180.0,90.0 ], [ 180.0,-90.0 ], [ -180.0,-90.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53b7b193e4b0388651d917de","contributors":{"authors":[{"text":"Serbina, Larisa O. lserbina@usgs.gov","contributorId":5474,"corporation":false,"usgs":true,"family":"Serbina","given":"Larisa","email":"lserbina@usgs.gov","middleInitial":"O.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":494571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Holly M. 0000-0003-0914-7570 millerh@usgs.gov","orcid":"https://orcid.org/0000-0003-0914-7570","contributorId":29544,"corporation":false,"usgs":true,"family":"Miller","given":"Holly","email":"millerh@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":false,"id":494572,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70187705,"text":"70187705 - 2014 - A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010","interactions":[],"lastModifiedDate":"2017-05-15T14:44:11","indexId":"70187705","displayToPublicDate":"2014-06-19T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010","docAbstract":"<p><span>Information on the present condition and spatiotemporal dynamics of mangrove forests is needed for land-change studies and integrated natural resources planning and management. Although several national mangrove estimates for the Philippines exist, information is unavailable at sufficient spatial and thematic detail for change analysis. Historical and contemporary mangrove distribution maps of the Philippines for 1990 and 2010 were prepared at nominal 30-m spatial resolution using Landsat satellite data. Image classification was performed using a supervised decision tree classification approach. Additionally, decadal land-cover change maps from 1990 to 2010 were prepared to depict changes in mangrove area. Total mangrove area decreased 10.5% from 1990 to 2010. Comparison of estimates produced from this study with selected historical mangrove area estimates revealed that total mangrove area decreased by approximately half (51.8%) from 1918 to 2010. This study provides the most current and reliable data regarding the Philippines mangrove area and spatial distribution and delineates where and when mangrove change has occurred in recent decades. The results from this study are useful for developing conservation strategies, biodiversity loss mitigation efforts, and future monitoring and analysis.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-13-00057.1","usgsCitation":"Long, J., Napton, D., Giri, C., and Graesser, J., 2014, A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010: Journal of Coastal Research, v. 30, no. 2, p. 260-271, https://doi.org/10.2112/JCOASTRES-D-13-00057.1.","productDescription":"12 p.","startPage":"260","endPage":"271","ipdsId":"IP-046144","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Phillippines","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[126.37681,8.41471],[126.47851,7.75035],[126.53742,7.18938],[126.19677,6.27429],[125.83142,7.29372],[125.36385,6.78649],[125.68316,6.04966],[125.39651,5.581],[124.21979,6.16136],[123.93872,6.88514],[124.24366,7.36061],[123.61021,7.83353],[123.29607,7.41888],[122.82551,7.45737],[122.0855,6.89942],[121.91993,7.19212],[122.31236,8.03496],[122.9424,8.31624],[123.48769,8.69301],[123.84115,8.24032],[124.60147,8.51416],[124.76461,8.96041],[125.47139,8.987],[125.41212,9.76033],[126.22271,9.28607],[126.30664,8.78249],[126.37681,8.41471]]],[[[123.98244,10.27878],[123.62318,9.95009],[123.30992,9.31827],[122.99588,9.02219],[122.38005,9.71336],[122.58609,9.98104],[122.83708,10.26116],[122.94741,10.88187],[123.49885,10.94062],[123.33777,10.26738],[124.07794,11.23273],[123.98244,10.27878]]],[[[118.50458,9.31638],[117.17427,8.3675],[117.66448,9.06689],[118.38691,9.6845],[118.98734,10.37629],[119.5115,11.36967],[119.68968,10.55429],[119.02946,10.00365],[118.50458,9.31638]]],[[[121.88355,11.89176],[122.48382,11.58219],[123.12022,11.58366],[123.10084,11.16593],[122.63771,10.74131],[122.00261,10.44102],[121.96737,10.90569],[122.03837,11.41584],[121.88355,11.89176]]],[[[125.50255,12.16269],[125.78346,11.04612],[125.01188,11.31145],[125.03276,10.97582],[125.27745,10.35872],[124.80182,10.13468],[124.76017,10.838],[124.4591,10.88993],[124.30252,11.49537],[124.89101,11.41558],[124.87799,11.79419],[124.26676,12.55776],[125.22712,12.53572],[125.50255,12.16269]]],[[[121.52739,13.06959],[121.26219,12.20556],[120.8339,12.7045],[120.32344,13.46641],[121.18013,13.4297],[121.52739,13.06959]]],[[[121.32131,18.50406],[121.9376,18.21855],[122.24601,18.47895],[122.33696,18.22488],[122.17428,17.81028],[122.51565,17.0935],[122.25231,16.26244],[121.66279,15.93102],[121.50507,15.12481],[121.72883,14.32838],[122.25893,14.2182],[122.70128,14.33654],[123.9503,13.78213],[123.85511,13.23777],[124.18129,12.99753],[124.07742,12.53668],[123.29804,13.02753],[122.92865,13.55292],[122.67136,13.18584],[122.03465,13.78448],[121.12638,13.63669],[120.62864,13.85766],[120.67938,14.27102],[120.99182,14.52539],[120.69334,14.75667],[120.56415,14.39628],[120.07043,14.97087],[119.92093,15.40635],[119.88377,16.3637],[120.28649,16.03463],[120.39005,17.59908],[120.71587,18.50523],[121.32131,18.50406]]]]},\"properties\":{\"name\":\"Philippines\"}}]}","volume":"30","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe38e4b0a7fdb43c8bfb","contributors":{"authors":[{"text":"Long, Jordan 0000-0002-4814-464X jlong@usgs.gov","orcid":"https://orcid.org/0000-0002-4814-464X","contributorId":3609,"corporation":false,"usgs":true,"family":"Long","given":"Jordan","email":"jlong@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Napton, Darrell","contributorId":176288,"corporation":false,"usgs":false,"family":"Napton","given":"Darrell","affiliations":[],"preferred":false,"id":695186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graesser, Jordan","contributorId":192030,"corporation":false,"usgs":false,"family":"Graesser","given":"Jordan","email":"","affiliations":[],"preferred":false,"id":695189,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70112906,"text":"70112906 - 2014 - Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data","interactions":[],"lastModifiedDate":"2014-06-18T13:37:34","indexId":"70112906","displayToPublicDate":"2014-06-18T13:28:00","publicationYear":"2014","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":"Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data","docAbstract":"Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"MDPI","doi":"10.3390/rs6065696","usgsCitation":"Liang, L., Chen, Y., Hawbaker, T., Zhu, Z., and Gong, P., 2014, Mapping mountain pine beetle mortality through growth trend analysis of time-series landsat data: Remote Sensing, v. 6, no. 6, p. 5696-5716, https://doi.org/10.3390/rs6065696.","productDescription":"21 p.","startPage":"5696","endPage":"5716","numberOfPages":"21","ipdsId":"IP-053363","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":472932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6065696","text":"Publisher Index Page"},{"id":288821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288757,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/rs6065696"}],"country":"United States","state":"Colorado","county":"Grand County","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -106.666667,39.666667 ], [ -106.666667,40.333333 ], [ -105.666667,40.333333 ], [ -105.666667,39.666667 ], [ -106.666667,39.666667 ] ] ] } } ] }","volume":"6","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-06-18","publicationStatus":"PW","scienceBaseUri":"53ae7773e4b0abf75cf2c133","contributors":{"authors":[{"text":"Liang, Lu","contributorId":72714,"corporation":false,"usgs":true,"family":"Liang","given":"Lu","affiliations":[],"preferred":false,"id":494906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Yanlei","contributorId":18276,"corporation":false,"usgs":true,"family":"Chen","given":"Yanlei","email":"","affiliations":[],"preferred":false,"id":494904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":494903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhi-Liang","contributorId":70726,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhi-Liang","affiliations":[],"preferred":false,"id":494905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gong, Peng","contributorId":102393,"corporation":false,"usgs":true,"family":"Gong","given":"Peng","affiliations":[],"preferred":false,"id":494907,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70104569,"text":"70104569 - 2014 - Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin","interactions":[],"lastModifiedDate":"2018-01-12T17:20:31","indexId":"70104569","displayToPublicDate":"2014-06-01T15:39:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin","docAbstract":"<p>The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1&thinsp;m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research: Biogeosciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/2013JG002594","usgsCitation":"Pastick, N.J., Jorgenson, M., Wylie, B.K., Rose, J.R., Rigge, M., and Walvoord, M.A., 2014, Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin: Journal of Geophysical Research: Biogeosciences, v. 119, no. 6, p. 1244-1265, https://doi.org/10.1002/2013JG002594.","productDescription":"22 p.","startPage":"1244","endPage":"1265","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056842","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013jg002594","text":"Publisher Index Page"},{"id":294946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294945,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/2013JG002594"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaskan Yukon River Basin","volume":"119","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-06-30","publicationStatus":"PW","scienceBaseUri":"542fbaaee4b092f17df61dfa","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","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},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":493735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M. Torre","contributorId":34848,"corporation":false,"usgs":true,"family":"Jorgenson","given":"M. Torre","affiliations":[],"preferred":false,"id":493738,"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":493734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rose, Joshua R.","contributorId":12395,"corporation":false,"usgs":true,"family":"Rose","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":493736,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rigge, Matthew 0000-0003-4471-8009","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":18295,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","affiliations":[],"preferred":false,"id":493737,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366 walvoord@usgs.gov","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":147211,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"walvoord@usgs.gov","middleInitial":"Ann","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":493739,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70124277,"text":"70124277 - 2014 - Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","interactions":[],"lastModifiedDate":"2014-09-11T13:56:39","indexId":"70124277","displayToPublicDate":"2014-06-01T13:46:29","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","docAbstract":"Agricultural production capacity contributes to food security in Afghanistan and is largely dependent on irrigated farming, mostly utilizing surface water fed by snowmelt. Because of the high contribution of irrigated crops (> 80%) to total agricultural production, knowing the spatial distribution and year-to-year variability in irrigated areas is imperative to monitoring food security for the country. We used 16-day composites of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to create 23-point time series for each year from 2000 through 2013. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm to map irrigated areas in Afghanistan for the last 14 years. In the absence of ground reference irrigated area information, we evaluated these maps with the irrigated areas classified from multiple snapshots of the landscape during the growing season from Landsat 5 optical and thermal sensor images. We were able to identify irrigated areas using Landsat imagery by selecting as irrigated those areas with Landsat-derived NDVI greater than 0.30–0.45, depending on the date of the Landsat image and surface temperature less than or equal to 310 Kelvin (36.9 ° C). Due to the availability of Landsat images, we were able to compare with the MODIS-derived maps for four years: 2000, 2009, 2010, and 2011. The irrigated areas derived from Landsat agreed well r<sup>2</sup> = 0.91 with the irrigated areas derived from MODIS, providing confidence in the MODIS NDVI threshold approach. The maps portrayed a highly dynamic irrigated agriculture practice in Afghanistan, where the amount of irrigated area was largely determined by the availability of surface water, especially snowmelt, and varied by as much as 30% between water surplus and water deficit years. During the past 14 years, 2001, 2004, and 2008 showed the lowest levels of irrigated area (~ 1.5 million hectares), attesting to the severe drought conditions in those years, whereas 2009, 2012 and 2013 registered the largest irrigated area (~ 2.5 million hectares) due to record snowpack and snowmelt in the region. The model holds promise the ability to provide near-real-time (by the end of the growing seasons) estimates of irrigated area, which are beneficial for food security monitoring as well as subsequent decision making for the country. While the model is developed for Afghanistan, it can be adopted with appropriate adjustments in the derived threshold values to map irrigated areas elsewhere.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.04.008","usgsCitation":"Pervez, M., Budde, M., and Rowland, J., 2014, Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI: Remote Sensing of Environment, v. 149, p. 155-165, https://doi.org/10.1016/j.rse.2014.04.008.","productDescription":"11 p.","startPage":"155","endPage":"165","numberOfPages":"11","ipdsId":"IP-049479","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":293759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293755,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2014.04.008"}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5412b9b1e4b0239f1986baa5","contributors":{"authors":[{"text":"Pervez, Md Shahriar 0000-0003-3417-1871 shahriar.pervez.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-3417-1871","contributorId":74230,"corporation":false,"usgs":true,"family":"Pervez","given":"Md Shahriar","email":"shahriar.pervez.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":500640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budde, Michael 0000-0002-9098-2751","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":36867,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","affiliations":[],"preferred":false,"id":500639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":3108,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":500638,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70104213,"text":"70104213 - 2014 - Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation","interactions":[],"lastModifiedDate":"2014-05-13T10:37:49","indexId":"70104213","displayToPublicDate":"2014-05-13T10:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation","docAbstract":"There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, <i>Typha</i> spp. and <i>Schoenoplectus acutus</i>, at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R<sup>2</sup> = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R<sup>2</sup> = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species (<i>Typha</i> spp.: %RMSE = 18.5%; <i>S. acutus</i>: %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R<sup>2</sup> = 0.56, %RMSE = 20.9%, compared to R<sup>2</sup> = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R<sup>2</sup> = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.04.003","usgsCitation":"Byrd, K.B., O'Connell, J., Di Tommaso, S., and Kelly, M., 2014, Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation: Remote Sensing of Environment, v. 149, p. 166-180, https://doi.org/10.1016/j.rse.2014.04.003.","productDescription":"15 p.","startPage":"166","endPage":"180","numberOfPages":"15","ipdsId":"IP-052200","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":287071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287072,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2014.04.003"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-san Joaquin River Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.7545,37.3797 ], [ -122.7545,38.2715 ], [ -121.2455,38.2715 ], [ -121.2455,37.3797 ], [ -122.7545,37.3797 ] ] ] } } ] }","volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"537330d2e4b04970612788ae","chorus":{"doi":"10.1016/j.rse.2014.04.003","url":"http://dx.doi.org/10.1016/j.rse.2014.04.003","publisher":"Elsevier BV","authors":"Byrd Kristin B., O'Connell Jessica L., Di Tommaso Stefania, Kelly Maggi","journalName":"Remote Sensing of Environment","publicationDate":"6/2014"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":493639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Connell, Jessica L.","contributorId":86265,"corporation":false,"usgs":true,"family":"O'Connell","given":"Jessica L.","affiliations":[],"preferred":false,"id":493642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Di Tommaso, Stefania","contributorId":9965,"corporation":false,"usgs":true,"family":"Di Tommaso","given":"Stefania","email":"","affiliations":[],"preferred":false,"id":493640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelly, Maggi","contributorId":14275,"corporation":false,"usgs":true,"family":"Kelly","given":"Maggi","affiliations":[],"preferred":false,"id":493641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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