To improve understanding of the distribution of ecologically important, ephemeral wetland habitats across the Great Plains, the occurrence and distribution of surface water in playa wetland complexes were documented for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. This information is important because it informs land and wildlife managers about the timing and location of habitat availability. Data with an accurate timestamp that indicate the presence of water, the percent of the area inundated with water, and the spatial distribution of playa wetlands with water are needed for a host of resource inventory, monitoring, and research applications. For example, the distribution of inundated wetlands forms the spatial pattern of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface waters that recharge the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Documentation of these spatially and temporally intricate processes, here, provides data required to assess connections between inundation and multiple environmental drivers, such as climate, land use, soil, and topography. Climate drivers are understood to interact with land cover, land use and soil attributes in determining the amount of water that flows overland into playa wetlands. Results indicated significant spatial variability represented by differences in the percent of playas inundated among States within the GPLCC. Further, analysis-of-variance comparison of differences in inundation between years showed significant differences in all cases. Although some connections with seasonal moisture patterns may be observed, the complex spatial-temporal gradients of precipitation, temperature, soils, and land use need to be combined as covariates in multivariate models to effectively account for these patterns. We demonstrate the feasibility of using classification of Landsat satellite imagery to describe playa-wetland inundation across years and seasons. Evaluating classifications representing only 4 years of imagery, we found significant year-to-year and state-to-state differences in inundation rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171166","collaboration":"Prepared in cooperation with the Great Plains Landscape Conservation Cooperative, U.S. Fish and Wildlife Service, Albuquerque, New Mexico","usgsCitation":"Manier, D.J., and Rover, J.R., 2018, Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region: U.S. Geological Survey Open-File Report 2017–1166, 20 p., https://doi.org/10.3133/ofr20171166.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-073569","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438012,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MW2GCN","text":"USGS data release","linkHelpText":"Landsat classification of surface water for multiple seasons to monitor inundation of playa wetlands"},{"id":351569,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1166/ofr20171166.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1166"},{"id":351568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1166/coverthb.jpg"}],"country":"United States","otherGeospatial":"Great Plains Landscape Conservation Cooperative","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 30\n ],\n [\n -96,\n 30\n ],\n [\n -96,\n 44\n ],\n [\n -106,\n 44\n ],\n [\n -106,\n 30\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
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In this study, we demonstrated that the Landsat-8 Operational Land Imager (OLI) sensor is a powerful tool that can provide periodic and system-wide information on the condition of drinking water reservoirs. The OLI is a multispectral radiometer (30 m spatial resolution) that allows ecosystem observations at spatial and temporal scales that allow the environmental community and water managers another means to monitor changes in water quality not feasible with field-based monitoring. Using the provisional Land Surface Reflectance product and field-collected chlorophyll-a (chl-a) concentrations from drinking water monitoring programs in North Carolina and Rhode Island, we compared five established approaches for estimating chl-aconcentrations using spectral data. We found that using the three band reflectance approach with a combination of OLI spectral bands 1, 3, and 5 produced the most promising results for accurately estimating chl-a concentrations in lakes (R2 value of 0.66; root mean square error value of 8.9 µg l−1). Using this model, we forecast the spatial and temporal variability of chl-a for Jordan Lake, a recreational and drinking water source in piedmont North Carolina and several small ponds that supply drinking water in southeastern Rhode Island.
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We used the isotopic evaporation signal in water and remote sensing to examine wetland‐stream hydrologic connectivity within the Pipestem Creek watershed, North Dakota, a watershed dominated by prairie‐pothole wetlands. Pipestem Creek exhibited an evaporated‐water signal that had approximately half the isotopic‐enrichment signal found in most evaporatively enriched prairie‐pothole wetlands. Groundwater adjacent to Pipestem Creek had isotopic values that indicated recharge from winter precipitation and had no significant evaporative enrichment, indicating that enriched surface water did not contribute significantly to groundwater discharging into Pipestem Creek. The estimated surface water area necessary to generate the evaporation signal within Pipestem Creek was highly dynamic, varied primarily with the amount of discharge, and was typically greater than the immediate Pipestem Creek surface water area, indicating that surficial flow from wetlands contributed to stream flow throughout the summer. We propose a dynamic range of spilling thresholds for prairie‐pothole wetlands across the watershed allowing for wetland inputs even during low‐flow periods. Combining Landsat estimates with the isotopic approach allowed determination of potential (Landsat) and actual (isotope) contributing areas in wetland‐dominated systems. This combined approach can give insights into the changes in location and magnitude of surface water and groundwater pathways over time. This approach can be used in other areas where evaporation from wetlands results in a sufficient evaporative isotopic signal.
","language":"English","publisher":"AGU","doi":"10.1002/2017WR021016","usgsCitation":"Brooks, J.R., Mushet, D.M., Vanderhoof, M.K., Leibowitz, S.G., Christensen, J.R., Neff, B., Rosenberry, D.O., Rugh, W.D., and Alexander, L., 2018, Estimating wetland connectivity to streams in the Prairie Pothole Region: An isotopic and remote sensing approach: Water Resources Research, v. 54, no. 2, p. 955-977, https://doi.org/10.1002/2017WR021016.","productDescription":"23 p.","startPage":"955","endPage":"977","ipdsId":"IP-086197","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469082,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5903587","text":"Publisher Index Page"},{"id":353957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","volume":"54","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-09","publicationStatus":"PW","scienceBaseUri":"5afee740e4b0da30c1bfc1cb","contributors":{"authors":[{"text":"Brooks, J. 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R.","contributorId":204686,"corporation":false,"usgs":false,"family":"Christensen","given":"J.","email":"","middleInitial":"R.","affiliations":[{"id":36974,"text":"U.S. Environmental Protection Agency, National Exposure Research Laboratory, Las Vegas, NV","active":true,"usgs":false}],"preferred":false,"id":734684,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Neff, Brian 0000-0003-3718-7350 bneff@usgs.gov","orcid":"https://orcid.org/0000-0003-3718-7350","contributorId":198885,"corporation":false,"usgs":true,"family":"Neff","given":"Brian","email":"bneff@usgs.gov","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":734685,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":734687,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rugh, W. 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For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.09.029","usgsCitation":"Healey, S.P., Cohen, W., Yang, Z., Brewer, C.K., Brooks, E.B., Gorelick, N., Hernandez, A.J., Huang, C., Hughes, M.J., Kennedy, R.E., Loveland, T., Moisen, G.G., Schroeder, T.A., Stehman, S.V., Vogelmann, J., Woodcock, C.E., Yang, L., and Zhu, Z., 2018, Mapping forest change using stacked generalization: An ensemble approach: Remote Sensing of Environment, v. 204, p. 717-728, https://doi.org/10.1016/j.rse.2017.09.029.","productDescription":"12 p.","startPage":"717","endPage":"728","ipdsId":"IP-087348","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469105,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.09.029","text":"Publisher Index 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The sagebrush (Artemisia spp.) steppe ecosystem of the northwest USA has been impacted by the invasion of exotic grasses, increasing fire return intervals, changing land management practices, and fragmentation, often lowering the overall resilience to change. We utilized contemporary and historical Landsat imagery, field data, and regression tree models to produce fractional cover maps of rangeland components (shrub, sagebrush, herbaceous, bare ground, and litter) through the last 30 years. Our main goals were to (1) investigate rangeland component trends over 30 years, (2) evaluate the magnitude and direction of trends in components and climate drivers and their relationship, and (3) assess component trends influenced by climate. Results indicated that over the study period, shrub, sage, herbaceous, and litter cover decreased, while bare ground cover increased. Measured rates of change ranged from − 0.14% decade−1 for shrub cover to 0.05% decade−1 for bare ground, whereas herbaceous and litter cover trends were negligible. Net landscape cover changes were consistent with expectations of climate change and disturbance producing a loss of biotic cover, and converting a portion of shrub and sagebrush to herbaceous cover. Overall, fire and related successional recovery was the greatest change agent for all components in terms of area and cover change, while increasing minimum temperature, at a rate of 0.66°C decade−1, was found to be the most significant climate driver.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-017-0191-3","usgsCitation":"Shi, H., Rigge, M.B., Homer, C.G., Xian, G.Z., Meyer, D., and Bunde, B., 2018, Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management: Ecosystems, v. 21, no. 5, p. 913-929, https://doi.org/10.1007/s10021-017-0191-3.","productDescription":"17 p.","startPage":"913","endPage":"929","ipdsId":"IP-079676","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.1904296875,\n 40.97575093157534\n ],\n [\n -118.16894531249999,\n 40.97575093157534\n ],\n [\n -118.16894531249999,\n 42.7349091465156\n ],\n [\n -120.1904296875,\n 42.7349091465156\n ],\n [\n -120.1904296875,\n 40.97575093157534\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f83a2de4b063d5d309808a","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717210,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192361,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717212,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bunde, Brett 0000-0003-0228-779X brett.bunde.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":198821,"corporation":false,"usgs":true,"family":"Bunde","given":"Brett","email":"brett.bunde.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717213,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190445,"text":"70190445 - 2018 - Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","interactions":[],"lastModifiedDate":"2018-03-05T15:50:35","indexId":"70190445","displayToPublicDate":"2017-08-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","title":"Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin","docAbstract":"Northern tamarisk beetles (Diorhabda carinulata) were released in the Upper Colorado River Basin in the United States in\r\n2004–2007 to defoliate introduced tamarisk shrubs (Tamarix spp.) in the region’s riparian zones. The primary purpose was\r\nto control the invasive shrub and reduce evapotranspiration (ET) by tamarisk in an attempt to increase stream flows. We\r\nevaluated beetle–tamarisk interactions with MODIS and Landsat imagery on 13 river systems, with vegetation indices used\r\nas indicators of the extent of defoliation and ET. Beetles are widespread and exhibit a pattern of colonize–defoliate–emigrate,\r\nso that riparian zones contain a mosaic of completely defoliated, partially defoliated, and refoliated tamarisk stands. Based\r\non satellite data and ET algorithms, mean ET before beetle release (2000–2006) was 416 mm/year compared to postrelease\r\n(2007–2015) ET of 355 mm/year (p<0.05) for a net reduction of 61 mm/year. This is lower than initial literature projections\r\nthat ET would be reduced by 300–460 mm/year. Reasons for the lower-than-expected ET reductions are because baseline ET\r\nrates are lower than initially projected, and percentage ET reduction is low because tamarisk stands tend to regrow new leaves\r\nafter defoliation and other plants help maintain canopy cover. Overall reductions in tamarisk green foliage during the study\r\nare 21%. However, ET in the Upper Basin has shown a steady decline since 2007 and equilibrium has not yet been reached.\r\nDefoliation is now proceeding from the Upper Basin into the Lower Basin at a rate of 40 km/year, much faster than initially\r\nprojected.","language":"English","publisher":"Wiley","doi":"10.1111/rec.12575","usgsCitation":"Nagler, P.L., Nguyen, U., Bateman, H.L., Jarchow, C., Glenn, E., Waugh, W.J., and van Riper, C., 2018, Northern tamarisk beetle (Diorhabda carinulata) and tamarisk (Tamarix spp.) interactions in the Colorado River basin: Restoration Ecology, v. 26, no. 2, p. 348-359, https://doi.org/10.1111/rec.12575.","productDescription":"12 p.","startPage":"348","endPage":"359","ipdsId":"IP-079583","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":345398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado River ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.78515624999999,\n 33.94335994657882\n ],\n [\n -107.68798828125,\n 33.94335994657882\n ],\n [\n -107.68798828125,\n 39.436192999314095\n ],\n [\n -114.78515624999999,\n 39.436192999314095\n ],\n [\n -114.78515624999999,\n 33.94335994657882\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-25","publicationStatus":"PW","scienceBaseUri":"59a9203ee4b07e1a023ccd95","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":709185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nguyen, Uyen","contributorId":71863,"corporation":false,"usgs":false,"family":"Nguyen","given":"Uyen","email":"","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":709186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bateman, Heather L.","contributorId":72294,"corporation":false,"usgs":true,"family":"Bateman","given":"Heather","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":709187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":709188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":709189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waugh, William J.","contributorId":196107,"corporation":false,"usgs":false,"family":"Waugh","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":709190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":709191,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70191887,"text":"70191887 - 2018 - Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery","interactions":[],"lastModifiedDate":"2018-03-05T15:48:21","indexId":"70191887","displayToPublicDate":"2017-08-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery","docAbstract":"In the USA, climate change is expected to have an adverse impact on slope stability in Alaska. However, to date, there has been limited work done in Alaska to assess if changes in slope stability are occurring. To address this issue, we used 30-m Landsat imagery acquired from 1984 to 2016 to establish an inventory of 24 rock avalanches in a 5000-km2 area of Glacier Bay National Park and Preserve in southeast Alaska. A search of available earthquake catalogs revealed that none of the avalanches were triggered by earthquakes. Analyses of rock-avalanche magnitude, mobility, and frequency reveal a cluster of large (areas ranging from 5.5 to 22.2 km2), highly mobile (height/length < 0.3) rock avalanches that occurred from June 2012 through June 2016 (near the end of the 33-year period of record). These rock avalanches began about 2 years after the long-term trend in mean annual maximum air temperature may have exceeded 0 °C. Possibly more important, most of these rock avalanches occurred during a multiple-year period of record-breaking warm winter and spring air temperatures. These observations suggested to us that rock avalanches in the study area may be becoming larger because of rock-permafrost degradation. However, other factors, such as accumulating elastic strain, glacial thinning, and increased precipitation, may also play an important role in preconditioning slopes for failure during periods of warm temperatures.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-017-0879-7","usgsCitation":"Coe, J.A., Bessette-Kirton, E., and Geertsema, M., 2018, Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery: Landslides, v. 15, no. 3, p. 393-407, https://doi.org/10.1007/s10346-017-0879-7.","productDescription":"15 p.","startPage":"393","endPage":"407","ipdsId":"IP-088763","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469192,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10346-017-0879-7","text":"Publisher Index Page"},{"id":346920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -137.076416015625,\n 59.07444815466584\n ],\n [\n -137.28515624999997,\n 59.012284446745966\n ],\n [\n -137.4554443359375,\n 58.91599192355906\n ],\n [\n -137.515869140625,\n 58.91031927906603\n ],\n [\n -138.0267333984375,\n 58.87910308023475\n ],\n [\n -138.01025390625,\n 58.77389612171181\n ],\n [\n -137.8070068359375,\n 58.60547223430314\n ],\n [\n -137.30712890625,\n 58.43335765926364\n ],\n [\n -136.6644287109375,\n 58.16780532399956\n ],\n [\n -136.5380859375,\n 58.208344086647706\n ],\n [\n -136.4007568359375,\n 58.28350775348904\n ],\n [\n -136.1865234375,\n 58.306602995027966\n ],\n [\n -136.0052490234375,\n 58.35563036280964\n ],\n [\n -136.07391357421875,\n 58.51234832198017\n ],\n [\n -136.29638671875,\n 58.69406761967911\n ],\n [\n -136.4227294921875,\n 58.79382463666851\n ],\n [\n -136.6094970703125,\n 58.87058467868075\n ],\n [\n -136.7962646484375,\n 58.89897119532359\n ],\n [\n -136.9061279296875,\n 58.91315571775059\n ],\n [\n -136.9281005859375,\n 58.96417180405949\n ],\n [\n -137.076416015625,\n 59.07444815466584\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-31","publicationStatus":"PW","scienceBaseUri":"59e86834e4b05fe04cd4d1ea","contributors":{"authors":[{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713543,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bessette-Kirton, Erin 0000-0002-2797-0694 ebessette-kirton@usgs.gov","orcid":"https://orcid.org/0000-0002-2797-0694","contributorId":177153,"corporation":false,"usgs":true,"family":"Bessette-Kirton","given":"Erin","email":"ebessette-kirton@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":713544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Geertsema, Marten","contributorId":197464,"corporation":false,"usgs":false,"family":"Geertsema","given":"Marten","email":"","affiliations":[],"preferred":false,"id":713545,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190589,"text":"70190589 - 2018 - The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware","interactions":[],"lastModifiedDate":"2018-03-29T12:51:13","indexId":"70190589","displayToPublicDate":"2017-06-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware","docAbstract":"The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-017-9554-y","usgsCitation":"Vanderhoof, M.K., Distler, H., Lang, M.W., and Alexander, L.C., 2018, The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware: Wetlands Ecology and Management, v. 26, no. 1, p. 63-86, https://doi.org/10.1007/s11273-017-9554-y.","productDescription":"24 p.","startPage":"63","endPage":"86","ipdsId":"IP-084257","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469198,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9534041","text":"External Repository"},{"id":438088,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4T8F","text":"USGS data release","linkHelpText":"Data Release for the influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware"},{"id":352120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.1,\n 38.5\n ],\n [\n -76.1,\n 39.1\n ],\n [\n -75.5,\n 39.1\n ],\n [\n -75.5,\n 38.5\n ],\n [\n -76.1,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-08","publicationStatus":"PW","scienceBaseUri":"5afee787e4b0da30c1bfc2b6","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":709917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Distler, Hayley 0000-0001-5006-1360 hdistler@usgs.gov","orcid":"https://orcid.org/0000-0001-5006-1360","contributorId":179359,"corporation":false,"usgs":true,"family":"Distler","given":"Hayley","email":"hdistler@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":709919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Laurie C.","contributorId":196285,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":709920,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192853,"text":"70192853 - 2017 - Radiometric characterization of Landsat Collection 1 products","interactions":[],"lastModifiedDate":"2017-12-20T11:00:05","indexId":"70192853","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Radiometric characterization of Landsat Collection 1 products","docAbstract":"Landsat data in the U.S. Geological Survey (USGS) archive are being reprocessed to generate a tiered collection of consistently geolocated and radiometrically calibrated products that are suitable for time series analyses. With the implementation of the collection management, no major updates will be made to calibration of the Landsat sensors within a collection. Only calibration parameters needed to maintain the established calibration trends without an effect on derived environmental records will be regularly updated, while all other changes will be deferred to a new collection. This first collection, Collection 1, incorporates various radiometric calibration updates to all Landsat sensors including absolute and relative gains for Landsat 8 Operational Land Imager (OLI), stray light correction for Landsat 8 Thermal Infrared Sensor (TIRS), absolute gains for Landsat 4 and 5 Thematic Mappers (TM), recalibration of Landsat 1-5 Multispectral Scanners (MSS) to ensure radiometric consistency among different formats of archived MSS data, and a transfer of Landsat 8 OLI reflectance based calibration to all previous Landsat sensors. While all OLI/TIRS, ETM+ and majority of TM data have already been reprocessed to Collection 1, a completion of MSS and remaining TM data reprocessing is expected by the end of this year. It is important to note that, although still available for download from the USGS web pages, the products generated using the Pre-Collection processing do not benefit from the latest radiometric calibration updates. In this paper, we are assessing radiometry of solar reflective bands in Landsat Collection 1 products through analysis of trends in on-board calibrator and pseudo invariant site (PICS) responses.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 10402, Earth Observing Systems XXII","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Society of Photo-Optical Instrumentation Engineers","doi":"10.1117/12.2276065","usgsCitation":"Micijevic, E., Haque, O., and Mishra, N., 2017, Radiometric characterization of Landsat Collection 1 products, in Proceedings Volume 10402, Earth Observing Systems XXII, v. 10402, 8 p., https://doi.org/10.1117/12.2276065.","productDescription":"8 p.","ipdsId":"IP-090400","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":350128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10402","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faf9e4b06e28e9c22a59","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":717214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":717215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mishra, Nischal nischal.mishra.ctr@usgs.gov","contributorId":198842,"corporation":false,"usgs":true,"family":"Mishra","given":"Nischal","email":"nischal.mishra.ctr@usgs.gov","affiliations":[],"preferred":false,"id":717216,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193361,"text":"70193361 - 2017 - Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting","interactions":[],"lastModifiedDate":"2018-03-23T12:24:21","indexId":"70193361","displayToPublicDate":"2017-11-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5098,"text":"Remote Sensing Applications: Society and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting","docAbstract":"The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2017.08.002","usgsCitation":"Dewitt, J., Chirico, P.G., Bergstresser, S.E., and Warner, T.A., 2017, Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting: Remote Sensing Applications: Society and Environment, v. 8, p. 126-139, https://doi.org/10.1016/j.rsase.2017.08.002.","productDescription":"14 p.","startPage":"126","endPage":"139","ipdsId":"IP-080382","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":469319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2017.08.002","text":"Publisher Index Page"},{"id":349018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Côte d′Ivoire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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0000-0002-0414-9748","orcid":"https://orcid.org/0000-0002-0414-9748","contributorId":195554,"corporation":false,"usgs":false,"family":"Warner","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":718841,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191544,"text":"ofr20171119 - 2017 - Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes","interactions":[],"lastModifiedDate":"2017-10-26T15:42:15","indexId":"ofr20171119","displayToPublicDate":"2017-10-26T00:00:00","publicationYear":"2017","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":"2017-1119","title":"Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes","docAbstract":"The National Land Cover Database (NLCD) provides thematic land cover and land cover change data at 30-meter spatial resolution for the United States. Although the NLCD is considered to be the leading thematic land cover/land use product and overall classification accuracy across the NLCD is high, performance and consistency in the vast shrub and grasslands of the Western United States is lower than desired. To address these issues and fulfill the needs of stakeholders requiring more accurate rangeland data, the USGS has developed a method to quantify these areas in terms of the continuous cover of several cover components. These components include the cover of shrub, sagebrush (Artemisia spp), big sagebrush (Artemisia tridentata spp.), herbaceous, annual herbaceous, litter, and bare ground, and shrub and sagebrush height. To produce maps of component cover, we collected field data that were then associated with spectral values in WorldView-2 and Landsat imagery using regression tree models. The current report outlines the procedures and results of converting these continuous cover components to three thematic NLCD classes: barren, shrubland, and grassland. To accomplish this, we developed a series of indices and conditional models using continuous cover of shrub, bare ground, herbaceous, and litter as inputs. The continuous cover data are currently available for two large regions in the Western United States. Accuracy of the “cross-walked” product was assessed relative to that of NLCD 2011 at independent validation points (n=787) across these two regions. Overall thematic accuracy of the “cross-walked” product was 0.70, compared to 0.63 for NLCD 2011. The kappa value was considerably higher for the “cross-walked” product at 0.41 compared to 0.28 for NLCD 2011. Accuracy was also evaluated relative to the values of training points (n=75,000) used in the development of the continuous cover components. Again, the “cross-walked” product outperformed NLCD 2011, with an overall accuracy of 0.81, compared to 0.66 for NLCD 2011. These results demonstrated that our continuous cover predictions and models were successful in increasing thematic classification accuracy in Western United States shrublands. We plan to directly use the “cross-walked” product, where available, in the NLCD 2016 product.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171119","usgsCitation":"Rigge, M.B., Gass, Leila, Homer, C.G., and Xian, G.Z., 2017, Methods for converting continuous shrubland ecosystem component values to thematic National Land Cover Database classes: U.S. Geological Survey Open-File Report 2017–1119, 10 p., https://doi.org/10.3133/ofr20171119.","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-089077","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347404,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1119/coverthb.jpg"},{"id":347405,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1119/ofr20171119.pdf","text":"Report","size":"2.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1119"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the Cascade Mountains, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras of logging trends that align with prevailing regulations and economic conditions. We used multiple logistic regression to determine the biophysical and anthropogenic factors that influence fine-scale selection of harvest stands in each time period. Results show that private lands forest cover became significantly reduced and more fragmented from 1985 to 2014. Variables linked to parameters of site conditions, location, climate, and vegetation greenness consistently distinguished harvest selection for each distinct era. This study demonstrates the utility of annual LULC data for investigating the underlying factors that influence land cover change.
","language":"English","publisher":"MDPI","doi":"10.3390/f8100383","usgsCitation":"Soulard, C.E., Walker, J.J., and Griffith, G.E., 2017, Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA: Forests, v. 8, no. 10, p. 1-18, https://doi.org/10.3390/f8100383.","productDescription":"Article 383; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-090964","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469435,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8100383","text":"Publisher Index Page"},{"id":438189,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X63KWW","text":"USGS data release","linkHelpText":"Data - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA"},{"id":347269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Cascade Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.33276367187499,\n 45.767522962149876\n ],\n [\n -119.80590820312499,\n 45.767522962149876\n ],\n [\n -119.80590820312499,\n 49.009050809382046\n ],\n [\n -122.33276367187499,\n 49.009050809382046\n ],\n [\n -122.33276367187499,\n 45.767522962149876\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-07","publicationStatus":"PW","scienceBaseUri":"59f05120e4b0220bbd9a1d79","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714098,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714099,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Griffith, Glenn E. 0000-0001-7966-4720 ggriffith@usgs.gov","orcid":"https://orcid.org/0000-0001-7966-4720","contributorId":4053,"corporation":false,"usgs":true,"family":"Griffith","given":"Glenn","email":"ggriffith@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714100,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249340,"text":"70249340 - 2017 - How similar are forest disturbance maps derived from different Landsat time series algorithms?","interactions":[],"lastModifiedDate":"2023-10-05T00:18:45.62362","indexId":"70249340","displayToPublicDate":"2017-10-01T12:47:32","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"How similar are forest disturbance maps derived from different Landsat time series algorithms?","docAbstract":"Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal data volume to mine subtle signals in Landsat time series, but as those signals become subtler, they are more likely to be mixed with noise in Landsat data. This study examines the similarity among seven different algorithms in their ability to map the full range of magnitudes of forest disturbance over six different Landsat scenes distributed across the conterminous US. The maps agreed very well in terms of the amount of undisturbed forest over time; however, for the ~30% of forest mapped as disturbed in a given year by at least one algorithm, there was little agreement about which pixels were affected. Algorithms that targeted higher-magnitude disturbances exhibited higher omission errors but lower commission errors than those targeting a broader range of disturbance magnitudes. These results suggest that a user of any given forest disturbance map should understand the map’s strengths and weaknesses (in terms of omission and commission error rates), with respect to the disturbance targets of interest.
","language":"English","publisher":"MDPI","doi":"10.3390/f8040098","usgsCitation":"Cohen, W., Healey, S.P., Yang, Z., Stehman, S.V., Brewer, C.K., Brooks, E.B., Gorelick, N., Huang, C., Hughes, M.J., Kennedy, R.E., Loveland, T., Moisen, G.G., Schroeder, T.A., Vogelmann, J., Woodcock, C.E., Yang, L., and Zhu, Z., 2017, How similar are forest disturbance maps derived from different Landsat time series algorithms?: Forests, v. 8, no. 4, 98, 19 p., https://doi.org/10.3390/f8040098.","productDescription":"98, 19 p.","ipdsId":"IP-085817","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":461391,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8040098","text":"Publisher Index Page"},{"id":421612,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175087","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"McShane, R.R., Driscoll, K.P., and Sando, Roy, 2017, A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents: U.S. Geological Survey Scientific Investigations Report 2017–5087, 19 p., https://doi.org/10.3133/sir20175087.","productDescription":"vi, 19 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-083112","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":346073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5087/coverthb.jpg"},{"id":346074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5087/sir20175087.pdf","text":"Report","size":"678 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5087"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9070743","usgsCitation":"Vanderhoof, M.K., Brunner, N.M., Beal, Y.G., and Hawbaker, T., 2017, Evaluation of the U.S. Geological Survey Landsat burned area essential climate variable across the conterminous U.S. using commercial high-resolution imagery: Remote Sensing, v. 9, no. 7, p. 1-24, https://doi.org/10.3390/rs9070743.","productDescription":"Article 743; 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-085973","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":461411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9070743","text":"Publisher Index Page"},{"id":345578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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nbrunner@usgs.gov","orcid":"https://orcid.org/0000-0001-9657-2860","contributorId":5323,"corporation":false,"usgs":true,"family":"Brunner","given":"Nicole","email":"nbrunner@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beal, Yen-Ju G. 0000-0002-5538-5687 ygbeal@usgs.gov","orcid":"https://orcid.org/0000-0002-5538-5687","contributorId":5328,"corporation":false,"usgs":true,"family":"Beal","given":"Yen-Ju","email":"ygbeal@usgs.gov","middleInitial":"G.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":709916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190587,"text":"70190587 - 2017 - Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States","interactions":[],"lastModifiedDate":"2017-09-08T11:49:44","indexId":"70190587","displayToPublicDate":"2017-09-08T00:00:00","publicationYear":"2017","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":"Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States","docAbstract":"The Landsat Burned Area Essential Climate Variable (BAECV), developed by the U.S. Geological Survey (USGS), capitalizes on the long temporal availability of Landsat imagery to identify burned areas across the conterminous United States (CONUS) (1984–2015). Adequate validation of such products is critical for their proper usage and interpretation. Validation of coarse-resolution products often relies on independent data derived from moderate-resolution sensors (e.g., Landsat). Validation of Landsat products, in turn, is challenging because there is no corresponding source of high-resolution, multispectral imagery that has been systematically collected in space and time over the entire temporal extent of the Landsat archive. Because of this, comparison between high-resolution images and Landsat science products can help increase user's confidence in the Landsat science products, but may not, alone, be adequate. In this paper, we demonstrate an approach to systematically validate the Landsat-derived BAECV product. Burned area extent was mapped for Landsat image pairs using a manually trained semi-automated algorithm that was manually edited across 28 path/rows and five different years (1988, 1993, 1998, 2003, 2008). Three datasets were independently developed by three analysts and the datasets were integrated on a pixel by pixel basis in which at least one to all three analysts were required to agree a pixel was burned. We found that errors within our Landsat reference dataset could be minimized by using the rendition of the dataset in which pixels were mapped as burned if at least two of the three analysts agreed. BAECV errors of omission and commission for the detection of burned pixels averaged 42% and 33%, respectively for CONUS across all five validation years. Errors of omission and commission were lowest across the western CONUS, for example in the shrub and scrublands of the Arid West (31% and 24%, respectively), and highest in the grasslands and agricultural lands of the Great Plains in central CONUS (62% and 57%, respectively). The BAECV product detected most (> 65%) fire events > 10 ha across the western CONUS (Arid and Mountain West ecoregions). Our approach and results demonstrate that a thorough validation of Landsat science products can be completed with independent Landsat-derived reference data, but could be strengthened by the use of complementary sources of high-resolution data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.06.025","usgsCitation":"Vanderhoof, M.K., Fairaux, N., Beal, Y.G., and Hawbaker, T., 2017, Validation of the USGS Landsat Burned Area Essential Climate Variable (BAECV) across the conterminous United States: Remote Sensing of Environment, v. 198, p. 393-406, https://doi.org/10.1016/j.rse.2017.06.025.","productDescription":"14 p.","startPage":"393","endPage":"406","ipdsId":"IP-079461","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":438220,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T151VX","text":"USGS data release","linkHelpText":"Data Release for the validation of the USGS Landsat Burned Area Product across the conterminous U.S."},{"id":345579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}\n","volume":"198","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b3ac31e4b08b1644d8f1ab","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fairaux, Nicole","contributorId":196288,"corporation":false,"usgs":false,"family":"Fairaux","given":"Nicole","email":"","affiliations":[],"preferred":false,"id":709910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beal, Yen-Ju G. 0000-0002-5538-5687 ygbeal@usgs.gov","orcid":"https://orcid.org/0000-0002-5538-5687","contributorId":5328,"corporation":false,"usgs":true,"family":"Beal","given":"Yen-Ju","email":"ygbeal@usgs.gov","middleInitial":"G.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709911,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":709912,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188428,"text":"70188428 - 2017 - Assessment of forest degradation in Vietnam using Landsat time series data","interactions":[],"lastModifiedDate":"2017-09-18T16:35:26","indexId":"70188428","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of forest degradation in Vietnam using Landsat time series data","docAbstract":"Landsat time series data were used to characterize forest degradation in Lam Dong Province, Vietnam. We conducted three types of image change analyses using Landsat time series data to characterize the land cover changes. Our analyses concentrated on the timeframe of 1973–2014, with much emphasis on the latter part of that range. We conducted a field trip through Lam Dong Province to develop a better understanding of the ground conditions of the region, during which we obtained many photographs of representative forest sites with Global Positioning System locations to assist us in our image interpretations. High-resolution Google Earth imagery and Landsat data of the region were used to validate results. In general, our analyses indicated that many land-use changes have occurred throughout Lam Dong Province, including gradual forest to non-forest transitions. Recent changes are most marked along the relatively narrow interfaces between agricultural and forest areas that occur towards the boundaries of the province. One important observation is that the most highly protected national reserves in the region have not changed much over the entire Landsat timeframe (1972–present). Spectral changes within these regions have not occurred at the same levels as those areas adjacent to the reserves.
","language":"English","publisher":"MDPI","doi":"10.3390/f8070238","usgsCitation":"Vogelmann, J., Van Khoa, P., Lan, X., Shermeyer, J.S., Shi, H., Wimberly, M.C., Tat Duong, H., and Van Huong, L., 2017, Assessment of forest degradation in Vietnam using Landsat time series data: Forests, v. 8, no. 7, p. 1-22, https://doi.org/10.3390/f8070238.","productDescription":"Article 238; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-087749","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469558,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8070238","text":"Publisher Index Page"},{"id":345866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Vietnam","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[108.05018,21.55238],[106.71507,20.69685],[105.88168,19.75205],[105.66201,19.05817],[106.42682,18.00412],[107.36195,16.69746],[108.2695,16.07974],[108.87711,15.27669],[109.33527,13.42603],[109.20014,11.66686],[108.36613,11.00832],[107.22093,10.36448],[106.40511,9.53084],[105.15826,8.59976],[104.79519,9.24104],[105.0762,9.91849],[104.33433,10.48654],[105.19991,10.88931],[106.24967,10.96181],[105.81052,11.56761],[107.4914,12.33721],[107.61455,13.53553],[107.38273,14.20244],[107.56453,15.20217],[107.31271,15.90854],[106.55601,16.60428],[105.92576,17.48532],[105.0946,18.66697],[103.89653,19.26518],[104.18339,19.62467],[104.82257,19.88664],[104.435,20.75873],[103.20386,20.76656],[102.7549,21.67514],[102.17044,22.46475],[102.70699,22.7088],[103.50451,22.70376],[104.47686,22.81915],[105.32921,23.35206],[105.81125,22.97689],[106.7254,22.79427],[106.56727,22.2182],[107.04342,21.8119],[108.05018,21.55238]]]},\"properties\":{\"name\":\"Vietnam\"}}]}","volume":"8","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-04","publicationStatus":"PW","scienceBaseUri":"59c0db1ee4b091459a5f4733","contributors":{"authors":[{"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":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":697698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Khoa, Phung","contributorId":192766,"corporation":false,"usgs":false,"family":"Van Khoa","given":"Phung","email":"","affiliations":[],"preferred":false,"id":697699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lan, Xuan","contributorId":192767,"corporation":false,"usgs":false,"family":"Lan","given":"Xuan","email":"","affiliations":[],"preferred":false,"id":697700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shermeyer, Jacob S. 0000-0002-8143-2790 jshermeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8143-2790","contributorId":5825,"corporation":false,"usgs":true,"family":"Shermeyer","given":"Jacob","email":"jshermeyer@usgs.gov","middleInitial":"S.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":697701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":192768,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":697702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":697703,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tat Duong, Hoang","contributorId":192770,"corporation":false,"usgs":false,"family":"Tat Duong","given":"Hoang","email":"","affiliations":[],"preferred":false,"id":697704,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Huong, Le","contributorId":192771,"corporation":false,"usgs":false,"family":"Van Huong","given":"Le","email":"","affiliations":[],"preferred":false,"id":697705,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70193256,"text":"70193256 - 2017 - Landsat-8 TIRS thermal radiometric calibration status","interactions":[],"lastModifiedDate":"2022-04-13T20:29:00.342257","indexId":"70193256","displayToPublicDate":"2017-09-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-8 TIRS thermal radiometric calibration status","docAbstract":"The Thermal Infrared Sensor (TIRS) instrument is the thermal-band imager on the Landsat-8 platform. The initial onorbit calibration estimates of the two TIRS spectral bands indicated large average radiometric calibration errors, -0.29 and -0.51 W/m2 sr μm or -2.1K and -4.4K at 300K in Bands 10 and 11, respectively, as well as high variability in the errors, 0.87K and 1.67K (1-σ), respectively. The average error was corrected in operational processing in January 2014, though, this adjustment did not improve the variability. The source of the variability was determined to be stray light from far outside the field of view of the telescope. An algorithm for modeling the stray light effect was developed and implemented in the Landsat-8 processing system in February 2017. The new process has improved the overall calibration of the two TIRS bands, reducing the residual variability in the calibration from 0.87K to 0.51K at 300K for Band 10 and from 1.67K to 0.84K at 300K for Band 11. There are residual average lifetime bias errors in each band: 0.04 W/m2 sr μm (0.30K) and -0.04 W/m2 sr μm (-0.29K), for Bands 10 and 11, respectively.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 10402, Earth Observing Systems XXII","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"SPIE","doi":"10.1117/12.2276045","usgsCitation":"Barsi, J.A., Markham, B.L., Montanaro, M., Gerace, A., Hook, S., Schott, J.R., Raqueno, N.G., and Morfitt, R., 2017, Landsat-8 TIRS thermal radiometric calibration status, in Proceedings Volume 10402, Earth Observing Systems XXII, 11 p., https://doi.org/10.1117/12.2276045.","productDescription":"11 p.","ipdsId":"IP-089775","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":350087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fb5ce4b06e28e9c22fc5","contributors":{"authors":[{"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":718387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markham, Brian L.","contributorId":90482,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":718388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Montanaro, Matthew","contributorId":147004,"corporation":false,"usgs":false,"family":"Montanaro","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":718389,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerace, Aaron","contributorId":199173,"corporation":false,"usgs":false,"family":"Gerace","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":718390,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hook, Simon","contributorId":150339,"corporation":false,"usgs":false,"family":"Hook","given":"Simon","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":718391,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schott, John R.","contributorId":199175,"corporation":false,"usgs":false,"family":"Schott","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":718392,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raqueno, Nina G.","contributorId":199176,"corporation":false,"usgs":false,"family":"Raqueno","given":"Nina","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":718393,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":718386,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70190369,"text":"70190369 - 2017 - Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery","interactions":[],"lastModifiedDate":"2019-12-09T11:37:14","indexId":"70190369","displayToPublicDate":"2017-08-31T11:29:23","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery","docAbstract":"In the USA, climate change is expected to have an adverse impact on slope stability in Alaska. However, to date, there has been limited work done in Alaska to assess if changes in slope stability are occurring. To address this issue, we used 30-m Landsat imagery acquired from 1984 to 2016 to establish an inventory of 24 rock avalanches in a 5000-km2 area of Glacier Bay National Park and Preserve in southeast Alaska. A search of available earthquake catalogs revealed that none of the avalanches were triggered by earthquakes. Analyses of rock-avalanche magnitude, mobility, and frequency reveal a cluster of large (areas ranging from 5.5 to 22.2 km2), highly mobile (height/length < 0.3) rock avalanches that occurred from June 2012 through June 2016 (near the end of the 33-year period of record). These rock avalanches began about 2 years after the long-term trend in mean annual maximum air temperature may have exceeded 0 °C. Possibly more important, most of these rock avalanches occurred during a multiple-year period of record-breaking warm winter and spring air temperatures. These observations suggested to us that rock avalanches in the study area may be becoming larger because of rock-permafrost degradation. However, other factors, such as accumulating elastic strain, glacial thinning, and increased precipitation, may also play an important role in preconditioning slopes for failure during periods of warm temperatures.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-017-0879-7","usgsCitation":"Coe, J.A., Bessette-Kirton, E., and Geertsema, M., 2017, Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery: Landslides, v. 15, no. 3, p. 393-407, https://doi.org/10.1007/s10346-017-0879-7.","productDescription":"15 p.","startPage":"393","endPage":"407","ipdsId":"IP-084975","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":469574,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10346-017-0879-7","text":"Publisher Index Page"},{"id":370107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -138.504638671875,\n 58.10110549730587\n ],\n [\n -135.6427001953125,\n 58.10110549730587\n ],\n [\n -135.6427001953125,\n 59.14213494974261\n ],\n [\n -138.504638671875,\n 59.14213494974261\n ],\n [\n -138.504638671875,\n 58.10110549730587\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":708745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bessette-Kirton, Erin 0000-0002-2797-0694 ebessette-kirton@usgs.gov","orcid":"https://orcid.org/0000-0002-2797-0694","contributorId":177153,"corporation":false,"usgs":true,"family":"Bessette-Kirton","given":"Erin","email":"ebessette-kirton@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":708746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Geertsema, M. 0000-0002-4650-8251","orcid":"https://orcid.org/0000-0002-4650-8251","contributorId":167412,"corporation":false,"usgs":false,"family":"Geertsema","given":"M.","affiliations":[],"preferred":false,"id":708747,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190406,"text":"70190406 - 2017 - Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest","interactions":[],"lastModifiedDate":"2017-08-30T14:09:12","indexId":"70190406","displayToPublicDate":"2017-08-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest","docAbstract":"Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.
","language":"English","publisher":"MDPI","doi":"10.3390/f8090322","usgsCitation":"Bright, B.C., Hudak, A.T., Meddens, A.J., Hawbaker, T., Briggs, J.S., and Kennedy, R.E., 2017, Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest: Forests, v. 9, no. 8, p. 1-22, https://doi.org/10.3390/f8090322.","productDescription":"Article 322; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-086687","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469578,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8090322","text":"Publisher Index Page"},{"id":345363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.04415893554688,\n 39.904469075530415\n ],\n [\n -105.70220947265625,\n 39.904469075530415\n ],\n [\n -105.70220947265625,\n 40.32246702124231\n ],\n [\n -106.04415893554688,\n 40.32246702124231\n ],\n [\n -106.04415893554688,\n 39.904469075530415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-30","publicationStatus":"PW","scienceBaseUri":"59a7ced1e4b0fd9b77d092a6","contributors":{"authors":[{"text":"Bright, Benjamin C.","contributorId":196021,"corporation":false,"usgs":false,"family":"Bright","given":"Benjamin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":709004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudak, Andrew T.","contributorId":196022,"corporation":false,"usgs":false,"family":"Hudak","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":709005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meddens, Arjan J.H.","contributorId":140349,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","email":"","middleInitial":"J.H.","affiliations":[{"id":13466,"text":"Univ. of Idaho","active":true,"usgs":false}],"preferred":false,"id":709007,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":709003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Jenny S. 0000-0001-7454-6928 jsbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-7454-6928","contributorId":3087,"corporation":false,"usgs":true,"family":"Briggs","given":"Jenny","email":"jsbriggs@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709008,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kennedy, Robert E.","contributorId":196025,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":709010,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190170,"text":"70190170 - 2017 - Evidence of compounded disturbance effects on vegetation recovery following high-severity wildfire and spruce beetle outbreak","interactions":[],"lastModifiedDate":"2017-08-15T17:45:02","indexId":"70190170","displayToPublicDate":"2017-08-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of compounded disturbance effects on vegetation recovery following high-severity wildfire and spruce beetle outbreak","docAbstract":"Spruce beetle (Dendroctonus rufipennis) outbreaks are rapidly spreading throughout subalpine forests of the Rocky Mountains, raising concerns that altered fuel structures may increase the ecological severity of wildfires. Although many recent studies have found no conclusive link between beetle outbreaks and increased fire size or canopy mortality, few studies have addressed whether these combined disturbances produce compounded effects on short-term vegetation recovery. We tested for an effect of spruce beetle outbreak severity on vegetation recovery in the West Fork Complex fire in southwestern Colorado, USA, where much of the burn area had been affected by severe spruce beetle outbreaks in the decade prior to the fire. Vegetation recovery was assessed using the Landsat-derived Normalized Difference Vegetation Index (NDVI) two years after the fire, which occurred in 2013. Beetle outbreak severity, defined as the basal area of beetle-killed trees within Landsat pixels, was estimated using vegetation index differences (dVIs) derived from pre-outbreak and post-outbreak Landsat images. Of the seven dVIs tested, the change in Normalized Difference Moisture Index (dNDMI) was most strongly correlated with field measurements of beetle-killed basal area (R2 = 0.66). dNDMI was included as an explanatory variable in sequential autoregressive (SAR) models of NDVI2015. Models also included pre-disturbance NDVI, topography, and weather conditions at the time of burning as covariates. SAR results showed a significant correlation between NDVI2015 and dNDMI, with more severe spruce beetle outbreaks corresponding to reduced post-fire vegetation cover. The correlation was stronger for models which were limited to locations in the red stage of outbreak (outbreak ≤ 5 years old at the time of fire) than for models of gray-stage locations (outbreak > 5 years old at the time of fire). These results indicate that vegetation recovery processes may be negatively impacted by severe spruce beetle outbreaks occurring within a decade of stand-replacing wildfire.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0181778","usgsCitation":"Carlson, A., Sibold, J.S., Assal, T.J., and Negron, J.F., 2017, Evidence of compounded disturbance effects on vegetation recovery following high-severity wildfire and spruce beetle outbreak: PLoS ONE, v. 12, no. 8, Article e0181778: 24 p., https://doi.org/10.1371/journal.pone.0181778.","productDescription":"Article e0181778: 24 p.","ipdsId":"IP-083553","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0181778","text":"Publisher Index Page"},{"id":344878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"West Fork Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.03955078125,\n 37.09023980307208\n ],\n [\n -106.3037109375,\n 37.09023980307208\n ],\n [\n -106.3037109375,\n 38.238180119798635\n ],\n [\n -108.03955078125,\n 38.238180119798635\n ],\n [\n -108.03955078125,\n 37.09023980307208\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-04","publicationStatus":"PW","scienceBaseUri":"59940845e4b0fe2b9fe8af8d","contributors":{"authors":[{"text":"Carlson, Amanda R. 0000-0002-0450-2636","orcid":"https://orcid.org/0000-0002-0450-2636","contributorId":195661,"corporation":false,"usgs":false,"family":"Carlson","given":"Amanda R.","affiliations":[],"preferred":false,"id":707799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sibold, Jason S.","contributorId":195662,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":707800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Assal, Timothy J. 0000-0001-6342-2954 assalt@usgs.gov","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":2203,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","email":"assalt@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":707798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Negron, Jose F.","contributorId":10734,"corporation":false,"usgs":true,"family":"Negron","given":"Jose","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":707801,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190052,"text":"70190052 - 2017 - Statistical relative gain calculation for Landsat 8","interactions":[],"lastModifiedDate":"2018-04-23T09:04:19","indexId":"70190052","displayToPublicDate":"2017-08-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Statistical relative gain calculation for Landsat 8","docAbstract":"The Landsat 8 Operational Land Imager (OLI) is an optical multispectral push-broom sensor with a focal plane consisting of over 7000 detectors per spectral band. Each of the individual imaging detectors contributes one column of pixels to an image. Any difference in the response between neighboring detectors may result in a visible stripe or band in the imagery. An accurate estimate of each detector’s relative gain is needed to account for any differences between detector responses. This paper describes a procedure for estimating relative gains which uses normally acquired Earth viewing statistics.","largerWorkTitle":"Proceedings SPIE: Optics and Photonics 2017: Remote Sensing","conferenceTitle":"SPIE Optics and Photonics","conferenceDate":"August 6-10, 2017","conferenceLocation":"San Diego, CA","language":"English","publisher":"SPIE","usgsCitation":"Anderson, C., Helder, D., and Jeno, D., 2017, Statistical relative gain calculation for Landsat 8, in Proceedings SPIE: Optics and Photonics 2017: Remote Sensing, v. 10402, San Diego, CA, August 6-10, 2017.","ipdsId":"IP-089100","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":344809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":344808,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://spie.org/conferences-and-exhibitions/optics-and-photonics/proceedings?SSO=1","linkFileType":{"id":5,"text":"html"}}],"volume":"10402","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b76ec3e4b08b1644ddfacc","contributors":{"authors":[{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":707335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":195522,"corporation":false,"usgs":false,"family":"Helder","given":"Dennis","affiliations":[],"preferred":false,"id":707336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeno, Drake 0000-0001-6962-6616 drake.jeno.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6962-6616","contributorId":195523,"corporation":false,"usgs":true,"family":"Jeno","given":"Drake","email":"drake.jeno.ctr@usgs.gov","affiliations":[],"preferred":true,"id":707337,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190024,"text":"70190024 - 2017 - Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software","interactions":[],"lastModifiedDate":"2017-08-04T10:07:58","indexId":"70190024","displayToPublicDate":"2017-08-04T00:00:00","publicationYear":"2017","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":"Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software","docAbstract":"Among the most pressing concerns of land managers in post-wildfire landscapes are the establishment and spread of invasive species. Land managers need accurate maps of invasive species cover for targeted management post-disturbance that are easily transferable across space and time. In this study, we sought to develop an iterative, replicable methodology based on limited invasive species occurrence data, freely available remotely sensed data, and open source software to predict the distribution of Bromus tectorum (cheatgrass) in a post-wildfire landscape. We developed four species distribution models using eight spectral indices derived from five months of Landsat 8 Operational Land Imager (OLI) data in 2014. These months corresponded to both cheatgrass growing period and time of field data collection in the study area. The four models were improved using an iterative approach in which a threshold for cover was established, and all models had high sensitivity values when tested on an independent dataset. We also quantified the area at highest risk for invasion in future seasons given 2014 distribution, topographic covariates, and seed dispersal limitations. These models demonstrate the effectiveness of using derived multi-date spectral indices as proxies for species occurrence on the landscape, the importance of selecting thresholds for invasive species cover to evaluate ecological risk in species distribution models, and the applicability of Landsat 8 OLI and the Software for Assisted Habitat Modeling for targeted invasive species management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2017.03.009","usgsCitation":"West, A.M., Evangelista, P.H., Jarnevich, C.S., Kumar, S., Swallow, A., Luizza, M., and Chignell, S., 2017, Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software: International Journal of Applied Earth Observation and Geoinformation, v. 59, p. 135-146, https://doi.org/10.1016/j.jag.2017.03.009.","productDescription":"12 p.","startPage":"135","endPage":"146","ipdsId":"IP-062677","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469621,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2017.03.009","text":"Publisher Index Page"},{"id":438249,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J67F5X","text":"USGS data release","linkHelpText":"Cheatgrass mapping in Squirrel Creek Wildfire, WY in 2014"},{"id":344581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59858804e4b05ba66e9ea291","contributors":{"authors":[{"text":"West, Amanda M.","contributorId":176705,"corporation":false,"usgs":false,"family":"West","given":"Amanda","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":707232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evangelista, Paul H.","contributorId":14747,"corporation":false,"usgs":true,"family":"Evangelista","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":707233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":707231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kumar, Sunil","contributorId":195493,"corporation":false,"usgs":false,"family":"Kumar","given":"Sunil","affiliations":[],"preferred":false,"id":707234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swallow, Aaron","contributorId":195494,"corporation":false,"usgs":false,"family":"Swallow","given":"Aaron","email":"","affiliations":[],"preferred":false,"id":707235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luizza, Matthew","contributorId":169629,"corporation":false,"usgs":false,"family":"Luizza","given":"Matthew","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":707236,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chignell, Steve","contributorId":195495,"corporation":false,"usgs":false,"family":"Chignell","given":"Steve","email":"","affiliations":[],"preferred":false,"id":707237,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70193043,"text":"70193043 - 2017 - Automated quantification of surface water inundation in wetlands using optical satellite imagery","interactions":[],"lastModifiedDate":"2017-11-12T11:13:09","indexId":"70193043","displayToPublicDate":"2017-08-01T00:00:00","publicationYear":"2017","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":"Automated quantification of surface water inundation in wetlands using optical satellite imagery","docAbstract":"We present a fully automated and scalable algorithm for quantifying surface water inundation in wetlands. Requiring no external training data, our algorithm estimates sub-pixel water fraction (SWF) over large areas and long time periods using Landsat data. We tested our SWF algorithm over three wetland sites across North America, including the Prairie Pothole Region, the Delmarva Peninsula and the Everglades, representing a gradient of inundation and vegetation conditions. We estimated SWF at 30-m resolution with accuracies ranging from a normalized root-mean-square-error of 0.11 to 0.19 when compared with various high-resolution ground and airborne datasets. SWF estimates were more sensitive to subtle inundated features compared to previously published surface water datasets, accurately depicting water bodies, large heterogeneously inundated surfaces, narrow water courses and canopy-covered water features. Despite this enhanced sensitivity, several sources of errors affected SWF estimates, including emergent or floating vegetation and forest canopies, shadows from topographic features, urban structures and unmasked clouds. The automated algorithm described in this article allows for the production of high temporal resolution wetland inundation data products to support a broad range of applications.
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