{"pageNumber":"26","pageRowStart":"625","pageSize":"25","recordCount":1873,"records":[{"id":70187704,"text":"70187704 - 2013 - An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD","interactions":[],"lastModifiedDate":"2018-03-08T13:04:32","indexId":"70187704","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD","docAbstract":"<p><span>Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2013.09.009","usgsCitation":"Xian, G.Z., Homer, C.G., Meyer, D., and Granneman, B.J., 2013, An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD: ISPRS Journal of Photogrammetry and Remote Sensing, v. 86, p. 136-149, https://doi.org/10.1016/j.isprsjprs.2013.09.009.","productDescription":"14 p.","startPage":"136","endPage":"149","ipdsId":"IP-046020","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Florida, Texas","volume":"86","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe39e4b0a7fdb43c8c01","contributors":{"authors":[{"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":695183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695182,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192028,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[],"preferred":false,"id":695180,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Granneman, Brian J. 0000-0002-1910-0955 grann@usgs.gov","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":4209,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"grann@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695181,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70049066,"text":"ds709Z - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan","interactions":[{"subject":{"id":70049066,"text":"ds709Z - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan","indexId":"ds709Z","publicationYear":"2013","noYear":false,"chapter":"Z","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan"},"predicate":"IS_PART_OF","object":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"id":1}],"isPartOf":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"lastModifiedDate":"2022-12-13T16:47:32.091965","indexId":"ds709Z","displayToPublicDate":"2013-11-11T13:21:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"Z","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Kandahar mineral district, which has bauxite deposits.\n\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA,2006,2007,2008), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement. \n\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar- elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image- registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative- reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area- enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Kandahar) and the WGS84 datum. The final image mosaics were subdivided into eight overlapping tiles or quadrants because of the large size of the target area. The eight image tiles (or quadrants) for the Kandahar area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. Within the Kandahar study area, two subareas were designated for detailed field investigations (that is, the Obatu-Shela and Sekhab-Zamto Kalay subareas); these subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709Z","collaboration":"Prepared in cooperation with the U.S. Department of Defense <a href=\"http://tfbso.defense.gov/www/\" target=\"_blank\">Task Force for Business and Stability Operations</a> and the <a href=\"http://www.bgs.ac.uk/AfghanMinerals/\" target=\"_blank\">Afghanistan Geological Survey</a>.","usgsCitation":"Davis, P.A., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Kandahar mineral district in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 maps: 69.11 x 73.07 inches and 11 x 8.5 inches; 20 Image Files; 20 Metadata Files; 1 Shapefile, https://doi.org/10.3133/ds709Z.","productDescription":"Readme; 2 maps: 69.11 x 73.07 inches and 11 x 8.5 inches; 20 Image Files; 20 Metadata Files; 1 Shapefile","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-051558","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":278986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709z.jpg"},{"id":278985,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/z/"},{"id":278990,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/z/index_maps/Kandahar_Area-of-Interest_Index_Map.pdf"},{"id":278992,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/z/metadata/metadata.html"},{"id":278994,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/z/image_files/image_files.html"},{"id":278989,"rank":1,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/z/1_readme.txt"},{"id":278991,"rank":1,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/z/index_maps/Kandahar_Image_Index_Map.pdf"},{"id":278995,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/z/index_maps/index_maps.html"},{"id":278993,"rank":1,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/z/shapefiles/shapefiles.html"}],"country":"Afghanistan","otherGeospatial":"Kandahar Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 65.416667,31.0 ], [ 65.416667,32.75 ], [ 65.75,32.75 ], [ 65.75,31.0 ], [ 65.416667,31.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5281fc5ee4b08f1425d63da1","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":486100,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70048826,"text":"70048826 - 2013 - Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission","interactions":[],"lastModifiedDate":"2021-04-22T19:32:37.892102","indexId":"70048826","displayToPublicDate":"2013-11-07T09:32:00","publicationYear":"2013","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}},"displayTitle":"Hyperspectral <i>versus</i> multispectral crop-productivity modeling and type discrimination for the HyspIRI mission","title":"Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission","docAbstract":"<p id=\"sp0005\">Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB)<span>&nbsp;</span><i>versus</i><span>&nbsp;</span>multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM&nbsp;+), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ<sub>1</sub><span>&nbsp;</span>(400–2500&nbsp;nm)<span>&nbsp;</span><i>versus</i><span>&nbsp;</span>λ<sub>2</sub><span>&nbsp;</span>(400–2500&nbsp;nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742&nbsp;nm and 1175&nbsp;nm (HVI742-1175), (ii) 1296&nbsp;nm and 1054&nbsp;nm (HVI1296-1054), (iii) 1225&nbsp;nm and 697&nbsp;nm (HVI1225-697), and (iv) 702&nbsp;nm and 1104&nbsp;nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331&nbsp;nm spectral range, followed by 10% in the moisture sensitive 970&nbsp;nm, 6% in the red and red-edge (630–752&nbsp;nm), and the remaining 10% distributed between blue (400–500&nbsp;nm), green (501–600&nbsp;nm), and NIR (760–900&nbsp;nm).</p><p id=\"sp0010\">Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies when using HNBs (&gt;&nbsp;90%) over MBBs data (varied between 45 and 84%).</p><p id=\"sp0015\">Finally, the study highlighted 29 HNBs of Hyperion that are optimal in the study of agricultural crops and potentially significant to the upcoming NASA HyspIRI mission. Determining optimal and redundant bands for a given application will help overcoming the Hughes' phenomenon (or curse of high dimensionality of data).</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.08.002","usgsCitation":"Mariotto, I., Thenkabail, P.S., Huete, A., Slonecker, E.T., and Platonov, A., 2013, Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission: Remote Sensing of Environment, v. 139, p. 291-305, https://doi.org/10.1016/j.rse.2013.08.002.","productDescription":"15 p.","startPage":"291","endPage":"305","ipdsId":"IP-037397","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":278897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Uzbekistan","otherGeospatial":"Syr Darya River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 68.704655,40.8555 ], [ 68.704655,40.885405 ], [ 68.719804,40.885405 ], [ 68.719804,40.8555 ], [ 68.704655,40.8555 ] ] ] } } ] }","volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"527cb952e4b0850ea050a8d2","contributors":{"authors":[{"text":"Mariotto, Isabella","contributorId":14140,"corporation":false,"usgs":true,"family":"Mariotto","given":"Isabella","email":"","affiliations":[],"preferred":false,"id":485722,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":485721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huete, Alfredo","contributorId":48337,"corporation":false,"usgs":true,"family":"Huete","given":"Alfredo","affiliations":[],"preferred":false,"id":485724,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slonecker, E. Terrence 0000-0002-5793-0503","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":67175,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"preferred":false,"id":485725,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Platonov, Alexander","contributorId":39965,"corporation":false,"usgs":true,"family":"Platonov","given":"Alexander","email":"","affiliations":[],"preferred":false,"id":485723,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70193295,"text":"70193295 - 2013 - A GIS and statistical approach to identify variables that control water quality in hydrothermally altered and mineralized watersheds, Silverton, Colorado, USA","interactions":[],"lastModifiedDate":"2017-11-06T14:20:55","indexId":"70193295","displayToPublicDate":"2013-10-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A GIS and statistical approach to identify variables that control water quality in hydrothermally altered and mineralized watersheds, Silverton, Colorado, USA","docAbstract":"<p><span>Hydrothermally altered bedrock in the Silverton mining area, southwest Colorado, USA, contains sulfide minerals that weather to produce acidic and metal-rich leachate that is toxic to aquatic life. This study utilized a geographic information system (GIS) and statistical approach to identify watershed-scale geologic variables in the Silverton area that influence water quality. GIS analysis of mineral maps produced using remote sensing datasets including Landsat Thematic Mapper, advanced spaceborne thermal emission and reflection radiometer, and a hybrid airborne visible infrared imaging spectrometer and field-based product enabled areas of alteration to be quantified. Correlations between water quality signatures determined at watershed outlets, and alteration types intersecting both total watershed areas and GIS-buffered areas along streams were tested using linear regression analysis. Despite remote sensing datasets having varying watershed area coverage due to vegetation cover and differing mineral mapping capabilities, each dataset was useful for delineating acid-generating bedrock. Areas of quartz–sericite–pyrite mapped by AVIRIS have the highest correlations with acidic surface water and elevated iron and aluminum concentrations. Alkalinity was only correlated with area of acid neutralizing, propylitically altered bedrock containing calcite and chlorite mapped by AVIRIS. Total watershed area of acid-generating bedrock is more significantly correlated with acidic and metal-rich surface water when compared with acid-generating bedrock intersected by GIS-buffered areas along streams. This methodology could be useful in assessing the possible effects that alteration type area has in either generating or neutralizing acidity in unmined watersheds and in areas where new mining is planned.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s12665-013-2229-y","usgsCitation":"Yager, D.B., Johnson, R.H., Rockwell, B.W., Caine, J.S., and Smith, K.S., 2013, A GIS and statistical approach to identify variables that control water quality in hydrothermally altered and mineralized watersheds, Silverton, Colorado, USA: Environmental Earth Sciences, v. 70, no. 3, p. 1057-1082, https://doi.org/10.1007/s12665-013-2229-y.","productDescription":"26 p.","startPage":"1057","endPage":"1082","ipdsId":"IP-031298","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":473517,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12665-013-2229-y","text":"Publisher Index Page"},{"id":348293,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Silverton","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.86445617675781,\n              37.621302013833\n            ],\n            [\n              -107.47169494628906,\n              37.621302013833\n            ],\n            [\n              -107.47169494628906,\n              37.98317483351337\n            ],\n            [\n              -107.86445617675781,\n              37.98317483351337\n            ],\n            [\n              -107.86445617675781,\n              37.621302013833\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"70","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2013-02-12","publicationStatus":"PW","scienceBaseUri":"5a07ef0ae4b09af898c8cd79","contributors":{"authors":[{"text":"Yager, Douglas B. 0000-0001-5074-4022 dyager@usgs.gov","orcid":"https://orcid.org/0000-0001-5074-4022","contributorId":798,"corporation":false,"usgs":true,"family":"Yager","given":"Douglas","email":"dyager@usgs.gov","middleInitial":"B.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718575,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Raymond H. rhjohnso@usgs.gov","contributorId":707,"corporation":false,"usgs":true,"family":"Johnson","given":"Raymond","email":"rhjohnso@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":718577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rockwell, Barnaby W. 0000-0002-9549-0617 barnabyr@usgs.gov","orcid":"https://orcid.org/0000-0002-9549-0617","contributorId":2195,"corporation":false,"usgs":true,"family":"Rockwell","given":"Barnaby","email":"barnabyr@usgs.gov","middleInitial":"W.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718574,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caine, Jonathan S. 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":1272,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan","email":"jscaine@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":718576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Kathleen S. 0000-0001-8547-9804 ksmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8547-9804","contributorId":182,"corporation":false,"usgs":true,"family":"Smith","given":"Kathleen","email":"ksmith@usgs.gov","middleInitial":"S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":720733,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70048192,"text":"70048192 - 2013 - LANDFIRE 2010 - updated data to support wildfire and ecological management","interactions":[],"lastModifiedDate":"2013-09-18T10:32:17","indexId":"70048192","displayToPublicDate":"2013-09-18T10:17:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1440,"text":"Earthzine","active":true,"publicationSubtype":{"id":10}},"title":"LANDFIRE 2010 - updated data to support wildfire and ecological management","docAbstract":"<p>Wildfire is a global phenomenon that affects human populations and ecosystems. Wildfire effects occur at local to global scales impacting many people in different ways (Figure 1). Ecological concerns due to land use, fragmentation, and climate change impact natural resource use, allocation, and conservation. Access to consistent and current environmental data is a constant challenge, yet necessary for understanding the complexities of wildfire and ecological management. Data products and tools from the LANDFIRE Program help decision-makers to clarify problems and identify possible solutions when managing fires and natural resources. LANDFIRE supports the reduction of risk from wildfire to human lives and property, monitoring of fire danger, prediction of fire behavior on active incidents, and assessment of fire severity and impacts on natural systems [1] [2] [3]. LANDFIRE products are unique in that they are nationally consistent and provide the only complete geospatial dataset describing vegetation and wildland fuel information for the entire U.S. As such, LANDFIRE data are useful for many ecological applications [3]. For example, LANDFIRE data were recently integrated into a decision-support system for resource management and conservation decision-making along the Appalachian Trail.</p>\n</br>\n<p>LANDFIRE is a joint effort between the U.S. Department of the Interior Office of Wildland Fire, U.S. Department of Agriculture Forest Service Fire & Aviation Management, and The Nature Conservancy. To date, seven versions of LANDFIRE data have been released, with each successive version improving the quality of the data, adding additional features, and/or updating the time period represented by the data. The latest version, LANDFIRE 2010 (LF 2010), released mid-2013, represents circa 2010 landscape conditions and succeeds LANDFIRE 2008 (LF 2008), which represented circa 2008 landscape conditions. LF 2010 used many of the same processes developed for the LF 2008 effort [3].</p>\n</br>\n<p>Ongoing refinement of the LANDFIRE vegetation and fuel data is necessary to improve the quality and usability of the data and to capture landscape disturbance. LANDFIRE relies on Landsat multi-spectral imagery to produce and update vegetation and fuel data. The deep Landsat archive provides data needed for vegetation classification, change analysis, and historical disturbance characterization, for which LANDFIRE has used more than 24,000 image scenes since the program’s inception. In addition, LF 2010 used airborne and spaceborne lidar, and spaceborne synthetic aperture radar (SAR) to map vegetation structure in areas where ground-based field information was lacking, including Alaska and U.S.-affiliated islands in the Caribbean and the Pacific. The mapping of insular areas is new for the 2010 data release; previous versions of LANDFIRE were limited to the conterminous U.S., Alaska, and Hawaii.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Earthzine","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","usgsCitation":"Nelson, K., Connot, J.A., Peterson, B.E., and Picotte, J.J., 2013, LANDFIRE 2010 - updated data to support wildfire and ecological management: Earthzine, no. September 2013.","ipdsId":"IP-050235","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":277580,"type":{"id":11,"text":"Document"},"url":"https://www.earthzine.org/2013/09/15/landfire-2010-updated-data-to-support-wildfire-and-ecological-management/"},{"id":277800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 144.616667,13.233333 ], [ 144.616667,71.833333 ], [ -64.566667,71.833333 ], [ -64.566667,13.233333 ], [ 144.616667,13.233333 ] ] ] } } ] }","issue":"September 2013","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"523abd74e4b08cabd166caf4","contributors":{"authors":[{"text":"Nelson, Kurtis J. 0000-0003-4911-4511","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":105629,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis J.","affiliations":[],"preferred":false,"id":483955,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connot, Joel A. 0000-0002-2556-3374 jconnot@usgs.gov","orcid":"https://orcid.org/0000-0002-2556-3374","contributorId":4436,"corporation":false,"usgs":true,"family":"Connot","given":"Joel","email":"jconnot@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":483953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Birgit E. 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":3599,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","middleInitial":"E.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":483952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","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":483954,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70048227,"text":"70048227 - 2013 - Estimating the extent of impervious surfaces and turf grass across large regions","interactions":[],"lastModifiedDate":"2018-03-13T15:41:00","indexId":"70048227","displayToPublicDate":"2013-09-17T14:46:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the extent of impervious surfaces and turf grass across large regions","docAbstract":"The ability of researchers to accurately assess the extent of impervious and pervious developed surfaces, e.g., turf grass, using land-cover data derived from Landsat satellite imagery in the Chesapeake Bay watershed is limited due to the resolution of the data and systematic discrepancies between developed land-cover classes, surface mines, forests, and farmlands. Estimates of impervious surface and turf grass area in the Mid-Atlantic, United States that were based on 2006 Landsat-derived land-cover data were substantially lower than estimates based on more authoritative and independent sources. New estimates of impervious surfaces and turf grass area derived using land-cover data combined with ancillary information on roads, housing units, surface mines, and sampled estimates of road width and residential impervious area were up to 57 and 45% higher than estimates based strictly on land-cover data. These new estimates closely approximate estimates derived from authoritative and independent sources in developed counties.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of the American Water Resources Association","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Water Resources Association","doi":"10.1111/jawr.12110","usgsCitation":"Claggett, P.R., Irani, F., and Thompson, R., 2013, Estimating the extent of impervious surfaces and turf grass across large regions: Journal of the American Water Resources Association, v. 49, no. 5, p. 1057-1077, https://doi.org/10.1111/jawr.12110.","productDescription":"21 p.","startPage":"1057","endPage":"1077","numberOfPages":"21","ipdsId":"IP-036883","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":277697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":277633,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/jawr.12110"},{"id":277634,"type":{"id":15,"text":"Index Page"},"url":"https://onlinelibrary.wiley.com/doi/10.1111/jawr.12110/full"}],"country":"United States","state":"Delaware;Maryl;New York;Pennsylvania","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -79.7717,37.3178 ], [ -79.7717,42.6582 ], [ -75.1904,42.6582 ], [ -75.1904,37.3178 ], [ -79.7717,37.3178 ] ] ] } } ] }","volume":"49","issue":"5","noUsgsAuthors":false,"publicationDate":"2013-09-04","publicationStatus":"PW","scienceBaseUri":"52396bd2e4b04b9308ae4e1c","contributors":{"authors":[{"text":"Claggett, Peter R. 0000-0002-5335-2857 pclaggett@usgs.gov","orcid":"https://orcid.org/0000-0002-5335-2857","contributorId":176287,"corporation":false,"usgs":true,"family":"Claggett","given":"Peter","email":"pclaggett@usgs.gov","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":484053,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irani, Frederick M. firani@usgs.gov","contributorId":2932,"corporation":false,"usgs":true,"family":"Irani","given":"Frederick M.","email":"firani@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":484054,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Renee L. rthompson1@usgs.gov","contributorId":2933,"corporation":false,"usgs":true,"family":"Thompson","given":"Renee L.","email":"rthompson1@usgs.gov","affiliations":[],"preferred":true,"id":484055,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70048136,"text":"ds787 - 2013 - Surface mineral maps of Afghanistan derived from HyMap imaging spectrometer data, version 2","interactions":[],"lastModifiedDate":"2013-09-11T17:13:14","indexId":"ds787","displayToPublicDate":"2013-09-11T16:11:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"787","subseriesTitle":"USGS Afghanistan Project Product","title":"Surface mineral maps of Afghanistan derived from HyMap imaging spectrometer data, version 2","docAbstract":"This report presents a new version of surface mineral maps derived from HyMap imaging spectrometer data collected over Afghanistan in the fall of 2007. This report also describes the processing steps applied to the imaging spectrometer data. The 218 individual flight lines composing the Afghanistan dataset, covering more than 438,000 square kilometers, were georeferenced to a mosaic of orthorectified Landsat images. The HyMap data were converted from radiance to reflectance using a radiative transfer program in combination with ground-calibration sites and a network of cross-cutting calibration flight lines. The U.S. Geological Survey Material Identification and Characterization Algorithm (MICA) was used to generate two thematic maps of surface minerals: a map of iron-bearing minerals and other materials, which have their primary absorption features at the shorter wavelengths of the reflected solar wavelength range, and a map of carbonates, phyllosilicates, sulfates, altered minerals, and other materials, which have their primary absorption features at the longer wavelengths of the reflected solar wavelength range. In contrast to the original version, version 2 of these maps is provided at full resolution of 23-meter pixel size. The thematic maps, MICA summary images, and the material fit and depth images are distributed in digital files linked to this report, in a format readable by remote sensing software and Geographic Information Systems (GIS). The digital files can be downloaded from http://pubs.usgs.gov/ds/787/downloads/.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds787","usgsCitation":"Kokaly, R., King, T., and Hoefen, T.M., 2013, Surface mineral maps of Afghanistan derived from HyMap imaging spectrometer data, version 2: U.S. Geological Survey Data Series 787, Report: iv, 29 p.; Downloads Directory, https://doi.org/10.3133/ds787.","productDescription":"Report: iv, 29 p.; Downloads Directory","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":277495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds787.gif"},{"id":277493,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/787/pdf/DS787.pdf"},{"id":277494,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/787/"}],"projection":"Tranverse Mercator","datum":"World Geodetic System, 1984","country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.5,29.39 ], [ 60.5,38.49 ], [ 74.89,38.49 ], [ 74.89,29.39 ], [ 60.5,29.39 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"523182dce4b079b6e76e60d2","contributors":{"authors":[{"text":"Kokaly, Raymond F. 0000-0003-0276-7101","orcid":"https://orcid.org/0000-0003-0276-7101","contributorId":81442,"corporation":false,"usgs":true,"family":"Kokaly","given":"Raymond F.","affiliations":[],"preferred":false,"id":483812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Trude","contributorId":29831,"corporation":false,"usgs":true,"family":"King","given":"Trude","email":"","affiliations":[],"preferred":false,"id":483811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoefen, Todd M. 0000-0002-3083-5987 thoefen@usgs.gov","orcid":"https://orcid.org/0000-0002-3083-5987","contributorId":403,"corporation":false,"usgs":true,"family":"Hoefen","given":"Todd","email":"thoefen@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":483810,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70123898,"text":"70123898 - 2013 - Landscape-scale effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park","interactions":[],"lastModifiedDate":"2014-09-10T09:51:19","indexId":"70123898","displayToPublicDate":"2013-09-10T09:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park","docAbstract":"<p>While fire shapes the structure of forests and acts as a keystone process, the details of how fire modifies forest structure have been difficult to evaluate because of the complexity of interactions between fires and forests. We studied this relationship across 69.2 km2 of Yosemite National Park, USA, that was subject to 32 fires ⩾40 ha between 1984 and 2010. Forests types included ponderosa pine (<i>Pinus ponderosa</i>), white fir-sugar pine (<i>Abies concolor/Pinus lambertiana</i>), and red fir (<i>Abies magnifica</i>). We estimated and stratified burned area by fire severity using the Landsat-derived Relativized differenced Normalized Burn Ratio (RdNBR). Airborne LiDAR data, acquired in July 2010, measured the vertical and horizontal structure of canopy material and landscape patterning of canopy patches and gaps. Increasing fire severity changed structure at the scale of fire severity patches, the arrangement of canopy patches and gaps within fire severity patches, and vertically within tree clumps. Each forest type showed an individual trajectory of structural change with increasing fire severity. As a result, the relationship between estimates of fire severity such as RdNBR and actual changes appears to vary among forest types. We found three arrangements of canopy patches and gaps associated with different fire severities: canopy-gap arrangements in which gaps were enclosed in otherwise continuous canopy (typically unburned and low fire severities); patch-gap arrangements in which tree clumps and gaps alternated and neither dominated (typically moderate fire severity); and open-patch arrangements in which trees were scattered across open areas (typically high fire severity).</p>\n<br>\n<p>Compared to stands outside fire perimeters, increasing fire severity generally resulted first in loss of canopy cover in lower height strata and increased number and size of gaps, then in loss of canopy cover in higher height strata, and eventually the transition to open areas with few or no trees. However, the estimated fire severities at which these transitions occurred differed for each forest type. Our work suggests that low severity fire in red fir forests and moderate severity fire in ponderosa pine and white fir-sugar pine forests would restore vertical and horizontal canopy structures believed to have been common prior to the start of widespread fire suppression in the early 1900s. The fusion of LiDAR and Landsat data identified post-fire structural conditions that would not be identified by Landsat alone, suggesting a broad applicability of combining Landsat and LiDAR data for landscape-scale structural analysis for fire management.</p>","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Forest Ecology and Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2012.08.044","usgsCitation":"Kane, V., Lutz, J.A., Roberts, S.L., Smith, D.F., McGaughey, R.J., Povak, N., and Brooks, M.L., 2013, Landscape-scale effects of fire severity on mixed-conifer and red fir forest structure in Yosemite National Park: Forest Ecology and Management, v. 287, p. 17-31, https://doi.org/10.1016/j.foreco.2012.08.044.","productDescription":"15 p.","startPage":"17","endPage":"31","ipdsId":"IP-038395","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":293584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293575,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.foreco.2012.08.044"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -119.886496,37.494762 ], [ -119.886496,38.185228 ], [ -119.195416,38.185228 ], [ -119.195416,37.494762 ], [ -119.886496,37.494762 ] ] ] } } ] }","volume":"287","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541165c3e4b0fe7e184a5562","chorus":{"doi":"10.1016/j.foreco.2012.08.044","url":"http://dx.doi.org/10.1016/j.foreco.2012.08.044","publisher":"Elsevier BV","authors":"Kane Van R., Lutz James A., Roberts Susan L., Smith Douglas F., McGaughey Robert J., Povak Nicholas A., Brooks Matthew L.","journalName":"Forest Ecology and Management","publicationDate":"1/2013","auditedOn":"11/1/2014"},"contributors":{"authors":[{"text":"Kane, Van R.","contributorId":25873,"corporation":false,"usgs":true,"family":"Kane","given":"Van R.","affiliations":[],"preferred":false,"id":500472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lutz, James A.","contributorId":61350,"corporation":false,"usgs":true,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":500475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, Susan L.","contributorId":85312,"corporation":false,"usgs":true,"family":"Roberts","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":500477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Douglas F.","contributorId":76235,"corporation":false,"usgs":true,"family":"Smith","given":"Douglas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":500476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGaughey, Robert J.","contributorId":36865,"corporation":false,"usgs":true,"family":"McGaughey","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":500473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Povak, Nicholas A.","contributorId":55749,"corporation":false,"usgs":true,"family":"Povak","given":"Nicholas A.","affiliations":[],"preferred":false,"id":500474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brooks, Matthew L. 0000-0002-3518-6787 mlbrooks@usgs.gov","orcid":"https://orcid.org/0000-0002-3518-6787","contributorId":393,"corporation":false,"usgs":true,"family":"Brooks","given":"Matthew","email":"mlbrooks@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":500471,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70208536,"text":"70208536 - 2013 - Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields","interactions":[],"lastModifiedDate":"2020-02-14T09:46:20","indexId":"70208536","displayToPublicDate":"2013-09-09T09:39:56","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2172,"text":"Journal of Applied Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields","docAbstract":"<p><span>Climate change may represent the greatest future risk to the sagebrush ecosystem. Improved ways to quantify and monitor gradual change resulting from climate influences in this ecosystem are vital to its future management. For this research, the change over time of five continuous field cover components including bare ground, herbaceous, litter, sagebrush, and shrub were measured on the ground and by satellite across six seasons and four years. Ground-measured litter and herbaceous cover exhibited the highest variation annually and herbaceous cover the highest variation seasonally. Correlation of ground measurements to corresponding remote-sensing predictions indicated that annual predictions tracked ground measurements more closely than seasonal ones, and QuickBird predictions tracked ground measurements more closely than Landsat predictions. When annual linear slope values from ground plots and sensor predictions were correlated by component, the direction of ground-measured change was tracked better with QuickBird components than with Landsat components. Component predictions were correlated to annual and seasonal DAYMET precipitation. QuickBird components on average had the best response to precipitation patterns, followed by Landsat components. Overall, these results demonstrate the ability of sagebrush ecosystem components as predicted by regression trees to incrementally measure changing components of a sagebrush ecosystem.</span></p>","language":"English","publisher":"SPIE","doi":"10.1117/1.JRS.7.073508","usgsCitation":"Homer, C.G., Meyer, D.K., Aldridge, C.L., and Schell, S., 2013, Detecting annual and seasonal changes in a sagebrush ecosystem with remote sensing-derived continuous fields: Journal of Applied Remote Sensing, v. 7, no. 1, 073508, 24 p., https://doi.org/10.1117/1.JRS.7.073508.","productDescription":"073508, 24 p.","ipdsId":"IP-043589","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1117/1.jrs.7.073508","text":"Publisher Index Page"},{"id":372340,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -110.950927734375,\n              42.779275360241904\n            ],\n            [\n              -111.37939453125,\n              41.178653972331674\n            ],\n            [\n              -108.885498046875,\n              40.95501133048621\n            ],\n            [\n              -108.39111328125,\n              42.391008609205045\n            ],\n            [\n              -110.950927734375,\n              42.779275360241904\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":782332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":782330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schell, Spencer 0000-0001-7732-1863 schells@usgs.gov","orcid":"https://orcid.org/0000-0001-7732-1863","contributorId":3357,"corporation":false,"usgs":true,"family":"Schell","given":"Spencer","email":"schells@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":782333,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70047982,"text":"fs20133060 - 2013 - Landsat 8","interactions":[{"subject":{"id":70047982,"text":"fs20133060 - 2013 - Landsat 8","indexId":"fs20133060","publicationYear":"2013","noYear":false,"title":"Landsat 8"},"predicate":"SUPERSEDED_BY","object":{"id":70159774,"text":"fs20153081 - 2015 - Landsat—Earth observation satellites","indexId":"fs20153081","publicationYear":"2015","noYear":false,"title":"Landsat—Earth observation satellites"},"id":1}],"supersededBy":{"id":70159774,"text":"fs20153081 - 2015 - Landsat—Earth observation satellites","indexId":"fs20153081","publicationYear":"2015","noYear":false,"title":"Landsat—Earth observation satellites"},"lastModifiedDate":"2017-03-27T15:32:05","indexId":"fs20133060","displayToPublicDate":"2013-09-04T15:22:04","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3060","title":"Landsat 8","docAbstract":"<p>The Landsat era that began in 1972 will continue into the future, since the February 2013 launch of the Landsat Data Continuity Mission (renamed Landsat 8 on May 30, 2013). The Landsat 8 satellite provides 16-bit high-quality land-surface data, with instruments advancing future measurement capabilities while ensuring compatibility with historical Landsat data. The Operational Land Imager sensor collects data in the visible, near infrared, and shortwave infrared wavelength regions as well as a panchromatic band. Two new spectral bands have been added: a deep-blue band for coastal water and aerosol studies (band 1), and a band for cirrus cloud detection (band 9). A Quality Assurance band is also included to indicate the presence of terrain shadowing, data artifacts, and clouds. The Thermal Infrared Sensor collects data in two long wavelength thermal infrared bands and has a 3-year design life.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133060","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2013, Landsat 8: U.S. Geological Survey Fact Sheet 2013-3060, 4 p., https://doi.org/10.3133/fs20133060.","productDescription":"4 p.","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":277290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133060.gif"},{"id":277288,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3060/"},{"id":277289,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3060/pdf/fs2013-3060.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5228485fe4b06291bed80394","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535585,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70074635,"text":"70074635 - 2013 - Recent land-use/land-cover change in the Central California Valley","interactions":[],"lastModifiedDate":"2014-01-31T09:33:11","indexId":"70074635","displayToPublicDate":"2013-09-01T09:22:30","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2367,"text":"Journal of Land Use Science","active":true,"publicationSubtype":{"id":10}},"title":"Recent land-use/land-cover change in the Central California Valley","docAbstract":"Open access to Landsat satellite data has enabled annual analyses of modern land-use and land-cover change (LULCC) for the Central California Valley ecoregion between 2005 and 2010. Our annual LULCC estimates capture landscape-level responses to water policy changes, climate, and economic instability. From 2005 to 2010, agriculture in the region fluctuated along with regulatory-driven changes in water allocation as well as persistent drought conditions. Grasslands and shrublands declined, while developed lands increased in former agricultural and grassland/shrublands. Development rates stagnated in 2007, coinciding with the onset of the historic foreclosure crisis in California and the global economic downturn. We utilized annual LULCC estimates to generate interval-based LULCC estimates (2000–2005 and 2005–2010) and extend existing 27 year interval-based land change monitoring through 2010. Resulting change data provides insights into the drivers of landscape change in the Central California Valley ecoregion and represents the first, continuous, 37 year mapping effort of its kind.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Land Use Science","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","doi":"10.1080/1747423X.2013.841297","usgsCitation":"Soulard, C.E., and Wilson, T.S., 2013, Recent land-use/land-cover change in the Central California Valley: Journal of Land Use Science, 22 p., https://doi.org/10.1080/1747423X.2013.841297.","productDescription":"22 p.","ipdsId":"IP-041215","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":473576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/1747423x.2013.841297","text":"Publisher Index Page"},{"id":281791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281790,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/1747423X.2013.841297"}],"country":"United States","state":"California","otherGeospatial":"Central California Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121.62,34.86 ], [ -121.62,39.22 ], [ -119.18,39.22 ], [ -119.18,34.86 ], [ -121.62,34.86 ] ] ] } } ] }","noUsgsAuthors":false,"publicationDate":"2013-09-25","publicationStatus":"PW","scienceBaseUri":"53cd6f50e4b0b29085106578","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":489618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tamara S.","contributorId":36640,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":489619,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70074779,"text":"70074779 - 2013 - Land-cover change in the conterminous United States from 1973 to 2000","interactions":[],"lastModifiedDate":"2017-04-06T16:09:10","indexId":"70074779","displayToPublicDate":"2013-08-01T13:30:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1841,"text":"Global Environmental Change","active":true,"publicationSubtype":{"id":10}},"title":"Land-cover change in the conterminous United States from 1973 to 2000","docAbstract":"Land-cover change in the conterminous United States was quantified by interpreting change from satellite imagery for a sample stratified by 84 ecoregions. Gross and net changes between 11 land-cover classes were estimated for 5 dates of Landsat imagery (1973, 1980, 1986, 1992, and 2000). An estimated 673,000 km<sup>2</sup>(8.6%) of the United States’ land area experienced a change in land cover at least one time during the study period. Forest cover experienced the largest net decline of any class with 97,000 km2 lost between 1973 and 2000. The large decline in forest cover was prominent in the two regions with the highest percent of overall change, the Marine West Coast Forests (24.5% of the region experienced a change in at least one time period) and the Eastern Temperate Forests (11.4% of the region with at least one change). Agriculture declined by approximately 90,000 km<sup>2</sup> with the largest annual net loss of 12,000 km<sup>2</sup> yr<sup>−1</sup> occurring between 1986 and 1992. Developed area increased by 33% and with the rate of conversion to developed accelerating rate over time. The time interval with the highest annual rate of change of 47,000 km<sup>2</sup> yr<sup>−1</sup> (0.6% per year) was 1986–1992. This national synthesis documents a spatially and temporally dynamic era of land change between 1973 and 2000. These results quantify land change based on a nationally consistent monitoring protocol and contribute fundamental estimates critical to developing understanding of the causes and consequences of land change in the conterminous United States.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloenvcha.2013.03.006","usgsCitation":"Sleeter, B.M., Sohl, T.L., Loveland, T., Auch, R.F., Acevedo, W., Drummond, M.A., Sayler, K., and Stehman, S.V., 2013, Land-cover change in the conterminous United States from 1973 to 2000: Global Environmental Change, v. 23, no. 4, p. 733-748, https://doi.org/10.1016/j.gloenvcha.2013.03.006.","productDescription":"16 p.","startPage":"733","endPage":"748","numberOfPages":"16","temporalStart":"1973-01-01","temporalEnd":"2000-12-31","ipdsId":"IP-035634","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":473622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloenvcha.2013.03.006","text":"Publisher Index Page"},{"id":282027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":282026,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.gloenvcha.2013.03.006"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","volume":"23","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd63f3e4b0b290850ff23f","contributors":{"authors":[{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 bsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-2371-9571","contributorId":3479,"corporation":false,"usgs":true,"family":"Sleeter","given":"Benjamin","email":"bsleeter@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":489873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","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":489867,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":3005,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":489871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":489868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Acevedo, William wacevedo@usgs.gov","contributorId":2689,"corporation":false,"usgs":true,"family":"Acevedo","given":"William","email":"wacevedo@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":489869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drummond, Mark A. 0000-0001-7420-3503 madrummond@usgs.gov","orcid":"https://orcid.org/0000-0001-7420-3503","contributorId":3053,"corporation":false,"usgs":true,"family":"Drummond","given":"Mark","email":"madrummond@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":489872,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sayler, Kristi L. 0000-0003-2514-242X sayler@usgs.gov","orcid":"https://orcid.org/0000-0003-2514-242X","contributorId":2988,"corporation":false,"usgs":true,"family":"Sayler","given":"Kristi","email":"sayler@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":489870,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":489874,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70187700,"text":"70187700 - 2013 - Modeling spatially explicit fire impact on gross primary production in interior Alaska using satellite images coupled with eddy covariance","interactions":[],"lastModifiedDate":"2017-05-15T14:37:25","indexId":"70187700","displayToPublicDate":"2013-08-01T00:00:00","publicationYear":"2013","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":"Modeling spatially explicit fire impact on gross primary production in interior Alaska using satellite images coupled with eddy covariance","docAbstract":"<p><span>In interior Alaska, wildfires change gross primary production (GPP) after the initial disturbance. The impact of fires on GPP is spatially heterogeneous, which is difficult to evaluate by limited point-based comparisons or is insufficient to assess by satellite vegetation index. The direct prefire and postfire comparison is widely used, but the recovery identification may become biased due to interannual climate variability. The objective of this study is to propose a method to quantify the spatially explicit GPP change caused by fires and succession. We collected three Landsat images acquired on 13 July 2004, 5 August 2004, and 6 September 2004 to examine the GPP recovery of burned area from 1987 to 2004. A prefire Landsat image acquired in 1986 was used to reconstruct satellite images assuming that the fires of 1987–2004 had not occurred. We used a light-use efficiency model to estimate the GPP. This model was driven by maximum light-use efficiency (E</span><sub>max</sub><span>) and fraction of photosynthetically active radiation absorbed by vegetation (F</span><sub>PAR</sub><span>). We applied this model to two scenarios (i.e., an actual postfire scenario and an assuming-no-fire scenario), where the changes in E</span><sub>max</sub><span> and F</span><sub>PAR</sub><span> were taken into account. The changes in E</span><sub>max</sub><span> were represented by the change in land cover of evergreen needleleaf forest, deciduous broadleaf forest, and shrub/grass mixed, whose E</span><sub>max</sub><span> was determined from three fire chronosequence flux towers as 1.1556, 1.3336, and 0.5098&nbsp;gC/MJ PAR. The changes in F</span><sub>PAR</sub><span> were inferred from NDVI change between the actual postfire NDVI and the reconstructed NDVI. After GPP quantification for July, August, and September 2004, we calculated the difference between the two scenarios in absolute and percent GPP changes. Our results showed rapid recovery of GPP post-fire with a 24% recovery immediately after burning and 43% one year later. For the fire scars with an age range of 2–17&nbsp;years, the recovery rate ranged from 54% to 95%. In addition to the averaging, our approach further revealed the spatial heterogeneity of fire impact on GPP, allowing one to examine the spatially explicit GPP change caused by fires.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.04.003","usgsCitation":"Huang, S., Liu, H., Dahal, D., Jin, S., Welp, L.R., Liu, J., and Liu, S., 2013, Modeling spatially explicit fire impact on gross primary production in interior Alaska using satellite images coupled with eddy covariance: Remote Sensing of Environment, v. 135, p. 178-188, https://doi.org/10.1016/j.rse.2013.04.003.","productDescription":"11 p.","startPage":"178","endPage":"188","ipdsId":"IP-045013","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341315,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"135","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"591abe3ae4b0a7fdb43c8c03","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":695166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Heping","contributorId":117909,"corporation":false,"usgs":true,"family":"Liu","given":"Heping","affiliations":[],"preferred":false,"id":695170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@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":695169,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":695167,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welp, Lisa R.","contributorId":192025,"corporation":false,"usgs":false,"family":"Welp","given":"Lisa","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":695171,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":695165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Shuguang 0000-0002-6027-3479 sliu@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3479","contributorId":147403,"corporation":false,"usgs":true,"family":"Liu","given":"Shuguang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695168,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70047284,"text":"dsDS709CC - 2013 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Parwan mineral district in Afghanistan: Chapter CC in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","interactions":[],"lastModifiedDate":"2013-07-30T09:40:27","indexId":"dsDS709CC","displayToPublicDate":"2013-07-29T20:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","chapter":"CC","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Parwan mineral district in Afghanistan: Chapter CC in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations, prepared databases for mineral-resource target areas in Afghanistan. The purpose of the databases is to (1) provide useful data to ground-survey crews for use in performing detailed assessments of the areas and (2) provide useful information to private investors who are considering investment in a particular area for development of its natural resources. The set of satellite-image mosaics provided in this Data Series (DS) is one such database. Although airborne digital color-infrared imagery was acquired for parts of Afghanistan in 2006, the image data have radiometric variations that preclude their use in creating a consistent image mosaic for geologic analysis. Consequently, image mosaics were created using ALOS (Advanced Land Observation Satellite; renamed Daichi) satellite images, whose radiometry has been well determined (Saunier, 2007a,b). This part of the DS consists of the locally enhanced ALOS image mosaics for the Parwan mineral district, which has gold and copper deposits.\n\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420–500 nanometer, nm), green (520–600 nm), red (610–690 nm), and near-infrared (760–890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520–770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency (©JAXA,2006, 2007), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement.\n\nelevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band’s picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Parwan) and the WGS84 datum. The final image mosaics were subdivided into two overlapping tiles or quadrants because of the large size of the target area. The two image tiles (or quadrants) for the North Bamyan area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (Data Series 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dsDS709CC","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey; This report is Chapter CC in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>. For more information, see: <a href=\"http://pubs.er.usgs.gov/publication/ds709\" target=\"_blank\">Data Series 709</a>.","usgsCitation":"Davis, P.A., 2013, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Parwan mineral district in Afghanistan: Chapter CC in <i>Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan</i>: U.S. Geological Survey Data Series 709, HTML Document; Readme Text; 4 Index Maps; 4 Image Files; 4 Metadata Files; Shapefiles, https://doi.org/10.3133/dsDS709CC.","productDescription":"HTML Document; Readme Text; 4 Index Maps; 4 Image Files; 4 Metadata Files; Shapefiles","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-049057","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":275537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/dsds709cc.PNG"},{"id":275531,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/cc/"},{"id":275536,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/cc/shapefiles/shapefiles.html"},{"id":275532,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/cc/1_readme.txt"},{"id":275533,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/cc/index_maps/index_maps.html"},{"id":275534,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/cc/image_files/image_files.html"},{"id":275535,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/cc/metadata/metadata.html"}],"country":"Afghanistan","otherGeospatial":"Parwan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 58.0,28.0 ], [ 58.0,40.0 ], [ 78.0,40.0 ], [ 78.0,28.0 ], [ 58.0,28.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51f780d6e4b02e26443a9329","contributors":{"authors":[{"text":"Davis, Philip A. pdavis@usgs.gov","contributorId":692,"corporation":false,"usgs":true,"family":"Davis","given":"Philip","email":"pdavis@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":481610,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70044756,"text":"70044756 - 2013 - Mapping wildfire burn severity in the Arctic Tundra from downsampled MODIS data","interactions":[],"lastModifiedDate":"2013-08-12T09:42:50","indexId":"70044756","displayToPublicDate":"2013-07-29T13:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":899,"text":"Arctic, Antarctic, and Alpine Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping wildfire burn severity in the Arctic Tundra from downsampled MODIS data","docAbstract":"Wildfires are historically infrequent in the arctic tundra, but are projected to increase with climate warming. Fire effects on tundra ecosystems are poorly understood and difficult to quantify in a remote region where a short growing season severely limits ground data collection. Remote sensing has been widely utilized to characterize wildfire regimes, but primarily from the Landsat sensor, which has limited data acquisition in the Arctic. Here, coarse-resolution remotely sensed data are assessed as a means to quantify wildfire burn severity of the 2007 Anaktuvuk River Fire in Alaska, the largest tundra wildfire ever recorded on Alaska's North Slope. Data from Landsat Thematic Mapper (TM) and downsampled Moderate-resolution Imaging Spectroradiometer (MODIS) were processed to spectral indices and correlated to observed metrics of surface, subsurface, and comprehensive burn severity. Spectral indices were strongly correlated to surface severity (maximum R2 = 0.88) and slightly less strongly correlated to substrate severity. Downsampled MODIS data showed a decrease in severity one year post-fire, corroborating rapid vegetation regeneration observed on the burned site. These results indicate that widely-used spectral indices and downsampled coarse-resolution data provide a reasonable supplement to often-limited ground data collection for analysis and long-term monitoring of wildfire effects in arctic ecosystems.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Arctic, Antarctic, and Alpine Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Institute of Arctic and Alpine Research (INSTAAR)","doi":"10.1657/1938-4246-45.1.64","usgsCitation":"Kolden, C.A., and Rogan, J., 2013, Mapping wildfire burn severity in the Arctic Tundra from downsampled MODIS data: Arctic, Antarctic, and Alpine Research, v. 45, no. 1, p. 64-76, https://doi.org/10.1657/1938-4246-45.1.64.","productDescription":"13 p.","startPage":"64","endPage":"76","ipdsId":"IP-018916","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":473641,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1657/1938-4246-45.1.64","text":"Publisher Index Page"},{"id":275517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275509,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1657/1938-4246-45.1.64"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -151.3861,68.8704 ], [ -151.3861,69.311 ], [ -149.7285,69.311 ], [ -149.7285,68.8704 ], [ -151.3861,68.8704 ] ] ] } } ] }","volume":"45","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-01-05","publicationStatus":"PW","scienceBaseUri":"51f780d6e4b02e26443a932d","contributors":{"authors":[{"text":"Kolden, Crystal A.","contributorId":98610,"corporation":false,"usgs":true,"family":"Kolden","given":"Crystal","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":476287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogan, John","contributorId":83008,"corporation":false,"usgs":true,"family":"Rogan","given":"John","email":"","affiliations":[],"preferred":false,"id":476286,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70043380,"text":"70043380 - 2013 - Plot- and landscape-level changes in climate and vegetation following defoliation of exotic saltcedar (Tamarix sp.) from the biocontrol agent Diorhabda carinulata along a stream in the Mojave Desert (USA)","interactions":[],"lastModifiedDate":"2019-12-10T12:14:15","indexId":"70043380","displayToPublicDate":"2013-07-22T16:07:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Plot- and landscape-level changes in climate and vegetation following defoliation of exotic saltcedar (Tamarix sp.) from the biocontrol agent Diorhabda carinulata along a stream in the Mojave Desert (USA)","docAbstract":"The biocontrol agent, northern tamarisk beetle (Diorhabda carinulata), has been used to defoliate non-native saltcedar (Tamarix spp.) in USA western riparian systems since 2001. Biocontrol has the potential to impact biotic communities and climatic conditions in affected riparian areas. To determine the relationships between biocontrol establishment and effects on vegetation and climate at the plot and landscape scales, we measured temperature, relative humidity, foliage canopy, solar radiation, and used satellite imagery to assess saltcedar defoliation and evapotranspiration (ET) along the Virgin River in the Mojave Desert. Following defoliation solar radiation increased, daily humidity decreased, and maximum daily temperatures tended to increase. MODIS and Landsat satellite imagery showed defoliation was widespread, resulting in reductions in ET and vegetation indices. Because biocontrol beetles are spreading into new saltcedar habitats on arid western rivers, and the eventual equilibrium between beetles and saltcedar is unknown, it is necessary to monitor trends for ecosystem functions and higher trophic-level responses in habitats impacted by biocontrol.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2012.09.011","usgsCitation":"Bateman, H., Nagler, P.L., and Glenn, E.P., 2013, Plot- and landscape-level changes in climate and vegetation following defoliation of exotic saltcedar (Tamarix sp.) from the biocontrol agent Diorhabda carinulata along a stream in the Mojave Desert (USA): Journal of Arid Environments, v. 89, p. 16-20, https://doi.org/10.1016/j.jaridenv.2012.09.011.","productDescription":"5 p.","startPage":"16","endPage":"20","ipdsId":"IP-034241","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":275255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Mojave Desert","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.49902343749999,\n              34.116352469972746\n            ],\n            [\n              -113.90625,\n              34.116352469972746\n            ],\n            [\n              -113.90625,\n              36.4566360115962\n            ],\n            [\n              -116.49902343749999,\n              36.4566360115962\n            ],\n            [\n              -116.49902343749999,\n              34.116352469972746\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"89","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee465ae4b00ffbed48f869","contributors":{"authors":[{"text":"Bateman, H.L.","contributorId":36036,"corporation":false,"usgs":true,"family":"Bateman","given":"H.L.","email":"","affiliations":[],"preferred":false,"id":473502,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":777038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, E. P.","contributorId":24463,"corporation":false,"usgs":false,"family":"Glenn","given":"E.","middleInitial":"P.","affiliations":[],"preferred":false,"id":473500,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70047128,"text":"70047128 - 2013 - Land loss due to recent hurricanes in coastal Louisiana, U.S.A.","interactions":[],"lastModifiedDate":"2013-07-22T08:55:49","indexId":"70047128","displayToPublicDate":"2013-07-22T08:43:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Land loss due to recent hurricanes in coastal Louisiana, U.S.A.","docAbstract":"The aim of this study is to improve estimates of wetland land loss in two study regions of coastal Louisiana, U.S.A., due to the extreme storms that impacted the region between 2004 and 2009. The estimates are based on change-detection-mapping analysis that incorporates pre and postlandfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional-water classifications using a combination of high-resolution (<5 m) QuickBird, IKONOS, and GeoEye-1, and medium-resolution (30 m) Landsat Thematic Mapper satellite imagery. This process was applied in two study areas: the Hackberry area located in the southwestern part of chenier plain that was impacted by Hurricanes Rita (September 24, 2005) and Ike (September 13, 2008), and the Delacroix area located in the eastern delta plain that was impacted by Hurricanes Katrina (August 29, 2005) and Gustav (September 1, 2008). In both areas, effects of the hurricanes include enlargement of existing bodies of open water and erosion of fringing marsh areas. Surge-removed marsh was easily identified in stable marshes but was difficult to identify in degraded or flooded marshes. Persistent land loss in the Hackberry area due to Hurricane Rita was approximately 5.8% and increased by an additional 7.9% due to Hurricane Ike, although this additional area may yet recover. About 80% of the Hackberry study area remained unchanged since 2003. In the Delacroix area, persistent land loss due to Hurricane Katrina measured approximately 4.9% of the study area, while Hurricane Gustav caused minimal impact of 0.6% land loss by November 2009. Continued recovery in this area may further erase Hurricane Gustav's impact in the absence of new storm events.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Coastal Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/SI63-009.1","usgsCitation":"Palaseanu-Lovejoy, M., Kranenburg, C., Barras, J., and Brock, J., 2013, Land loss due to recent hurricanes in coastal Louisiana, U.S.A.: Journal of Coastal Research, no. 63, p. 97-109, https://doi.org/10.2112/SI63-009.1.","productDescription":"14 p.","startPage":"97","endPage":"109","numberOfPages":"14","ipdsId":"IP-035278","costCenters":[],"links":[{"id":275192,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275191,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2112/SI63-009.1"}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -90.491638,29.75484 ], [ -90.491638,30.071471 ], [ -90.129089,30.071471 ], [ -90.129089,29.75484 ], [ -90.491638,29.75484 ] ] ] } } ] }","issue":"63","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51ee4656e4b00ffbed48f855","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":481134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. ckranenburg@usgs.gov","contributorId":3924,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine J.","email":"ckranenburg@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":481135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barras, John A. jbarras@usgs.gov","contributorId":2425,"corporation":false,"usgs":true,"family":"Barras","given":"John A.","email":"jbarras@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":481133,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brock, John 0000-0002-5289-9332 jbrock@usgs.gov","orcid":"https://orcid.org/0000-0002-5289-9332","contributorId":2261,"corporation":false,"usgs":true,"family":"Brock","given":"John","email":"jbrock@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":481132,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70047101,"text":"70047101 - 2013 - Deforestation trends of tropical dry forests in central Brazil","interactions":[],"lastModifiedDate":"2013-07-18T14:44:51","indexId":"70047101","displayToPublicDate":"2013-07-17T14:39:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1045,"text":"Biotropica","active":true,"publicationSubtype":{"id":10}},"title":"Deforestation trends of tropical dry forests in central Brazil","docAbstract":"Tropical dry forests are the most threatened forest type in the world yet a paucity of research about them stymies development of appropriate conservation actions. The Paranã River Basin has the most significant dry forest formations in the Cerrado biome of central Brazil and is threatened by intense land conversion to pastures and agriculture. We examined changes in Paranã River Basin deforestation rates and fragmentation across three time intervals that covered 31 yr using Landsat imagery. Our results indicated a 66.3 percent decrease in forest extent between 1977 and 2008, with an annual rate of forest cover change of 3.5 percent. Landscape metrics further indicated severe forest loss and fragmentation, resulting in an increase in the number of fragments and reduction in patch sizes. Forest fragments in flatlands have virtually disappeared and the only significant forest remnants are mostly found over limestone outcrops in the eastern part of the basin. If current patterns persist, we project that these forests will likely disappear within 25 yr. These patterns may be reversed with creation of protected areas and involvement of local people to preserve small fragments that can be managed for restoration.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Biotropica","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"The Association for Tropical Biology and Conservation","doi":"10.1111/btp.12010","usgsCitation":"Bianchi, C.A., and Haig, S.M., 2013, Deforestation trends of tropical dry forests in central Brazil: Biotropica, v. 45, no. 3, p. 395-400, https://doi.org/10.1111/btp.12010.","productDescription":"6 p.","startPage":"395","endPage":"400","ipdsId":"IP-040376","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":275151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":275150,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/btp.12010"}],"country":"Brazil","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.98,-33.75 ], [ -73.98,5.27 ], [ -34.79,5.27 ], [ -34.79,-33.75 ], [ -73.98,-33.75 ] ] ] } } ] }","volume":"45","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-12-15","publicationStatus":"PW","scienceBaseUri":"51e90e5fe4b0e157e9e86f01","contributors":{"authors":[{"text":"Bianchi, Carlos A.","contributorId":77026,"corporation":false,"usgs":true,"family":"Bianchi","given":"Carlos","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":481058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":481057,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70188072,"text":"70188072 - 2013 - Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA","interactions":[],"lastModifiedDate":"2024-07-02T16:43:04.264131","indexId":"70188072","displayToPublicDate":"2013-07-10T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA","docAbstract":"<p><span class=\"paraNumber\">[1] <span>Linkages between permafrost distribution and lake surface-area changes in cold regions have not been previously examined over a large scale because of the paucity of subsurface permafrost information. Here, a first large-scale examination of these linkages is made over a 5150 km</span><sup>2</sup><span>&nbsp;area of Yukon Flats, Alaska, USA, by evaluating the relationship between lake surface-area changes during 1979–2009, derived from Landsat satellite data, and sublacustrine groundwater flow-path connectivity inferred from a pioneering, airborne geophysical survey of permafrost. The results suggest that the shallow (few tens of meters) thaw state of permafrost has more influence than deeper permafrost conditions on the evolving water budgets of lakes on a multidecadal time scale. In the region studied, these key shallow aquifers have high hydraulic conductivity and great spatial variability in thaw state, making groundwater flow and associated lake level evolution particularly sensitive to climate change owing to the close proximity of these aquifers to the atmosphere.</span></span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1002/grl.50187","usgsCitation":"Jepsen, S.M., Voss, C.I., Walvoord, M.A., Minsley, B.J., and Rover, J., 2013, Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA: Geophysical Research Letters, v. 40, no. 5, p. 882-887, https://doi.org/10.1002/grl.50187.","productDescription":"6 p.","startPage":"882","endPage":"887","ipdsId":"IP-040838","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":473701,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/grl.50187","text":"Publisher Index Page"},{"id":341849,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -148,\n              66.07\n            ],\n            [\n              -145,\n              66.07\n            ],\n            [\n              -145,\n              66.775\n            ],\n            [\n              -148,\n              66.775\n            ],\n            [\n              -148,\n              66.07\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"40","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2013-03-14","publicationStatus":"PW","scienceBaseUri":"592e84c8e4b092b266f10dc2","contributors":{"authors":[{"text":"Jepsen, Steven M. sjepsen@usgs.gov","contributorId":3892,"corporation":false,"usgs":true,"family":"Jepsen","given":"Steven","email":"sjepsen@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":696399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Clifford I. 0000-0001-5923-2752 cvoss@usgs.gov","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":1559,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford","email":"cvoss@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":696397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366 walvoord@usgs.gov","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":147211,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"walvoord@usgs.gov","middleInitial":"Ann","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":696400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":696396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rover, Jennifer 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":192333,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696398,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70044221,"text":"70044221 - 2013 - Metrically preserving the USGS aerial film archive","interactions":[],"lastModifiedDate":"2013-07-01T11:50:17","indexId":"70044221","displayToPublicDate":"2013-07-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Metrically preserving the USGS aerial film archive","docAbstract":"Since 1972, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, has provided fi lm-based products to the public. EROS is home to an archive of 12 million frames of analog photography ranging from 1937 to the present. The archive contains collections from both aerial and satellite platforms including programs such as the National High Altitude Program (NHAP), National Aerial Photography Program (NAPP), U.S. Antarctic Resource Center (USARC), Declass 1(CORONA, ARGON, and LANYARD), Declass 2 (KH-7 and KH-9), and Landsat (1972 – 1992, Landsat 1–5).","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Photogrammetric Engineering and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"ASPRS","usgsCitation":"Moe, D., and Longhenry, R., 2013, Metrically preserving the USGS aerial film archive: Photogrammetric Engineering and Remote Sensing, v. 79, no. 3, p. 225-228.","productDescription":"4 p.","startPage":"225","endPage":"228","ipdsId":"IP-042507","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":274358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274357,"type":{"id":11,"text":"Document"},"url":"https://digital.ipcprintservices.com/publication/?i=147261"}],"volume":"79","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51d296d7e4b0ca18483389a7","contributors":{"authors":[{"text":"Moe, Donald dmoe@usgs.gov","contributorId":3761,"corporation":false,"usgs":true,"family":"Moe","given":"Donald","email":"dmoe@usgs.gov","affiliations":[],"preferred":true,"id":475130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Longhenry, Ryan 0000-0002-9995-3690 rlonghenry@usgs.gov","orcid":"https://orcid.org/0000-0002-9995-3690","contributorId":4012,"corporation":false,"usgs":true,"family":"Longhenry","given":"Ryan","email":"rlonghenry@usgs.gov","affiliations":[],"preferred":true,"id":475131,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70041208,"text":"70041208 - 2013 - Assessment of the NASA-USGS Global Land Survey (GLS) Datasets","interactions":[],"lastModifiedDate":"2017-04-06T16:00:45","indexId":"70041208","displayToPublicDate":"2013-07-01T00:00:00","publicationYear":"2013","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":"Assessment of the NASA-USGS Global Land Survey (GLS) Datasets","docAbstract":"<p><span>The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.02.026","usgsCitation":"Gutman, G., Huang, C., Chander, G., Noojipady, P., and Masek, J.G., 2013, Assessment of the NASA-USGS Global Land Survey (GLS) Datasets: Remote Sensing of Environment, v. 134, p. 249-265, https://doi.org/10.1016/j.rse.2013.02.026.","productDescription":"17 p.","startPage":"249","endPage":"265","ipdsId":"IP-037259","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":339371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"UNITED STATES","volume":"134","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e753eee4b09da6799c0c53","contributors":{"authors":[{"text":"Gutman, Garik","contributorId":190654,"corporation":false,"usgs":false,"family":"Gutman","given":"Garik","email":"","affiliations":[],"preferred":false,"id":690210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":690211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":690212,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noojipady, Praveen","contributorId":24260,"corporation":false,"usgs":true,"family":"Noojipady","given":"Praveen","email":"","affiliations":[],"preferred":false,"id":690213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":690214,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70040016,"text":"70040016 - 2013 - Estimating suitable environments for invasive plant species across large landscapes: a remote sensing strategy using Landsat 7 ETM+","interactions":[],"lastModifiedDate":"2020-09-11T17:36:23.36493","indexId":"70040016","displayToPublicDate":"2013-06-21T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2030,"text":"International Journal of Biodiversity and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating suitable environments for invasive plant species across large landscapes: a remote sensing strategy using Landsat 7 ETM+","docAbstract":"<p><span>The key to reducing ecological and economic damage caused by invasive plant species is to locate and eradicate new invasions before they threaten native biodiversity and ecological processes. We used Landsat Enhanced Thematic Mapper Plus imagery to estimate suitable environments for four invasive plants in Big Bend National Park, southwest Texas, using a presence-only modeling approach. Giant reed (</span><i>Arundo donax</i><span>), Lehmann lovegrass (</span><i>Eragrostis lehmanniana</i><span>), horehound (</span><i>Marrubium vulgare</i><span>) and buffelgrass (</span><i>Pennisteum ciliare</i><span>) were selected for remote sensing spatial analyses. Multiple dates/seasons of imagery were used to account for habitat conditions within the study area and to capture phenological differences among targeted species and the surrounding landscape. Individual species models had high (0.91 to 0.99) discriminative ability to differentiate invasive plant suitable environments from random background locations. Average test area under the receiver operating characteristic curve (AUC) ranged from 0.91 to 0.99, indicating that plant predictive models exhibited high discriminative ability to differentiate suitable environments for invasive plant species from random locations. Omission rates ranged from &lt;1.0 to 18%. We demonstrated that useful models estimating suitable environments for invasive plants may be created with &lt;50 occurrence locations and that reliable modeling using presence-only datasets can be powerful tools for land managers.</span></p>","language":"English","publisher":"Academic Journals","doi":"10.5897/IJBC12.057","usgsCitation":"Young, K.E., Abbott, L.B., Caldwell, C.A., and Schrader, T.S., 2013, Estimating suitable environments for invasive plant species across large landscapes: a remote sensing strategy using Landsat 7 ETM+: International Journal of Biodiversity and Conservation, v. 5, no. 3, p. 122-134, https://doi.org/10.5897/IJBC12.057.","productDescription":"13 p.","startPage":"122","endPage":"134","ipdsId":"IP-041046","costCenters":[{"id":471,"text":"New Mexico Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":274063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378343,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://academicjournals.org/journal/IJBC/article-stat/73700A410650"}],"country":"United States","state":"Texas","otherGeospatial":"Big Bend National Park","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -104.556884765625,\n              28.98892237190413\n            ],\n            [\n              -102.7001953125,\n              28.98892237190413\n            ],\n            [\n              -102.7001953125,\n              29.935895213372444\n            ],\n            [\n              -104.556884765625,\n              29.935895213372444\n            ],\n            [\n              -104.556884765625,\n              28.98892237190413\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"5","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51c567d3e4b0c89b8f120dff","contributors":{"authors":[{"text":"Young, Kendal E.","contributorId":76212,"corporation":false,"usgs":true,"family":"Young","given":"Kendal","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":467484,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abbott, Laurie B.","contributorId":57352,"corporation":false,"usgs":true,"family":"Abbott","given":"Laurie","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":467483,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caldwell, Colleen A. 0000-0002-4730-4867 ccaldwel@usgs.gov","orcid":"https://orcid.org/0000-0002-4730-4867","contributorId":3050,"corporation":false,"usgs":true,"family":"Caldwell","given":"Colleen","email":"ccaldwel@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":467481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schrader, T. Scott","contributorId":43260,"corporation":false,"usgs":true,"family":"Schrader","given":"T.","email":"","middleInitial":"Scott","affiliations":[],"preferred":false,"id":467482,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70132430,"text":"70132430 - 2013 - Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach","interactions":[],"lastModifiedDate":"2020-12-29T13:05:54.132252","indexId":"70132430","displayToPublicDate":"2013-06-01T11:45:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach","docAbstract":"<p>The increasing availability of multi-scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model-assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the \"hot\" and \"cold\" reference conditions. The SSEBop model was used for computing ET for 12 years (2000-2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000-2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.</p>","language":"English","publisher":"American Water Resources Association","doi":"10.1111/jawr.12057","usgsCitation":"Senay, G.B., Bohms, S., Singh, R.K., Gowda, P.H., Velpuri, N.M., Alemu, H., and Verdin, J.P., 2013, Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach: Journal of the American Water Resources Association, v. 49, no. 3, p. 577-591, https://doi.org/10.1111/jawr.12057.","productDescription":"15 p.","startPage":"577","endPage":"591","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-037720","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":438789,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L2YMV","text":"USGS data release","linkHelpText":"Daily SSEBop Evapotranspiration Data from 2000 to 2018"},{"id":381655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-05-13","publicationStatus":"PW","scienceBaseUri":"5465d635e4b04d4b7dbd6624","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":522829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohms, Stefanie 0000-0002-2979-4655 sbohms@usgs.gov","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":3148,"corporation":false,"usgs":true,"family":"Bohms","given":"Stefanie","email":"sbohms@usgs.gov","affiliations":[{"id":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":525156,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singh, Ramesh K. 0000-0002-8164-3483 rsingh@usgs.gov","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":3895,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","email":"rsingh@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":525157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gowda, Prasanna H.","contributorId":127439,"corporation":false,"usgs":false,"family":"Gowda","given":"Prasanna","email":"","middleInitial":"H.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":525158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926 nvelpuri@usgs.gov","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":4441,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga","email":"nvelpuri@usgs.gov","middleInitial":"Manohar","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":525159,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alemu, Henok","contributorId":124527,"corporation":false,"usgs":false,"family":"Alemu","given":"Henok","email":"","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":525160,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Verdin, James P. 0000-0003-0238-9657 verdin@usgs.gov","orcid":"https://orcid.org/0000-0003-0238-9657","contributorId":720,"corporation":false,"usgs":true,"family":"Verdin","given":"James","email":"verdin@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":525161,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70046136,"text":"sim3252 - 2013 - Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado","interactions":[],"lastModifiedDate":"2013-05-28T14:30:22","indexId":"sim3252","displayToPublicDate":"2013-05-28T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3252","title":"Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado","docAbstract":"Multispectral satellite data acquired by the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and Landsat 7 Enhanced Thematic Mapper Plus (TM) sensors are being used to populate an online Geographic Information System (GIS) of the spatial occurrence of mineral groups and green vegetation across the western conterminous United States and Alaska. These geospatial data are supporting U.S. Geological Survey national-scale mineral deposit database development and other mineral resource and geoenvironmental research as a means of characterizing mineral exposures related to mined and unmined hydrothermally altered rocks and mine waste.\n\nThis report introduces a new methodology for the automated analysis of Landsat TM data that has been applied to more than 180 scenes covering the western United States. A map of mineral groups and green vegetation produced using this new methodology that covers the western San Juan Mountains, Colorado, and the Four Corners Region is presented. The map is provided as a layered GeoPDF and in GIS-ready digital format. TM data analysis results from other well-studied and mineralogically characterized areas with strong hydrothermal alteration and (or) supergene weathering of near-surface sulfide minerals are also shown and compared with results derived from ASTER data analysis.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3252","usgsCitation":"Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3252, iv, 25 p.; Map: 1 Sheet: 36 x 40 inches; Downloads Directory, https://doi.org/10.3133/sim3252.","productDescription":"iv, 25 p.; Map: 1 Sheet: 36 x 40 inches; Downloads Directory","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":272912,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3252.gif"},{"id":272908,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3252/"},{"id":272909,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3252/downloads/pdfs/SIM3252_pamphlet.pdf"},{"id":272910,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3252/downloads/pdfs/SIM3252_map.pdf"},{"id":272911,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3252/downloads/"}],"country":"United States","state":"Colorado","otherGeospatial":"San Juan Mountains","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.0,37.0 ], [ -109.0,41.0 ], [ -102.0,41.0 ], [ -102.0,37.0 ], [ -109.0,37.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51a5c3e2e4b0605bc571ef5a","contributors":{"authors":[{"text":"Rockwell, Barnaby W. 0000-0002-9549-0617 barnabyr@usgs.gov","orcid":"https://orcid.org/0000-0002-9549-0617","contributorId":2195,"corporation":false,"usgs":true,"family":"Rockwell","given":"Barnaby","email":"barnabyr@usgs.gov","middleInitial":"W.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":479002,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70045993,"text":"ofr20131049 - 2013 - Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado","interactions":[],"lastModifiedDate":"2018-03-08T13:01:51","indexId":"ofr20131049","displayToPublicDate":"2013-05-17T00:00:00","publicationYear":"2013","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":"2013-1049","title":"Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado","docAbstract":"North American sagebrush-steppe ecosystems have decreased by about 50 percent since European settlement. As a result, sagebrush-steppe dependent species, such as the Gunnison sage-grouse, have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, is needed to help maintain existing sagebrush habitats; however, products that accurately model and map sagebrush habitats in detail over the Gunnison Basin in Colorado are still unavailable. The goal of this project is to provide a rigorous large-area sagebrush habitat classification and inventory with statistically validated products and estimates of precision across the Gunnison Basin. This research employs a combination of methods, including (1) modeling sagebrush rangeland as a series of independent objective components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground measured plot data on 2.4-meter QuickBird satellite imagery in the same season the imagery is acquired; (3) modeling of ground measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of Landsat Thematic Mapper imagery (30-meter) for optimal modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution Landsat Thematic Mapper; and 6) employing accuracy assessment of model predictions to enable users to understand their dependencies. Results include the prediction of four primary components including percent bare ground, percent herbaceous, percent shrub, and percent litter, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and shrub height (centimeters). Results were validated with an independent accuracy assessment, with root mean square error values ranging from 3.5 (percent big sagebrush) to 10.8 (percent bare ground) at the QuickBird scale, and from 4.5 (percent Wyoming sagebrush) to 12.4 (percent herbaceous) at the full Landsat scale. These results offer significant improvement in sagebrush ecosystem quantification across the Gunnison Basin, and also provide maximum flexibility to users to employ for a wide variety of applications. Further refinement of these remote sensing component predictions in the future will be most likely achieved by focusing on more extensive ground plot sampling, employing new high and moderate-resolution satellite sensors that offer additional spectral bands for vegetation discrimination, and capturing more dates of satellite imagery to better represent phenological variation.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131049","usgsCitation":"Homer, C.G., Aldridge, C.L., Meyer, D., and Schell, S., 2013, Multiscale sagebrush rangeland habitat modeling in the Gunnison Basin of Colorado: U.S. Geological Survey Open-File Report 2013-1049, iv, 12 p., https://doi.org/10.3133/ofr20131049.","productDescription":"iv, 12 p.","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-041635","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131049.gif"},{"id":272336,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1049/"},{"id":272337,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1049/of13-1049.pdf"}],"country":"United States","state":"Colorado","otherGeospatial":"Gunnison Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109.0,37.0 ], [ -109.0,41.0 ], [ -102.0,41.0 ], [ -102.0,37.0 ], [ -109.0,37.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51974367e4b09a9cb58d5ede","contributors":{"authors":[{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":478661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":478658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Debra K. 0000-0002-8841-697X","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":72282,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra K.","affiliations":[],"preferred":false,"id":478660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schell, Spencer J.","contributorId":50432,"corporation":false,"usgs":true,"family":"Schell","given":"Spencer J.","affiliations":[],"preferred":false,"id":478659,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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