Drained thermokarst lake basins accumulate significant amounts of soil organic carbon in the form of peat, which is of interest to understanding carbon cycling and climate change feedbacks associated with thermokarst in the Arctic. Remote sensing is a tool useful for understanding temporal and spatial dynamics of drained basins. In this study, we tested the application of high-resolution X-band Synthetic Aperture Radar (SAR) data of the German TerraSAR-X satellite from the 2009 growing season (July–September) for characterizing drained thermokarst lake basins of various age in the ice-rich permafrost region of the northern Seward Peninsula, Alaska. To enhance interpretation of patterns identified in X-band SAR for these basins, we also analyzed the Normalized Difference Vegetation Index (NDVI) calculated from a Landsat-5 Thematic Mapper image acquired on July 2009 and compared both X-band SAR and NDVI data with observations of basin age. We found significant logarithmic relationships between (a) TerraSAR-X backscatter and basin age from 0 to 10,000 years, (b) Landat-5 TM NDVI and basin age from 0 to 10,000 years, and (c) TerraSAR-X backscatter and basin age from 50 to 10,000 years. NDVI was a better indicator of basin age over a period of 0–10,000 years. However, TerraSAR-X data performed much better for discriminating radiocarbon-dated basins (50–10,000 years old). No clear relationships were found for either backscatter or NDVI and basin age from 0 to 50 years. We attribute the decreasing trend of backscatter and NDVI with increasing basin age to post-drainage changes in the basin surface. Such changes include succession in vegetation, soils, hydrology, and renewed permafrost aggradation, ground ice accumulation and localized frost heave. Results of this study show the potential application of X-band SAR data in combination with NDVI data to map long-term succession dynamics of drained thermokarst lake basins.
","language":"English","publisher":"Remote Sensing","doi":"10.3390/rs4123741","usgsCitation":"Regmi, P., Grosse, G., Jones, M.C., Jones, B.M., and Walter Anthony, K., 2012, Characterizing post-drainage succession in Thermokarst Lake Basins on the Seward Peninsula, Alaska with TerraSAR-X Backscatter and Landsat-based NDVI data: Remote Sensing, v. 4, p. 3741-3765, https://doi.org/10.3390/rs4123741.","productDescription":"25 p. ","startPage":"3741","endPage":"3765","ipdsId":"IP-041633","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":474246,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs4123741","text":"Publisher Index Page"},{"id":342501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Seward Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.4443359375,\n 66.46066349658045\n ],\n [\n -161.806640625,\n 66.23145747862573\n ],\n [\n -162.3779296875,\n 66.12496236487968\n ],\n [\n -163.30078125,\n 66.16051056018838\n ],\n [\n -163.564453125,\n 66.42553717157787\n ],\n [\n -163.564453125,\n 66.65297740055279\n ],\n [\n -164.35546875,\n 66.75724984139227\n ],\n [\n -165.9375,\n 66.58321725728175\n ],\n [\n -167.2119140625,\n 66.31986144668052\n ],\n [\n -168.00292968749997,\n 66.01801815922045\n ],\n [\n -168.7060546875,\n 65.4034447883078\n ],\n [\n -167.6953125,\n 64.4348920430406\n ],\n [\n -165.9814453125,\n 64.01449619484472\n ],\n [\n -163.30078125,\n 63.93737246791484\n ],\n [\n -162.20214843749997,\n 64.35893097894458\n ],\n [\n -161.3232421875,\n 64.60503753178527\n ],\n [\n -161.0595703125,\n 64.77412531292873\n ],\n [\n -160.4443359375,\n 65.164578884019\n ],\n [\n -160.26855468749997,\n 65.56754970214311\n ],\n [\n -160.048828125,\n 65.92855383515203\n ],\n [\n -160.4443359375,\n 66.46066349658045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-11-27","publicationStatus":"PW","scienceBaseUri":"59424b3de4b0764e6c65dc75","contributors":{"authors":[{"text":"Regmi, Prajna","contributorId":192910,"corporation":false,"usgs":false,"family":"Regmi","given":"Prajna","email":"","affiliations":[],"preferred":false,"id":698124,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grosse, Guido","contributorId":146182,"corporation":false,"usgs":false,"family":"Grosse","given":"Guido","email":"","affiliations":[{"id":12916,"text":"Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":698123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":698122,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":698121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter Anthony, Katey","contributorId":192911,"corporation":false,"usgs":false,"family":"Walter Anthony","given":"Katey","affiliations":[],"preferred":false,"id":698125,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70041041,"text":"70041041 - 2012 - Peat accumulation in drained thermokarst lake basins in continuous, ice-rich permafrost, northern Seward Peninsula, Alaska","interactions":[],"lastModifiedDate":"2013-02-23T22:15:04","indexId":"70041041","displayToPublicDate":"2012-11-29T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2319,"text":"Journal of Geophysical Research G: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Peat accumulation in drained thermokarst lake basins in continuous, ice-rich permafrost, northern Seward Peninsula, Alaska","docAbstract":"Thermokarst lakes and peat-accumulating drained lake basins cover a substantial portion of Arctic lowland landscapes, yet the role of thermokarst lake drainage and ensuing peat formation in landscape-scale carbon (C) budgets remains understudied. Here we use measurements of terrestrial peat thickness, bulk density, organic matter content, and basal radiocarbon age from permafrost cores, soil pits, and exposures in vegetated, drained lake basins to characterize regional lake drainage chronology, C accumulation rates, and the role of thermokarst-lake cycling in carbon dynamics throughout the Holocene on the northern Seward Peninsula, Alaska. Most detectable lake drainage events occurred within the last 4,000 years with the highest drainage frequency during the medieval climate anomaly. Peat accumulation rates were highest in young (50–500 years) drained lake basins (35.2 g C m−2 yr−1) and decreased exponentially with time since drainage to 9 g C m−2 yr−1 in the oldest basins. Spatial analyses of terrestrial peat depth, basal peat radiocarbon ages, basin geomorphology, and satellite-derived land surface properties (Normalized Difference Vegetation Index (NDVI); Minimum Noise Fraction (MNF)) from Landsat satellite data revealed significant relationships between peat thickness and mean basin NDVI or MNF. By upscaling observed relationships, we infer that drained thermokarst lake basins, covering 391 km2 (76%) of the 515 km2 study region, store 6.4–6.6 Tg organic C in drained lake basin terrestrial peat. Peat accumulation in drained lake basins likely serves to offset greenhouse gas release from thermokarst-impacted landscapes and should be incorporated in landscape-scale C budgets.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research G: Biogeosciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1029/2011JG001766","usgsCitation":"Jones, M.C., Grosse, G., Jones, B.M., and Anthony, K.W., 2012, Peat accumulation in drained thermokarst lake basins in continuous, ice-rich permafrost, northern Seward Peninsula, Alaska: Journal of Geophysical Research G: Biogeosciences, v. 117, https://doi.org/10.1029/2011JG001766.","productDescription":"16 p.","startPage":"G00M07","additionalOnlineFiles":"N","ipdsId":"IP-035962","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":263483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":263480,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1029/2011JG001766"}],"country":"United States","state":"Alaska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 172.45,51.21 ], [ 172.45,71.39 ], [ -130.0,71.39 ], [ -130.0,51.21 ], [ 172.45,51.21 ] ] ] } } ] }","volume":"117","noUsgsAuthors":false,"publicationDate":"2012-04-07","publicationStatus":"PW","scienceBaseUri":"50e0f56ce4b0fec3206f1c76","contributors":{"authors":[{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":469233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":469235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":469232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anthony, Katey Walter","contributorId":77441,"corporation":false,"usgs":true,"family":"Anthony","given":"Katey","email":"","middleInitial":"Walter","affiliations":[],"preferred":false,"id":469234,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70041372,"text":"ds709F - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Badakhshan mineral district in Afghanistan: Chapter F in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:14:25","indexId":"ds709F","displayToPublicDate":"2012-11-21T00:00:00","publicationYear":"2012","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":"F","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Badakhshan mineral district in Afghanistan: Chapter F in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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 Badakhshan mineral district, which has gold deposits. ALOS 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,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. The 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Badakhshan) and the WGS84 datum. The final image mosaics were subdivided into six overlapping tiles or quadrants because of the large size of the target area. The six image tiles (or quadrants) for the Badakhshan 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 Badakhshan study area, three subareas were designated for detailed field investigations (that is, the Bharak, Fayz-Abad, and Ragh 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/ds709F","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 F in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Badakhshan mineral district in Afghanistan: Chapter F in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 45.64 x 48.46 inches; 18 Image Files; 18 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709F.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 45.64 x 48.46 inches; 18 Image Files; 18 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":263684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_F.jpg"},{"id":263675,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/f/"},{"id":263676,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/f/1_readme.txt"},{"id":263677,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/f/index_maps/Badakhshan_Area-of-Interest_Index_Map.pdf"},{"id":263678,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/f/index_maps/Badakhshan_Image_Mosaic_Index_Map.pdf"},{"id":263679,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/f/index_maps/Badakhshan_Subarea_Image_Mosaic_Index_Map.pdf"},{"id":263680,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/f/image_files/image_files.html"},{"id":263681,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/f/metadata/metadata.html"},{"id":263682,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/f/shapefiles/shapefiles.html"},{"id":263683,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/index.html"}],"country":"Afghanistan","state":"Badakhshan","otherGeospatial":"Badakhshan Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 70.25,37.0 ], [ 70.25,37.75 ], [ 71.25,37.75 ], [ 71.25,37.0 ], [ 70.25,37.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50bfbda6e4b01744973f7813","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":469650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":469652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":469651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70041250,"text":"ds709E - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Aynak mineral district in Afghanistan: Chapter E in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:14:45","indexId":"ds709E","displayToPublicDate":"2012-11-21T00:00:00","publicationYear":"2012","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":"E","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Aynak mineral district in Afghanistan: Chapter E in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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 Aynak mineral district, which has copper deposits. ALOS 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,2008,2010), 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. The 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then coregistered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image coregistration was 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 315-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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area’s local zone (42 for Aynak) and the WGS84 datum. The final image mosaics were subdivided into four overlapping tiles or quadrants because of the large size of the target area. The four image tiles (or quadrants) for the Aynak 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 Aynak study area, five subareas were designated for detailed field investigations (that is, the Bakhel-Charwaz, Kelaghey-Kakhay, Kharuti-Dawrankhel, Logar Valley, and Yagh-Darra/Gul-Darra 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/ds709E","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 E in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: Data Series 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Aynak mineral district in Afghanistan: Chapter E in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 42.79 x 29.78 inches; 18 Image Files; 18 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709E.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 42.79 x 29.78 inches; 18 Image Files; 18 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":263605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_E.jpg"},{"id":263595,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/e/"},{"id":263600,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/e/index_maps/Aynak_Subarea_Image_Index_Map.pdf"},{"id":263601,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/e/image_files/image_files.html"},{"id":263597,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/e/1_readme.txt"},{"id":263598,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/e/index_maps/Aynak_Area-of-Interest_Index_Map.pdf"},{"id":263599,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/e/index_maps/Aynak_Image_Index_Map.pdf"},{"id":263602,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/e/metadata/metadata.html"},{"id":263603,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/e/shapefiles/shapefiles.html"},{"id":263604,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/"}],"country":"Afghanistan","state":"Kabul;Logar;Paktya;Wardak","otherGeospatial":"Aynak Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 68.633333,34.083333 ], [ 68.633333,34.533333 ], [ 69.5,34.533333 ], [ 69.5,34.083333 ], [ 68.633333,34.083333 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50bd1396e4b069d93eefc4ec","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":469455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":469456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":469458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":469457,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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},{"subject":{"id":70101717,"text":"ds709DD - 2014 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni1 mineral district in Afghanistan","indexId":"ds709DD","publicationYear":"2014","noYear":false,"chapter":"DD","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni1 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":2},{"subject":{"id":70101718,"text":"ds709EE - 2014 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni2 mineral district in Afghanistan","indexId":"ds709EE","publicationYear":"2014","noYear":false,"chapter":"EE","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni2 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":3},{"subject":{"id":70101719,"text":"ds709FF - 2014 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Farah mineral district in Afghanistan","indexId":"ds709FF","publicationYear":"2014","noYear":false,"chapter":"FF","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Farah 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":4}],"lastModifiedDate":"2013-02-01T11:10:22","indexId":"ds709","displayToPublicDate":"2012-11-02T00:00:00","publicationYear":"2012","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","title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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 DS consists of the locally enhanced ALOS image mosaics for each of the 24 mineral project areas (referred to herein as areas of interest), whose locality names, locations, and main mineral occurrences are shown on the index map of Afghanistan (fig. 1). ALOS 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, 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. The 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. PRISM image orthorectification for one-half of the target areas was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). 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 SPARKLE logic, which is described in Davis (2006). Each of the four-band images within each 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 specified radius that was usually 500 m. 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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (either 41 or 42) and the WGS84 datum. Most final image mosaics were subdivided into overlapping tiles or quadrants because of the large size of the target areas. The image tiles (or quadrants) for each area of interest 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. Approximately one-half of the study areas have at least one subarea designated for detailed field investigations; the subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709","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 composed of 24 chapters. Please visit DS 709 to view available chapters.","usgsCitation":"Davis, P.A., 2012, Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, 24 Chapters, https://doi.org/10.3133/ds709.","productDescription":"24 Chapters","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":262620,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709.jpg"},{"id":262613,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/","linkFileType":{"id":5,"text":"html"}}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"507ee041e4b022001d87bb82","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":468184,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70156665,"text":"70156665 - 2012 - Dark and background response stability for the Landsat 8 Thermal Infrared Sensor","interactions":[],"lastModifiedDate":"2017-04-25T16:31:21","indexId":"70156665","displayToPublicDate":"2012-10-15T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Dark and background response stability for the Landsat 8 Thermal Infrared Sensor","docAbstract":"The Thermal Infrared Sensor (TIRS) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM), which is a joint mission between NASA and the USGS. The TIRS instrument will continue to collect the thermal infrared data that are currently being collected by the Thematic Mapper and the Enhanced Thematic Mapper Plus on Landsats 5 and 7, respectively. One of the key requirements of the new sensor is that the dark and background response be stable to ensure proper data continuity from the legacy Landsat instruments. Pre launch testing of the instrument has recently been completed at the NASA Goddard Space Flight Center (GSFC), which included calibration collects that mimic those that will be performed on orbit. These collects include images of a cold plate meant to simulate the deep space calibration source as viewed by the instrument in flight. The data from these collects give insight into the stability of the instrument’s dark and background response, as well as factors that may cause these responses to vary. This paper quantifies the measured background and dark response of TIRS as well as its stability.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of SPIE volume 8510","conferenceTitle":"Earth Observing Systems XVII","conferenceDate":"August 13-16, 2012","conferenceLocation":"San Diego, California","language":"English","publisher":"SPIE","doi":"10.1117/12.930139","usgsCitation":"Vanderwerff, K., and Montanaro, M., 2012, Dark and background response stability for the Landsat 8 Thermal Infrared Sensor, in Proceedings of SPIE volume 8510, San Diego, California, August 13-16, 2012, 9 p., https://doi.org/10.1117/12.930139.","productDescription":"9 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-039642","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":307455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55dd91b0e4b0518e354dd147","contributors":{"authors":[{"text":"Vanderwerff, Kelly kvanderwerff@usgs.gov","contributorId":4617,"corporation":false,"usgs":true,"family":"Vanderwerff","given":"Kelly","email":"kvanderwerff@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":569859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Montanaro, Matthew","contributorId":147004,"corporation":false,"usgs":false,"family":"Montanaro","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":569860,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70040318,"text":"ofr20121185 - 2012 - 2011 Year in review - Earth Resources Observation and Science Center","interactions":[],"lastModifiedDate":"2018-03-08T14:26:49","indexId":"ofr20121185","displayToPublicDate":"2012-10-15T00:00:00","publicationYear":"2012","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":"2012-1185","title":"2011 Year in review - Earth Resources Observation and Science Center","docAbstract":"The USGS Earth Resources Observation and Science (EROS) Center's 2011 Year in Review is an annual report recounting the broad scope of the Center's 2011 accomplishments. The report covers preparations for the Landsat Data Continuity Mission (LDCM) launch, the ever-increasing use of free Landsat data, monitoring the effects of natural hazards, and more to emphasize the importance of innovation in using satellite data to study change over time.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121185","usgsCitation":"2012, 2011 Year in review - Earth Resources Observation and Science Center: U.S. Geological Survey Open-File Report 2012-1185, iv, 30 p., https://doi.org/10.3133/ofr20121185.","productDescription":"iv, 30 p.","numberOfPages":"38","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":262591,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1185.jpg"},{"id":262580,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1185/","linkFileType":{"id":5,"text":"html"}},{"id":262581,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1185/ofr2012-1185.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -96.840384,43.465702 ], [ -96.840384,43.798528 ], [ -96.530628,43.798528 ], [ -96.530628,43.465702 ], [ -96.840384,43.465702 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd4927e4b0b290850eeeba","contributors":{"compilers":[{"text":"Johnson, Rebecca L. 0000-0002-8771-6161 rljohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-8771-6161","contributorId":178874,"corporation":false,"usgs":true,"family":"Johnson","given":"Rebecca","email":"rljohnson@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":730608,"contributorType":{"id":3,"text":"Compilers"},"rank":1}]}} ,{"id":70040217,"text":"ofr20121199 - 2012 - Hydrological information products for the Off-Project Water Program of the Klamath Basin Restoration Agreement","interactions":[],"lastModifiedDate":"2013-06-18T10:59:41","indexId":"ofr20121199","displayToPublicDate":"2012-10-09T00:00:00","publicationYear":"2012","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":"2012-1199","title":"Hydrological information products for the Off-Project Water Program of the Klamath Basin Restoration Agreement","docAbstract":"The Klamath Basin Restoration Agreement (KBRA) was developed by a diverse group of stakeholders, Federal and State resource management agencies, Tribal representatives, and interest groups to provide a comprehensive solution to ecological and water-supply issues in the Klamath Basin. The Off-Project Water Program (OPWP), one component of the KBRA, has as one of its purposes to permanently provide an additional 30,000 acre-feet of water per year on an average annual basis to Upper Klamath Lake through \"voluntary retirement of water rights or water uses or other means as agreed to by the Klamath Tribes, to improve fisheries habitat and also provide for stability of irrigation water deliveries.\" The geographic area where the water rights could be retired encompasses approximately 1,900 square miles. The OPWP area is defined as including the Sprague River drainage, the Sycan River drainage downstream of Sycan Marsh, the Wood River drainage, and the Williamson River drainage from Kirk Reef at the southern end of Klamath Marsh downstream to the confluence with the Sprague River. Extensive, broad, flat, poorly drained uplands, valleys, and wetlands characterize much of the study area. Irrigation is almost entirely used for pasture. To assist parties involved with decisionmaking and implementation of the OPWP, the U.S. Geological Survey (USGS), in cooperation with the Klamath Tribes and other stakeholders, created five hydrological information products. These products include GIS digital maps and datasets containing spatial information on evapotranspiration, subirrigation indicators, water rights, subbasin streamflow statistics, and return-flow indicators. The evapotranspiration (ET) datasets were created under contract for this study by Evapotranspiration, Plus, LLC, of Twin Falls, Idaho. A high-resolution remote sensing technique known as Mapping Evapotranspiration at High Resolution and Internalized Calibration (METRIC) was used to create estimates of the spatial distribution of ET. The METRIC technique uses thermal infrared Landsat imagery to quantify actual evapotranspiration at a 30-meter resolution that can be related to individual irrigated fields. Because evaporation uses heat energy, ground surfaces with large ET rates are left cooler as a result of ET than ground surfaces that have less ET. As a consequence, irrigated fields appear in the Landsat images as cooler than nonirrigated fields. Products produced from this study include total seasonal and total monthly (April-October) actual evapotranspiration maps for 2004 (a dry year) and 2006 (a wet year). Maps showing indicators of natural subirrigation were also provided by this study. \"Subirrigation\" as used here is the evapotranspiration of shallow groundwater by plants with roots that penetrate to or near the water table. Subirrigation often occurs at locations where the water table is at or above the plant rooting depth. Natural consumptive use by plants diminishes the benefit of retiring water rights in subirrigated areas. Some agricultural production may be possible, however, on subirrigated lands for which water rights are retired. Because of the difficulty in precisely mapping and quantifying subirrigation, this study presents several sources of spatially mapped data that can be used as indicators of higher subirrigation probability. These include the floodplain boundaries defined by stream geomorphology, water-table depth defined in Natural Resources Conservation Service (NRCS) soil surveys, and soil rooting depth defined in NRCS soil surveys. The two water-rights mapping products created in the study were \"points of diversion\" (POD) and \"place of use\" (POU) for surface-water irrigation rights. To create these maps, all surface-water rights data, decrees, certificates, permits, and unadjudicated claims within the entire 1,900 square mile study area were aggregated into a common GIS geodatabase. Surface-water irrigation rights within a 5-mile buffer of the study area were then selected and identified. The POU area was then totaled by water right for primary and supplemental water rights. The maximum annual volume (acre-feet) allowed under each water right also was calculated using the POU area and duty (allowable annual irrigation application in feet). In cases where a water right has more than one designated POD, the total volume for the water right was equally distributed to each POD listed for the water right. Because of this, mapped distribution of diversion rates for some rights may differ from actual practice. Water-right information in the map products was from digital datasets obtained from the Oregon Water Resources Department and was, at the time acquired, the best available compilation of water-right information available. Because the completeness and accuracy of the water-right data could not be verified, users are encouraged to check directly with the Oregon Water Resources Department where specific information on individual rights or locations is essential. A dataset containing streamflow statistics for 72 subbasins in the study area was created for the study area. The statistics include annual flow durations (5-, 10-, 25-, 50-, and 95-percent exceedances) and 7-day, 10-year (7Q10) and 7-day, 2-year (7Q2) low flows, and were computed using regional regression equations based on measured streamflow records in the region. Daily streamflow records used were adjusted as needed for crop consumptive use; therefore the statistics represent streamflow under more natural conditions as though irrigation diversions did not exist. Statistics are provided for flow rates resulting from streamflow originating from within the entire drainage area upstream of the subbasin pour point (referring to the outlet of the contributing drainage basin). The statistics were computed for the purpose of providing decision makers with the ability to estimate streamflow that would be expected after water conservation techniques have been implemented or a water right has been retired. A final product from the study are datasets of indicators of the potential for subsurface return flow of irrigation water from agricultural areas to nearby streams. The datasets contain information on factors such as proximity to surface-water features, geomorphic floodplain characteristics, and depth to water. The digital data, metadata, and example illustrations for the datasets described in this report are available on-line from the USGS Water Resources National Spatial Data Infrastructure (NSDI) Node Website http://water.usgs.gov/lookup/getgislist or from the U.S. Government website DATA.gov at http://www.data.gov with links provided in a Microsoft® Excel® workbook in appendix A.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121199","collaboration":"Prepared in cooperation with the Klamath Tribes and in collaboration with Klamath Basin Rangeland Trust, Klamath Watershed Partnership, Sustainable Northwest, The Nature Conservancy, Upper Klamath Water Users Association, and U.S. Fish and Wildlife Service","usgsCitation":"Snyder, D.T., Risley, J.C., and Haynes, J.V., 2012, Hydrological information products for the Off-Project Water Program of the Klamath Basin Restoration Agreement: U.S. Geological Survey Open-File Report 2012-1199, iv; 20 p.; Appendix A, https://doi.org/10.3133/ofr20121199.","productDescription":"iv; 20 p.; Appendix A","numberOfPages":"27","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":262474,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1199.jpg"},{"id":262417,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1199/","linkFileType":{"id":5,"text":"html"}},{"id":262418,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1199/pdf/ofr20121199.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":273905,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_distance_to_gaining_streams_and_lakes.xml"},{"id":273906,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_distance_to_perennial_streams_and_lakes.xml"},{"id":273913,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_subbasin_analysis_pour_points_v3.xml"},{"id":273914,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_subbasin_analysis_v3.xml"},{"id":273915,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_water_rights_pod_20110909.xml"},{"id":273916,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_water_rights_pou_20110909.xml"},{"id":273911,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_sprague_river_oregon_geomorphology_return_flow.xml"},{"id":273912,"type":{"id":16,"text":"Metadata"},"url":"https://water.usgs.gov/GIS/metadata/usgswrd/XML/kbra_opwp_sprague_river_oregon_geomorphology_subirrigation.xml"}],"country":"United States","state":"California;Oregon","otherGeospatial":"Klamath Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.33333333333333,42.166666666666664 ], [ -122.33333333333333,43.416666666666664 ], [ -120.5,43.416666666666664 ], [ -120.5,42.166666666666664 ], [ -122.33333333333333,42.166666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50defea8e4b0dfbe79e682c8","contributors":{"authors":[{"text":"Snyder, Daniel T. dtsnyder@usgs.gov","contributorId":820,"corporation":false,"usgs":true,"family":"Snyder","given":"Daniel","email":"dtsnyder@usgs.gov","middleInitial":"T.","affiliations":[],"preferred":true,"id":467921,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Risley, John C. 0000-0002-8206-5443 jrisley@usgs.gov","orcid":"https://orcid.org/0000-0002-8206-5443","contributorId":2698,"corporation":false,"usgs":true,"family":"Risley","given":"John","email":"jrisley@usgs.gov","middleInitial":"C.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":467922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haynes, Jonathan V. 0000-0001-6530-6252 jhaynes@usgs.gov","orcid":"https://orcid.org/0000-0001-6530-6252","contributorId":3113,"corporation":false,"usgs":true,"family":"Haynes","given":"Jonathan","email":"jhaynes@usgs.gov","middleInitial":"V.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":467923,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70040060,"text":"70040060 - 2012 - Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery","interactions":[],"lastModifiedDate":"2017-11-25T13:49:53","indexId":"70040060","displayToPublicDate":"2012-09-28T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery","docAbstract":"The spread of tamarisk (Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle (Diorhabda carinulata) as a biological control agent. Following its initial release along the Colorado River near Moab, Utah in 2004, the beetle has successfully established and defoliated tamarisk across much of the upper Colorado River Basin. However, the spatial distribution and seasonal timing of defoliation are complex and difficult to quantify over large areas. To address this challenge, we tested and compared two remote sensing approaches to mapping tamarisk defoliation: Disturbance Index (DI) and a decision tree method called Random Forest (RF). Based on multitemporal Landsat 5 TM imagery for 2006-2010, changes in DI and defoliation probability from RF were calculated to detect tamarisk defoliation along the banks of Green, Colorado, Dolores and San Juan rivers within the Colorado Plateau area. Defoliation mapping accuracy was assessed based on field surveys partitioned into 10 km sections of river and on regions of interest created for continuous riparian vegetation. The DI method detected 3711 ha of defoliated area in 2007, 7350 ha in 2008, 10,457 ha in 2009 and 5898 ha in 2010. The RF method detected much smaller areas of defoliation but proved to have higher accuracy, as demonstrated by accuracy assessment and sensitivity analysis, with 784 ha in 2007, 960 ha in 2008, 934 ha in 2009, and 1008 ha in 2010. Results indicate that remote sensing approaches are likely to be useful for studying spatiotemporal patterns of tamarisk defoliation as the tamarisk leaf beetle spreads throughout the western United States.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"GIScience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Bellwether Publishing, Ltd.","publisherLocation":"Columbia, MD","doi":"10.2747/1548-1603.49.4.510","usgsCitation":"Meng, R., Dennison, P.E., Jamison, L., van Riper, C., Nager, P., Hultine, K.R., Bean, D., and Dudley, T., 2012, Detection of tamarisk defoliation by the northern tamarisk beetle based on multitemporal Landsat 5 thematic mapper imagery: GIScience and Remote Sensing, v. 49, no. 4, p. 510-537, https://doi.org/10.2747/1548-1603.49.4.510.","productDescription":"28 p.","startPage":"510","endPage":"537","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":474343,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2747/1548-1603.49.4.510","text":"Publisher Index Page"},{"id":262152,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":262146,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2747/1548-1603.49.4.510"}],"country":"United States","volume":"49","issue":"4","noUsgsAuthors":false,"publicationDate":"2013-05-15","publicationStatus":"PW","scienceBaseUri":"5066250fe4b053bff18e1bef","contributors":{"authors":[{"text":"Meng, Ran","contributorId":105955,"corporation":false,"usgs":true,"family":"Meng","given":"Ran","email":"","affiliations":[],"preferred":false,"id":467592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dennison, Philip E.","contributorId":105132,"corporation":false,"usgs":true,"family":"Dennison","given":"Philip","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":467591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jamison, Levi R.","contributorId":45163,"corporation":false,"usgs":true,"family":"Jamison","given":"Levi R.","affiliations":[],"preferred":false,"id":467587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":467590,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nager, Pamela","contributorId":11046,"corporation":false,"usgs":true,"family":"Nager","given":"Pamela","email":"","affiliations":[],"preferred":false,"id":467585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hultine, Kevin R. 0000-0001-9747-6037","orcid":"https://orcid.org/0000-0001-9747-6037","contributorId":23772,"corporation":false,"usgs":true,"family":"Hultine","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":467586,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bean, Dan W.","contributorId":58133,"corporation":false,"usgs":true,"family":"Bean","given":"Dan W.","affiliations":[],"preferred":false,"id":467588,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dudley, Tom","contributorId":64496,"corporation":false,"usgs":true,"family":"Dudley","given":"Tom","affiliations":[],"preferred":false,"id":467589,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70255881,"text":"70255881 - 2012 - An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data","interactions":[],"lastModifiedDate":"2024-07-09T13:32:55.362554","indexId":"70255881","displayToPublicDate":"2012-09-25T08:28:03","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data","docAbstract":"The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (∼80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (khat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (khat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (∼90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ∼30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms. The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.
","language":"English","publisher":"MDPI","doi":"10.3390/rs4102890","usgsCitation":"Thenkabail, P.S., and Wu, Z., 2012, An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data: Remote Sensing, v. 4, no. 10, p. 2890-2918, https://doi.org/10.3390/rs4102890.","productDescription":"29 p.","startPage":"2890","endPage":"2918","ipdsId":"IP-035313","costCenters":[{"id":273,"text":"Flagstaff Science Center","active":false,"usgs":true}],"links":[{"id":474346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs4102890","text":"Publisher Index Page"},{"id":430841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tajikistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[71.0142,40.24437],[70.64802,39.93575],[69.55961,40.10321],[69.46489,39.52668],[70.54916,39.6042],[71.78469,39.27946],[73.67538,39.43124],[73.92885,38.50582],[74.25751,38.60651],[74.86482,38.37885],[74.82999,37.99001],[74.98,37.41999],[73.9487,37.42157],[73.26006,37.49526],[72.63689,37.04756],[72.19304,36.94829],[71.84464,36.73817],[71.44869,37.06564],[71.54192,37.90577],[71.2394,37.95327],[71.34813,38.25891],[70.80682,38.48628],[70.3763,38.1384],[70.27057,37.73516],[70.11658,37.58822],[69.51879,37.609],[69.19627,37.15114],[68.85945,37.34434],[68.13556,37.02312],[67.83,37.14499],[68.39203,38.15703],[68.17603,38.90155],[67.44222,39.14014],[67.70143,39.58048],[68.53642,39.53345],[69.01163,40.08616],[69.32949,40.72782],[70.66662,40.96021],[70.45816,40.49649],[70.60141,40.21853],[71.0142,40.24437]]]},\"properties\":{\"name\":\"Tajikistan\"}}]}","volume":"4","issue":"10","noUsgsAuthors":false,"publicationDate":"2012-09-25","publicationStatus":"PW","contributors":{"authors":[{"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":905870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":905871,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70040023,"text":"ds709C - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Haji-Gak mineral district in Afghanistan: Chapter C in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:13:40","indexId":"ds709C","displayToPublicDate":"2012-09-24T00:00:00","publicationYear":"2012","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":"C","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Haji-Gak mineral district in Afghanistan: Chapter C in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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 Haji-Gak mineral district, which has iron ore deposits. ALOS 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), 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. The 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. For this particular area, PRISM image orthorectification was performed by the Alaska Satellite Facility, applying its photogrammetric software to PRISM stereo images with vertical control points obtained from the digital elevation database produced by the Shuttle Radar Topography Mission (Farr and others, 2007) and horizontal adjustments based on a controlled Landsat image base (Davis, 2006). The 10-m AVNIR multispectral imagery was then co-registered to the orthorectified PRISM images and individual multispectral and panchromatic images were mosaicked into single images of the entire area of interest. The image-coregistration was 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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Haji-Gak) and the WGS84 datum. The final image mosaics were subdivided into three overlapping tiles or quadrants because of the large size of the target area. The three image tiles (or quadrants) for the Haji-Gak 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 Haji-Gak study area, three subareas were designated for detailed field investigations (that is, the Haji-Gak Prospect, Farenjal, and NE Haji-Gak subareas); these subareas were extracted from the area's image mosaic and are provided as separate embedded geotiff images.","largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709C","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 C in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: DS 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Haji-Gak mineral district in Afghanistan: Chapter C in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 3 Maps: 11 x 8.5 inches and 50.51 x 34.26 inches; 12 Image Files; 12 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709C.","productDescription":"Readme; 3 Maps: 11 x 8.5 inches and 50.51 x 34.26 inches; 12 Image Files; 12 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":262047,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_C.jpg"},{"id":262265,"rank":400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/c/index_maps/Haji-Gak_Image_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":262266,"rank":401,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/c/index_maps/Haji-Gak_Subarea_Image_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":262264,"rank":400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/c/index_maps/Haji-Gak_Area-of-Interest_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":262040,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/c/","linkFileType":{"id":5,"text":"html"}},{"id":263628,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/"},{"id":263629,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/c/image_files/image_files.html"},{"id":263626,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/c/metadata/metadata.html"},{"id":263627,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/c/shapefiles/shapefiles.html"},{"id":263625,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/c/1_readme.txt"}],"country":"Afghanistan","state":"Bamyan;Parwan;Wardak","otherGeospatial":"Haji-gak Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 67.75,34.5 ], [ 67.75,35.166667 ], [ 68.916667,35.166667 ], [ 68.916667,34.5 ], [ 67.75,34.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50788e1ce4b0cfc2d59f5ad8","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":467492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":467493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":467495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":467494,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70040327,"text":"ds709B - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Balkhab mineral district in Afghanistan: Chapter B in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:14:10","indexId":"ds709B","displayToPublicDate":"2012-09-05T00:00:00","publicationYear":"2012","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":"B","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Balkhab mineral district in Afghanistan: Chapter B in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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 Balkhab mineral district, which has copper deposits. ALOS 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,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. The 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 315-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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Balkhab) 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 Balkhab 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 Balkhab study area, one subarea was designated for detailed field investigations (that is, the Balkhab Prospect subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.","largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709B","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 B in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: DS 709.","usgsCitation":"Davis, P.A., and Cagney, L.E., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Balkhab mineral district in Afghanistan: Chapter B in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Maps: 11 x 8.5 inches and 46.68 x 35.61 inches; 6 Image Files; 6 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709B.","productDescription":"Readme; 2 Maps: 11 x 8.5 inches and 46.68 x 35.61 inches; 6 Image Files; 6 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":262587,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_B.jpg"},{"id":262584,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/b/","linkFileType":{"id":5,"text":"html"}},{"id":262586,"rank":400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/b/index_maps/Balkhab_Image_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":262585,"rank":400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/b/index_maps/Balkhab_Area-of-Interest_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":263621,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/b/image_files/image_files.html"},{"id":263620,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/b/1_readme.txt"},{"id":263622,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/b/metadata/metadata.html"},{"id":263623,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/b/shapefiles/shapefiles.html"},{"id":263624,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/"}],"country":"Afghanistan","state":"Balkh;Samangan;Sari-Pul","otherGeospatial":"Balkhab Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 66.25,35.25 ], [ 66.25,35.916667 ], [ 67.25,35.916667 ], [ 67.25,35.25 ], [ 66.25,35.25 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"507d2378e4b0905c2a76c025","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":468095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":468096,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70039813,"text":"sim3222 - 2012 - Earthquakes and faults in southern California (1970-2010)","interactions":[],"lastModifiedDate":"2014-04-24T15:37:33","indexId":"sim3222","displayToPublicDate":"2012-09-05T00:00:00","publicationYear":"2012","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":"3222","title":"Earthquakes and faults in southern California (1970-2010)","docAbstract":"The map depicts both active and inactive faults and earthquakes magnitude 1.5 to 7.3 in southern California (1970–2010). The bathymetry was generated from digital files from the California Department of Fish And Game, Marine Region, Coastal Bathymetry Project. Elevation data are from the U.S. Geological Survey National Elevation Database. Landsat satellite image is from fourteen Landsat 5 Thematic Mapper scenes collected between 2009 and 2010. Fault data are reproduced with permission from 2006 California Geological Survey and U.S. Geological Survey data. The earthquake data are from the U.S. Geological Survey National Earthquake Information Center.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3222","usgsCitation":"Sleeter, B.M., Calzia, J.P., and Walter, S.R., 2012, Earthquakes and faults in southern California (1970-2010): U.S. Geological Survey Scientific Investigations Map 3222, Map: 1 Sheet: 48 x 36 inches, https://doi.org/10.3133/sim3222.","productDescription":"Map: 1 Sheet: 48 x 36 inches","numberOfPages":"1","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1970-01-01","temporalEnd":"2010-12-31","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":260172,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3222.png"},{"id":260170,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3222/","linkFileType":{"id":5,"text":"html"}},{"id":260171,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3222/sim3222_poster.pdf","linkFileType":{"id":1,"text":"pdf"}}],"scale":"450000","projection":"Albers Conical Equal Area Projection","datum":"North American Horizontal Datum of 1983; National Geodetic Vertical Datum of 1929","country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -121,32.75 ], [ -121,35.083333333333336 ], [ -115,35.083333333333336 ], [ -115,32.75 ], [ -121,32.75 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a050fe4b0c8380cd50c48","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":466977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calzia, James P. jcalzia@usgs.gov","contributorId":2801,"corporation":false,"usgs":true,"family":"Calzia","given":"James","email":"jcalzia@usgs.gov","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":466976,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walter, Stephen R.","contributorId":34954,"corporation":false,"usgs":true,"family":"Walter","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":466978,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70005018,"text":"70005018 - 2012 - Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery","interactions":[],"lastModifiedDate":"2020-09-04T13:19:56.447766","indexId":"70005018","displayToPublicDate":"2012-08-31T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1175,"text":"Canadian Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery","docAbstract":"Programs to monitor lake area change are becoming increasingly important in high latitude regions, and their development often requires evaluating tradeoffs among different approaches in terms of accuracy of measurement, consistency across multiple users over long time periods, and efficiency. We compared three supervised methods for lake classification from Landsat imagery (density slicing, classification trees, and feature extraction). The accuracy of lake area and number estimates was evaluated relative to high-resolution aerial photography acquired within two days of satellite overpasses. The shortwave infrared band 5 was better at separating surface water from nonwater when used alone than when combined with other spectral bands. The simplest of the three methods, density slicing, performed best overall. The classification tree method resulted in the most omission errors (approx. 2x), feature extraction resulted in the most commission errors (approx. 4x), and density slicing had the least directional bias (approx. half of the lakes with overestimated area and half of the lakes with underestimated area). Feature extraction was the least consistent across training sets (i.e., large standard error among different training sets). Density slicing was the best of the three at classifying small lakes as evidenced by its lower optimal minimum lake size criterion of 5850 m2 compared with the other methods (8550 m2). Contrary to conventional wisdom, the use of additional spectral bands and a more sophisticated method not only required additional processing effort but also had a cost in terms of the accuracy and consistency of lake classifications.","language":"English","publisher":"Canadian Aeronautics and Space Institute","publisherLocation":"Kanata, Ontario","doi":"10.5589/m12-035","usgsCitation":"Roach, J., Griffith, B., and Verbyla, D., 2012, Comparison of three methods for long-term monitoring of boreal lake area using Landsat TM and ETM+ imagery: Canadian Journal of Remote Sensing, v. 38, no. 4, p. 427-440, https://doi.org/10.5589/m12-035.","productDescription":"14 p.","startPage":"427","endPage":"440","ipdsId":"IP-031187","costCenters":[{"id":108,"text":"Alaska Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":260115,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":378113,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.tandfonline.com/doi/full/10.5589/m12-035","linkFileType":{"id":5,"text":"html"}}],"volume":"38","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059f8b2e4b0c8380cd4d232","contributors":{"authors":[{"text":"Roach, Jennifer K.","contributorId":30861,"corporation":false,"usgs":true,"family":"Roach","given":"Jennifer K.","affiliations":[],"preferred":false,"id":351825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Griffith, Brad 0000-0001-8698-6859","orcid":"https://orcid.org/0000-0001-8698-6859","contributorId":82571,"corporation":false,"usgs":true,"family":"Griffith","given":"Brad","email":"","affiliations":[{"id":108,"text":"Alaska Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":true,"id":351826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Verbyla, David","contributorId":87795,"corporation":false,"usgs":true,"family":"Verbyla","given":"David","affiliations":[],"preferred":false,"id":351827,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70040328,"text":"ds709A - 2012 - Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Khanneshin mineral district in Afghanistan: Chapter A in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","interactions":[],"lastModifiedDate":"2013-02-01T11:13:25","indexId":"ds709A","displayToPublicDate":"2012-08-17T00:00:00","publicationYear":"2012","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":"A","title":"Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Khanneshin mineral district in Afghanistan: Chapter A in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts 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 Khanneshin mineral district, which has uranium, thorium, rare-earth-element, and apatite deposits. ALOS 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,2007,2008,2010), 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. The 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). All image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Khanneshin) and the WGS84 datum. The final image mosaics were subdivided into nine overlapping tiles or quadrants because of the large size of the target area. The nine image tiles (or quadrants) for the Khanneshin 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 Khanneshin study area, one subarea was designated for detailed field investigations (that is, the Khanneshin volcano subarea); this subarea was extracted from the area's image mosaic and is provided as separate embedded geotiff images.","largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (DS 709)","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709A","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 A in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan. For more information, see: DS 709.","usgsCitation":"Davis, P.A., Cagney, L.E., Arko, S.A., and Harbin, M., 2012, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Khanneshin mineral district in Afghanistan: Chapter A in Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan: U.S. Geological Survey Data Series 709, Readme; 2 Index Maps: 11 x 8.5 inches and 76.14 x 50.07 inches; 20 Image Files; 20 Metadata Files; Shapefiles; DS 709, https://doi.org/10.3133/ds709A.","productDescription":"Readme; 2 Index Maps: 11 x 8.5 inches and 76.14 x 50.07 inches; 20 Image Files; 20 Metadata Files; Shapefiles; DS 709","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":262599,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_709_A.jpg"},{"id":262598,"rank":401,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/a/index_maps/Khanneshin_Image_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":262596,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/a/","linkFileType":{"id":5,"text":"html"}},{"id":262597,"rank":400,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/a/index_maps/Khanneshin_Area-of-Interest_Index_Map.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":263615,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/a/1_readme.txt"},{"id":263616,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/a/image_files/image_files.html"},{"id":263617,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/a/metadata/metadata.html"},{"id":263618,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/ds/709/"},{"id":263619,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/a/shapefiles/shapefiles.html"}],"country":"Afghanistan","state":"Helm;Nimroz","otherGeospatial":"Khanneshin Mineral District","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 62.75,29.916667 ], [ 62.75,30.833333 ], [ 64.416667,30.833333 ], [ 64.416667,29.916667 ], [ 62.75,29.916667 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"507ee039e4b022001d87bb7e","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":468097,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagney, Laura E. 0000-0003-3282-2458 lcagney@usgs.gov","orcid":"https://orcid.org/0000-0003-3282-2458","contributorId":4744,"corporation":false,"usgs":true,"family":"Cagney","given":"Laura","email":"lcagney@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":468098,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arko, Scott A.","contributorId":101929,"corporation":false,"usgs":true,"family":"Arko","given":"Scott","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":468100,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harbin, Michelle L.","contributorId":20590,"corporation":false,"usgs":true,"family":"Harbin","given":"Michelle L.","affiliations":[],"preferred":false,"id":468099,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70006049,"text":"70006049 - 2012 - The next Landsat satellite: The Landsat Data Continuity Mission","interactions":[],"lastModifiedDate":"2022-01-25T12:26:43.71883","indexId":"70006049","displayToPublicDate":"2012-08-01T00:00:00","publicationYear":"2012","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":"The next Landsat satellite: The Landsat Data Continuity Mission","docAbstract":"The National Aeronautics and Space Administration (NASA) and the Department of Interior United States Geological Survey (USGS) are developing the successor mission to Landsat 7 that is currently known as the Landsat Data Continuity Mission (LDCM). NASA is responsible for building and launching the LDCM satellite observatory. USGS is building the ground system and will assume responsibility for satellite operations and for collecting, archiving, and distributing data following launch. The observatory will consist of a spacecraft in low-Earth orbit with a two-sensor payload. One sensor, the Operational Land Imager (OLI), will collect image data for nine shortwave spectral bands over a 185 km swath with a 30 m spatial resolution for all bands except a 15 m panchromatic band. The other instrument, the Thermal Infrared Sensor (TIRS), will collect image data for two thermal bands with a 100 m resolution over a 185 km swath. Both sensors offer technical advancements over earlier Landsat instruments. OLI and TIRS will coincidently collect data and the observatory will transmit the data to the ground system where it will be archived, processed to Level 1 data products containing well calibrated and co-registered OLI and TIRS data, and made available for free distribution to the general public. The LDCM development is on schedule for a December 2012 launch. The USGS intends to rename the satellite \"Landsat 8\" following launch. By either name a successful mission will fulfill a mandate for Landsat data continuity. The mission will extend the almost 40-year Landsat data archive with images sufficiently consistent with data from the earlier missions to allow long-term studies of regional and global land cover change.","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.rse.2011.08.026","usgsCitation":"Irons, J.R., Dwyer, J.L., and Barsi, J.A., 2012, The next Landsat satellite: The Landsat Data Continuity Mission: Remote Sensing of Environment, v. 122, p. 11-21, https://doi.org/10.1016/j.rse.2011.08.026.","productDescription":"11 p.","startPage":"11","endPage":"21","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":474392,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2060/20120013292","text":"External Repository"},{"id":259394,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505bae19e4b08c986b323f02","contributors":{"authors":[{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":353734,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":353733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":353735,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70007154,"text":"70007154 - 2012 - Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems","interactions":[],"lastModifiedDate":"2012-08-30T17:16:17","indexId":"70007154","displayToPublicDate":"2012-07-28T00:00:00","publicationYear":"2012","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":"Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems","docAbstract":"The focus of the study was to assess gradual changes occurring throughout a range of natural ecosystems using decadal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) time series data. Time series data stacks were generated for four study areas: (1) a four scene area dominated by forest and rangeland ecosystems in the southwestern United States, (2) a sagebrush-dominated rangeland in Wyoming, (3) woodland adjacent to prairie in northwestern Nebraska, and (4) a forested area in the White Mountains of New Hampshire. Through analyses of time series data, we found evidence of gradual systematic change in many of the natural vegetation communities in all four areas. Many of the conifer forests in the southwestern US are showing declines related to insects and drought, but very few are showing evidence of improving conditions or increased greenness. Sagebrush communities are showing decreases in greenness related to fire, mining, and probably drought, but very few of these communities are showing evidence of increased greenness or improving conditions. In Nebraska, forest communities are showing local expansion and increased canopy densification in the prairie–woodland interface, and in the White Mountains high elevation understory conifers are showing range increases towards lower elevations. The trends detected are not obvious through casual inspection of the Landsat images. Analyses of time series data using many scenes and covering multiple years are required in order to develop better impressions and representations of the changing ecosystem patterns and trends that are occurring. The approach described in this paper demonstrates that Landsat time series data can be used operationally for assessing gradual ecosystem change across large areas. Local knowledge and available ancillary data are required in order to fully understand the nature of these trends.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.rse.2011.06.027","usgsCitation":"Vogelmann, J., Xian, G., Homer, C.G., and Tolk, B., 2012, Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems: Remote Sensing of Environment, v. 122, p. 92-105, https://doi.org/10.1016/j.rse.2011.06.027.","productDescription":"14 p.","startPage":"92","endPage":"105","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":259239,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":257288,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.rse.2011.06.027","linkFileType":{"id":5,"text":"html"}}],"country":"United States","volume":"122","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5dafe4b0c8380cd70523","contributors":{"authors":[{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":355952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":76589,"corporation":false,"usgs":true,"family":"Xian","given":"George","affiliations":[],"preferred":false,"id":355955,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":355953,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tolk, Brian 0000-0002-9060-0266","orcid":"https://orcid.org/0000-0002-9060-0266","contributorId":62426,"corporation":false,"usgs":true,"family":"Tolk","given":"Brian","affiliations":[],"preferred":false,"id":355954,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70037787,"text":"70037787 - 2012 - Late twentieth century land-cover change in the basin and range ecoregions of the United States","interactions":[],"lastModifiedDate":"2012-11-14T14:57:22","indexId":"70037787","displayToPublicDate":"2012-07-27T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3242,"text":"Regional Environmental Change","active":true,"publicationSubtype":{"id":10}},"title":"Late twentieth century land-cover change in the basin and range ecoregions of the United States","docAbstract":"As part of the US Geological Survey's Land Cover Trends project, land-use/land-cover change estimates between 1973 and 2000 are presented for the basin and range ecoregions, including Northern, Central, Mojave, and Sonoran. Landsat data were employed to estimate and characterize land-cover change from 1973, 1980, 1986, 1992, and 2000 using a post-classification comparison. Overall, spatial change was 2.5% (17,830 km2). Change increased steadily between 1973 and 1986 but decreased slightly between 1992 and 2000. The grassland/shrubland class, frequently used for livestock grazing, constituted the majority of the study area and had a net decrease from an estimated 83.8% (587,024 km2) in 1973 to 82.6% (578,242 km2) in 2000. The most common land-use/land-cover conversions across the basin and range ecoregions were indicative of the changes associated with natural, nonmechanical disturbances (i.e., fire), and grassland/shrubland loss to development, agriculture, and mining. This comprehensive look at contemporary land-use/land-cover change provides critical insight into how the deserts of the United States have changed and can be used to inform adaptive management practices of public lands.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Regional Environmental Change","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","publisherLocation":"Amsterdam, Netherlands","doi":"10.1007/s10113-012-0296-3","usgsCitation":"Soulard, C.E., and Sleeter, B.M., 2012, Late twentieth century land-cover change in the basin and range ecoregions of the United States: Regional Environmental Change, v. 12, no. 4, p. 813-823, https://doi.org/10.1007/s10113-012-0296-3.","productDescription":"11 p.","startPage":"813","endPage":"823","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":259228,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10113-012-0296-3","linkFileType":{"id":5,"text":"html"}},{"id":259231,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-03-14","publicationStatus":"PW","scienceBaseUri":"505a4560e4b0c8380cd6727c","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":462727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":462728,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70038646,"text":"sim3207 - 2012 - Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009","interactions":[],"lastModifiedDate":"2012-06-09T01:01:37","indexId":"sim3207","displayToPublicDate":"2012-06-08T00:00:00","publicationYear":"2012","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":"3207","title":"Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009","docAbstract":"The purpose of this project is to provide improved estimates of Louisiana wetland land loss due to hurricane impacts between 2004 and 2009 based upon a change detection mapping analysis that incorporates pre- and post-landfall (Hurricanes Katrina, Rita, Gustav, and Ike) fractional water classification of a combination of high resolution (QuickBird, IKONOS and Geoeye-1) and medium resolution (Landsat) satellite imagery. This second dataset focuses on Hurricanes Katrina and Gustav, which made landfall on August 29, 2005, and September 1, 2008, respectively. The study area is an approximately 1208-square-kilometer region surrounding Delacroix, Louisiana, in the eastern Delta Plain. Overall, 77 percent of the area remained unchanged between 2004 and 2009, and over 11 percent of the area was changed permanently by Hurricane Katrina (including both land gain and loss). Less than 3 percent was affected, either temporarily or permanently, by Hurricane Gustav. A related dataset (SIM 3141) focused on Hurricane Rita, which made landfall on the Louisiana/Texas border on September 24, 2005, as a Category 3 hurricane.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3207","usgsCitation":"Palaseanu-Lovejoy, M., Kranenburg, C., and Brock, J., 2012, Land area change analysis following hurricane impacts in Delacroix, Louisiana, 2004--2009: U.S. Geological Survey Scientific Investigations Map 3207, ii, 9 p.; PDF Download of Map: 48.01 x 36.01 inches; ZIP Download of Data Files; General Metadata File; Readme File, https://doi.org/10.3133/sim3207.","productDescription":"ii, 9 p.; PDF Download of Map: 48.01 x 36.01 inches; ZIP Download of Data Files; General Metadata File; Readme File","startPage":"i","endPage":"9","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2004-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":257375,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3207/","linkFileType":{"id":5,"text":"html"}},{"id":257376,"rank":300,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3207/pdf/SIM_3207_poster.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":257386,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3207.bmp"}],"country":"United States","state":"Louisiana","city":"Delacroix","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a419ee4b0c8380cd6566d","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":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"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}],"preferred":true,"id":464590,"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":464591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":464589,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70038466,"text":"70038466 - 2012 - Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests","interactions":[],"lastModifiedDate":"2012-06-12T01:01:51","indexId":"70038466","displayToPublicDate":"2012-06-06T10:27:00","publicationYear":"2012","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":"Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests","docAbstract":"We examined the effects of three early season (spring) prescribed fires on burn severity patterns of summer wildfires that occurred 1–3 years post-treatment in a mixed conifer forest in central Idaho. Wildfire and prescribed fire burn severities were estimated as the difference in normalized burn ratio (dNBR) using Landsat imagery. We used GIS derived vegetation, topography, and treatment variables to generate models predicting the wildfire burn severity of 1286–5500 30-m pixels within and around treated areas. We found that wildfire severity was significantly lower in treated areas than in untreated areas and significantly lower than the potential wildfire severity of the treated areas had treatments not been implemented. At the pixel level, wildfire severity was best predicted by an interaction between prescribed fire severity, topographic moisture, heat load, and pre-fire vegetation volume. Prescribed fire severity and vegetation volume were the most influential predictors. Prescribed fire severity, and its influence on wildfire severity, was highest in relatively warm and dry locations, which were able to burn under spring conditions. In contrast, wildfire severity peaked in cooler, more mesic locations that dried later in the summer and supported greater vegetation volume. We found considerable evidence that prescribed fires have landscape-level influences within treatment boundaries; most notable was an interaction between distance from the prescribed fire perimeter and distance from treated patch edges, which explained up to 66% of the variation in wildfire severity. Early season prescribed fires may not directly target the locations most at risk of high severity wildfire, but proximity of these areas to treated patches and the discontinuity of fuels following treatment may influence wildfire severity and explain how even low severity treatments can be effective management tools in fire-prone landscapes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Forest Ecology and Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.foreco.2012.04.002","usgsCitation":"Arkle, R., Pilliod, D., and Welty, J., 2012, Pattern and process of prescribed fires influence effectiveness at reducing wildfire severity in dry coniferous forests: Forest Ecology and Management, v. 276, p. 174-184, https://doi.org/10.1016/j.foreco.2012.04.002.","productDescription":"11 p.l","startPage":"174","endPage":"184","numberOfPages":"11","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":257437,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":257420,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.foreco.2012.04.002","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a75b2e4b0c8380cd77cb3","contributors":{"authors":[{"text":"Arkle, Robert S.","contributorId":55679,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert S.","affiliations":[],"preferred":false,"id":464291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pilliod, David S.","contributorId":101760,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[],"preferred":false,"id":464293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Welty, Justin L.","contributorId":80558,"corporation":false,"usgs":true,"family":"Welty","given":"Justin L.","affiliations":[],"preferred":false,"id":464292,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70038609,"text":"fs20123072 - 2012 - Landsat: A global land-imaging mission","interactions":[{"subject":{"id":70038609,"text":"fs20123072 - 2012 - Landsat: A global land-imaging mission","indexId":"fs20123072","publicationYear":"2012","noYear":false,"title":"Landsat: A global land-imaging mission"},"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":"2017-03-28T11:08:59","indexId":"fs20123072","displayToPublicDate":"2012-06-06T00:00:00","publicationYear":"2012","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":"2012-3072","title":"Landsat: A global land-imaging mission","docAbstract":"Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123072","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2012, Landsat: A global land-imaging mission: U.S. Geological Survey Fact Sheet 2012-3072, 4 p., https://doi.org/10.3133/fs20123072.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":257250,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3072.gif"},{"id":299698,"rank":101,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2012/3072/fs2012-3072.pdf","text":"Report","size":"3.95 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":257249,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3072/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43f3e4b0c8380cd6670d","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":535188,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70038426,"text":"fs20123066 - 2012 - Landsat Data Continuity Mission","interactions":[],"lastModifiedDate":"2012-05-26T01:01:37","indexId":"fs20123066","displayToPublicDate":"2012-05-25T00:00:00","publicationYear":"2012","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":"2012-3066","title":"Landsat Data Continuity Mission","docAbstract":"The Landsat Data Continuity Mission (LDCM) is a partnership formed between the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) to place the next Landsat satellite in orbit in January 2013. The Landsat era that began in 1972 will become a nearly 41-year global land record with the successful launch and operation of the LDCM. The LDCM will continue the acquisition, archiving, and distribution of multispectral imagery affording global, synoptic, and repetitive coverage of the Earth's land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time.\r\nThe mission objectives of the LDCM are to (1) collect and archive medium resolution (30-meter spatial resolution) multispectral image data affording seasonal coverage of the global landmasses for a period of no less than 5 years; (2) ensure that LDCM data are sufficiently consistent with data from the earlier Landsat missions in terms of acquisition geometry, calibration, coverage characteristics, spectral characteristics, output product quality, and data availability to permit studies of landcover and land-use change over time; and (3) distribute LDCM data products to the general public on a nondiscriminatory basis at no cost to the user.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20123066","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2012, Landsat Data Continuity Mission: U.S. Geological Survey Fact Sheet 2012-3066, 4 p., https://doi.org/10.3133/fs20123066.","productDescription":"4 p.","onlineOnly":"Y","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":256974,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs_2012_3066.gif"},{"id":256968,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2012/3066/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a43cfe4b0c8380cd66631","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":535185,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}