The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Geophysical Research: Biogeosciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/2013JG002594","usgsCitation":"Pastick, N.J., Jorgenson, M., Wylie, B.K., Rose, J.R., Rigge, M., and Walvoord, M.A., 2014, Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin: Journal of Geophysical Research: Biogeosciences, v. 119, no. 6, p. 1244-1265, https://doi.org/10.1002/2013JG002594.","productDescription":"22 p.","startPage":"1244","endPage":"1265","numberOfPages":"22","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056842","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013jg002594","text":"Publisher Index Page"},{"id":294946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294945,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/2013JG002594"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaskan Yukon River Basin","volume":"119","issue":"6","noUsgsAuthors":false,"publicationDate":"2014-06-30","publicationStatus":"PW","scienceBaseUri":"542fbaaee4b092f17df61dfa","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":493735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M. Torre","contributorId":34848,"corporation":false,"usgs":true,"family":"Jorgenson","given":"M. Torre","affiliations":[],"preferred":false,"id":493738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":493734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rose, Joshua R.","contributorId":12395,"corporation":false,"usgs":true,"family":"Rose","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":493736,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rigge, Matthew 0000-0003-4471-8009","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":18295,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","affiliations":[],"preferred":false,"id":493737,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366 walvoord@usgs.gov","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":147211,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"walvoord@usgs.gov","middleInitial":"Ann","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":493739,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70124277,"text":"70124277 - 2014 - Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","interactions":[],"lastModifiedDate":"2014-09-11T13:56:39","indexId":"70124277","displayToPublicDate":"2014-06-01T13:46:29","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI","docAbstract":"Agricultural production capacity contributes to food security in Afghanistan and is largely dependent on irrigated farming, mostly utilizing surface water fed by snowmelt. Because of the high contribution of irrigated crops (> 80%) to total agricultural production, knowing the spatial distribution and year-to-year variability in irrigated areas is imperative to monitoring food security for the country. We used 16-day composites of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to create 23-point time series for each year from 2000 through 2013. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm to map irrigated areas in Afghanistan for the last 14 years. In the absence of ground reference irrigated area information, we evaluated these maps with the irrigated areas classified from multiple snapshots of the landscape during the growing season from Landsat 5 optical and thermal sensor images. We were able to identify irrigated areas using Landsat imagery by selecting as irrigated those areas with Landsat-derived NDVI greater than 0.30–0.45, depending on the date of the Landsat image and surface temperature less than or equal to 310 Kelvin (36.9 ° C). Due to the availability of Landsat images, we were able to compare with the MODIS-derived maps for four years: 2000, 2009, 2010, and 2011. The irrigated areas derived from Landsat agreed well r2 = 0.91 with the irrigated areas derived from MODIS, providing confidence in the MODIS NDVI threshold approach. The maps portrayed a highly dynamic irrigated agriculture practice in Afghanistan, where the amount of irrigated area was largely determined by the availability of surface water, especially snowmelt, and varied by as much as 30% between water surplus and water deficit years. During the past 14 years, 2001, 2004, and 2008 showed the lowest levels of irrigated area (~ 1.5 million hectares), attesting to the severe drought conditions in those years, whereas 2009, 2012 and 2013 registered the largest irrigated area (~ 2.5 million hectares) due to record snowpack and snowmelt in the region. The model holds promise the ability to provide near-real-time (by the end of the growing seasons) estimates of irrigated area, which are beneficial for food security monitoring as well as subsequent decision making for the country. While the model is developed for Afghanistan, it can be adopted with appropriate adjustments in the derived threshold values to map irrigated areas elsewhere.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.04.008","usgsCitation":"Pervez, M., Budde, M., and Rowland, J., 2014, Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI: Remote Sensing of Environment, v. 149, p. 155-165, https://doi.org/10.1016/j.rse.2014.04.008.","productDescription":"11 p.","startPage":"155","endPage":"165","numberOfPages":"11","ipdsId":"IP-049479","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":293759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":293755,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2014.04.008"}],"country":"Afghanistan","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 60.52,29.38 ], [ 60.52,38.49 ], [ 74.89,38.49 ], [ 74.89,29.38 ], [ 60.52,29.38 ] ] ] } } ] }","volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5412b9b1e4b0239f1986baa5","contributors":{"authors":[{"text":"Pervez, Md Shahriar 0000-0003-3417-1871 shahriar.pervez.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-3417-1871","contributorId":74230,"corporation":false,"usgs":true,"family":"Pervez","given":"Md Shahriar","email":"shahriar.pervez.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":500640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budde, Michael 0000-0002-9098-2751","orcid":"https://orcid.org/0000-0002-9098-2751","contributorId":36867,"corporation":false,"usgs":true,"family":"Budde","given":"Michael","affiliations":[],"preferred":false,"id":500639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rowland, James 0000-0003-4837-3511 rowland@usgs.gov","orcid":"https://orcid.org/0000-0003-4837-3511","contributorId":3108,"corporation":false,"usgs":true,"family":"Rowland","given":"James","email":"rowland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":500638,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70104213,"text":"70104213 - 2014 - Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation","interactions":[],"lastModifiedDate":"2014-05-13T10:37:49","indexId":"70104213","displayToPublicDate":"2014-05-13T10:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation","docAbstract":"There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, Typha spp. and Schoenoplectus acutus, at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R2 = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R2 = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species (Typha spp.: %RMSE = 18.5%; S. acutus: %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R2 = 0.56, %RMSE = 20.9%, compared to R2 = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R2 = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing of Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.04.003","usgsCitation":"Byrd, K.B., O'Connell, J., Di Tommaso, S., and Kelly, M., 2014, Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation: Remote Sensing of Environment, v. 149, p. 166-180, https://doi.org/10.1016/j.rse.2014.04.003.","productDescription":"15 p.","startPage":"166","endPage":"180","numberOfPages":"15","ipdsId":"IP-052200","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":287071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":287072,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2014.04.003"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-san Joaquin River Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.7545,37.3797 ], [ -122.7545,38.2715 ], [ -121.2455,38.2715 ], [ -121.2455,37.3797 ], [ -122.7545,37.3797 ] ] ] } } ] }","volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"537330d2e4b04970612788ae","chorus":{"doi":"10.1016/j.rse.2014.04.003","url":"http://dx.doi.org/10.1016/j.rse.2014.04.003","publisher":"Elsevier BV","authors":"Byrd Kristin B., O'Connell Jessica L., Di Tommaso Stefania, Kelly Maggi","journalName":"Remote Sensing of Environment","publicationDate":"6/2014"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":493639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Connell, Jessica L.","contributorId":86265,"corporation":false,"usgs":true,"family":"O'Connell","given":"Jessica L.","affiliations":[],"preferred":false,"id":493642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Di Tommaso, Stefania","contributorId":9965,"corporation":false,"usgs":true,"family":"Di Tommaso","given":"Stefania","email":"","affiliations":[],"preferred":false,"id":493640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelly, Maggi","contributorId":14275,"corporation":false,"usgs":true,"family":"Kelly","given":"Maggi","affiliations":[],"preferred":false,"id":493641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70099906,"text":"gip133A - 2014 - Tracking change over time: River flooding","interactions":[],"lastModifiedDate":"2017-03-28T11:14:52","indexId":"gip133A","displayToPublicDate":"2014-05-12T08:10:20","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"133","chapter":"A","title":"Tracking change over time: River flooding","docAbstract":"Landsat satellites have been capturing images of Earth from space since 1972. These images provide a long-term record of natural and human-induced changes on the global landscape. Comparing images from multiple years reveals slow and subtle changes as well as rapid and devastating ones. Landsat images are available from the Internet at no charge. Using the free software MultiSpec, students can track changes to the landscape over time—just like remote sensing scientists do!
\nThe objective of the Tracking Change Over Time lesson plan is to get students excited about studying the changing Earth. Intended for students in grades 5-8, the lesson plan is flexible and may be used as a student self-guided tutorial or as a teacher-led class lesson. Enhance students' learning of geography, map reading, earth science, and problem solving by seeing landscape changes from space.
\nThe objective of the Tracking Change Over Time lesson plan is to get students excited about studying the changing Earth. Intended for students in grades 5-8, the lesson plan is flexible and may be used as a student self-guided tutorial or as a teacher-led class lesson. Enhance students' learning of geography, map reading, earth science, and problem solving by seeing landscape changes from space.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip133A","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2014, Tracking change over time: River flooding (Originally posted: May 9, 2014; Version 2.0: March 10, 2016): U.S. Geological Survey General Information Product 133, Tracking change over time—River flooding - Teacher: 4 p.; Tracking change over time—River flooding - Student: 2 p., https://doi.org/10.3133/gip133A.","productDescription":"Tracking change over time—River flooding - Teacher: 4 p.; Tracking change over time—River flooding - Student: 2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-038840","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":318950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/gip133A.JPG"},{"id":287035,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/gip/133a/"},{"id":287040,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/133a/pdf/gip133a_teacher.pdf","text":"Teacher","description":"Teacher"},{"id":287041,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/133a/pdf/gip133a_student.pdf","text":"Student","description":"Student"}],"country":"United States","state":"Illinois, Indiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.51,36.97 ], [ -91.51,42.51 ], [ -84.78,42.51 ], [ -84.78,36.97 ], [ -91.51,36.97 ] ] ] } } ] }","edition":"Originally posted: May 9, 2014; Version 2.0: March 10, 2016","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5371df54e4b08449547883e8","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":535644,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"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","interactions":[{"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":1}],"isPartOf":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"lastModifiedDate":"2022-12-09T20:57:06.961314","indexId":"ds709FF","displayToPublicDate":"2014-05-07T12:42:20","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","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","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 Farah mineral district, which has spectral reflectance anomalies indicative of copper, zinc, lead, silver, and gold deposits.
\n\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 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.
\n\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).
\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (41 for Farah) 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 Farah 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 Farah study area, five subareas were designated for detailed field investigations (that is, the FarahA through FarahE 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 (Data Series 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709FF","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"Davis, P.A., 2014, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Farah mineral district in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles, https://doi.org/10.3133/ds709FF.","productDescription":"HTML Document; Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055960","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":286977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709ff.jpg"},{"id":286974,"rank":11,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/ff/image_files/image_files.html","text":"Image Files"},{"id":286972,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/ff/1_readme.doc"},{"id":286975,"rank":1,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/ff/metadata/metadata.html"},{"id":286971,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/ff/"},{"id":286973,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/ff/index_maps/index_maps.html","text":"Index Maps"},{"id":286976,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/ff/shapefiles/shapefiles.html"}],"country":"Afghanistan","otherGeospatial":"Farah Mineral District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 62.73978191168703,\n 32.9985359214848\n ],\n [\n 60.74259795471943,\n 32.9985359214848\n ],\n [\n 60.74259795471943,\n 31.463985777157987\n ],\n [\n 62.73978191168703,\n 31.463985777157987\n ],\n [\n 62.73978191168703,\n 32.9985359214848\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d1e4b0a51a87c4b125","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":492735,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"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","interactions":[{"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":1}],"isPartOf":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"lastModifiedDate":"2022-12-09T20:57:48.473565","indexId":"ds709EE","displayToPublicDate":"2014-05-07T12:17:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","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","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 Ghazni2 mineral district, which has spectral reflectance anomalies indicative of gold, mercury, and sulfur deposits.
\n\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 2008, 2009), but the image processing has altered the original pixel structure and all image values of the JAXA ALOS data, such that original image values cannot be recreated from this DS. As such, the DS products match JAXA criteria for value added products, which are not copyrighted, according to the ALOS end-user license agreement.
\n\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).
\n\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Ghazni2) and the WGS84 datum. The images for the Ghazni2 area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (Data Series 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709EE","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"Davis, P.A., 2014, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni2 mineral district in Afghanistan: U.S. Geological Survey Data Series 709, HTML Document; Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles, https://doi.org/10.3133/ds709EE.","productDescription":"HTML Document; Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles","onlineOnly":"Y","ipdsId":"IP-054394","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":286970,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709ee.jpg"},{"id":286964,"rank":11,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/ee/","linkFileType":{"id":5,"text":"html"}},{"id":286967,"rank":4,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/ee/image_files/image_files.html","text":"Image Files","linkFileType":{"id":5,"text":"html"}},{"id":286965,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/ee/1_readme.doc"},{"id":286968,"rank":1,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/ee/metadata/metadata.html","linkFileType":{"id":5,"text":"html"}},{"id":286969,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/ee/shapefiles/shapefiles.html"},{"id":286966,"rank":1,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/ee/index_maps/index_maps.html","text":"Index Maps","linkFileType":{"id":5,"text":"html"}}],"country":"Afghanistan","otherGeospatial":"Ghazni2 Mineral District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 68.87252342306118,\n 33.400794951645565\n ],\n [\n 68.5382513185329,\n 33.71681758841511\n ],\n [\n 68.30727405623549,\n 33.72224478024398\n ],\n [\n 68.08601269164748,\n 34.1414227946527\n ],\n [\n 67.2638552357352,\n 34.07256923954678\n ],\n [\n 67.23635530353977,\n 33.52157172943353\n ],\n [\n 66.91878464275106,\n 33.262493443111566\n ],\n [\n 67.42957551466375,\n 32.86586167317263\n ],\n [\n 67.7125922483599,\n 32.561357509486314\n ],\n [\n 68.87252342306118,\n 33.400794951645565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d3e4b0a51a87c4b12f","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":492734,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"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","interactions":[{"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":1}],"isPartOf":{"id":70040370,"text":"ds709 - 2012 - Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan","indexId":"ds709","publicationYear":"2012","noYear":false,"title":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan"},"lastModifiedDate":"2022-12-09T20:58:26.596731","indexId":"ds709DD","displayToPublicDate":"2014-05-07T12:01:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"709","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","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 Ghazni1 mineral district, which has spectral reflectance anomalies indicative of clay, aluminum, gold, silver, mercury, and sulfur deposits.
\nALOS was launched on January 24, 2006, and provides multispectral images from the AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor in blue (420-500 nanometer, nm), green (520-600 nm), red (610-690 nm), and near-infrared (760-890 nm) wavelength bands with an 8-bit dynamic range and a 10-meter (m) ground resolution. The satellite also provides a panchromatic band image from the PRISM (Panchromatic Remote-sensing Instrument for Stereo Mapping) sensor (520-770 nm) with the same dynamic range but a 2.5-m ground resolution. The image products in this DS incorporate copyrighted data provided by the Japan Aerospace Exploration Agency ((c)JAXA, 2008, 2009), 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.
\nThe selection criteria for the satellite imagery used in our mosaics were images having (1) the highest solar-elevation angles (near summer solstice) and (2) the least cloud, cloud-shadow, and snow cover. The multispectral and panchromatic data were orthorectified with ALOS satellite ephemeris data, a process which is not as accurate as orthorectification using digital elevation models (DEMs); however, the ALOS processing center did not have a precise DEM. As a result, the multispectral and panchromatic image pairs were generally not well registered to the surface and not coregistered well enough to perform resolution enhancement on the multispectral data. Therefore, it was necessary to (1) register the 10-m AVNIR multispectral imagery to a well-controlled Landsat image base, (2) mosaic the individual multispectral images into a single image of the entire area of interest, (3) register each panchromatic image to the registered multispectral image base, and (4) mosaic the individual panchromatic images into a single image of the entire area of interest. The two image-registration steps were facilitated using an automated control-point algorithm developed by the USGS that allows image coregistration to within one picture element. Before rectification, the multispectral and panchromatic images were converted to radiance values and then to relative-reflectance values using the methods described in Davis (2006). Mosaicking the multispectral or panchromatic images started with the image with the highest sun-elevation angle and the least atmospheric scattering, which was treated as the standard image. The band-reflectance values of all other multispectral or panchromatic images within the area were sequentially adjusted to that of the standard image by determining band-reflectance correspondence between overlapping images using linear least-squares analysis. The resolution of the multispectral image mosaic was then increased to that of the panchromatic image mosaic using the SPARKLE logic, which is described in Davis (2006). Each of the four-band images within the resolution-enhanced image mosaic was individually subjected to a local-area histogram stretch algorithm (described in Davis, 2007), which stretches each band's picture element based on the digital values of all picture elements within a 500-m radius. The final databases, which are provided in this DS, are three-band, color-composite images of the local-area-enhanced, natural-color data (the blue, green, and red wavelength bands) and color-infrared data (the green, red, and near-infrared wavelength bands).
\nAll image data were initially projected and maintained in Universal Transverse Mercator (UTM) map projection using the target area's local zone (42 for Ghazni1) and the WGS84 datum. The images for the Ghazni1 area are provided as embedded geotiff images, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Local-area-enhanced, high-resolution natural-color and color-infrared satellite-image mosaics of mineral districts in Afghanistan (Data Series 709)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds709DD","collaboration":"Prepared in cooperation with the U.S. Department of Defense Task Force for Business and Stability Operations and the Afghanistan Geological Survey","usgsCitation":"Davis, P.A., 2014, Local-area-enhanced, 2.5-meter resolution natural-color and color-infrared satellite-image mosaics of the Ghazni1 mineral district in Afghanistan: U.S. Geological Survey Data Series 709, Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles, https://doi.org/10.3133/ds709DD.","productDescription":"Readme Text; Index Maps; Image Files; Metadata Files; Shapefiles","onlineOnly":"Y","ipdsId":"IP-054392","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":286957,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds709dd.jpg"},{"id":286960,"rank":11,"type":{"id":14,"text":"Image"},"url":"https://pubs.usgs.gov/ds/709/dd/image_files/image_files.html","text":"Image Files","linkFileType":{"id":5,"text":"html"}},{"id":286958,"rank":2,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/ds/709/dd/1_readme.doc"},{"id":286961,"rank":1,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/ds/709/dd/metadata/metadata.html","linkFileType":{"id":5,"text":"html"}},{"id":286963,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/709/dd/"},{"id":286959,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/ds/709/dd/index_maps/index_maps.html","text":"Index Maps","linkFileType":{"id":5,"text":"html"}},{"id":286962,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/ds/709/dd/shapefiles/shapefiles.html"}],"scale":"7000000","country":"Afghanistan","otherGeospatial":"Ghazni1 Mineral District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 68.87252342306118,\n 33.400794951645565\n ],\n [\n 68.5382513185329,\n 33.71681758841511\n ],\n [\n 68.30727405623549,\n 33.72224478024398\n ],\n [\n 68.08601269164748,\n 34.1414227946527\n ],\n [\n 67.2638552357352,\n 34.07256923954678\n ],\n [\n 67.23635530353977,\n 33.52157172943353\n ],\n [\n 66.91878464275106,\n 33.262493443111566\n ],\n [\n 67.42957551466375,\n 32.86586167317263\n ],\n [\n 67.7125922483599,\n 32.561357509486314\n ],\n [\n 68.87252342306118,\n 33.400794951645565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"536b47d2e4b0a51a87c4b12a","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":492733,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188051,"text":"70188051 - 2014 - Landsat-8: Science and product vision for terrestrial global change research","interactions":[],"lastModifiedDate":"2017-05-30T16:17:41","indexId":"70188051","displayToPublicDate":"2014-04-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Landsat-8: Science and product vision for terrestrial global change research","docAbstract":"Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.
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One of the affected areas was the Breton National Wildlife Refuge, which consists of a chain of low-lying islands, including Breton Island and the Chandeleur Islands, and their surrounding waters. The island chain is located approximately 115–150 kilometers (km) north-northwest of the spill site. A sand berm was constructed seaward of, and on, the island chain. Construction began at the northern end of Chandeleur Islands in June 2010 and ended in April 2011 after 14 km of berm had been constructed. The berm consisted of three distinct sections based on where the berm was placed relative to the islands. The northern section of the berm was built in open water on a submerged portion of the Chandeleur Islands platform. The middle section was built approximately 70–90 meters (m) seaward of the Chandeleur Islands. The southern section was built on the islands’ beaches. 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Climate Data Records, as defined by the National Research Council, are a time series of measurements with sufficient length, consistency, and continuity to identify climate variability and change. 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","language":"English","publisher":"MDPI","doi":"10.3390/w6030694","usgsCitation":"Gallant, A.L., Kaya, S.G., White, L., Brisco, B., Roth, M.F., Sadinski, W.J., and Rover, J., 2014, Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data: Water, v. 6, no. 3, p. 694-722, https://doi.org/10.3390/w6030694.","productDescription":"29 p.","startPage":"694","endPage":"722","ipdsId":"IP-053361","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w6030694","text":"Publisher Index Page"},{"id":341958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-03-24","publicationStatus":"PW","scienceBaseUri":"592fd640e4b0e9bd0ea8970a","contributors":{"authors":[{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":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":696252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaya, Shannon G.","contributorId":192330,"corporation":false,"usgs":false,"family":"Kaya","given":"Shannon","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":696253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Lori","contributorId":192557,"corporation":false,"usgs":false,"family":"White","given":"Lori","email":"","affiliations":[],"preferred":false,"id":696254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brisco, Brian","contributorId":37665,"corporation":false,"usgs":true,"family":"Brisco","given":"Brian","email":"","affiliations":[],"preferred":false,"id":696255,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Mark F. 0000-0001-5095-1865 mroth@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-1865","contributorId":3286,"corporation":false,"usgs":true,"family":"Roth","given":"Mark","email":"mroth@usgs.gov","middleInitial":"F.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":696256,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sadinski, Walter J. wsadinski@usgs.gov","contributorId":3287,"corporation":false,"usgs":true,"family":"Sadinski","given":"Walter","email":"wsadinski@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":696257,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rover, Jennifer 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":192333,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":696258,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70100631,"text":"70100631 - 2014 - Use of multi-sensor active fire detections to map fires in the United States: the future of monitoring trends in burn severity","interactions":[],"lastModifiedDate":"2017-04-21T16:02:42","indexId":"70100631","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Use of multi-sensor active fire detections to map fires in the United States: the future of monitoring trends in burn severity","docAbstract":"The effort to utilize satellite-based MODIS, AVHRR, and GOES fire detections from the Hazard Monitoring System (HMS) to identify undocumented fires in Florida and improve the Monitoring Trends in Burn Severity (MTBS) mapping process has yielded promising results. This method was augmented using regression tree models to identify burned/not-burned pixels (BnB) in every Landsat scene (1984–2012) in Worldwide Referencing System 2 Path/Rows 16/40, 17/39, and 1839. The burned area delineations were combined with the HMS detections to create burned area polygons attributed with their date of fire detection. Within our study area, we processed 88,000 HMS points (2003–2012) and 1,800 Landsat scenes to identify approximately 300,000 burned area polygons. Six percent of these burned area polygons were larger than the 500-acre MTBS minimum size threshold. From this study, we conclude that the process can significantly improve understanding of fire occurrence and improve the efficiency and timeliness of assessing its impacts upon the landscape.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Proceedings: Wildland fire in the Appalachians: Discussions among managers and scientists. General Technical Report SRS-199","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"USDA Forest Service","publisherLocation":"Asheville, NC","usgsCitation":"Picotte, J.J., Coan, M., and Howard, S.M., 2014, Use of multi-sensor active fire detections to map fires in the United States: the future of monitoring trends in burn severity, in Proceedings: Wildland fire in the Appalachians: Discussions among managers and scientists. General Technical Report SRS-199, p. 155-161.","productDescription":"7 p.","startPage":"155","endPage":"161","ipdsId":"IP-053728","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340098,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":340097,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.srs.fs.usda.gov/pubs/gtr/gtr_srs199/gtr_srs199.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4fe4b0c3010a8087d5","contributors":{"authors":[{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":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":518668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coan, Michael mcoan@usgs.gov","contributorId":5398,"corporation":false,"usgs":true,"family":"Coan","given":"Michael","email":"mcoan@usgs.gov","affiliations":[],"preferred":true,"id":692439,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Stephen M. 0000-0001-5255-5882 smhoward@usgs.gov","orcid":"https://orcid.org/0000-0001-5255-5882","contributorId":3483,"corporation":false,"usgs":true,"family":"Howard","given":"Stephen","email":"smhoward@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":692440,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188320,"text":"70188320 - 2014 - Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data","interactions":[],"lastModifiedDate":"2017-06-06T14:00:55","indexId":"70188320","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data","docAbstract":"Various best management practices (BMPs) have been implemented on rangelands with the goals of controlling nonpoint source pollution, reducing the impact of livestock in ecologically important riparian areas, and improving grazing distribution. Providing off-stream water sources to livestock in pastures, cross-fencing, and rotational grazing are common rangeland BMPs that have demonstrated success in drawing livestock grazing pressure away from streams. We evaluated the effects of rangeland BMP implementation with six commercial-scale pastures in the northern mixed-grass prairie. Four pastures received a BMP suite consisting of off-stream water, cross-fencing, and deferred-rotation grazing, and two pastures did not receive BMPs. We hypothesized that the BMPs increased the quantity of riparian vegetation cover relative to the conditions in these pastures during the pre-BMP period and to the two pastures that did not receive BMPs. We used a series of 30-m Landsat normalized difference vegetation index (NDVI) images to track the spatial and temporal changes (1984–2010, n = 24) in vegetation cover, to which NDVI has been well correlated. Validation indicated that the remotely sensed signal from in-channel vegetation was representative of ground conditions. The BMP suite was associated with a 15% increase in the in-channel NDVI (0–30 m from stream centerline) and 18% increase in the riparian NDVI (30–180 m from stream center line). Conversely, the in-channel and riparian NDVI of non-BMP pastures declined 30% and 18% over the study period. The majority of change occurred within 2 yr of BMP implementation. The patterns of in-channel NDVI among pastures suggested that BMP implementation likely altered grazing distribution by decreasing the preferential use of riparian and in-channel areas. We demonstrated that satellite imagery time series are useful in retrospectively evaluating the efficacy of conservation practices, providing critical information to guide adaptive management and decision makers.
","language":"English","publisher":"Elsevier","doi":"10.2111/REM-D-12-00185.1","usgsCitation":"Rigge, M.B., Smart, A., Wylie, B.K., and de Van Kamp, K., 2014, Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data: Rangeland Ecology and Management, v. 67, no. 1, p. 1-8, https://doi.org/10.2111/REM-D-12-00185.1.","productDescription":"8 p.","startPage":"1","endPage":"8","ipdsId":"IP-035745","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":342160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2fe4b0f6c2d0d9c781","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":697194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smart, Alexander","contributorId":24262,"corporation":false,"usgs":true,"family":"Smart","given":"Alexander","affiliations":[],"preferred":false,"id":697310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":697311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Van Kamp, Kendall","contributorId":192662,"corporation":false,"usgs":false,"family":"de Van Kamp","given":"Kendall","email":"","affiliations":[],"preferred":false,"id":697312,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187708,"text":"70187708 - 2014 - Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","interactions":[],"lastModifiedDate":"2017-05-31T16:12:20","indexId":"70187708","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","docAbstract":"Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2013.08.014","usgsCitation":"Hansen, M., Egorov, A., Potapov, P., Stehman, S., Tyukavina, A., Turubanova, S., Roy, D.P., Goetz, S., Loveland, T., Ju, J., Kommareddy, A., Kovalskyy, V., Forsyth, C., and Bents, T., 2014, Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD): Remote Sensing of Environment, v. 140, p. 466-484, https://doi.org/10.1016/j.rse.2013.08.014.","productDescription":"19 p.","startPage":"466","endPage":"484","ipdsId":"IP-046262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","publishingServiceCenter":{"id":4,"text":"Rolla 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Valeriy","contributorId":192062,"corporation":false,"usgs":false,"family":"Kovalskyy","given":"Valeriy","email":"","affiliations":[{"id":26958,"text":"South Dakota State University, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":695318,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Forsyth, C.","contributorId":192034,"corporation":false,"usgs":false,"family":"Forsyth","given":"C.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":695319,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bents, T.","contributorId":139577,"corporation":false,"usgs":false,"family":"Bents","given":"T.","email":"","affiliations":[{"id":33302,"text":"University of Kansas, Lawrence","active":true,"usgs":false}],"preferred":false,"id":695320,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70187766,"text":"70187766 - 2014 - Landsat: Sustaining earth observations beyond Landsat 8","interactions":[],"lastModifiedDate":"2017-05-17T11:43:12","indexId":"70187766","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat: Sustaining earth observations beyond Landsat 8","docAbstract":"The Landsat series of Earth-observing satellites began 41-years ago as a partnership between the U.S. Geological Survey (USGS) of the Department of the Interior (DOI) and The National Aeronautics and Space Administration (NASA). For the past 41 years, Landsat satellites and associated U.S. Government ground processing, distribution, and archiving systems have acquired and made available global, moderate-resolution, multispectral measurements of land and coastal regions, providing humankind’s longest record of our planet from space. Landsat information is truly a national asset, providing an important and unique capability that benefits abroad community, including Federal, state, and local governments; globalchange science; academia, and the private sector.
","language":"English","publisher":"ASPRS","usgsCitation":"Kelly, F.P., and Holm, T.M., 2014, Landsat: Sustaining earth observations beyond Landsat 8: Photogrammetric Engineering and Remote Sensing, v. 80, no. 1, p. 15-15.","productDescription":"1 p.","startPage":"15","endPage":"15","ipdsId":"IP-052876","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":341438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593e2855e4b0764e6c61b779","contributors":{"authors":[{"text":"Kelly, Francis P. fkelly@usgs.gov","contributorId":5099,"corporation":false,"usgs":true,"family":"Kelly","given":"Francis","email":"fkelly@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":true,"id":695535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holm, Thomas M. holm@usgs.gov","contributorId":261,"corporation":false,"usgs":true,"family":"Holm","given":"Thomas","email":"holm@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":695536,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194461,"text":"70194461 - 2014 - Utilizing multi-sensor fire detections to map fires in the United States","interactions":[],"lastModifiedDate":"2018-04-23T09:10:31","indexId":"70194461","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Utilizing multi-sensor fire detections to map fires in the United States","docAbstract":"In 2006, the Monitoring Trends in Burn Severity (MTBS) project began a cooperative effort between the US Forest Service (USFS) and the U.S.Geological Survey (USGS) to map and assess burn severity all large fires that have occurred in the United States since 1984. Using Landsat imagery, MTBS is mandated to map wildfire and prescribed fire that meet specific size criteria: greater than 1000 acres in the west and 500 acres in the east, regardless of ownership. Relying mostly on federal and state fire occurrence records, over 15,300 individual fires have been mapped. While mapping recorded fires, an additional 2,700 “unknown” or undocumented fires were discovered and assessed. It has become apparent that there are perhaps thousands of undocumented fires in the US that are yet to be mapped. Fire occurrence records alone are inadequate if MTBS is to provide a comprehensive accounting of fire across the US. Additionally, the sheer number of fires to assess has overwhelmed current manual procedures. To address these problems, the National Aeronautics and Space Administration (NASA) Applied Sciences Program is helping to fund the efforts of the USGS and its MTBS partners (USFS, National Park Service) to develop, and implement a system to automatically identify fires using satellite data. In near real time, USGS will combine active fire satellite detections from MODIS, AVHRR and GOES satellites with Landsat acquisitions. Newly acquired Landsat imagery will be routinely scanned to identify freshly burned area pixels, derive an initial perimeter and tag the burned area with the satellite date and time of detection. Landsat imagery from the early archive will be scanned to identify undocumented fires. Additional automated fire assessment processes will be developed. The USGS will develop these processes using open source software packages in order to provide freely available tools to local land managers providing them with the capability to assess fires at the local level.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1,","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"ISPRS Technical Commission I Symposium","conferenceDate":"November 17-20, 2014","conferenceLocation":"Denver, CO","language":"English","publisher":"ISPRS","doi":"10.5194/isprsarchives-XL-1-161-2014","usgsCitation":"Howard, S.M., Picotte, J.J., and Coan, M., 2014, Utilizing multi-sensor fire detections to map fires in the United States, in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1,, v. XL-1, Denver, CO, November 17-20, 2014, p. 161-166, https://doi.org/10.5194/isprsarchives-XL-1-161-2014.","productDescription":"6 p.","startPage":"161","endPage":"166","ipdsId":"IP-060379","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473416,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprsarchives-xl-1-161-2014","text":"Publisher Index Page"},{"id":350086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"XL-1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-11-07","publicationStatus":"PW","scienceBaseUri":"5a6100c8e4b06e28e9c2540f","contributors":{"authors":[{"text":"Howard, Stephen M. 0000-0001-5255-5882 smhoward@usgs.gov","orcid":"https://orcid.org/0000-0001-5255-5882","contributorId":3483,"corporation":false,"usgs":true,"family":"Howard","given":"Stephen","email":"smhoward@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":723939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":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":725216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coan, Michael mcoan@usgs.gov","contributorId":5398,"corporation":false,"usgs":true,"family":"Coan","given":"Michael","email":"mcoan@usgs.gov","affiliations":[],"preferred":true,"id":725217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70193016,"text":"70193016 - 2014 - Centralized mission planning and scheduling system for the Landsat Data Continuity Mission","interactions":[],"lastModifiedDate":"2017-12-20T10:45:00","indexId":"70193016","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Centralized mission planning and scheduling system for the Landsat Data Continuity Mission","docAbstract":"Satellites in Low Earth Orbit provide missions with closer range for studying aspects such as geography and topography, but often require efficient utilization of space and ground assets. Optimizing schedules for these satellites amounts to a complex planning puzzle since it requires operators to face issues such as discontinuous ground contacts, limited onboard memory storage, constrained downlink margin, and shared ground antenna resources. To solve this issue for the Landsat Data Continuity Mission (LDCM, Landsat 8), all the scheduling exchanges for science data request, ground/space station contact, and spacecraft maintenance and control will be coordinated through a centralized Mission Planning and Scheduling (MPS) engine, based upon GMV’s scheduling system flexplan9 . The synchronization between all operational functions must be strictly maintained to ensure efficient mission utilization of ground and spacecraft activities while working within the bounds of the space and ground resources, such as Solid State Recorder (SSR) and available antennas. This paper outlines the functionalities that the centralized planning and scheduling system has in its operational control and management of the Landsat 8 spacecraft.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SpaceOps 2014 Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SpaceOps 2014 Conference","conferenceDate":"May 5-9, 2014","conferenceLocation":"Pasadena, CA","language":"English","publisher":"American Institute of Aeronautics and Astronautics","doi":"10.2514/6.2014-1642","usgsCitation":"Kavelaars, A., Barnoy, A.M., Gregory, S., Garcia, G., Talon, C., Greer, G., Williams, J., and Dulski, V., 2014, Centralized mission planning and scheduling system for the Landsat Data Continuity Mission, in SpaceOps 2014 Conference, Pasadena, CA, May 5-9, 2014, 9 p., https://doi.org/10.2514/6.2014-1642.","productDescription":"9 p.","ipdsId":"IP-027697","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":350121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-05-02","publicationStatus":"PW","scienceBaseUri":"5a6100c9e4b06e28e9c25429","contributors":{"authors":[{"text":"Kavelaars, Alicia","contributorId":201423,"corporation":false,"usgs":false,"family":"Kavelaars","given":"Alicia","email":"","affiliations":[],"preferred":false,"id":725239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnoy, Assaf M.","contributorId":198935,"corporation":false,"usgs":false,"family":"Barnoy","given":"Assaf","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":725240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregory, Shawna","contributorId":198939,"corporation":false,"usgs":false,"family":"Gregory","given":"Shawna","email":"","affiliations":[],"preferred":false,"id":725241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garcia, Gonzalo","contributorId":201424,"corporation":false,"usgs":false,"family":"Garcia","given":"Gonzalo","email":"","affiliations":[],"preferred":false,"id":725242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Talon, Cesar","contributorId":201425,"corporation":false,"usgs":false,"family":"Talon","given":"Cesar","email":"","affiliations":[],"preferred":false,"id":725243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greer, Gregory","contributorId":198937,"corporation":false,"usgs":false,"family":"Greer","given":"Gregory","email":"","affiliations":[],"preferred":false,"id":725244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Jason jason@usgs.gov","contributorId":3397,"corporation":false,"usgs":true,"family":"Williams","given":"Jason","email":"jason@usgs.gov","affiliations":[],"preferred":true,"id":725245,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dulski, Vicki","contributorId":201427,"corporation":false,"usgs":false,"family":"Dulski","given":"Vicki","email":"","affiliations":[],"preferred":false,"id":725246,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70191819,"text":"70191819 - 2014 - Landsat Science Team meeting — First Landsat 8 evaluations","interactions":[],"lastModifiedDate":"2022-04-01T22:46:47.053949","indexId":"70191819","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3555,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":10}},"title":"Landsat Science Team meeting — First Landsat 8 evaluations","docAbstract":"The U.S. Geological Survey (USGS)-NASA Landsat Science Team (LST) met at the USGS’ Earth Resources Observation and Science (EROS) Center near Sioux Falls, SD, from October 29-31, 2013. All meeting presentations can be downloaded from landsat.usgs.gov/science_LST_October_29_31_2013.php.
","language":"English","publisher":"NASA","usgsCitation":"Loveland, T.R., Wulder, M.A., and Irons, J.R., 2014, Landsat Science Team meeting — First Landsat 8 evaluations: The Earth Observer, v. 26, no. 2, p. 24-28.","productDescription":"5 p.","startPage":"24","endPage":"28","ipdsId":"IP-046236","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":346819,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/Mar-Apr2014_508finalcolor.pdf"},{"id":347358,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a8e4b0220bbd9d9fa4","contributors":{"authors":[{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":715666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulder, Michael A.","contributorId":189990,"corporation":false,"usgs":false,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":715667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":715668,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188057,"text":"70188057 - 2014 - Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data","interactions":[],"lastModifiedDate":"2017-05-30T13:33:33","indexId":"70188057","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data","docAbstract":"Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.
","language":"English","publisher":"MDPI","doi":"10.3390/rs6109494","usgsCitation":"Giri, C., and Long, J., 2014, Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data: Remote Sensing, v. 6, no. 10, p. 9494-9510, https://doi.org/10.3390/rs6109494.","productDescription":"17 p.","startPage":"9494","endPage":"9510","ipdsId":"IP-059806","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":473301,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs6109494","text":"Publisher Index Page"},{"id":341862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"South America","volume":"6","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-08","publicationStatus":"PW","scienceBaseUri":"592e84c6e4b092b266f10d9f","contributors":{"authors":[{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, Jordan 0000-0002-4814-464X jlong@usgs.gov","orcid":"https://orcid.org/0000-0002-4814-464X","contributorId":3609,"corporation":false,"usgs":true,"family":"Long","given":"Jordan","email":"jlong@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696341,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70132431,"text":"70132431 - 2014 - Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model","interactions":[],"lastModifiedDate":"2017-01-18T11:37:09","indexId":"70132431","displayToPublicDate":"2013-12-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model","docAbstract":"Accurately estimating consumptive water use in the Colorado River Basin (CRB) is important for assessing and managing limited water resources in the basin. Increasing water demand from various sectors may threaten long-term sustainability of the water supply in the arid southwestern United States. We have developed a first-ever basin-wide actual evapotranspiration (ETa) map of the CRB at the Landsat scale for water use assessment at the field level. We used the operational Simplified Surface Energy Balance (SSEBop) model for estimating ETa using 328 cloud-free Landsat images acquired during 2010. Our results show that cropland had the highest ETa among all land cover classes except for water. Validation using eddy covariance measured ETa showed that the SSEBop model nicely captured the variability in annual ETa with an overall R2 of 0.78 and a mean bias error of about 10%. Comparison with water balance-based ETa showed good agreement (R2 = 0.85) at the sub-basin level. Though there was good correlation (R2 = 0.79) between Moderate Resolution Imaging Spectroradiometer (MODIS)-based ETa (1 km spatial resolution) and Landsat-based ETa (30 m spatial resolution), the spatial distribution of MODIS-based ETa was not suitable for water use assessment at the field level. In contrast, Landsat-based ETa has good potential to be used at the field level for water management. With further validation using multiple years and sites, our methodology can be applied for regular production of ETa maps of larger areas such as the conterminous United States.
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McCullough","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":641009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":641008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sader, Steven A.","contributorId":171436,"corporation":false,"usgs":false,"family":"Sader","given":"Steven","email":"","middleInitial":"A.","affiliations":[{"id":25572,"text":"University of Maine, Orono","active":true,"usgs":false}],"preferred":false,"id":641010,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70099274,"text":"70099274 - 2013 - Spatiotemporal variation in range-wide Golden-cheeked Warbler habitat","interactions":[],"lastModifiedDate":"2014-03-24T13:16:24","indexId":"70099274","displayToPublicDate":"2014-03-24T12:48:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal variation in range-wide Golden-cheeked Warbler habitat","docAbstract":"Habitat availability ultimately limits the distribution and abundance of wildlife species. Consequently, it is paramount to identify where wildlife habitat is and understand how it changes over time in order to implement large scale wildlife conservation plans. Yet, no work has quantified the degree of change in range-wide breeding habitat for the golden-cheeked warbler (Setophaga chrysoparia), despite the species being listed as endangered by the U.S. federal government. Thus, using available geographic information system (GIS) data and Landsat satellite imagery we quantified range-wide warbler breeding habitat change from 1999-2001 to 2010-2011. We detected a 29% reduction in total warbler breeding habitat and found that warbler breeding habitat was removed and became more fragmented at uneven rates across the warbler’s breeding range during this time period. This information will assist researchers and managers in prioritizing breeding habitat conservation efforts for the species and provide a foundation for more realistic carrying capacity scenarios when modeling golden-cheeked warbler populations over time. Additionally, this study highlights the need for future work centered on quantifying golden-cheeked warbler movement rates and distances in order to assess the degree of connectivity between increasingly fragmented habitat patches.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecosphere","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Ecological Society of America","doi":"10.1890/ES13-00229.1","usgsCitation":"Duarte, A., Jensen, J., Hatfield, J., and Weckerly, F., 2013, Spatiotemporal variation in range-wide Golden-cheeked Warbler habitat: Ecosphere, v. 4, no. 12, 12 p., https://doi.org/10.1890/ES13-00229.1.","productDescription":"12 p.","ipdsId":"IP-051894","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":473357,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1890/es13-00229.1","text":"Publisher Index Page"},{"id":284399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":284397,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1890/ES13-00229.1"}],"country":"United States","state":"Texas","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -101.14,29.80 ], [ -101.14,32.95 ], [ -97.68,32.95 ], [ -97.68,29.80 ], [ -101.14,29.80 ] ] ] } } ] }","volume":"4","issue":"12","noUsgsAuthors":false,"publicationDate":"2013-12-30","publicationStatus":"PW","scienceBaseUri":"53517065e4b05569d805a3cd","contributors":{"authors":[{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":491929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jensen, Jennifer","contributorId":78239,"corporation":false,"usgs":true,"family":"Jensen","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":491930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatfield, Jeffrey S. jhatfield@usgs.gov","contributorId":151,"corporation":false,"usgs":true,"family":"Hatfield","given":"Jeffrey S.","email":"jhatfield@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":491928,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weckerly, Floyd","contributorId":93813,"corporation":false,"usgs":true,"family":"Weckerly","given":"Floyd","affiliations":[],"preferred":false,"id":491931,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173625,"text":"70173625 - 2013 - Landsat imagery reveals declining clarity of Maine’s lakes during 1995-2010","interactions":[],"lastModifiedDate":"2016-06-09T15:10:12","indexId":"70173625","displayToPublicDate":"2014-01-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Landsat imagery reveals declining clarity of Maine’s lakes during 1995-2010","docAbstract":"Water clarity is a strong indicator of regional water quality. Unlike other common water-quality metrics, such as chlorophyll a, total P, or trophic status, clarity can be accurately and efficiently estimated remotely on a regional scale. Satellite-based remote sensing is useful in regions with many lakes where traditional field-sampling techniques may be prohibitively expensive. Repeated sampling of easily accessed lakes can lead to spatially irregular, nonrandom samples of a region. Remote sensing remedies this problem. We applied a remote monitoring protocol we had previously developed for Maine lakes >8 ha based on Landsat satellite data recorded during 1995–2010 to identify spatial and temporal patterns in Maine lake clarity. We focused on the overlapping region of Landsat paths 11 and 12 to increase availability of cloud-free images in August and early September, a period of relative lake stability and seasonal poor-clarity conditions well suited for annual monitoring. We divided Maine into 3 regions (northeastern, south-central, western) based on morphometric and chemical lake features. We found a general decrease in average statewide lake clarity from 4.94 to 4.38 m during 1995–2010. Water clarity ranged from 4 to 6 m during 1995–2010, but it decreased consistently during 2005–2010. Clarity in both the northeastern and western lake regions has decreased from 5.22 m in 1995 to 4.36 and 4.21 m, respectively, in 2010, whereas lake clarity in the south-central lake region (4.50 m) has not changed since 1995. Climate change, timber harvesting, or watershed morphometry may be responsible for regional water-clarity decline. Remote sensing of regional water clarity provides a more complete spatial perspective of lake water quality than existing, interest-based sampling. However, field sampling done under existing monitoring programs can be used to calibrate accurate models designed to estimate water clarity remotely.
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A.","affiliations":[],"preferred":false,"id":638298,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70049058,"text":"ofr20131269 - 2013 - Users, uses, and value of Landsat satellite imagery: results from the 2012 survey of users","interactions":[],"lastModifiedDate":"2013-12-04T08:29:12","indexId":"ofr20131269","displayToPublicDate":"2013-12-02T13:14:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-1269","title":"Users, uses, and value of Landsat satellite imagery: results from the 2012 survey of users","docAbstract":"Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). Social scientists at the USGS Fort Collins Science Center conducted an extensive survey in early 2012 to explore who uses Landsat imagery, how they use the imagery, and what the value of the imagery is to them. The survey was sent to all users registered with USGS who had accessed Landsat imagery in the year prior to the survey and over 11,000 current Landsat imagery users responded. The results of the survey revealed that respondents from many sectors use Landsat imagery in myriad project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance of and dependence on the imagery, the numerous environmental and societal benefits observed from projects using Landsat imagery, the potential negative impacts on users’ work if Landsat imagery was no longer available, and the substantial aggregated annual economic benefit from the imagery. These results represent only the value of Landsat to users registered with USGS; further research would help to determine what the value of the imagery is to a greater segment of the population, such as downstream users of the imagery and imagery-derived products.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131269","usgsCitation":"Miller, H., Richardson, L.A., Koontz, S.R., Loomis, J., and Koontz, L., 2013, Users, uses, and value of Landsat satellite imagery: results from the 2012 survey of users: U.S. Geological Survey Open-File Report 2013-1269, ix, 51 p., https://doi.org/10.3133/ofr20131269.","productDescription":"ix, 51 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-050650","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":280099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131269.jpg"},{"id":280098,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2013/1269/pdf/of2013-1269.pdf"},{"id":280096,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1269/"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"529dac19e4b0516126f66b69","contributors":{"authors":[{"text":"Miller, Holly M. 0000-0003-0914-7570 millerh@usgs.gov","orcid":"https://orcid.org/0000-0003-0914-7570","contributorId":4577,"corporation":false,"usgs":true,"family":"Miller","given":"Holly M.","email":"millerh@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":486071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, Leslie A. lrichardson@usgs.gov","contributorId":4810,"corporation":false,"usgs":true,"family":"Richardson","given":"Leslie","email":"lrichardson@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":486072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koontz, Stephen R.","contributorId":69272,"corporation":false,"usgs":true,"family":"Koontz","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":486074,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loomis, John","contributorId":60746,"corporation":false,"usgs":true,"family":"Loomis","given":"John","affiliations":[],"preferred":false,"id":486073,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":2174,"corporation":false,"usgs":false,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":7016,"text":"Environmental Quality Division, National Park Service, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":486070,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}