Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha−1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha−1(relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2015.1004764","usgsCitation":"Ji, L., Wylie, B.K., Brown, D.R., Peterson, B.E., Alexander, H.D., Mack, M., Rover, J.R., Waldrop, M.P., McFarland, J.W., Chen, X., and Pastick, N.J., 2015, Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska: International Journal of Remote Sensing, v. 36, no. 4, p. 939-953, https://doi.org/10.1080/01431161.2015.1004764.","productDescription":"15 p.","startPage":"939","endPage":"953","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":299844,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.53125,\n 60.973107109199404\n ],\n [\n -158.466796875,\n 61.60639637138628\n ],\n [\n -155.0390625,\n 63.11463763252091\n ],\n [\n -152.490234375,\n 63.704722429433225\n ],\n [\n -150.99609375,\n 63.54855223203644\n ],\n [\n -151.435546875,\n 62.99515845212052\n ],\n [\n -150.556640625,\n 62.2679226294176\n ],\n [\n -147.744140625,\n 62.63376960786813\n ],\n [\n -144.580078125,\n 62.3903694381427\n ],\n [\n -141.064453125,\n 61.22795717667785\n ],\n [\n -141.15234374999997,\n 69.06856318696033\n ],\n [\n -145.37109375,\n 69.47296854140573\n ],\n [\n -156.357421875,\n 69.2249968541159\n ],\n [\n -157.763671875,\n 69.38031271734351\n ],\n [\n -157.763671875,\n 68.8159271333607\n ],\n [\n -159.873046875,\n 66.89559561140706\n ],\n [\n -160.6640625,\n 63.93737246791484\n ],\n [\n -164.61914062499997,\n 63.23362741232569\n ],\n [\n -166.2890625,\n 61.77312286453148\n ],\n [\n -164.53125,\n 60.973107109199404\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-17","publicationStatus":"PW","scienceBaseUri":"553a09d0e4b0c1efddaed145","contributors":{"authors":[{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":545380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":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":545383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Dana R. N.","contributorId":140386,"corporation":false,"usgs":false,"family":"Brown","given":"Dana","email":"","middleInitial":"R. N.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":545482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Birgit E. 0000-0002-4356-1540 bpeterson@usgs.gov","orcid":"https://orcid.org/0000-0002-4356-1540","contributorId":3599,"corporation":false,"usgs":true,"family":"Peterson","given":"Birgit","email":"bpeterson@usgs.gov","middleInitial":"E.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":545378,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexander, Heather D.","contributorId":140365,"corporation":false,"usgs":false,"family":"Alexander","given":"Heather","email":"","middleInitial":"D.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":545385,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mack, Michelle C.","contributorId":140367,"corporation":false,"usgs":false,"family":"Mack","given":"Michelle C.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":545387,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rover, Jennifer R. 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":2941,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":545379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waldrop, Mark P. 0000-0003-1829-7140 mwaldrop@usgs.gov","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":1599,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","email":"mwaldrop@usgs.gov","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":545381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McFarland, Jack W.","contributorId":140366,"corporation":false,"usgs":false,"family":"McFarland","given":"Jack","email":"","middleInitial":"W.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":545386,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chen, Xuexia","contributorId":140368,"corporation":false,"usgs":false,"family":"Chen","given":"Xuexia","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":545388,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":545382,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70145997,"text":"fs20153034 - 2015 - Landsat surface reflectance data","interactions":[],"lastModifiedDate":"2020-03-04T14:20:31","indexId":"fs20153034","displayToPublicDate":"2015-04-21T09:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-3034","displayTitle":"Landsat Surface Reflectance Data","title":"Landsat surface reflectance data","docAbstract":"Landsat satellite data have been produced, archived, and distributed by the U.S. Geological Survey since 1972. Users rely on these data for historical study of land surface change and require consistent radiometric data processed to the highest science standards. In support of the guidelines established through the Global Climate Observing System, the U.S. Geological Survey has embarked on production of higher-level Landsat data products to support land surface change studies. One such product is Landsat surface reflectance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153034","usgsCitation":"U.S. Geological Survey, 2015, Landsat surface reflectance data (ver. 1.1, March 27, 2019): U.S. Geological Survey Fact Sheet 2015-3034, 1 p., https://doi.org/10.3133/fs20153034.","productDescription":"1 p.","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-063045","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":299806,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3034/coverthb2.jpg"},{"id":299805,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3034/pdf/fs20153034.pdf","size":"286 KB","linkFileType":{"id":1,"text":"pdf"}}],"edition":"Version 1: Originally posted April 20, 2015; Version 1.1: June 16, 2015; Version 1.1 updated: March 27, 2019","contact":"Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, South Dakota 57198
Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km2 (1.1%) across the study area under the A1B scenario and 41.15 km2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km2 (4.1%) under A1B and 50.8 km2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km2 (3.8%) under A1B and 40.59 km2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2015.03.002","usgsCitation":"Homer, C.G., Xian, G.Z., Aldridge, C.L., Meyer, D.K., Loveland, T.R., and O’Donnell, M.S., 2015, Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future: Ecological Indicators, v. 55, p. 131-145, https://doi.org/10.1016/j.ecolind.2015.03.002.","productDescription":"15 p.","startPage":"131","endPage":"145","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061116","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2015.03.002","text":"Publisher Index Page"},{"id":299699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.96240234375,\n 41.48080459927738\n ],\n [\n -110.00473022460938,\n 41.6770148220322\n ],\n [\n -109.69573974609375,\n 42.56926437219384\n ],\n [\n -108.65341186523436,\n 42.37883631647602\n ],\n [\n -108.96240234375,\n 41.48080459927738\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"552f7d9ae4b0b22a158031c7","contributors":{"authors":[{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":544946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":544947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":544948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debra K. 0000-0002-8841-697X dkmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":3145,"corporation":false,"usgs":true,"family":"Meyer","given":"Debra","email":"dkmeyer@usgs.gov","middleInitial":"K.","affiliations":[{"id":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":544950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":544949,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":3351,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":544951,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70148094,"text":"70148094 - 2015 - Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches","interactions":[],"lastModifiedDate":"2017-01-18T10:04:27","indexId":"70148094","displayToPublicDate":"2015-03-24T14:00:00","publicationYear":"2015","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":"Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches","docAbstract":"The satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. The 250-m GSN data estimated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have been used for terrestrial ecosystem modeling and monitoring. High temporal resolution with a wide range of wavelengths make the MODIS land surface products robust and reliable. The long-term 30-m Landsat data provide spatial detailed information for characterizing human-scale processes and have been used for land cover and land change studies. The main goal of this study is to combine 250-m MODIS GSN and 30-m Landsat observations to generate a quality-improved high spatial resolution (30-m) GSN database. A rule-based piecewise regression GSN model based on MODIS and Landsat data was developed. Results show a strong correlation between predicted GSN and actual GSN (r = 0.97, average error = 0.026). The most important Landsat variables in the GSN model are Normalized Difference Vegetation Indices (NDVIs) in May and August. The derived MODIS-Landsat-based 30-m GSN map provides biophysical information for moderate-scale ecological features. This multiple sensor study retains the detailed seasonal dynamic information captured by MODIS and leverages the high-resolution information from Landsat, which will be useful for regional ecosystem studies.
","language":"English","publisher":"Molecular Diversity Preservation International","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs70403489","usgsCitation":"Gu, Y., and Wylie, B.K., 2015, Downscaling 250-m MODIS growing season NDVI based on multiple-date landsat images and data mining approaches: Remote Sensing, v. 7, no. 4, p. 3489-3506, https://doi.org/10.3390/rs70403489.","productDescription":"18 p.","startPage":"3489","endPage":"3506","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064005","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472202,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70403489","text":"Publisher Index Page"},{"id":300605,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-24","publicationStatus":"PW","scienceBaseUri":"555db03ee4b0a92fa7eb82fc","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":547324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":547325,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70141305,"text":"70141305 - 2015 - Landsat-8 Operational Land Imager (OLI) radiometric performance on-orbit","interactions":[],"lastModifiedDate":"2017-01-18T10:05:02","indexId":"70141305","displayToPublicDate":"2015-02-20T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat-8 Operational Land Imager (OLI) radiometric performance on-orbit","docAbstract":"Expectations of the Operational Land Imager (OLI) radiometric performance onboard Landsat-8 have been met or exceeded. The calibration activities that occurred prior to launch provided calibration parameters that enabled ground processing to produce imagery that met most requirements when data were transmitted to the ground. Since launch, calibration updates have improved the image quality even more, so that all requirements are met. These updates range from detector gain coefficients to reduce striping and banding to alignment parameters to improve the geometric accuracy. This paper concentrates on the on-orbit radiometric performance of the OLI, excepting the radiometric calibration performance. Topics discussed in this paper include: signal-to-noise ratios that are an order of magnitude higher than previous Landsat missions; radiometric uniformity that shows little residual banding and striping, and continues to improve; a dynamic range that limits saturation to extremely high radiance levels; extremely stable detectors; slight nonlinearity that is corrected in ground processing; detectors that are stable and 100% operable; and few image artifacts.
","language":"English","publisher":"MDPI","doi":"10.3390/rs70202208","usgsCitation":"Morfitt, R., Barsi, J.A., Levy, R., Markham, B.L., Micijevic, E., Ong, L., Scaramuzza, P., and Vanderwerff, K., 2015, Landsat-8 Operational Land Imager (OLI) radiometric performance on-orbit: Remote Sensing, v. 7, no. 2, p. 2208-2237, https://doi.org/10.3390/rs70202208.","productDescription":"30 p.","startPage":"2208","endPage":"2237","numberOfPages":"30","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059104","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472266,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70202208","text":"Publisher Index Page"},{"id":298068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-02-17","publicationStatus":"PW","scienceBaseUri":"54e85aabe4b02d776a67c5b3","contributors":{"authors":[{"text":"Morfitt, Ron 0000-0002-4777-4877 rmorfitt@usgs.gov","orcid":"https://orcid.org/0000-0002-4777-4877","contributorId":4097,"corporation":false,"usgs":true,"family":"Morfitt","given":"Ron","email":"rmorfitt@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":540661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":540662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levy, Raviv","contributorId":131008,"corporation":false,"usgs":false,"family":"Levy","given":"Raviv","email":"","affiliations":[{"id":7209,"text":"SSAI / NASA / GSFC","active":true,"usgs":false}],"preferred":false,"id":540663,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markham, Brian L.","contributorId":90482,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":540664,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":540665,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ong, Lawrence","contributorId":139287,"corporation":false,"usgs":false,"family":"Ong","given":"Lawrence","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":540666,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scaramuzza, Pat 0000-0002-2616-8456 pscar@usgs.gov","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":3970,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","email":"pscar@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":540667,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vanderwerff, Kelly kvanderwerff@usgs.gov","contributorId":4617,"corporation":false,"usgs":true,"family":"Vanderwerff","given":"Kelly","email":"kvanderwerff@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":540668,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70127397,"text":"70127397 - 2015 - Vegetation burn severity mapping using Landsat-8 and WorldView-2","interactions":[],"lastModifiedDate":"2016-07-08T15:05:35","indexId":"70127397","displayToPublicDate":"2015-02-01T00:00:00","publicationYear":"2015","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":"Vegetation burn severity mapping using Landsat-8 and WorldView-2","docAbstract":"We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.
","language":"English","publisher":"Ingenta Connect","doi":"10.14358/PERS.81.2.143","usgsCitation":"Wu, Z., Middleton, B.R., Hetzler, R., Vogel, J.M., and Dye, D.G., 2015, Vegetation burn severity mapping using Landsat-8 and WorldView-2: Photogrammetric Engineering and Remote Sensing, v. 2, p. 143-154, https://doi.org/10.14358/PERS.81.2.143.","productDescription":"12 p.","startPage":"143","endPage":"154","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-055282","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":472311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.81.2.143","text":"Publisher Index Page"},{"id":324949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5780cec2e4b0811616822402","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":519606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":519604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hetzler, Robert","contributorId":117299,"corporation":false,"usgs":true,"family":"Hetzler","given":"Robert","affiliations":[],"preferred":false,"id":519607,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vogel, John M. 0000-0002-8226-1188 jvogel@usgs.gov","orcid":"https://orcid.org/0000-0002-8226-1188","contributorId":3167,"corporation":false,"usgs":true,"family":"Vogel","given":"John","email":"jvogel@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":519603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dye, Dennis G. 0000-0002-7100-272X ddye@usgs.gov","orcid":"https://orcid.org/0000-0002-7100-272X","contributorId":4233,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","email":"ddye@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":519605,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70093447,"text":"70093447 - 2015 - Distribution and dynamics of mangrove forests of South Asia","interactions":[],"lastModifiedDate":"2024-06-13T16:40:05.28636","indexId":"70093447","displayToPublicDate":"2015-01-15T10:05:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Distribution and dynamics of mangrove forests of South Asia","docAbstract":"Mangrove forests in South Asia occur along the tidal sea edge of Bangladesh, India, Pakistan, and Sri Lanka. These forests provide important ecosystem goods and services to the region's dense coastal populations and support important functions of the biosphere. Mangroves are under threat from both natural and anthropogenic stressors; however the current status and dynamics of the region's mangroves are poorly understood. We mapped the current extent of mangrove forests in South Asia and identified mangrove forest cover change (gain and loss) from 2000 to 2012 using Landsat satellite data. We also conducted three case studies in Indus Delta (Pakistan), Goa (India), and Sundarbans (Bangladesh and India) to identify rates, patterns, and causes of change in greater spatial and thematic details compared to regional assessment of mangrove forests.
\n\n
Our findings revealed that the areal extent of mangrove forests in South Asia is approximately 1,187,476 ha representing ∼7% of the global total. Our results showed that from 2000 to 2012, 92,135 ha of mangroves were deforested and 80,461 ha were reforested with a net loss of 11,673 ha. In all three case studies, mangrove areas have remained the same or increased slightly, however, the turnover was greater than the net change. Both, natural and anthropogenic factors are responsible for the change and turnover. The major causes of forest cover change are similar throughout the region; however, specific factors may be dominant in specific areas. Major causes of deforestation in South Asia include (i) conversion to other land use (e.g. conversion to agriculture, shrimp farms, development, and human settlement), (ii) over-harvesting (e.g. grazing, browsing and lopping, and fishing), (iii) pollution, (iv) decline in freshwater availability, (v) floodings, (vi) reduction of silt deposition, (vii) coastal erosion, and (viii) disturbances from tropical cyclones and tsunamis. Our analysis in the region's diverse socio-economic and environmental conditions highlights complex patterns of mangrove distribution and change. Results from this study provide important insight to the conservation and management of the important and threatened South Asian mangrove ecosystem.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2014.01.020","usgsCitation":"Giri, C., Long, J., Abbas, S., Murali, R.M., Qamer, F.M., Pengra, B., and Thau, D., 2015, Distribution and dynamics of mangrove forests of South Asia: Journal of Environmental Management, v. 148, p. 10-111, https://doi.org/10.1016/j.jenvman.2014.01.020.","productDescription":"11 p.","startPage":"10","endPage":"111","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053476","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":282104,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Bangladesh, India, Pakistan, Sri Lanka","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 92.63975132917415,\n 21.34644755383752\n ],\n [\n 92.69636375423357,\n 23.093195331917457\n ],\n [\n 87.03406063425564,\n 22.227478454576186\n ],\n [\n 79.87319001569784,\n 16.25741892146837\n ],\n [\n 79.40076707254411,\n 11.408756432754643\n ],\n [\n 77.40592579978806,\n 8.46469922275864\n ],\n [\n 73.18695686131176,\n 20.790094297154695\n ],\n [\n 72.87074998253325,\n 23.046181487791515\n ],\n [\n 69.00936975468215,\n 25.351149951891756\n ],\n [\n 64.32547281330233,\n 26.060745107911004\n ],\n [\n 61.76183043871745,\n 25.261499790160414\n ],\n [\n 64.21367275692924,\n 24.50573492635114\n ],\n [\n 66.3733274284561,\n 24.544017863708063\n ],\n [\n 68.99849415883313,\n 22.58798715943358\n ],\n [\n 70.015738206263,\n 20.80686582819048\n ],\n [\n 71.72493350853802,\n 19.930230848875425\n ],\n [\n 72.76848440374161,\n 16.482368346929064\n ],\n [\n 75.30190954383363,\n 9.756672722882257\n ],\n [\n 77.5455349971038,\n 6.816882624722439\n ],\n [\n 80.79401300568276,\n 4.854563949254953\n ],\n [\n 82.92883335032462,\n 6.407766545075063\n ],\n [\n 80.88295396453611,\n 11.692367112504456\n ],\n [\n 81.05675184751397,\n 15.286942844019848\n ],\n [\n 86.20876185660052,\n 19.103228519336653\n ],\n [\n 87.5529994234904,\n 20.938076078708463\n ],\n [\n 92.63975132917415,\n 21.34644755383752\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53ae7685e4b0abf75cf2bf89","contributors":{"authors":[{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":2403,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":490014,"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":490015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abbas, Sawaid","contributorId":29304,"corporation":false,"usgs":true,"family":"Abbas","given":"Sawaid","email":"","affiliations":[],"preferred":false,"id":490018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murali, R. 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We assessed temporal and spatial patterns in meadow plant biomass over a 23-year period for meadow complexes that were spatially linked to three distinct populations of Roosevelt elk (Cervus elaphus roosevelti) in northwestern California. Our objectives were to determine whether the plant community exhibited a tolerant or resistant response when elk population growth became irruptive. Plant biomass for the three meadow complexes inhabited by the elk populations was measured using Normalized Difference Vegetation Index (NDVI), which was derived from Landsat 5 Thematic Mapper imagery. Elk populations exhibited different patterns of growth through the time series, whereby one population underwent a complete four-stage irruptive growth pattern while the other two did not. 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\nTo achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.
\nThis report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II) (Open File Report 2013-1280)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131280B1","collaboration":"Prepared in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania","usgsCitation":"Finn, C.A., and Horton, J.D., 2015, Crystalline basement map of Mauritania derived from filtered aeromagnetic data (deliverable 54_1), Aeromagnetic and geological structure map of Mauritania (phase V, deliverable 54_2), Maximum depth to basement map of Mauritania derived from Euler analysis of Aeromagnetic data (phase V, deliverable 54_3), and color composite image of radioelement data (added value): U.S. Geological Survey Open-File Report 2013-1280, 8 Plates: 54.0 x 60.0 inches; Data; Metadata, https://doi.org/10.3133/ofr20131280B1.","productDescription":"8 Plates: 54.0 x 60.0 inches; Data; Metadata","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056844","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":319079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20131280B1.PNG"},{"id":319078,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1280/GIS_and_Maps/Chapter_B1_deliverables_54_and_added_value-Geophysics/","text":"Maps, Data, and Metadata"}],"country":"Mauritania","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-12.17075,14.61683],[-12.83066,15.30369],[-13.43574,16.03938],[-14.09952,16.3043],[-14.57735,16.59826],[-15.13574,16.58728],[-15.62367,16.36934],[-16.12069,16.45566],[-16.4631,16.13504],[-16.54971,16.67389],[-16.27055,17.16696],[-16.14635,18.10848],[-16.25688,19.09672],[-16.37765,19.59382],[-16.27784,20.09252],[-16.53632,20.56787],[-17.06342,20.99975],[-16.84519,21.33332],[-12.9291,21.32707],[-13.11875,22.77122],[-12.87422,23.28483],[-11.93722,23.37459],[-11.96942,25.93335],[-8.68729,25.88106],[-8.6844,27.39574],[-4.92334,24.97457],[-6.45379,24.95659],[-5.97113,20.64083],[-5.48852,16.3251],[-5.31528,16.20185],[-5.53774,15.50169],[-9.55024,15.4865],[-9.70026,15.26411],[-10.08685,15.33049],[-10.65079,15.13275],[-11.3491,15.41126],[-11.66608,15.38821],[-11.83421,14.7991],[-12.17075,14.61683]]]},\"properties\":{\"name\":\"Mauritania\"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f11b3be4b0f59b85ddc33a","contributors":{"authors":[{"text":"Finn, Carol A. 0000-0002-6178-0405 cfinn@usgs.gov","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":1326,"corporation":false,"usgs":true,"family":"Finn","given":"Carol","email":"cfinn@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":622309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, John D. 0000-0003-2969-9073 jhorton@usgs.gov","orcid":"https://orcid.org/0000-0003-2969-9073","contributorId":1227,"corporation":false,"usgs":true,"family":"Horton","given":"John","email":"jhorton@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622310,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191883,"text":"70191883 - 2015 - Phenological response of an Arizona dryland forest to short-term climatic extremes","interactions":[],"lastModifiedDate":"2017-10-18T16:36:22","indexId":"70191883","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","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":"Phenological response of an Arizona dryland forest to short-term climatic extremes","docAbstract":"Baseline information about dryland forest phenology is necessary to accurately anticipate future ecosystem shifts. The overarching goal of our study was to investigate the variability of vegetation phenology across a dryland forest landscape in response to climate alterations. We analyzed the influence of site characteristics and climatic conditions on the phenological patterns of an Arizona, USA, ponderosa pine (Pinus ponderosa) forest during a five-year period (2005 to 2009) that encompassed extreme wet and dry precipitation regimes. We assembled 80 synthetic Landsat images by applying the spatial and temporal adaptive reflectance fusion method (STARFM) to 500 m MODIS and 30 m Landsat-5 Thematic Mapper (TM) data. We tested relationships between site characteristics and the timing of peak Normalized Difference Vegetation Index (NDVI) to assess the effect of climatic stress on the green-up of individual pixels during or after the summer monsoon. Our results show that drought-induced stress led to a fragmented phenological response that was highly dependent on microsite parameters, as both the spatial autocorrelation of peak timing and the number of significant site variables increased during the drought year. Pixels at lower elevations and with higher proportions of herbaceous vegetation were more likely to exhibit dynamic responses to changes in precipitation conditions. Our study demonstrates the complexity of responses within dryland forest ecosystems and highlights the need for standardized monitoring of phenology trends in these areas. The spatial and temporal variability of phenological signals may provide a quantitative solution to the problem of how to evaluate dryland land surface trends across time.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute (MDPI)","doi":"10.3390/rs70810832","usgsCitation":"Walker, J.J., de Beurs, K., and Wynne, R., 2015, Phenological response of an Arizona dryland forest to short-term climatic extremes: Remote Sensing, v. 7, no. 8, p. 10832-10855, https://doi.org/10.3390/rs70810832.","productDescription":"24 p.","startPage":"10832","endPage":"10855","ipdsId":"IP-063470","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":472391,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs70810832","text":"Publisher Index Page"},{"id":346919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.25,\n 34.5\n ],\n [\n -111.25,\n 34.5\n ],\n [\n -111.25,\n 35.5\n ],\n [\n -112.25,\n 35.5\n ],\n [\n -112.25,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-24","publicationStatus":"PW","scienceBaseUri":"59e8683ce4b05fe04cd4d241","contributors":{"authors":[{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Beurs, Kirsten","contributorId":197460,"corporation":false,"usgs":false,"family":"de Beurs","given":"Kirsten","email":"","affiliations":[],"preferred":false,"id":713534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wynne, Randolph","contributorId":197461,"corporation":false,"usgs":false,"family":"Wynne","given":"Randolph","email":"","affiliations":[],"preferred":false,"id":713535,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70159517,"text":"ofr20131280T - 2015 - Geographic Information Systems (GIS) for la République Islamique de Mauritanie (PRISM-II) phase V (phase V, deliverable 92)","interactions":[{"subject":{"id":70159517,"text":"ofr20131280T - 2015 - Geographic Information Systems (GIS) for la République Islamique de Mauritanie (PRISM-II) phase V (phase V, deliverable 92)","indexId":"ofr20131280T","publicationYear":"2015","noYear":false,"chapter":"T","title":"Geographic Information Systems (GIS) for la République Islamique de Mauritanie (PRISM-II) phase V (phase V, deliverable 92)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":1}],"isPartOf":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"lastModifiedDate":"2022-12-08T16:55:22.019544","indexId":"ofr20131280T","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","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":"2013-1280","chapter":"T","title":"Geographic Information Systems (GIS) for la République Islamique de Mauritanie (PRISM-II) phase V (phase V, deliverable 92)","docAbstract":"In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.
\nTo achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.
\nThis report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II) (Open File Report 2013-1280)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131280T","collaboration":"Prepared in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania","usgsCitation":"Horton, J.D., and Taylor, C.D., 2015, Geographic Information Systems (GIS) for la République Islamique de Mauritanie (PRISM-II) phase V (phase V, deliverable 92): U.S. Geological Survey Open-File Report 2013-1280, Data; Metadata; Readme, https://doi.org/10.3133/ofr20131280T.","productDescription":"Data; Metadata; Readme","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052725","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":319099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":319098,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1280/GIS_and_Maps/Chapter_T_deliverable_92-Basemap_GIS/","text":"Data, Metadata, and Readme File","linkFileType":{"id":1,"text":"pdf"}}],"country":"Mauritania","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-12.17075,14.61683],[-12.83066,15.30369],[-13.43574,16.03938],[-14.09952,16.3043],[-14.57735,16.59826],[-15.13574,16.58728],[-15.62367,16.36934],[-16.12069,16.45566],[-16.4631,16.13504],[-16.54971,16.67389],[-16.27055,17.16696],[-16.14635,18.10848],[-16.25688,19.09672],[-16.37765,19.59382],[-16.27784,20.09252],[-16.53632,20.56787],[-17.06342,20.99975],[-16.84519,21.33332],[-12.9291,21.32707],[-13.11875,22.77122],[-12.87422,23.28483],[-11.93722,23.37459],[-11.96942,25.93335],[-8.68729,25.88106],[-8.6844,27.39574],[-4.92334,24.97457],[-6.45379,24.95659],[-5.97113,20.64083],[-5.48852,16.3251],[-5.31528,16.20185],[-5.53774,15.50169],[-9.55024,15.4865],[-9.70026,15.26411],[-10.08685,15.33049],[-10.65079,15.13275],[-11.3491,15.41126],[-11.66608,15.38821],[-11.83421,14.7991],[-12.17075,14.61683]]]},\"properties\":{\"name\":\"Mauritania\"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f11b55e4b0f59b85ddc41c","contributors":{"authors":[{"text":"Horton, John D. 0000-0003-2969-9073 jhorton@usgs.gov","orcid":"https://orcid.org/0000-0003-2969-9073","contributorId":1227,"corporation":false,"usgs":true,"family":"Horton","given":"John","email":"jhorton@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622240,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159509,"text":"ofr20131280E - 2015 - Landsat maps (phase V, deliverable 60), ASTER maps (phase V, deliverable 62), ASTER_DEM maps (phase V, deliverable 63), and spectral remote sensing in support of PRISM-II mineral resource assessment project, Islamic Republic of Mauritania (phase V, deliverables 61 and 64)","interactions":[{"subject":{"id":70159509,"text":"ofr20131280E - 2015 - Landsat maps (phase V, deliverable 60), ASTER maps (phase V, deliverable 62), ASTER_DEM maps (phase V, deliverable 63), and spectral remote sensing in support of PRISM-II mineral resource assessment project, Islamic Republic of Mauritania (phase V, deliverables 61 and 64)","indexId":"ofr20131280E","publicationYear":"2015","noYear":false,"chapter":"E","title":"Landsat maps (phase V, deliverable 60), ASTER maps (phase V, deliverable 62), ASTER_DEM maps (phase V, deliverable 63), and spectral remote sensing in support of PRISM-II mineral resource assessment project, Islamic Republic of Mauritania (phase V, deliverables 61 and 64)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 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This report and accompanying maps constitute project deliverables 60–64. All digital data for use in Geographic Information System (GIS) and image processing software will be included in the GIS deliverable 92. Image maps in PDF format of the processed Landsat and ASTER scenes are referenced in the appendixes.
\nSamples of rock, alluvium, colluvium, and (or) eolian sediments were collected at 41 locations during the 2007 field campaign. A point shapefile (“IRM07_sample_gps_points.shp”) containing these locations will be included in GIS deliverable 92. Most of the samples were characterized in the laboratory using a full-range ASD™ (Analytical Spectral Devices) FieldSpec II spectrometer.
\nThe image products derived from Landsat TM and ASTER data enable the delineation of mineral groups across wide areas based on color response. Guides are provided that allow users to interpret these colors as to mineral group occurrence over lithologic units and known deposits. This information can be extrapolated to other geologically permissive tracts for various deposit types in the search for similar mineralogic responses that may be indicative of concealed deposits.
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2015 - Structure map of Mauritania (phase V, deliverables 52a and 52b)","interactions":[{"subject":{"id":70159524,"text":"ofr20131280A2 - 2015 - Structure map of Mauritania (phase V, deliverables 52a and 52b)","indexId":"ofr20131280A2","publicationYear":"2015","noYear":false,"chapter":"A2","title":"Structure map of Mauritania (phase V, deliverables 52a and 52b)"},"predicate":"IS_PART_OF","object":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"id":1}],"isPartOf":{"id":70160523,"text":"ofr20131280 - 2015 - Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V","indexId":"ofr20131280","publicationYear":"2015","noYear":false,"title":"Second Projet de Renforcement Institutionnel du Secteur Minier de la République Islamique de Mauritanie (PRISM-II) Phase V"},"lastModifiedDate":"2022-12-08T16:46:56.782656","indexId":"ofr20131280A2","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","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":"2013-1280","chapter":"A2","title":"Structure map of Mauritania (phase V, deliverables 52a and 52b)","docAbstract":"In 1996, at the request of the Government of the Islamic Republic of Mauritania, a team of U.S. Geological Survey (USGS) scientists produced a strategic plan for the acquisition, improvement and modernization of multidisciplinary sets of data to support the growth of the Mauritanian minerals sector and to highlight the geological and mineral exploration potential of the country. In 1999, the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania implemented a program for the acquisition of the recommended basic geoscientific information, termed the first Projet de Renforcement Institutionnel du Secteur Minier (Project for Institutional Capacity Building in the Mining Sector, PRISM-I). As a result of the PRISM-I efforts, a great deal of new geological, geophysical, geochemical, remote sensing, and hydrological data became available for evaluation and synthesis. However, the Ministry of Petroleum, Energy, and Mines recognized that additional work was required to extract the full benefit of the data before it could be of greatest use to the international community and of benefit to the Mauritanian minerals and development sector.
\nTo achieve this benefit, the Ministry of Petroleum, Energy, and Mines implemented a second Projet de Renforcement Institutionnel du Secteur Minier (PRISM-II) in 2006 to consolidate, synthesize, and interpret all of the existing data, create a new 1:1,000,000 scale geologic map, and define the mineral resource potential of the country. A consortium in which the USGS was the lead scientific agency carried out the majority of the PRISM-II work. In 2008, the USGS Mauritania Minerals Project was interrupted due to political changes in Mauritania. PRISM-II work resumed in 2011, and was completed in 2013 with the delivery of over 40 separate written reports and plates, an access file containing the Mauritanian National Mineral Deposits Database, and an interactive GIS containing all of the multi-disciplinary data and interpretive areas of mineral resource potential in Mauritania.
\nThis report contains the USGS results of the PRISM-II Mauritania Minerals Project and is presented in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania. The Report is composed of separate chapters consisting of multidisciplinary interpretive reports with accompanying plates on the geology, structure, geochronology, geophysics, hydrogeology, geochemistry, remote sensing (Landsat TM and ASTER), and SRTM and ASTER digital elevation models of Mauritania. The syntheses of these multidisciplinary data formed the basis for additional chapters containing interpretive reports on 12 different commodities and deposit types known to occur in Mauritania, accompanied by countrywide mineral resource potential maps of each commodity/deposit type. The commodities and deposit types represented include: (1) Ni, Cu, PGE, and Cr deposits hosted in ultramafic rocks; (2) orogenic, Carlin-like, and epithermal gold deposits; (3) polymetallic Pb-Zn-Cu vein deposits; (4) sediment-hosted Pb-Zn-Ag deposits of the SEDEX and Mississippi Valley-type; (5) sediment-hosted copper deposits; ( 6) volcanogenic massive sulfide deposits; (7) iron oxide copper-gold deposits; (8) uranium deposits; (9) Algoma-, Superior-, and oolitic-type iron deposits; (10) shoreline Ti-Zr placer deposits; (11) incompatible element deposits hosted in pegmatites, alkaline rocks, and carbonatites, and; (12) industrial mineral deposits. Additional chapters include the Mauritanian National Mineral Deposits Database are accompanied by an explanatory text and the Mauritania Minerals Project GIS that contains all of the interpretive layers created by USGS scientists. Raw data not in the public domain may be obtained from the Ministry of Petroleum, Energy, and Mines in Nouakchott, Mauritania.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Second projet de renforcement institutionnel du secteur minier de la République Islamique de Mauritanie (PRISM-II) (Open File Report 2013-1280)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20131280A2","collaboration":"Prepared in cooperation with the Ministry of Petroleum, Energy, and Mines of the Islamic Republic of Mauritania","usgsCitation":"Bradley, D., Horton, J.D., Motts, H.A., and Taylor, C.D., 2015, Structure map of Mauritania (phase V, deliverables 52a and 52b): U.S. Geological Survey Open-File Report 2013-1280, 2 Plates: 54.0 x 60.0 inches; Data; Metadata, https://doi.org/10.3133/ofr20131280A2.","productDescription":"2 Plates: 54.0 x 60.0 inches; Data; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-056944","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":319064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":319063,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2013/1280/GIS_and_Maps/Chapter_A2_deliverable_52-Structure/","text":"Map, Data, and Metadata"}],"country":"Mauritania","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-12.17075,14.61683],[-12.83066,15.30369],[-13.43574,16.03938],[-14.09952,16.3043],[-14.57735,16.59826],[-15.13574,16.58728],[-15.62367,16.36934],[-16.12069,16.45566],[-16.4631,16.13504],[-16.54971,16.67389],[-16.27055,17.16696],[-16.14635,18.10848],[-16.25688,19.09672],[-16.37765,19.59382],[-16.27784,20.09252],[-16.53632,20.56787],[-17.06342,20.99975],[-16.84519,21.33332],[-12.9291,21.32707],[-13.11875,22.77122],[-12.87422,23.28483],[-11.93722,23.37459],[-11.96942,25.93335],[-8.68729,25.88106],[-8.6844,27.39574],[-4.92334,24.97457],[-6.45379,24.95659],[-5.97113,20.64083],[-5.48852,16.3251],[-5.31528,16.20185],[-5.53774,15.50169],[-9.55024,15.4865],[-9.70026,15.26411],[-10.08685,15.33049],[-10.65079,15.13275],[-11.3491,15.41126],[-11.66608,15.38821],[-11.83421,14.7991],[-12.17075,14.61683]]]},\"properties\":{\"name\":\"Mauritania\"}}]}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56f11b71e4b0f59b85ddc51a","contributors":{"authors":[{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":622216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, John D. 0000-0003-2969-9073 jhorton@usgs.gov","orcid":"https://orcid.org/0000-0003-2969-9073","contributorId":1227,"corporation":false,"usgs":true,"family":"Horton","given":"John","email":"jhorton@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Motts, Holly A.","contributorId":149752,"corporation":false,"usgs":false,"family":"Motts","given":"Holly","email":"","middleInitial":"A.","affiliations":[{"id":17814,"text":"USGS GIS compilation","active":true,"usgs":false}],"preferred":false,"id":622218,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":622219,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188059,"text":"70188059 - 2015 - Validation of geometric accuracy of Global Land Survey (GLS) 2000 data","interactions":[],"lastModifiedDate":"2018-05-29T08:59:06","indexId":"70188059","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","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":"Validation of geometric accuracy of Global Land Survey (GLS) 2000 data","docAbstract":"The Global Land Survey (GLS) 2000 data were generated from Geocover™ 2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™ 2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, orthorectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™ 2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.81.2.131","usgsCitation":"Rengarajan, R., Sampath, A., Storey, J.C., and Choate, M., 2015, Validation of geometric accuracy of Global Land Survey (GLS) 2000 data: Photogrammetric Engineering and Remote Sensing, v. 2, p. 131-141, https://doi.org/10.14358/PERS.81.2.131.","productDescription":"11 p.","startPage":"131","endPage":"141","ipdsId":"IP-055114","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472419,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.81.2.131","text":"Publisher Index Page"},{"id":341860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84bee4b092b266f10d55","contributors":{"authors":[{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110 rrengarajan@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":192376,"corporation":false,"usgs":true,"family":"Rengarajan","given":"Rajagopalan","email":"rrengarajan@contractor.usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":696344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":696343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696345,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696346,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70146301,"text":"70146301 - 2015 - Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information","interactions":[],"lastModifiedDate":"2023-02-10T17:39:44.675385","indexId":"70146301","displayToPublicDate":"2015-01-01T00:00:00","publicationYear":"2015","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":"Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information","docAbstract":"The National Land Cover Database (NLCD) provides nationwide data on land cover and land cover change at the native 30-m spatial resolution of the Landsat Thematic Mapper (TM). The database is designed to provide five-year cyclical updating of United States land cover and associated changes. The recent release of NLCD 2011 products now represents a decade of consistently produced land cover and impervious surface for the Nation across three periods: 2001, 2006, and 2011 (Homer et al., 2007; Fry et al., 2011). Tree canopy cover has also been produced for 2011 (Coluston et al., 2012; Coluston et al., 2013). With the release of NLCD 2011, the database provides the ability to move beyond simple change detection to monitoring and trend assessments. NLCD 2011 represents the latest evolution of NLCD products, continuing its focus on consistency, production, efficiency, and product accuracy. NLCD products are designed for widespread application in biology, climate, education, land management, hydrology, environmental planning, risk and disease analysis, telecommunications and visualization, and are available for no cost at http://www.mrlc.gov. 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Kevin","contributorId":140260,"corporation":false,"usgs":false,"family":"Megown","given":"Kevin","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":544961,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70128794,"text":"70128794 - 2015 - Machine learning for predicting soil classes in three semi-arid landscapes","interactions":[],"lastModifiedDate":"2014-10-14T15:20:17","indexId":"70128794","displayToPublicDate":"2014-10-14T15:14:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning for predicting soil classes in three semi-arid landscapes","docAbstract":"Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes.
\nOur objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination.
\nOverall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used.
\nOverall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geoderma","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.geoderma.2014.09.019","usgsCitation":"Brungard, C.W., Boettinger, J.L., Duniway, M.C., Wills, S., and Edwards, T.C., 2015, Machine learning for predicting soil classes in three semi-arid landscapes: Geoderma, v. 239-240, p. 68-83, https://doi.org/10.1016/j.geoderma.2014.09.019.","productDescription":"16 p.","startPage":"68","endPage":"83","ipdsId":"IP-055747","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":295328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":295318,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geoderma.2014.09.019"}],"country":"United States","state":"New Mexico, Utah, Wyoming","volume":"239-240","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"543e2d06e4b0fd76af69cedc","contributors":{"authors":[{"text":"Brungard, Colby W.","contributorId":99488,"corporation":false,"usgs":true,"family":"Brungard","given":"Colby","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":503225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boettinger, Janis L.","contributorId":82239,"corporation":false,"usgs":true,"family":"Boettinger","given":"Janis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":503223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":503222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wills, Skye A.","contributorId":92600,"corporation":false,"usgs":true,"family":"Wills","given":"Skye A.","affiliations":[],"preferred":false,"id":503224,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, Thomas C. Jr. 0000-0002-0773-0909 tce@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-0909","contributorId":2061,"corporation":false,"usgs":true,"family":"Edwards","given":"Thomas","suffix":"Jr.","email":"tce@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":false,"id":503221,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70125289,"text":"70125289 - 2015 - Long-term decrease in satellite vegetation indices in response to environmental variables in an iconic desert riparian ecosystem: the Upper San Pedro, Arizona, United States","interactions":[],"lastModifiedDate":"2015-07-01T15:50:43","indexId":"70125289","displayToPublicDate":"2014-09-18T13:43:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Long-term decrease in satellite vegetation indices in response to environmental variables in an iconic desert riparian ecosystem: the Upper San Pedro, Arizona, United States","docAbstract":"The Upper San Pedro River is one of the few remaining undammed rivers that maintain a vibrant riparian ecosystem in the southwest United States. However, its riparian forest is threatened by diminishing groundwater and surface water inputs, due to either changes in watershed characteristics such as changes in riparian and upland vegetation, or human activities such as regional groundwater pumping. We used satellite vegetation indices to quantify the green leaf density of the groundwater-dependent riparian forest from 1984 to 2012. The river was divided into a southern, upstream (mainly perennial flow) reach and a northern, downstream (mainly intermittent and ephemeral flow) reach. Pre-monsoon (June) Landsat normalized difference vegetation index (NDVI) values showed a 20% drop for the northern reach (P < 0·001) and no net change for the southern reach (P > 0·05). NDVI and enhanced vegetation index values were positively correlated (P < 0·05) with river flows, which decreased over the study period in the northern reach, and negatively correlated (P < 0·05) with air temperatures in both reaches, which have increased by 1·4 °C from 1932 to 2012. NDVI in the uplands around the river did not increase from 1984 to 2012, suggesting that increased evapotranspiration in the uplands was not a factor in reducing river flows. Climate change, regional groundwater pumping, changes in the intensity of monsoon rain events and lack of overbank flooding are feasible explanations for deterioration of the riparian forest in the northern reach.
","language":"English","publisher":"John Wiley & Sons Ltd.","doi":"10.1002/eco.1529","usgsCitation":"Nguyen, U., Glenn, E.P., Nagler, P.L., and Scott, R.L., 2015, Long-term decrease in satellite vegetation indices in response to environmental variables in an iconic desert riparian ecosystem: the Upper San Pedro, Arizona, United States: Ecohydrology, v. 8, no. 4, p. 610-625, https://doi.org/10.1002/eco.1529.","productDescription":"16 p.","startPage":"610","endPage":"625","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052717","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":294183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294181,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/eco.1529"}],"country":"United States","state":"Arizona","otherGeospatial":"Upper San Pedro River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -110.433333,31.25 ], [ -110.433333,32.166667 ], [ -109.816667,32.166667 ], [ -109.816667,31.25 ], [ -110.433333,31.25 ] ] ] } } ] }","volume":"8","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"541be60de4b0e96537dda074","contributors":{"authors":[{"text":"Nguyen, Uyen","contributorId":71863,"corporation":false,"usgs":false,"family":"Nguyen","given":"Uyen","email":"","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":501145,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glenn, Edward P.","contributorId":19289,"corporation":false,"usgs":true,"family":"Glenn","given":"Edward","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":501143,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":501142,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Russell L.","contributorId":39875,"corporation":false,"usgs":false,"family":"Scott","given":"Russell","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":501144,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173949,"text":"70173949 - 2014 - Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches","interactions":[],"lastModifiedDate":"2016-06-20T10:22:54","indexId":"70173949","displayToPublicDate":"2016-01-19T07:45:00","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":"Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches","docAbstract":"Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.
","language":"English","publisher":"MDPI","doi":"10.3390/rs61212478","usgsCitation":"Selkowitz, D.J., Forster, R., and Caldwell, M.K., 2014, Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches: Remote Sensing, v. 6, no. 12, p. 12478-12508, https://doi.org/10.3390/rs61212478.","productDescription":"30 p.","startPage":"12478","endPage":"12508","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060122","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":472504,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61212478","text":"Publisher Index Page"},{"id":323952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-11","publicationStatus":"PW","scienceBaseUri":"576913e4e4b07657d19ff238","contributors":{"authors":[{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":639740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forster, Richard","contributorId":172149,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","affiliations":[{"id":26993,"text":"University of Utah, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":639741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caldwell, Megan K. mcaldwell@usgs.gov","contributorId":4243,"corporation":false,"usgs":true,"family":"Caldwell","given":"Megan","email":"mcaldwell@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":639742,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70139553,"text":"70139553 - 2014 - Modeling a historical mountain pine beetle outbreak using Landsat MSS and multiple lines of evidence","interactions":[],"lastModifiedDate":"2015-01-28T14:59:14","indexId":"70139553","displayToPublicDate":"2015-01-28T14:45:00","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":"Modeling a historical mountain pine beetle outbreak using Landsat MSS and multiple lines of evidence","docAbstract":"Mountain pine beetles are significant forest disturbance agents, capable of inducing widespread mortality in coniferous forests in western North America. Various remote sensing approaches have assessed the impacts of beetle outbreaks over the last two decades. However, few studies have addressed the impacts of historical mountain pine beetle outbreaks, including the 1970s event that impacted Glacier National Park. The lack of spatially explicit data on this disturbance represents both a major data gap and a critical research challenge in that wildfire has removed some of the evidence from the landscape. We utilized multiple lines of evidence to model forest canopy mortality as a proxy for outbreak severity. We incorporate historical aerial and landscape photos, aerial detection survey data, a nine-year collection of satellite imagery and abiotic data. This study presents a remote sensing based framework to (1) relate measurements of canopy mortality from fine-scale aerial photography to coarse-scale multispectral imagery and (2) classify the severity of mountain pine beetle affected areas using a temporal sequence of Landsat data and other landscape variables. We sampled canopy mortality in 261 plots from aerial photos and found that insect effects on mortality were evident in changes to the Normalized Difference Vegetation Index (NDVI) over time. We tested multiple spectral indices and found that a combination of NDVI and the green band resulted in the strongest model. We report a two-step process where we utilize a generalized least squares model to account for the large-scale variability in the data and a binary regression tree to describe the small-scale variability. The final model had a root mean square error estimate of 9.8% canopy mortality, a mean absolute error of 7.6% and an R2 of 0.82. The results demonstrate that a model of percent canopy mortality as a continuous variable can be developed to identify a gradient of mountain pine beetle severity on the landscape.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2014.09.002","usgsCitation":"Assal, T.J., Sibold, J., and Reich, R.M., 2014, Modeling a historical mountain pine beetle outbreak using Landsat MSS and multiple lines of evidence: Remote Sensing of Environment, v. 155, p. 275-288, https://doi.org/10.1016/j.rse.2014.09.002.","productDescription":"14 p.","startPage":"275","endPage":"288","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057978","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":297600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.5654296875,\n 48.22101291025667\n ],\n [\n -114.5654296875,\n 49.11523132594606\n ],\n [\n -113.15093994140625,\n 49.11523132594606\n ],\n [\n -113.15093994140625,\n 48.22101291025667\n ],\n [\n -114.5654296875,\n 48.22101291025667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"155","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2a98e4b08de9379b3128","contributors":{"authors":[{"text":"Assal, Timothy J. 0000-0001-6342-2954 assalt@usgs.gov","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":2203,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","email":"assalt@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":539441,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sibold, Jason","contributorId":10724,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","affiliations":[],"preferred":false,"id":539442,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reich, Robin M.","contributorId":98578,"corporation":false,"usgs":false,"family":"Reich","given":"Robin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":539443,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70039984,"text":"70039984 - 2014 - Comparison of simulated HyspIRI with two multispectral sensors for invasive species mapping","interactions":[],"lastModifiedDate":"2020-12-09T13:25:52.841036","indexId":"70039984","displayToPublicDate":"2014-12-31T13:50:24","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":"Comparison of simulated HyspIRI with two multispectral sensors for invasive species mapping","docAbstract":"This study evaluates the radiometric consistency between Landsat-8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) using cross calibration techniques. Two approaches are used, one based on cross calibration between the two sensors using simultaneous image pairs, acquired during an underfly event on 29–30 March 2013. The other approach is based on using time series of image statistics acquired by these two sensors over the Libya 4 pseudo invariant calibration site (PICS) (+28.55°N, +23.39°E). Analyses from these approaches show that the reflectance calibration of OLI is generally within ±3% of the ETM+ radiance calibration for all the reflective bands from visible to short wave infrared regions when the ChKur solar spectrum is used to convert the ETM+ radiance to reflectance. Similar results are obtained comparing the OLI radiance calibration directly with the ETM+ radiance calibration and the results in these two different physical units (radiance and reflectance) agree to within ±2% for all the analogous bands. These results will also be useful to tie all the Landsat heritage sensors from Landsat 1 MultiSpectral Scanner (MSS) through Landsat-8 OLI to a consistent radiometric scale.
","language":"English","publisher":"MDPI","doi":"10.3390/rs61212619","usgsCitation":"Mishra, N., Haque, O., Leigh, L., Aaron, D., Helder, D., and Markham, B.L., 2014, Radiometric cross calibration of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+): Remote Sensing, v. 6, no. 12, p. 12619-12638, https://doi.org/10.3390/rs61212619.","productDescription":"20 p.","startPage":"12619","endPage":"12638","ipdsId":"IP-058467","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472569,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61212619","text":"Publisher Index Page"},{"id":341875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"12","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-16","publicationStatus":"PW","scienceBaseUri":"592e84c0e4b092b266f10d64","contributors":{"authors":[{"text":"Mishra, Nischal nischal.mishra.ctr@usgs.gov","contributorId":148000,"corporation":false,"usgs":false,"family":"Mishra","given":"Nischal","email":"nischal.mishra.ctr@usgs.gov","affiliations":[],"preferred":false,"id":696489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":696334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leigh, Larry","contributorId":192383,"corporation":false,"usgs":false,"family":"Leigh","given":"Larry","email":"","affiliations":[],"preferred":false,"id":696490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aaron, David","contributorId":83809,"corporation":false,"usgs":false,"family":"Aaron","given":"David","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":696491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":99714,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","affiliations":[],"preferred":false,"id":696492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Markham, Brian L. 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":121488,"corporation":false,"usgs":true,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696493,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70141389,"text":"70141389 - 2014 - Establishing a baseline for regional scale monitoring of eelgrass (Zostera marina) habitat on the lower Alaska Peninsula","interactions":[],"lastModifiedDate":"2015-02-18T15:04:15","indexId":"70141389","displayToPublicDate":"2014-12-10T00:00:00","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":"Establishing a baseline for regional scale monitoring of eelgrass (Zostera marina) habitat on the lower Alaska Peninsula","docAbstract":"Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass (Zostera marina) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis.
","language":"English","publisher":"MDPI AG","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs61212447","usgsCitation":"Hogrefe, K.R., Ward, D.H., Donnelly, T.F., and Dau, N., 2014, Establishing a baseline for regional scale monitoring of eelgrass (Zostera marina) habitat on the lower Alaska Peninsula: Remote Sensing, v. 6, no. 12, p. 12447-12477, https://doi.org/10.3390/rs61212447.","productDescription":"31 p.","startPage":"12447","endPage":"12477","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-054532","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":472581,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61212447","text":"Publisher Index Page"},{"id":438736,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","linkHelpText":"Mapping Data of Eelgrass (Zostera marina) Distribution, Alaska and Baja California, Mexico"},{"id":298041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.71826171875,\n 54.54020652089137\n ],\n [\n -163.71826171875,\n 56.15166933290848\n ],\n [\n -160.1971435546875,\n 56.15166933290848\n ],\n [\n -160.1971435546875,\n 54.54020652089137\n ],\n [\n -163.71826171875,\n 54.54020652089137\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-12-10","publicationStatus":"PW","scienceBaseUri":"54e5c5c0e4b02d776a669eb9","contributors":{"authors":[{"text":"Hogrefe, Kyle R. khogrefe@usgs.gov","contributorId":4264,"corporation":false,"usgs":true,"family":"Hogrefe","given":"Kyle","email":"khogrefe@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":540748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":540749,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Tyrone F. tfdonnelly@usgs.gov","contributorId":4369,"corporation":false,"usgs":true,"family":"Donnelly","given":"Tyrone","email":"tfdonnelly@usgs.gov","middleInitial":"F.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":540750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dau, Niels","contributorId":139333,"corporation":false,"usgs":false,"family":"Dau","given":"Niels","email":"","affiliations":[],"preferred":false,"id":540829,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70133365,"text":"70133365 - 2014 - Landsat 8 operational land imager on-orbit geometric calibration and performance","interactions":[],"lastModifiedDate":"2017-01-18T11:25:22","indexId":"70133365","displayToPublicDate":"2014-11-14T15:15:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 8 operational land imager on-orbit geometric calibration and performance","docAbstract":"The Landsat 8 spacecraft was launched on 11 February 2013 carrying the Operational Land Imager (OLI) payload for moderate resolution imaging in the visible, near infrared (NIR), and short-wave infrared (SWIR) spectral bands. During the 90-day commissioning period following launch, several on-orbit geometric calibration activities were performed to refine the prelaunch calibration parameters. The results of these calibration activities were subsequently used to measure geometric performance characteristics in order to verify the OLI geometric requirements. Three types of geometric calibrations were performed including: (1) updating the OLI-to-spacecraft alignment knowledge; (2) refining the alignment of the sub-images from the multiple OLI sensor chips; and (3) refining the alignment of the OLI spectral bands. The aspects of geometric performance that were measured and verified included: (1) geolocation accuracy with terrain correction, but without ground control (L1Gt); (2) Level 1 product accuracy with terrain correction and ground control (L1T); (3) band-to-band registration accuracy; and (4) multi-temporal image-to-image registration accuracy. Using the results of the on-orbit calibration update, all aspects of geometric performance were shown to meet or exceed system requirements.
","language":"English","publisher":"MDPI","doi":"10.3390/rs61111127","usgsCitation":"Storey, J.C., Choate, M., and Lee, K., 2014, Landsat 8 operational land imager on-orbit geometric calibration and performance: Remote Sensing, v. 6, no. 11, p. 11127-11152, https://doi.org/10.3390/rs61111127.","productDescription":"26 p.","startPage":"11127","endPage":"11152","numberOfPages":"26","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056529","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":472636,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs61111127","text":"Publisher Index Page"},{"id":296113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"11","noUsgsAuthors":false,"publicationDate":"2014-11-11","publicationStatus":"PW","scienceBaseUri":"5467199be4b04d4b7dbde521","contributors":{"authors":[{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":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":525030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":525031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Kenton","contributorId":127404,"corporation":false,"usgs":false,"family":"Lee","given":"Kenton","email":"","affiliations":[{"id":6944,"text":"Ball Aerospace Technologies Corporation","active":true,"usgs":false}],"preferred":false,"id":525032,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}