This research considers the applicability of different vegetation indices at 30 m resolution for mapping and monitoring desert wetland (cienega) health and spatial extent through time at Cienega Creek in southeastern Arizona, USA. Multiple stressors including the risk of decadal-scale drought, the effects of current and predicted global warming, and continued anthropogenic pressures threaten aquatic habitats in the southwest and cienegas are recognized as important sites for conservation and restoration efforts. However, cienegas present a challenge to satellite-imagery based analysis due to their small size and mixed surface cover of open water, exposed soils, and vegetation. We created time series of five well-known vegetation indices using annual Landsat Thematic Mapper (TM) images retrieved during the April–June dry season, from 1984 to 2011 to map landscape-level distribution of wetlands and monitor the temporal dynamics of individual sites. Indices included the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII). One topographic index, the Topographic Wetness Index (TWI), was analyzed to examine the utility of topography in mapping distribution of cienegas. Our results indicate that the NDII, calculated using Landsat TM band 5, outperforms the other indices at differentiating cienegas from riparian and upland sites, and was the best means to analyze change. As such, it offers a critical baseline for future studies that seek to extend the analysis of cienegas to other regions and time scales, and has broader applicability to the remote sensing of wetland features in arid landscapes.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15324982.2016.1170076","usgsCitation":"Wilson, N.R., Norman, L.M., Villarreal, M.L., Gass, L., Tiller, R., and Salywon, A., 2016, Comparison of remote sensing indices for monitoring of desert cienegas: Arid Land Research and Management, v. 30, no. 4, p. 460-478, https://doi.org/10.1080/15324982.2016.1170076.","productDescription":"19 p.","startPage":"460","endPage":"478","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068692","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15324982.2016.1170076","text":"Publisher Index Page"},{"id":324640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cienega Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.5667495727539,\n 31.894319802510566\n ],\n [\n -110.5667495727539,\n 31.970512683093744\n ],\n [\n -110.5063247680664,\n 31.970512683093744\n ],\n [\n -110.5063247680664,\n 31.894319802510566\n ],\n [\n -110.5667495727539,\n 31.894319802510566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-18","publicationStatus":"PW","scienceBaseUri":"5774e32fe4b07dd077c5fbff","contributors":{"authors":[{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":5770,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tiller, Ron","contributorId":169496,"corporation":false,"usgs":false,"family":"Tiller","given":"Ron","email":"","affiliations":[{"id":25532,"text":"Arizona Department of Transportation, Environmental Planning Group","active":true,"usgs":false}],"preferred":false,"id":629795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Salywon, Andrew","contributorId":169497,"corporation":false,"usgs":false,"family":"Salywon","given":"Andrew","email":"","affiliations":[{"id":25533,"text":"Desert Botanical Garden","active":true,"usgs":false}],"preferred":false,"id":629796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170970,"text":"70170970 - 2016 - Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US","interactions":[],"lastModifiedDate":"2017-01-17T19:16:04","indexId":"70170970","displayToPublicDate":"2016-06-29T15:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US","docAbstract":"Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/17538947.2016.1158876","usgsCitation":"Schleeweis, K., Goward, S.N., Huang, C., Dwyer, J.L., Dungan, J.L., Lindsey, M.A., Michaelis, A., Rishmawi, K., and Masek, J.G., 2016, Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US: International Journal of Digital Earth, v. 9, no. 10, p. 963-980, https://doi.org/10.1080/17538947.2016.1158876.","productDescription":"18 p.","startPage":"963","endPage":"980","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073476","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":324639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-20","publicationStatus":"PW","scienceBaseUri":"5774e34fe4b07dd077c5fcf9","contributors":{"authors":[{"text":"Schleeweis, Karen","contributorId":169308,"corporation":false,"usgs":false,"family":"Schleeweis","given":"Karen","email":"","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":629277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goward, Samuel N.","contributorId":44459,"corporation":false,"usgs":true,"family":"Goward","given":"Samuel","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":629278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":629279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":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":629276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dungan, Jennifer L.","contributorId":172579,"corporation":false,"usgs":false,"family":"Dungan","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":641329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindsey, Mary A.","contributorId":169309,"corporation":false,"usgs":false,"family":"Lindsey","given":"Mary","email":"","middleInitial":"A.","affiliations":[{"id":25467,"text":"Climate Program Office, NOAA","active":true,"usgs":false}],"preferred":false,"id":629280,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michaelis, Andrew","contributorId":169311,"corporation":false,"usgs":false,"family":"Michaelis","given":"Andrew","email":"","affiliations":[{"id":25468,"text":"University Corporation Monterey Bay / NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":629282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rishmawi, Khaldoun","contributorId":169310,"corporation":false,"usgs":false,"family":"Rishmawi","given":"Khaldoun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":629281,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":629283,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70177751,"text":"70177751 - 2016 - The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","interactions":[],"lastModifiedDate":"2018-08-08T10:25:00","indexId":"70177751","displayToPublicDate":"2016-06-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","docAbstract":"Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.
","language":"English","publisher":"Pergamon Press","doi":"10.1016/j.marpolbul.2016.02.076","usgsCitation":"Slonecker, E.T., Jones, D.K., and Pellerin, B.A., 2016, The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM): Marine Pollution Bulletin, v. 107, no. 2, p. 518-527, https://doi.org/10.1016/j.marpolbul.2016.02.076.","productDescription":"10 p.","startPage":"518","endPage":"527","ipdsId":"IP-069654","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470815,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpolbul.2016.02.076","text":"Publisher Index Page"},{"id":438605,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7125QQM","text":"USGS data release","linkHelpText":"CDOM/fDOM and Landsat 8 Comparisons"},{"id":330242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5809d7c4e4b0f497e78fca62","chorus":{"doi":"10.1016/j.marpolbul.2016.02.076","url":"http://dx.doi.org/10.1016/j.marpolbul.2016.02.076","publisher":"Elsevier BV","authors":"Slonecker E. Terrence, Jones Daniel K., Pellerin Brian A.","journalName":"Marine Pollution Bulletin","publicationDate":"6/2016","auditedOn":"3/21/2016","publiclyAccessibleDate":"3/4/2016"},"contributors":{"authors":[{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":651634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Daniel K. 0000-0003-0724-8001 dkjones@usgs.gov","orcid":"https://orcid.org/0000-0003-0724-8001","contributorId":4959,"corporation":false,"usgs":true,"family":"Jones","given":"Daniel","email":"dkjones@usgs.gov","middleInitial":"K.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. bpeller@usgs.gov","contributorId":1451,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":651653,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170462,"text":"70170462 - 2016 - Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)","interactions":[],"lastModifiedDate":"2019-12-14T06:31:12","indexId":"70170462","displayToPublicDate":"2016-06-28T16:45:00","publicationYear":"2016","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":"Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)","docAbstract":"Remote sensing has proven a useful way of evaluating long-term trends in vegetation “greenness” through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends.
\nAn assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.03.036","usgsCitation":"Zhu, Z., Fu, Y., Woodcock, C., Olofsson, P., Vogelmann, J., Holden, C., Wang, M., Dai, S., and Yu, Y., 2016, Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014): Remote Sensing of Environment, v. 185, p. 243-257, https://doi.org/10.1016/j.rse.2016.03.036.","productDescription":"15 p.","startPage":"243","endPage":"257","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068963","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470818,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.03.036","text":"Publisher Index Page"},{"id":324550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","city":"Guangzhou","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 112.5,\n 21.453068633086783\n ],\n [\n 116.71874999999999,\n 21.453068633086783\n ],\n [\n 116.71874999999999,\n 26.86328062676624\n ],\n [\n 112.5,\n 26.86328062676624\n ],\n [\n 112.5,\n 21.453068633086783\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"577391a6e4b07657d1a88bd0","contributors":{"authors":[{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":627309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fu, Yingchun","contributorId":172520,"corporation":false,"usgs":false,"family":"Fu","given":"Yingchun","email":"","affiliations":[],"preferred":false,"id":641108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":641109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olofsson, Pontus","contributorId":131007,"corporation":false,"usgs":false,"family":"Olofsson","given":"Pontus","email":"","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":641110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":127752,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":641111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holden, Christopher","contributorId":172521,"corporation":false,"usgs":false,"family":"Holden","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":641112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Min","contributorId":145692,"corporation":false,"usgs":false,"family":"Wang","given":"Min","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":641113,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dai, Shu","contributorId":172522,"corporation":false,"usgs":false,"family":"Dai","given":"Shu","email":"","affiliations":[],"preferred":false,"id":641114,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yu, Yang","contributorId":172524,"corporation":false,"usgs":false,"family":"Yu","given":"Yang","email":"","affiliations":[],"preferred":false,"id":641115,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70174046,"text":"fs20163037 - 2016 - Mapping water use—Landsat and water resources in the United States","interactions":[],"lastModifiedDate":"2019-09-20T10:50:09","indexId":"fs20163037","displayToPublicDate":"2016-06-27T00:00:00","publicationYear":"2016","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":"2016-3037","displayTitle":"Mapping Water Use—Landsat and Water Resources in the United States","title":"Mapping water use—Landsat and water resources in the United States","docAbstract":"Using Landsat satellite data, scientists with the U.S. Geological Survey have helped to refine a technique called evapotranspiration mapping to measure how much water crops are using across landscapes and through time. These water-use maps are created using a computer model that integrates Landsat and weather data.
Crucial to the process is the thermal (infrared) band from Landsat. Using the Landsat thermal band with its 100-meter resolution, water-use maps can be created at a scale detailed enough to show how much water crops are using at the level of individual fields anywhere in the world.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163037","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"U.S. Geological Survey, 2016, Mapping water use—Landsat and water resources in the United States (ver. 1.1, September 2019): U.S. Geological Survey Fact Sheet 2016–3037, 2 p., https://doi.org/10.3133/fs20163037.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075235","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":367504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3037/fs20163037_2.pdf","text":"Report","size":"5.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3037"},{"id":324411,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3037/coverthb2.jpg"},{"id":367505,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3037/versionHist.txt","size":"1.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"edition":"Version 1.0: June 27, 2016; Version 1.1 September 18, 2019","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2016.01.003","usgsCitation":"Mitchell, M., Wilson, R.R., Twedt, D.J., Mini, A., and James, J.D., 2016, Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley: Remote Sensing Applications: Society and Environment, v. 4, p. 55-60, https://doi.org/10.1016/j.rsase.2016.01.003.","productDescription":"6 p.","startPage":"55","endPage":"60","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061883","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470856,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rsase.2016.01.003","text":"Publisher Index Page"},{"id":324251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Missouri","otherGeospatial":"Mississippi Alluvial Valley","volume":"4","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576ba89fe4b07657d1a17683","chorus":{"doi":"10.1016/j.rsase.2016.01.003","url":"http://dx.doi.org/10.1016/j.rsase.2016.01.003","publisher":"Elsevier BV","authors":"Mitchell Michael, Wilson R. Randy, Twedt Daniel J., Mini Anne E., James J. Dale","journalName":"Remote Sensing Applications: Society and Environment","publicationDate":"10/2016"},"contributors":{"authors":[{"text":"Mitchell, Michael","contributorId":98207,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","affiliations":[],"preferred":false,"id":638374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, R. Randy","contributorId":100287,"corporation":false,"usgs":true,"family":"Wilson","given":"R.","email":"","middleInitial":"Randy","affiliations":[],"preferred":false,"id":638375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Twedt, Daniel J. 0000-0003-1223-5045 dtwedt@usgs.gov","orcid":"https://orcid.org/0000-0003-1223-5045","contributorId":398,"corporation":false,"usgs":true,"family":"Twedt","given":"Daniel","email":"dtwedt@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":638373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mini, Anne","contributorId":171716,"corporation":false,"usgs":false,"family":"Mini","given":"Anne","affiliations":[{"id":26934,"text":"Lower Mississippi Valley Joint Venture and American Bird Conservancy, 193 Business Park Drive, Suite E, Ridgeland, MS 39157","active":true,"usgs":false}],"preferred":false,"id":638376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"James, J. Dale","contributorId":172350,"corporation":false,"usgs":false,"family":"James","given":"J.","email":"","middleInitial":"Dale","affiliations":[],"preferred":false,"id":640427,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170079,"text":"70170079 - 2016 - Landsat Science Team: 2016 winter meeting summary","interactions":[],"lastModifiedDate":"2017-01-18T09:26:26","indexId":"70170079","displayToPublicDate":"2016-06-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3555,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":10}},"title":"Landsat Science Team: 2016 winter meeting summary","docAbstract":"The winter meeting of the joint U.S. Geological Survey (USGS)–NASA Landsat Science Team (LST) was held January 12-14, 2016, at Virginia Tech University in Blacksburg, VA. LST co-chairs Tom Loveland [USGS’s Earth Resources Observation and Science Data Center (EROS)—Senior Scientist] and Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Landsat 8 Project Scientist] welcomed more than 50 participants to the three-day meeting. The main objectives of this meeting focused on identifying priorities and approaches to improve the global moderate-resolution satellite record. Overall, the meeting was geared more towards soliciting team member recommendations on several rapidly evolving issues, than on providing updates on individual research activities. All the presentations given at the meeting are available at landsat.usgs. gov//science_LST_january2016.php.
","language":"English","publisher":"NASA","usgsCitation":"Schroeder, T., Loveland, T., Wulder, M.A., and Irons, J.R., 2016, Landsat Science Team: 2016 winter meeting summary: The Earth Observer, p. 19-23.","productDescription":"5 p.","startPage":"19","endPage":"23","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074277","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":324210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":324209,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/May_June_2016_color%20508.pdf"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576bb6b6e4b07657d1a228c9","contributors":{"authors":[{"text":"Schroeder, Todd tschroeder@usgs.gov","contributorId":149137,"corporation":false,"usgs":true,"family":"Schroeder","given":"Todd","email":"tschroeder@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":626045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":626046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":626047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":626048,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171415,"text":"70171415 - 2016 - Forest disturbance interactions and successional pathways in the Southern Rocky Mountains","interactions":[],"lastModifiedDate":"2016-06-01T15:59:35","indexId":"70171415","displayToPublicDate":"2016-05-20T02:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Forest disturbance interactions and successional pathways in the Southern Rocky Mountains","docAbstract":"The pine forests in the southern portion of the Rocky Mountains are a heterogeneous mosaic of disturbance and recovery. The most extensive and intensive stress and mortality are received from human activity, fire, and mountain pine beetles (MPB;Dendroctonus ponderosae). Understanding disturbance interactions and disturbance-succession pathways are crucial for adapting management strategies to mitigate their impacts and anticipate future ecosystem change. Driven by this goal, we assessed the forest disturbance and recovery history in the Southern Rocky Mountains Ecoregion using a 13-year time series of Landsat image stacks. An automated classification workflow that integrates temporal segmentation techniques and a random forest classifier was used to examine disturbance patterns. To enhance efficiency in selecting representative samples at the ecoregion scale, a new sampling strategy that takes advantage of the scene-overlap among adjacent Landsat images was designed. The segment-based assessment revealed that the overall accuracy for all 14 scenes varied from 73.6% to 92.5%, with a mean of 83.1%. A design-based inference indicated the average producer’s and user’s accuracies for MPB mortality were 85.4% and 82.5% respectively. We found that burn severity was largely unrelated to the severity of pre-fire beetle outbreaks in this region, where the severity of post-fire beetle outbreaks generally decreased in relation to burn severity. Approximately half the clear-cut and burned areas were in various stages of recovery, but the regeneration rate was much slower for MPB-disturbed sites. Pre-fire beetle outbreaks and subsequent fire produced positive compound effects on seedling reestablishment in this ecoregion. Taken together, these results emphasize that although multiple disturbances do play a role in the resilience mechanism of the serotinous lodgepole pine, the overall recovery could be slow due to the vast area of beetle mortality.
","language":"English","publisher":"Elsevier Science Pub. Co.","doi":"10.1016/j.foreco.2016.05.010","usgsCitation":"Liang, L., Hawbaker, T., Zhu, Z., Li, X., and Gong, P., 2016, Forest disturbance interactions and successional pathways in the Southern Rocky Mountains: Forest Ecology and Management, no. 375, p. 35-45, https://doi.org/10.1016/j.foreco.2016.05.010.","productDescription":"11 p.","startPage":"35","endPage":"45","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066422","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.05.010","text":"Publisher Index Page"},{"id":322049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Wyoming","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 35\n ],\n [\n -111,\n 44\n ],\n [\n -103,\n 44\n ],\n [\n -103,\n 35\n ],\n [\n -111,\n 35\n ]\n ]\n ]\n }\n }\n ]\n}","issue":"375","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500763e4b0ee97d51bb5f9","chorus":{"doi":"10.1016/j.foreco.2016.05.010","url":"http://dx.doi.org/10.1016/j.foreco.2016.05.010","publisher":"Elsevier BV","authors":"Liang Lu, Hawbaker Todd J., Zhu Zhiliang, Li Xuecao, Gong Peng","journalName":"Forest Ecology and Management","publicationDate":"9/2016"},"contributors":{"authors":[{"text":"Liang, Lu","contributorId":169730,"corporation":false,"usgs":false,"family":"Liang","given":"Lu","email":"","affiliations":[{"id":25576,"text":"Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA 94720","active":true,"usgs":false}],"preferred":false,"id":630922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":630921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":630923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Xuecao","contributorId":169731,"corporation":false,"usgs":false,"family":"Li","given":"Xuecao","email":"","affiliations":[{"id":25577,"text":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":630924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gong, Peng","contributorId":169732,"corporation":false,"usgs":false,"family":"Gong","given":"Peng","affiliations":[{"id":25576,"text":"Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA 94720","active":true,"usgs":false}],"preferred":false,"id":630925,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70171057,"text":"70171057 - 2016 - Continuous 1985-2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California","interactions":[],"lastModifiedDate":"2016-05-18T09:17:59","indexId":"70171057","displayToPublicDate":"2016-05-18T10:15:00","publicationYear":"2016","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":"Continuous 1985-2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California","docAbstract":"To assess how montane meadow vegetation recovered after a wildfire that occurred in Yosemite National Park, CA in 1996, Google Earth Engine image processing was applied to leverage the entire Landsat Thematic Mapper archive from 1985 to 2012. Vegetation greenness (normalized difference vegetation index [NDVI]) was summarized every 16 days across the 28-year Landsat time series for 26 meadows. Disturbance event detection was hindered by the subtle influence of low-severity fire on meadow vegetation. A hard break (August 1996) was identified corresponding to the Ackerson Fire, and monthly composites were used to compare NDVI values and NDVI trends within burned and unburned meadows before, immediately after, and continuously for more than a decade following the fire date. Results indicate that NDVI values were significantly lower at 95% confidence level for burned meadows following the fire date, yet not significantly lower at 95% confidence level in the unburned meadows. Burned meadows continued to exhibit lower monthly NDVI in the dormant season through 2012. Over the entire monitoring period, the negative-trending, dormant season NDVI slopes in the burned meadows were also significantly lower than unburned meadows at 90% confidence level. Lower than average NDVI values and slopes in the dormant season compared to unburned meadows, coupled with photographic evidence, strongly suggest that evergreen vegetation was removed from the periphery of some meadows after the fire. These analyses provide insight into how satellite imagery can be used to monitor low-severity fire effects on meadow vegetation.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8050371","usgsCitation":"Soulard, C.E., Albano, C.M., Villarreal, M.L., and Walker, J.J., 2016, Continuous 1985-2012 Landsat monitoring to assess fire effects on meadows in Yosemite National Park, California: Remote Sensing, v. 8, no. 5, Article 371; 16 p., https://doi.org/10.3390/rs8050371.","productDescription":"Article 371; 16 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070750","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8050371","text":"Publisher Index Page"},{"id":321373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.79904174804688,\n 37.821175249016726\n ],\n [\n -119.79904174804688,\n 37.970725990064786\n ],\n [\n -119.59991455078124,\n 37.970725990064786\n ],\n [\n -119.59991455078124,\n 37.821175249016726\n ],\n [\n -119.79904174804688,\n 37.821175249016726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-29","publicationStatus":"PW","scienceBaseUri":"573d841be4b0dae0d5e4c049","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albano, Christine M.","contributorId":169455,"corporation":false,"usgs":false,"family":"Albano","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":629694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629696,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":629698,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171046,"text":"70171046 - 2016 - Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass","interactions":[],"lastModifiedDate":"2017-11-22T17:34:52","indexId":"70171046","displayToPublicDate":"2016-05-18T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass","docAbstract":"The Landsat 8 mission provides new opportunities for quantifying the distribution of above-ground carbon at moderate spatial resolution across the globe, and in particular drylands. Furthermore, coupled with structural information from space-based and airborne laser altimetry, Landsat 8 provides powerful capabilities for large-area, long-term studies that quantify temporal and spatial changes in above-ground biomass and cover. With the planned launch of ICESat-2 in 2017 and thus the potential to couple Landsat 8 and ICESat-2 data, we have unprecedented opportunities to address key challenges in drylands, including quantifying fuel loads, habitat quality, biodiversity, carbon cycling, and desertification.
\nIn this study, we explore the strengths of Landsat 8's Operational Land Imager (OLI) in estimating vegetation structure in a dryland ecosystem, and compare these results to Landsat 5's Thematic Mapper (TM). We also demonstrate the potential of OLI when coupled with light detection and ranging (lidar) in estimating vegetation cover and biomass in a dryland ecosystem. The OLI and TM predictions were similarly positive, indicating data from these sensors may be used in tandem for long-term time-series analysis. Results indicate shrub and herbaceous cover are well predicted with multi-temporal OLI data, and a combination of OLI and lidar derivatives improves most of these estimates and reduces uncertainty. For example, significant improvements were made for shrub cover (R2 = 0.64 and 0.78 using OLI only and both OLI and lidar data, respectively). Importantly, a time series of OLI, with some improvement from lidar, provides strong estimates of herbaceous cover (68% of the variance is explained with OLI alone). In contrast, OLI data explain roughly 59% of the variance in total shrub biomass, however approximately 71% of the variance is explained when combined with lidar derivatives.
\nTo estimate the potential synergies of OLI and ICESat-2 we used simulated ICESat-2 photon data to predict vegetation structure. In a shrubland environment with a vegetation mean height of 1 m and mean vegetation cover of 33%, vegetation photons are able to explain nearly 50% of the variance in vegetation height. These results, and those from a comparison site, suggest that a lower detection threshold of ICESat-2 may be in the range of 30% canopy cover and roughly 1 m height in comparable dryland environments and these detection thresholds could be used to combine future ICESat-2 photon data with OLI spectral data for improved vegetation structure. Overall, the synergistic use of Landsat 8 and ICESat-2 may improve estimates of above-ground biomass and carbon storage in drylands that meet these minimum thresholds, increasing our ability to monitor drylands for fuel loading and the potential to sequester carbon.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.02.039","usgsCitation":"Glenn, N.F., Neuenschwander, A., Vierling, L.A., Spaete, L., Li, A., Shinneman, D.J., Pilliod, D.S., Arkle, R., and McIlroy, S., 2016, Landsat 8 and ICESat-2: Performance and potential synergies for quantifying dryland ecosystem vegetation cover and biomass: Remote Sensing of Environment, v. 185, p. 233-242, https://doi.org/10.1016/j.rse.2016.02.039.","productDescription":"10 p.","startPage":"233","endPage":"242","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065442","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":321374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"573d841ce4b0dae0d5e4c05b","contributors":{"authors":[{"text":"Glenn, Nancy F.","contributorId":95321,"corporation":false,"usgs":true,"family":"Glenn","given":"Nancy","email":"","middleInitial":"F.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":629668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neuenschwander, Amy","contributorId":169442,"corporation":false,"usgs":false,"family":"Neuenschwander","given":"Amy","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":629669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vierling, Lee A.","contributorId":169443,"corporation":false,"usgs":false,"family":"Vierling","given":"Lee","email":"","middleInitial":"A.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":629670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spaete, Lucas","contributorId":169444,"corporation":false,"usgs":false,"family":"Spaete","given":"Lucas","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":629671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Aihua","contributorId":169445,"corporation":false,"usgs":false,"family":"Li","given":"Aihua","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":629672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629673,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":629667,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Arkle, Robert 0000-0003-3021-1389 rarkle@usgs.gov","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":149893,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","email":"rarkle@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":629674,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McIlroy, Susan K. 0000-0001-5088-3700 smcilroy@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3700","contributorId":169446,"corporation":false,"usgs":true,"family":"McIlroy","given":"Susan","email":"smcilroy@usgs.gov","middleInitial":"K.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":629675,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70159049,"text":"70159049 - 2016 - Estimating forest and woodland aboveground biomass using active and passive remote sensing","interactions":[],"lastModifiedDate":"2016-06-01T13:43:53","indexId":"70159049","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","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":"Estimating forest and woodland aboveground biomass using active and passive remote sensing","docAbstract":"Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.82.4.271","usgsCitation":"Wu, Z., Dye, D.G., Vogel, J.M., and Middleton, B.R., 2016, Estimating forest and woodland aboveground biomass using active and passive remote sensing: Photogrammetric Engineering and Remote Sensing, v. 82, no. 4, p. 271-281, https://doi.org/10.14358/PERS.82.4.271.","productDescription":"11 p.","startPage":"271","endPage":"281","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058621","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.82.4.271","text":"Publisher Index Page"},{"id":322022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500761e4b0ee97d51bb5ca","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":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":577544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":577542,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":577543,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70175897,"text":"70175897 - 2016 - Using NDVI to measure precipitation in semi-arid landscapes","interactions":[],"lastModifiedDate":"2017-02-08T11:18:50","indexId":"70175897","displayToPublicDate":"2016-04-20T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Using NDVI to measure precipitation in semi-arid landscapes","docAbstract":"Measuring precipitation in semi-arid landscapes is important for understanding the processes related to rainfall and run-off; however, measuring precipitation accurately can often be challenging especially within remote regions where precipitation instruments are scarce. Typically, rain-gauges are sparsely distributed and research comparing rain-gauge and RADAR precipitation estimates reveal that RADAR data are often misleading, especially for monsoon season convective storms. This study investigates an alternative way to map the spatial and temporal variation of precipitation inputs along ephemeral stream channels using Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper imagery. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG), a region covering 3,367 km2 of semiarid landscapes in southwestern Arizona, USA. The change in NDVI from a pre-to post-monsoon season image along ephemeral stream channels explained 73% of the variance in annual monsoonal precipitation totals from a nearby rain-gauge. In addition, large seasonal changes in NDVI along channels were useful in determining when and where flow events have occurred.
","language":"English","publisher":"Academic Press","doi":"10.1016/j.jaridenv.2016.04.004","usgsCitation":"Birtwhistle, A.N., Laituri, M., Bledsoe, B., and Friedman, J.M., 2016, Using NDVI to measure precipitation in semi-arid landscapes: Journal of Arid Environments, v. 131, p. 15-24, https://doi.org/10.1016/j.jaridenv.2016.04.004.","productDescription":"10 p.","startPage":"15","endPage":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070425","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471060,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2016.04.004","text":"Publisher Index Page"},{"id":438618,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZK5DSB","text":"USGS data release","linkHelpText":"Mean of the Top Ten Percent of NDVI Values in the Yuma Proving Ground during Monsoon Season, 1986-2011"},{"id":327106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":334957,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ZK5DSB","text":"Mean of the top ten percent of NDVI values in the Yuma Proving Ground during monsoon season, 1986-2011"}],"country":"United States","state":"Arizona","otherGeospatial":"Sonoran Desert, Yuma Proving Ground","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.49676513671875,\n 33.55741786324217\n ],\n [\n -114.23858642578125,\n 33.56199537293026\n ],\n [\n -114.2083740234375,\n 32.95797741405949\n ],\n [\n -113.9666748046875,\n 32.94875863715422\n ],\n [\n -113.9501953125,\n 33.0316929997831\n ],\n [\n -113.74420166015624,\n 33.04320549616557\n ],\n [\n -113.7469482421875,\n 33.33511774753217\n ],\n [\n -113.587646484375,\n 33.33741240611175\n ],\n [\n -113.56842041015625,\n 32.930318199070534\n ],\n [\n -113.90625,\n 32.76880048488168\n ],\n [\n -114.466552734375,\n 32.7295304134847\n ],\n [\n -114.466552734375,\n 32.85421076375023\n ],\n [\n -114.46105957031249,\n 32.877280526855394\n ],\n [\n -114.44183349609375,\n 32.89342578969231\n ],\n [\n -114.46380615234375,\n 32.92109653816924\n ],\n [\n -114.46380615234375,\n 32.94414888814148\n ],\n [\n -114.46105957031249,\n 32.9648908656902\n ],\n [\n -114.46929931640624,\n 32.98562797456918\n ],\n [\n -114.49127197265625,\n 32.98562797456918\n ],\n [\n -114.49676513671875,\n 33.00636021320537\n ],\n [\n -114.52972412109375,\n 33.02939031998959\n ],\n [\n -114.55169677734375,\n 33.038600678138195\n ],\n [\n -114.576416015625,\n 33.04090311724091\n ],\n [\n -114.60113525390625,\n 33.050112271849656\n ],\n [\n -114.5928955078125,\n 33.4955977448657\n ],\n [\n -114.49676513671875,\n 33.55741786324217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b82deee4b03fd6b7da3a5e","contributors":{"authors":[{"text":"Birtwhistle, Amy N.","contributorId":173888,"corporation":false,"usgs":false,"family":"Birtwhistle","given":"Amy","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laituri, Melinda","contributorId":173889,"corporation":false,"usgs":false,"family":"Laituri","given":"Melinda","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bledsoe, Brian","contributorId":173890,"corporation":false,"usgs":false,"family":"Bledsoe","given":"Brian","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":646516,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168812,"text":"ofr20161032 - 2016 - Users and uses of Landsat 8 satellite imagery—2014 survey results","interactions":[],"lastModifiedDate":"2016-04-18T11:34:54","indexId":"ofr20161032","displayToPublicDate":"2016-04-18T10:45:00","publicationYear":"2016","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":"2016-1032","title":"Users and uses of Landsat 8 satellite imagery—2014 survey results","docAbstract":"In 2013, Landsat 8 began adding high quality, global, moderate-resolution imagery to the more than 40-year archive of Landsat imagery. To assess the potential effects of the availability of Landsat 8 imagery on users and their work, the U.S. Geological Survey (USGS) Land Remote Sensing Program (LRS) initiated a survey of Landsat users. The objectives of the survey were to
\n1. Characterize various Landsat user groups, such as United States (U.S.) and international users and Landsat 8 and non-Landsat 8 users;
2. Identify any differences among user groups in uses and preferences;
3. Measure the importance of and satisfaction with Landsat 8 attributes;
4. Assess the importance to users of the frequency of usable imagery; and
5. Determine any challenges in using Landsat 8.
The online survey was sent to 51,617 Landsat users registered with USGS in May 2014. Almost 13,000 people responded to the survey for a response rate of 25 percent (n = 12,966). Current Landsat users (users who had used Landsat in their work in the year prior to the survey) composed 89 percent of the sample (n = 11,549) and past Landsat users composed 11 percent (n = 1,417). The results reported here apply to current Landsat users registered with the USGS Earth Resources Observation and Science (EROS) Center.
\nUsers from 161 countries responded to the survey. Of those, 19 percent were citizens or permanent residents of the United States and 81 percent resided in other countries. More than 70 percent of current users had used Landsat 8 in the year prior to the survey. The majority of Landsat 8 users (65 percent) were established users who used Landsat imagery regularly both before and after Landsat 8 imagery became available. The average current Landsat user was male, 36 years old, and highly educated, with 9 years of experience using satellite imagery or geographic information system (GIS) software. Landsat 8 users had, on average, two more years of experience than non-Landsat 8 users. Users were employed predominantly by academic institutions (65 percent), followed by private businesses (13 percent), Federal governments (10 percent), State and local governments (6 percent), and nonprofit organizations (6 percent).
\nOf the Landsat imagery obtained in the past year by current users, on average 31 percent came from a Landsat 8 sensor. An equivalent amount came from the Landsat 7 ETM+ sensor (33 percent); slightly less came from Landsats 4 and 5 TM sensors (27 percent). Much less came from Landsats 1 through 5 MSS sensors (5 percent). Overall, more than a third of users’ work used Landsat imagery (38 percent). Of this work, on average, 37 percent of the work was operational. Landsat 8 users considered a greater proportion of their work operational than non-Landsat 8 users (39 percent compared with 29 percent). Environmental sciences and management were the most commonly selected primary applications (selected by 42 percent of users). Land use/land cover (23 percent) was the second most commonly selected primary application, followed by education (12 percent), agriculture (9 percent), and planning and development (6 percent).
\nLandsat 8 users were asked to rank the importance of certain attributes in determining whether to use Landsat 8 imagery in their work. The archive was ranked most important, followed by cost, spatial resolution, extent of coverage, data quality, and frequency of revisit. Users were asked how satisfied they were with these same attributes as they currently apply to Landsat 8 imagery. On average, users were most satisfied with lack of cost, extent of coverage, data quality, and the archive, but they were satisfied with all attributes.
\nUsers were asked how often they needed Landsat imagery to meet various requirements for their primary application. The survey question specifically asked how often users needed usable imagery, which differs from how often they would like the Landsat satellites to acquire an image. Users were asked to identify their needed frequency of usable imagery for the following levels:
\n1. Threshold level—the minimum frequency of usable imagery needed to be of any value to their primary application.
2. Breakthrough level—the frequency of usable imagery that would result in a significant improvement for their primary application of the imagery.
3. Target level—the frequency of usable imagery that would only provide a limited additional increase in the expected performance for their primary application.
To meet the threshold level, three-quarters of users needed usable imagery every 17 days or less frequently. At the breakthrough level, two-thirds of users (64 percent) needed a usable image every 5–16 days. The current constellation of two satellites (Landsat 7 and 8) is capable of meeting the threshold and breakthrough needs of most users at least some of the time, but a single satellite would be highly unlikely to do so. Two-fifths of users (40 percent) felt that usable imagery provided every 4 days or more frequently would meet their target level which the current Landsat constellation cannot provide. Landsat 8 users were significantly more likely than non-Landsat 8 users to need usable imagery more frequently to meet their target levels. Additionally, U.S. Landsat 8 users were significantly more likely than other Landsat users to need usable imagery more frequently in order meet both their breakthrough and target levels.
\nTo explore the effect of the availability of Landsat 8 imagery on Landsat imagery use in general, established users (those who had consistently used Landsat imagery both before and after Landsat 8 imagery became available) using Landsat 8 imagery were asked about changes in the amount of Landsat imagery they used. The majority of established users using Landsat 8 imagery (60 percent) reported an average increase of 51 percent in the number of scenes obtained after Landsat 8 imagery became available. Landsat 8 users were asked if they had encountered challenges in using Landsat 8 whereas non-Landsat 8 users were asked if such challenges had played a role in why they were not using Landsat 8 imagery. Although many users did not encounter challenges when using or trying to use Landsat 8 data, slightly less than 30 percent did encounter issues with processing the data to a usable point. The most common issue reported was not being able to create or have access to a surface reflectance corrected product. Other challenges were related to the file sizes of images being too large to download, store, or analyze. There were no statistically significant differences between Landsat 8 and non-Landsat 8 users in terms of challenges encountered when using or trying to use the imagery, which indicates that users were not unduly discouraged by the challenges they may have encountered. When asked about potential consequences of not using Landsat 8, more than half of the non-Landsat 8 users did not report detrimental effects on their work from not using the imagery. Of those who did report detrimental effects, decreased quality of work, decreased scope of work, and increased time spent on work were the most common.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161032","usgsCitation":"Miller, H.M., 2016, Users and uses of Landsat 8 satellite imagery—2014 survey results: U.S. Geological Survey Open-File Report 2016–1032, 27 p., https://dx.doi/org/10.3133/ofr20161032.","productDescription":"vi, 27 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069774","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1032/ofr20161032.pdf","text":"Report","size":"2.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1032"},{"id":320058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1032/coverthb.jpg"}],"contact":"Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Box 25046, MS-939
Fort Collins, CO 80526-8118
Landsat missions have always been an important component of U.S. foreign policy, as well as science and technology policy. The Landsat program’s longstanding network of International Cooperators (ICs), which operates numerous International Ground Stations (IGS) around the world, embodies the United States’ policy of peaceful use of outer space and the worldwide dissemination of civil space technology for public benefit. Thus, the ICs provide an essential dimension to the Landsat mission.
In 2010, the Landsat Global Archive Consolidation (LGAC) initiative began, with goals to consolidate the Landsat data archives of all IGSs, make the data more accessible to the global Landsat user community, and significantly increase the frequency of observations over a given area of interest to contribute to the understanding of a changing Earth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163018","usgsCitation":"U.S. Geological Survey, 2016, Landsat International Cooperators and Global Archive Consolidation (ver. 1.3, April 2023): U.S. Geological Survey Fact Sheet 2016–3018, 2 p., https://doi.org/10.3133/fs20163018.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072463","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":416367,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3018/versionHist.txt","text":"Version History","size":"3.57 kB","linkFileType":{"id":2,"text":"txt"}},{"id":416366,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3018/fs20163018.pdf","text":"Report","size":"572 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2016–3018"},{"id":319664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3018/coverthb4.jpg"}],"edition":"Version 1.0: April 7, 2016; Version 1.1: December 8, 2016; Version 1.2: June 10, 2019; Version 1.3: April 26, 2023","contact":"Landsat User Services
Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
custserv@usgs.gov
https://www.usgs.gov/land-resources/national-land-imaging-program
","tableOfContents":"Context. Quantifying variability in landscape-scale surface water connectivity can help improve our understanding of the multiple effects of wetlands on downstream waterways. Objectives. We examined how wetland merging and the coalescence of wetlands with streams varied both spatially (among ecoregions) and interannually (from drought to deluge) across parts of the Prairie Pothole Region. Methods. Wetland extent was derived over a time series (1990-2011) using Landsat imagery. Changes in landscape-scale connectivity, generated by the physical coalescence of wetlands with other surface water features, were quantified by fusing static wetland and stream datasets with Landsat-derived wetland extent maps, and related to multiple wetness indices. The usage of Landsat allows for decadal-scale analysis, but limits the types of surface water connections that can be detected. Results. Wetland extent correlated positively with the merging of wetlands and wetlands with streams. Wetness conditions, as defined by drought indices and runoff, were positively correlated with wetland extent, but less consistently correlated with measures of surface water connectivity. The degree of wetland-wetland merging was found to depend less on total wetland area or density, and more on climate conditions, as well as the threshold for how wetland/upland was defined. In contrast, the merging of wetlands with streams was positively correlated with stream density, and inversely related to wetland density. Conclusions. Characterizing the degree of surface water connectivity within the Prairie Pothole Region in North America requires consideration of 1) climate-driven variation in wetness conditions and 2) within-region variation in wetland and stream spatial arrangements.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-015-0290-5","usgsCitation":"Vanderhoof, M.K., Alexander, L., and Todd, J., 2016, Temporal and spatial patterns of wetland extent influence variability of surface water connectivity in the Prairie Pothole Region, United States: Landscape Ecology, v. 31, no. 4, p. 805-824, https://doi.org/10.1007/s10980-015-0290-5.","productDescription":"20 p.","startPage":"805","endPage":"824","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069152","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-015-0290-5","text":"Publisher Index Page"},{"id":319678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.1005859375,\n 24.462150693715266\n ],\n [\n -83.1005859375,\n 24.77177232822881\n ],\n [\n -82.6171875,\n 24.77177232822881\n ],\n [\n -82.6171875,\n 24.462150693715266\n ],\n [\n -83.1005859375,\n 24.462150693715266\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.1513671875,\n 46.46813299215554\n ],\n [\n -100.1513671875,\n 48.545705491847464\n ],\n [\n -97.20703125,\n 48.545705491847464\n ],\n [\n -97.20703125,\n 46.46813299215554\n ],\n [\n -100.1513671875,\n 46.46813299215554\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.06396484375,\n 43.77109381775651\n ],\n [\n -98.06396484375,\n 45.55252525134013\n ],\n [\n -95.38330078125,\n 45.55252525134013\n ],\n [\n -95.38330078125,\n 43.77109381775651\n ],\n [\n -98.06396484375,\n 43.77109381775651\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-06","publicationStatus":"PW","scienceBaseUri":"56fe3c48e4b075ab2b2aa0d8","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":625787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander, Laurie C.","contributorId":138989,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":625788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Todd, Jason","contributorId":168396,"corporation":false,"usgs":false,"family":"Todd","given":"Jason","email":"","affiliations":[{"id":25279,"text":"U.S. EPA NCEA","active":true,"usgs":false}],"preferred":false,"id":625789,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160072,"text":"gip161 - 2016 - Earth as art 4","interactions":[],"lastModifiedDate":"2017-01-18T09:24:15","indexId":"gip161","displayToPublicDate":"2016-03-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"161","title":"Earth as art 4","docAbstract":"Landsat 8 is the latest addition to the long-running series of Earth-observing satellites in the Landsat program that began in 1972. The images featured in this fourth installment of the Earth As Art collection were all acquired by Landsat 8. They show our planet’s diverse landscapes with remarkable clarity.
Landsat satellites see the Earth as no human can. Not only do they acquire images from the vantage point of space, but their sensors record infrared as well as visible wavelengths of light. The resulting images often reveal “hidden” details of the Earth’s land surface, making them invaluable for scientific research.
As with previous Earth As Art exhibits, these Landsat images were selected solely for their aesthetic appeal. Many of the images have been manipulated to enhance color variations or details. They are not intended for scientific interpretation—only for your viewing pleasure. What do you see in these unique glimpses of the Earth’s continents, islands, and coastlines?
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip161","usgsCitation":"U.S. Geological Survey, 2016, Earth as art 4: U.S. Geological Survey General Information Product 161, 20 p., https://dx.doi.org/10.3133/gip161.","productDescription":"24 p.","numberOfPages":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066375","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":318786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0161/coverthb.jpg"},{"id":318787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0161/gip161.pdf","text":"Report","size":"36.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 161"}],"contact":"U.S. Geological Survey
Earth Resources Observation
and Science (EROS) Center
47914 252nd Street
Sioux Falls, SD 57198–0001
http://eros.usgs.gov/imagegallery
Images from Landsat 8, launched in 2013, already stand out as stellar additions to our popular Earth As Art series. We are proud to present the fourth collection—Earth As Art 4!
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip162","usgsCitation":"U.S. Geological Survey, 2016, Earth as art 4 bookmark: U.S. Geological Survey General Information Product 162, https://dx.doi.org/10.3133/gip162.","productDescription":"Bookmark","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072150","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":318857,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0162/gip162.pdf","text":"Bookmark","size":"249 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 162"},{"id":318856,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0162/coverthb.jpg"}],"contact":"U.S. Geological Survey
Earth Resources Observation
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47914 252nd Street
Sioux Falls, SD 57198–0001
http://eros.usgs.gov/imagegallery
The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
","language":"English","publisher":"Cloud Publications","usgsCitation":"Wu, Z., Dye, D.G., Stoker, J.M., Vogel, J.M., Velasco, M.G., and Middleton, B.R., 2016, Evaluating lidar point densities for effective estimation of aboveground biomass: International Journal of Advanced Remote Sensing and GIS, v. 5, no. 1, p. 1483-1499.","productDescription":"17 p.","startPage":"1483","endPage":"1499","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068758","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":323961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":319210,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-559"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576913b9e4b07657d19ff045","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":623214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":623216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":623215,"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":623218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Velasco, Miguel G. 0000-0003-2559-7934 mvelasco@usgs.gov","orcid":"https://orcid.org/0000-0003-2559-7934","contributorId":2103,"corporation":false,"usgs":true,"family":"Velasco","given":"Miguel","email":"mvelasco@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":623219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":623217,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70164497,"text":"sir20165023 - 2016 - Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013","interactions":[],"lastModifiedDate":"2016-05-18T08:54:58","indexId":"sir20165023","displayToPublicDate":"2016-03-11T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5023","title":"Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013","docAbstract":"A 15-year estimated Trophic State Index (eTSI) for Michigan inland lakes is available, and it spans seven datasets, each representing 1 to 3 years of data from 1999 to 2013. On average, 3,000 inland lake eTSI values are represented in each of the datasets by a process that relates field-measured Secchi-disk transparency (SDT) to Landsat satellite imagery to provide eTSI values for unsampled inland lakes. The correlation between eTSI values and field-measured Trophic State Index (TSI) values from SDT was strong as shown by R2 values from 0.71 to 0.83. Mean eTSI values ranged from 42.7 to 46.8 units, which when converted to estimated SDT (eSDT) ranged from 8.9 to 12.5 feet for the datasets. Most eTSI values for Michigan inland lakes are in the mesotrophic TSI class. The Environmental Protection Agency (EPA) Level III Ecoregions were used to illustrate and compare the spatial distribution of eTSI classes for Michigan inland lakes. Lakes in the Northern Lakes and Forests, North Central Hardwood Forests, and Southern Michigan/Northern Indiana Drift Plains ecoregions are predominantly in the mesotrophic TSI class. The Huron/Erie Lake Plains and Eastern Corn Belt Plains ecoregions, had predominantly eutrophic class lakes and also the highest percent of hypereutrophic lakes than other ecoregions in the State. Data from multiple sampling programs—including data collected by volunteers with the Cooperative Lakes Monitoring Program (CLMP) through the Michigan Department of Environmental Quality (MDEQ), and the 2007 National Lakes Assessment (NLA)—were compiled to compare the distribution of lake TSI classes between each program. The seven eTSI datasets are available for viewing and download with eSDT from the Michigan Lake Water Clarity Interactive Map Viewer at http://mi.water.usgs.gov/projects/RemoteSensing/index.html.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165023","collaboration":"Prepared in cooperation with the Michigan Department of Environmental Quality","usgsCitation":"Fuller, L.M., and Jodoin, R.S., 2016, Estimation of a Trophic State Index for selected inland lakes in Michigan, 1999–2013: U.S. Geological Survey Scientific Investigations Report 2016–5023, 16 p., https://dx.doi.org/10.3133/sir20165023.","productDescription":"vii, 16 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-067016","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":318806,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5023/sir20165023.pdf","text":"Report","size":"1.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Michigan Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911-5991
http://mi.water.usgs.gov/
Drought has long been recognized as a driving mechanism in the forests of western North America and drought-induced mortality has been documented across genera in recent years. Given the frequency of these events are expected to increase in the future, understanding patterns of mortality and plant response to severe drought is important to resource managers. Drought can affect the functional, physiological, structural, and demographic properties of forest ecosystems. Remote sensing studies have documented changes in forest properties due to direct and indirect effects of drought; however, few studies have addressed this at local scales needed to characterize highly heterogeneous ecosystems in the forest-shrubland ecotone. We analyzed a 22-year Landsat time series (1985–2012) to determine changes in forest in an area that experienced a relatively dry decade punctuated by two years of extreme drought. We assessed the relationship between several vegetation indices and field measured characteristics (e.g. plant area index and canopy gap fraction) and applied these indices to trend analysis to uncover the location, direction and timing of change. Finally, we assessed the interaction of climate and topography by forest functional type. The Normalized Difference Moisture Index (NDMI), a measure of canopy water content, had the strongest correlation with short-term field measures of plant area index (R2 = 0.64) and canopy gap fraction (R2 = 0.65). Over the entire time period, 25% of the forested area experienced a significant (p-value < 0.05) negative trend in NDMI, compared to less than 10% in a positive trend. Coniferous forests were more likely to be associated with a negative NDMI trend than deciduous forest. Forests on southern aspects were least likely to exhibit a negative trend while north aspects were most prevalent. Field plots with a negative trend had a lower live density, and higher amounts of standing dead and down trees compared to plots with no trend. Our analysis identifies spatially explicit patterns of long-term trends anchored with ground based evidence to highlight areas of forest that are resistant, persistent or vulnerable to severe drought. The results provide a long-term perspective for the resource management of this area and can be applied to similar ecosystems throughout western North America.
","language":"English","publisher":"Elsevier Science Pub. Co.","publisherLocation":"New York, NY","doi":"10.1016/j.foreco.2016.01.017","usgsCitation":"Assal, T.J., Anderson, P.J., and Sibold, J., 2016, Spatial and temporal trends of drought effects in a heterogeneous semi-arid forest ecosystem: Forest Ecology and Management, v. 365, p. 137-151, https://doi.org/10.1016/j.foreco.2016.01.017.","productDescription":"15 p.","startPage":"137","endPage":"151","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070450","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2016.01.017","text":"Publisher Index Page"},{"id":318076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Wyoming Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.5,\n 40.5\n ],\n [\n -109.5,\n 41.5\n ],\n [\n -108.5,\n 41.5\n ],\n [\n -108.5,\n 40.5\n ],\n [\n -109.5,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"365","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56c44831e4b0946c65211715","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":620492,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Patrick J. 0000-0003-2281-389X andersonpj@usgs.gov","orcid":"https://orcid.org/0000-0003-2281-389X","contributorId":3590,"corporation":false,"usgs":true,"family":"Anderson","given":"Patrick","email":"andersonpj@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":620493,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sibold, Jason","contributorId":10724,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","affiliations":[],"preferred":false,"id":620494,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70168367,"text":"70168367 - 2016 - Normalized burn ratios link fire severity with patterns of avian occurrence","interactions":[],"lastModifiedDate":"2018-12-20T13:00:09","indexId":"70168367","displayToPublicDate":"2016-02-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Normalized burn ratios link fire severity with patterns of avian occurrence","docAbstract":"Remotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains.
We evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire.
We identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales.
Agreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire.
DNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.
Floodplain red gum forests (Eucalyptus camaldulensis plus associated grasses, reeds and sedges) are sites of high biodiversity in otherwise arid regions of southeastern Australia. They depend on periodic floods from rivers, but dams and diversions have reduced flood frequencies and volumes, leading to deterioration of trees and associated biota. There is a need to determine their water requirements so environmental flows can be administered to maintain or restore the forests. Their water requirements include the frequency and extent of overbank flooding, which recharges the floodplain soils with water, as well as the actual amount of water consumed in evapotranspiration (ET). We estimated the flooding requirements and ET for a 38 134 ha area of red gum forest fed by the Murrumbidgee River in Yanga National Park, New South Wales. ET was estimated by three methods: sap flux sensors placed in individual trees; a remote sensing method based on the Enhanced Vegetation Index from MODIS satellite imagery and a water balance method based on differences between river flows into and out of the forest. The methods gave comparable estimates yet covered different spatial and temporal scales. We estimated flood frequency and volume requirements by comparing Normalized Difference Vegetation Index values from Landsat images with flood history from 1995 to 2014, which included both wet periods and dry periods. ET during wet years is about 50% of potential ET but is much less in dry years because of the trees' ability to control stomatal conductance. Based on our analyses plus other studies, red gum trees at this location require environmental flows of 2000 GL yr−1 every other year, with peak flows of 20 000 ML d−1, to produce flooding sufficient to keep them in good condition. However, only about 120–200 GL yr−1 of river water is consumed in ET, with the remainder flowing out of the forest where it enters the Murray River system.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.10734","usgsCitation":"Nagler, P.L., Doody, T.M., Glenn, E.P., Jarchow, C.J., Barreto-Munoz, A., and Didan, K., 2016, Wide-area estimates of evapotranspiration by red gum (Eucalyptus camaldulensis) and associated vegetation in the Murray-Darling River Basin, Australia: Hydrological Processes, v. 30, no. 9, p. 1376-1387, https://doi.org/10.1002/hyp.10734.","productDescription":"12 p.","startPage":"1376","endPage":"1387","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064981","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":317918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","otherGeospatial":"Murray-Darling River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 143.4814453125,\n -34.73032697882121\n ],\n [\n 143.4814453125,\n -34.31394984163212\n ],\n [\n 144.35623168945312,\n -34.31394984163212\n ],\n [\n 144.35623168945312,\n -34.73032697882121\n ],\n [\n 143.4814453125,\n -34.73032697882121\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-29","publicationStatus":"PW","scienceBaseUri":"56bc5f35e4b08d617f660028","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":619773,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doody, Tanya M.","contributorId":138691,"corporation":false,"usgs":false,"family":"Doody","given":"Tanya","email":"","middleInitial":"M.","affiliations":[{"id":12494,"text":"CSIRO Land and Water, Australia","active":true,"usgs":false}],"preferred":false,"id":619774,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Edward P.","contributorId":19289,"corporation":false,"usgs":true,"family":"Glenn","given":"Edward","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":619775,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":5813,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":619776,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":619777,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":619778,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70168326,"text":"70168326 - 2016 - The global Landsat archive: Status, consolidation, and direction","interactions":[],"lastModifiedDate":"2017-01-17T19:17:44","indexId":"70168326","displayToPublicDate":"2016-02-10T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"The global Landsat archive: Status, consolidation, and direction","docAbstract":"New and previously unimaginable Landsat applications have been fostered by a policy change in 2008 that made analysis-ready Landsat data free and open access. Since 1972, Landsat has been collecting images of the Earth, with the early years of the program constrained by onboard satellite and ground systems, as well as limitations across the range of required computing, networking, and storage capabilities. Rather than robust on-satellite storage for transmission via high bandwidth downlink to a centralized storage and distribution facility as with Landsat-8, a network of receiving stations, one operated by the U.S. government, the other operated by a community of International Cooperators (ICs), were utilized. ICs paid a fee for the right to receive and distribute Landsat data and over time, more Landsat data was held outside the archive of the United State Geological Survey (USGS) than was held inside, much of it unique. Recognizing the critical value of these data, the USGS began a Landsat Global Archive Consolidation (LGAC) initiative in 2010 to bring these data into a single, universally accessible, centralized global archive, housed at the Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota. The primary LGAC goals are to inventory the data held by ICs, acquire the data, and ingest and apply standard ground station processing to generate an L1T analysis-ready product. As of January 1, 2015 there were 5,532,454 images in the USGS archive. LGAC has contributed approximately 3.2 million of those images, more than doubling the original USGS archive holdings. Moreover, an additional 2.3 million images have been identified to date through the LGAC initiative and are in the process of being added to the archive. The impact of LGAC is significant and, in terms of images in the collection, analogous to that of having had twoadditional Landsat-5 missions. As a result of LGAC, there are regions of the globe that now have markedly improved Landsat data coverage, resulting in an enhanced capacity for mapping, monitoring change, and capturing historic conditions. Although future missions can be planned and implemented, the past cannot be revisited, underscoring the value and enhanced significance of historical Landsat data and the LGAC initiative. The aim of this paper is to report the current status of the global USGS Landsat archive, document the existing and anticipated contributions of LGAC to the archive, and characterize the current acquisitions of Landsat-7 and Landsat-8. Landsat-8 is adding data to the archive at an unprecedented rate as nearly all terrestrial images are now collected. We also offer key lessons learned so far from the LGAC initiative, plus insights regarding other critical elements of the Landsat program looking forward, such as acquisition, continuity, temporal revisit, and the importance of continuing to operationalize the Landsat program.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2015.11.032","usgsCitation":"Wulder, M.A., White, J.C., Loveland, T., Woodcock, C., Belward, A., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., and Roy, D.P., 2016, The global Landsat archive: Status, consolidation, and direction: Remote Sensing of Environment, v. 185, p. 271-283, https://doi.org/10.1016/j.rse.2015.11.032.","productDescription":"13 p.","startPage":"271","endPage":"283","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071343","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":471246,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2015.11.032","text":"Publisher Index Page"},{"id":317904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56bc5f35e4b08d617f660026","contributors":{"authors":[{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":619672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Joanne C.","contributorId":63362,"corporation":false,"usgs":true,"family":"White","given":"Joanne","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":619673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":619671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":619674,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belward, Alan","contributorId":166667,"corporation":false,"usgs":false,"family":"Belward","given":"Alan","affiliations":[{"id":18032,"text":"European Commission, Joint Research Centere, Institute for Environment and Sustainability, Ispra Varese, Italy","active":true,"usgs":false}],"preferred":false,"id":619675,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cohen, Warren B.","contributorId":100093,"corporation":false,"usgs":true,"family":"Cohen","given":"Warren","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":619676,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fosnight, Eugene A. 0000-0002-8557-3697 fosnight@usgs.gov","orcid":"https://orcid.org/0000-0002-8557-3697","contributorId":2961,"corporation":false,"usgs":true,"family":"Fosnight","given":"Eugene","email":"fosnight@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":619677,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaw, Jerad 0000-0002-8319-2778 jshaw@usgs.gov","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":3564,"corporation":false,"usgs":true,"family":"Shaw","given":"Jerad","email":"jshaw@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":619678,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":619679,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Roy, David P.","contributorId":71083,"corporation":false,"usgs":true,"family":"Roy","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":619680,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70117462,"text":"70117462 - 2016 - Spatial variations in immediate greenhouse gases and aerosol emissions and resulting radiative forcing from wildfires in interior Alaska","interactions":[],"lastModifiedDate":"2017-01-17T19:18:05","indexId":"70117462","displayToPublicDate":"2016-02-01T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5175,"text":"Theoretical and Applied Climatology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variations in immediate greenhouse gases and aerosol emissions and resulting radiative forcing from wildfires in interior Alaska","docAbstract":"Boreal fires can cool the climate; however, this conclusion came from individual fires and may not represent the whole story. We hypothesize that the climatic impact of boreal fires depends on local landscape heterogeneity such as burn severity, prefire vegetation type, and soil properties. To test this hypothesis, spatially explicit emission of greenhouse gases (GHGs) and aerosols and their resulting radiative forcing are required as an important and necessary component towards a full assessment. In this study, we integrated remote sensing (Landsat and MODIS) and models (carbon consumption model, emission factors model, and radiative forcing model) to calculate the carbon consumption, GHGs and aerosol emissions, and their radiative forcing of 2001–2010 fires at 30 m resolution in the Yukon River Basin of Alaska. Total carbon consumption showed significant spatial variation, with a mean of 2,615 g C m−2 and a standard deviation of 2,589 g C m−2. The carbon consumption led to different amounts of GHGs and aerosol emissions, ranging from 593.26 Tg (CO2) to 0.16 Tg (N2O). When converted to equivalent CO2 based on global warming potential metric, the maximum 20 years equivalent CO2 was black carbon (713.77 Tg), and the lowest 20 years equivalent CO2 was organic carbon (−583.13 Tg). The resulting radiative forcing also showed significant spatial variation: CO2, CH4, and N2O can cause a 20-year mean radiative forcing of 7.41 W m−2 with a standard deviation of 2.87 W m−2. This emission forcing heterogeneity indicates that different boreal fires have different climatic impacts. When considering the spatial variation of other forcings, such as surface shortwave forcing, we may conclude that some boreal fires, especially boreal deciduous fires, can warm the climate.
","language":"English","publisher":"Springer","publisherLocation":"New York","doi":"10.1007/s00704-015-1379-0","usgsCitation":"Huang, S., Liu, H., Dahal, D., Jin, S., Li, S., and Liu, S., 2016, Spatial variations in immediate greenhouse gases and aerosol emissions and resulting radiative forcing from wildfires in interior Alaska: Theoretical and Applied Climatology, v. 123, no. 3, p. 581-592, https://doi.org/10.1007/s00704-015-1379-0.","startPage":"581","endPage":"592","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058246","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":326645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"123","issue":"3","noUsgsAuthors":false,"publicationDate":"2015-01-18","publicationStatus":"PW","scienceBaseUri":"57b58b5de4b03bcb0104bc6e","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":519096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liu, Heping","contributorId":117909,"corporation":false,"usgs":true,"family":"Liu","given":"Heping","affiliations":[],"preferred":false,"id":519100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":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":519098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":519097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Shuang","contributorId":116219,"corporation":false,"usgs":true,"family":"Li","given":"Shuang","email":"","affiliations":[],"preferred":false,"id":519099,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Shu-Guang sliu@usgs.gov","contributorId":984,"corporation":false,"usgs":true,"family":"Liu","given":"Shu-Guang","email":"sliu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":519095,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70162073,"text":"70162073 - 2016 - Comparison of four different energy balance models for estimating evapotranspiration in the Midwestern United States","interactions":[],"lastModifiedDate":"2017-01-18T09:24:56","indexId":"70162073","displayToPublicDate":"2016-01-14T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of four different energy balance models for estimating evapotranspiration in the Midwestern United States","docAbstract":"The development of different energy balance models has allowed users to choose a model based on its suitability in a region. We compared four commonly used models—Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model, Surface Energy Balance Algorithm for Land (SEBAL) model, Surface Energy Balance System (SEBS) model, and the Operational Simplified Surface Energy Balance (SSEBop) model—using Landsat images to estimate evapotranspiration (ET) in the Midwestern United States. Our models validation using three AmeriFlux cropland sites at Mead, Nebraska, showed that all four models captured the spatial and temporal variation of ET reasonably well with an R2 of more than 0.81. Both the METRIC and SSEBop models showed a low root mean square error (<0.93 mm·day−1) and a high Nash–Sutcliffe coefficient of efficiency (>0.80), whereas the SEBAL and SEBS models resulted in relatively higher bias for estimating daily ET. The empirical equation of daily average net radiation used in the SEBAL and SEBS models for upscaling instantaneous ET to daily ET resulted in underestimation of daily ET, particularly when the daily average net radiation was more than 100 W·m−2. Estimated daily ET for both cropland and grassland had some degree of linearity with METRIC, SEBAL, and SEBS, but linearity was stronger for evaporative fraction. Thus, these ET models have strengths and limitations for applications in water resource management.
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