{"pageNumber":"19","pageRowStart":"450","pageSize":"25","recordCount":1869,"records":[{"id":70189116,"text":"70189116 - 2017 - Landsat-based trend analysis of lake dynamics across northern permafrost regions","interactions":[],"lastModifiedDate":"2019-12-21T08:24:38","indexId":"70189116","displayToPublicDate":"2017-06-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat-based trend analysis of lake dynamics across northern permafrost regions","docAbstract":"Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions.  Here we present, a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM,ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e. upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.","language":"English","publisher":"Multidisciplinary Digital Publishing Institute (MDPI)","doi":"10.3390/rs9070640","usgsCitation":"Nitze, I., Grosse, G., Jones, B.M., Arp, C.D., Ulrich, M., Federov, A., and Veremeeva, A., 2017, Landsat-based trend analysis of lake dynamics across northern permafrost regions: Remote Sensing, v. 9, no. 7, 640, 28 p., https://doi.org/10.3390/rs9070640.","productDescription":"640, 28 p.","ipdsId":"IP-087096","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":469730,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9070640","text":"Publisher Index 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,{"id":70192992,"text":"70192992 - 2017 - A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011","interactions":[],"lastModifiedDate":"2018-03-08T13:03:59","indexId":"70192992","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","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":"A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011","docAbstract":"<p><span>Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.04.021","usgsCitation":"Jin, S., Yang, L., Zhu, Z., and Homer, C.G., 2017, A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011: Remote Sensing of Environment, v. 195, p. 44-55, https://doi.org/10.1016/j.rse.2017.04.021.","productDescription":"12 p.","startPage":"44","endPage":"55","ipdsId":"IP-082390","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"195","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f83a36e4b063d5d30980dc","contributors":{"authors":[{"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":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":717548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":717550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717549,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70187550,"text":"sir20175044 - 2017 - Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013","interactions":[],"lastModifiedDate":"2017-05-30T12:46:29","indexId":"sir20175044","displayToPublicDate":"2017-05-30T00:00:00","publicationYear":"2017","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":"2017-5044","title":"Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013","docAbstract":"<p>The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175044","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management","usgsCitation":"Hartley, S.B., Couvillion, B.R., and Enwright, N.M., 2017, Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013: U.S. Geological Survey Scientific Investigations Report 2017–5044, 6 p., https://doi.org/10.3133/20175044.","productDescription":"Report: vi, 6 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084395","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research 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PSC"},"publishedDate":"2017-05-30","noUsgsAuthors":false,"publicationDate":"2017-05-30","publicationStatus":"PW","scienceBaseUri":"592e84b8e4b092b266f10d29","contributors":{"authors":[{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":694488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Couvillion, Brady R. 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":3829,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","middleInitial":"R.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":694489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Enwright, Nicholas M. 0000-0002-7887-3261 enwrightn@usgs.gov","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":4880,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","email":"enwrightn@usgs.gov","middleInitial":"M.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":694490,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70187773,"text":"70187773 - 2017 - Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.","interactions":[],"lastModifiedDate":"2017-05-18T12:57:32","indexId":"70187773","displayToPublicDate":"2017-05-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.","docAbstract":"<p>A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate <i>in situ</i> micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature (<i>T<sub>s</sub></i>)<span>&nbsp;normalized difference vegetation index (NDVI) and daily maximum air temperature (<i>T<sub>a</sub></i>)</span><span>. The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using&nbsp;<i>T<sub>s</sub></i></span><span> and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the&nbsp;<i>T<sub>s</sub></i><sub>&nbsp;</sub></span><span>from Landsat relative to the&nbsp;<i>T<sub>s&nbsp;</sub></i></span><span>from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30&nbsp;m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.</span><br></p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2017.1312033","usgsCitation":"Yagci, A.L., Santanello, J.A., Jones, J., and Barr, J.G., 2017, Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.: International Journal of Remote Sensing, v. 38, no. 14, p. 3981-4007, https://doi.org/10.1080/01431161.2017.1312033.","productDescription":"27 p.","startPage":"3981","endPage":"4007","ipdsId":"IP-073615","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":341456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"14","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-04","publicationStatus":"PW","scienceBaseUri":"591eb2e2e4b0a7fdb4418b89","contributors":{"authors":[{"text":"Yagci, Ali Levent 0000-0003-1094-9204","orcid":"https://orcid.org/0000-0003-1094-9204","contributorId":192125,"corporation":false,"usgs":false,"family":"Yagci","given":"Ali","email":"","middleInitial":"Levent","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":695554,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Santanello, Joseph A. 0000-0002-0807-6590","orcid":"https://orcid.org/0000-0002-0807-6590","contributorId":192126,"corporation":false,"usgs":false,"family":"Santanello","given":"Joseph","email":"","middleInitial":"A.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":695555,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":695553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barr, Jordan G.","contributorId":85809,"corporation":false,"usgs":false,"family":"Barr","given":"Jordan","email":"","middleInitial":"G.","affiliations":[{"id":13531,"text":"South Florida Natural Resource Center, Everglades National Park","active":true,"usgs":false}],"preferred":false,"id":695556,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70269685,"text":"70269685 - 2017 - Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States","interactions":[],"lastModifiedDate":"2025-07-31T13:22:08.551955","indexId":"70269685","displayToPublicDate":"2017-05-18T00:00:00","publicationYear":"2017","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":"Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States","docAbstract":"<p><span>Remote sensing-based field-scale&nbsp;evapotranspiration&nbsp;(ET) maps are useful for characterizing water use patterns and assessing crop performance. The relative impact of&nbsp;climate variability&nbsp;and water management decisions are better studied and quantified using historical data that are derived using a set of consistent datasets and methodology. Historical (1984–2014) Landsat-based ET maps were generated for major irrigation districts in California, i.e., Palo Verde and eight other sub-basins in parts of the middle and lower Central Valley. A total of 3396&nbsp;Landsat&nbsp;images were processed using the Operational Simplified Surface Energy Balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study sites over the 31</span><span>&nbsp;</span><span>years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively good correspondence (R</span><sup>2</sup><span>&nbsp;up to 0.88,&nbsp;root mean square error&nbsp;as low as 14</span><span>&nbsp;</span><span>mm/month) between SSEBop ET and validation datasets. In a pairwise comparison, annual variability of agro-hydrologic parameters of actual evapotranspiration (</span><i>ET</i><sub><i>a</i></sub><span>), land surface temperature (</span><i>T</i><sub><i>s</i></sub><span>), and runoff (</span><i>Q</i><span>) were found to be more variable than their corresponding climatic counterparts of atmospheric water demand (</span><i>ET</i><sub><i>o</i></sub><span>), air temperature (</span><i>T</i><sub><i>a</i></sub><span>), and precipitation (</span><i>P</i><span>), revealing process differences between regional climatic drivers and localized agro-hydrologic responses. However, only&nbsp;</span><i>T</i><sub><i>a</i></sub><span>&nbsp;showed a consistent increase (up to 1.2</span><span>&nbsp;</span><span>K) over study sites during the 31</span><span>&nbsp;</span><span>years, whereas other climate variables such as&nbsp;</span><i>ET</i><sub><i>o</i></sub><span>&nbsp;and&nbsp;</span><i>P</i><span>&nbsp;showed a generally neutral trend. This study demonstrates a useful application of “Big Data” science where large volumes of historical Landsat and weather datasets were used to quantify and understand the relative importance of water management and climate variability in crop water use dynamics in regards to the linkages among water management decisions, hydrologic processes and economic transactions. Irrigation district-wide&nbsp;</span><i>ET</i><sub><i>a</i></sub><span>&nbsp;estimates were used to compute historical crop water use volumes and monetary equivalents of water savings for the Palo Verde Irrigation District (PVID). During the peak crop fallowing year in PVID, the water saved reached a maximum of ~</span><span>&nbsp;</span><span>107,200</span><span>&nbsp;</span><span>acre-feet in 2011 with an estimated monetary payout value of $20.5 million. A significant decreasing trend in actual ET despite an increasing atmospheric demand in PVID highlights the role of management decisions in affecting local hydrologic processes. This study has importance for planning water resource allocation, managing water rights, sustaining agricultural production, and quantifying impacts of climate and land use/land cover changes on water resources. With increased computational efficiency, similar studies can be conducted in other parts of the world to help policy and decision makers understand and quantify various aspects of&nbsp;water resources management.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.05.005","usgsCitation":"Senay, G.B., Schauer, M., Friedrichs, M., Velpuri, N., and Singh, R., 2017, Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States: Remote Sensing of Environment, v. 202, p. 98-112, https://doi.org/10.1016/j.rse.2017.05.005.","productDescription":"15 p.","startPage":"98","endPage":"112","ipdsId":"IP-084512","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493296,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.05.005","text":"Publisher Index Page"},{"id":493191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.40977836532282,\n              42.04933677309765\n            ],\n            [\n              -123.16675091874134,\n              36.46673710104686\n            ],\n            [\n              -118.58307879273526,\n              32.705501119947826\n            ],\n            [\n              -113.73717874196483,\n              32.12914120007623\n            ],\n            [\n              -113.88408063137007,\n              35.268837865873046\n            ],\n            [\n              -114.8258516238531,\n              35.43740401775197\n            ],\n            [\n              -117.48081678552326,\n              37.20400442230724\n            ],\n            [\n              -120.03564230020528,\n              39.18700095919894\n            ],\n            [\n              -120.18220374157768,\n              41.98136883976758\n            ],\n            [\n              -125.40977836532282,\n              42.04933677309765\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"202","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":216909,"corporation":false,"usgs":true,"family":"Schauer","given":"Matthew","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@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":944427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velpuri, Naga Manohar  0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Ramesh 0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":210983,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70184212,"text":"gip172 - 2017 - Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research","interactions":[],"lastModifiedDate":"2017-05-03T10:03:12","indexId":"gip172","displayToPublicDate":"2017-05-02T00:00:00","publicationYear":"2017","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":"172","title":"Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research","docAbstract":"<p>When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.</p><p>Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.</p><p>In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.</p><p>Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.</p><p>Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip172","usgsCitation":"Young, S.M., 2017, Land Change Monitoring, Assessment, and Projection (LCMAP) revolutionizes land cover and land change research: U.S. Geological Survey General Information Product 172, 4 p., https://doi.org/10.3133/gip172.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-083103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0172/coverthb.jpg"},{"id":340453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0172/gip172.pdf","size":"7.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 172"}],"contact":"<p>Director,&nbsp;Earth Resources Observation and Science (EROS) Center<br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198</p><p><a href=\"https://eros.usgs.gov\" data-mce-href=\"https://eros.usgs.gov\">https://eros.usgs.gov</a></p>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2017-05-02","noUsgsAuthors":false,"publicationDate":"2017-05-02","publicationStatus":"PW","scienceBaseUri":"59099aade4b0fc4e449157e6","contributors":{"authors":[{"text":"Young, Steven 0000-0002-7904-9696 steven.young.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7904-9696","contributorId":173131,"corporation":false,"usgs":true,"family":"Young","given":"Steven","email":"steven.young.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":680572,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70188109,"text":"70188109 - 2017 - Climate legacy and lag effects on dryland plant communities in the southwestern U.S.","interactions":[],"lastModifiedDate":"2017-05-31T13:23:34","indexId":"70188109","displayToPublicDate":"2017-05-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Climate legacy and lag effects on dryland plant communities in the southwestern U.S.","docAbstract":"<p><span>Climate change effects on vegetation will likely be strong in the southwestern U.S., which is projected to experience large increases in temperature and changes in precipitation. Plant communities in the southwestern U.S. may be particularly vulnerable to climate change as the productivity of many plant species is strongly water-limited. This study examines the relationship between climate and vegetation condition using a time-series of Landsat imagery across grassland, shrubland, and woodland communities on the Colorado Plateau, USA. We improve on poorly understood inter-annual climate-vegetation relationships by exploring how the responses of different plant communities depend on climate legacies (&gt;12&nbsp;months) and lag behind shorter-term (3–12 month) changes in water availability. Our results show a prolonged drying trend on the Colorado Plateau since the early 1990s that was punctuated in several years by intense droughts. In areas that experienced sustained dry conditions or a drying trend, vegetation greenness (a proxy for production) increased linearly when conditions were interrupted by wetting events. In contrast, in areas that experienced sustained wet conditions or a wetting trend, vegetation greenness was weakly or not related to wetting events, indicating that production may saturate if vegetation experiences sufficient water availability. Shrubland and woodland communities had stronger relationships with climate at long lags (6–12 months) and many maintained greenness under sustained water deficit, whereas grassland communities had stronger relationships at short lags (3–6 months) and lost greenness even in periods of short-term drought. The results of our study show the importance of identifying climate legacies and lags when assessing indicators of ecological drought, which can be used to improve forecasts of which plant communities will be vulnerable under future climate change.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2016.10.024","usgsCitation":"Bunting, E., Munson, S.M., and Villarreal, M.L., 2017, Climate legacy and lag effects on dryland plant communities in the southwestern U.S.: Ecological Indicators, v. 74, p. 216-229, https://doi.org/10.1016/j.ecolind.2016.10.024.","productDescription":"14 p.","startPage":"216","endPage":"229","ipdsId":"IP-080256","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438355,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90CRK5N","text":"USGS data release","linkHelpText":"Dataset for climate legacy and lag effects on dryland plant communities in the southwestern U.S."},{"id":341940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.115234375,\n              35.8356283888737\n            ],\n            [\n              -106.94091796875,\n              35.8356283888737\n            ],\n            [\n              -106.94091796875,\n              40.96330795307353\n            ],\n            [\n              -113.115234375,\n              40.96330795307353\n            ],\n            [\n              -113.115234375,\n              35.8356283888737\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"74","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592fd63ce4b0e9bd0ea896e6","contributors":{"authors":[{"text":"Bunting, Erin 0000-0001-9103-6065 ebunting@usgs.gov","orcid":"https://orcid.org/0000-0001-9103-6065","contributorId":168488,"corporation":false,"usgs":true,"family":"Bunting","given":"Erin","email":"ebunting@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":696776,"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":696777,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70186881,"text":"70186881 - 2017 - Cloud detection algorithm comparison and validation for operational Landsat data products","interactions":[],"lastModifiedDate":"2017-04-13T09:40:27","indexId":"70186881","displayToPublicDate":"2017-04-13T00:00:00","publicationYear":"2017","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":"Cloud detection algorithm comparison and validation for operational Landsat data products","docAbstract":"<p><span>Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM&nbsp;+) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.03.026","usgsCitation":"Foga, S.C., Scaramuzza, P., Guo, S., Zhu, Z., Dilley, R., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M., and Laue, B., 2017, Cloud detection algorithm comparison and validation for operational Landsat data products: Remote Sensing of Environment, v. 194, p. 379-390, https://doi.org/10.1016/j.rse.2017.03.026.","productDescription":"12 p.","startPage":"379","endPage":"390","ipdsId":"IP-076780","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.03.026","text":"Publisher Index Page"},{"id":339659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"194","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f08e5ee4b06911a29fa842","contributors":{"authors":[{"text":"Foga, Steven Curtis 0000-0003-1835-1987 sfoga@usgs.gov","orcid":"https://orcid.org/0000-0003-1835-1987","contributorId":5703,"corporation":false,"usgs":true,"family":"Foga","given":"Steven","email":"sfoga@usgs.gov","middleInitial":"Curtis","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scaramuzza, Pat 0000-0002-2616-8456 pscar@usgs.gov","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":3970,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","email":"pscar@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guo, Song 0000-0001-8823-188X sguo@usgs.gov","orcid":"https://orcid.org/0000-0001-8823-188X","contributorId":5245,"corporation":false,"usgs":true,"family":"Guo","given":"Song","email":"sguo@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":690808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dilley, Ronald 0000-0002-6960-1125 ronald.dilley.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-6960-1125","contributorId":190829,"corporation":false,"usgs":true,"family":"Dilley","given":"Ronald","email":"ronald.dilley.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beckmann, Tim 0000-0002-2557-0638 tim.beckmann.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-2557-0638","contributorId":190830,"corporation":false,"usgs":true,"family":"Beckmann","given":"Tim","email":"tim.beckmann.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690811,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690810,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":690812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hughes, MJ","contributorId":190831,"corporation":false,"usgs":false,"family":"Hughes","given":"MJ","email":"","affiliations":[],"preferred":false,"id":690813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Laue, Brady 0000-0002-4559-3618 brady.laue.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4559-3618","contributorId":190832,"corporation":false,"usgs":true,"family":"Laue","given":"Brady","email":"brady.laue.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690814,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70185763,"text":"70185763 - 2017 - Landsat Science Team: 2017 Winter meeting summary","interactions":[],"lastModifiedDate":"2020-12-17T17:35:38.83827","indexId":"70185763","displayToPublicDate":"2017-04-12T00:00:00","publicationYear":"2017","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: 2017 Winter meeting summary","docAbstract":"<p>The winter meeting of the NASA-U.S. Geological Survey (USGS) Landsat Science Team (LST) was held January 10-12, 2017, at Boston University. LST co-chairs Tom Loveland [USGS’s Earth Resources Observation and Science Center (EROS)—Senior Scientist], Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Deputy Director, Earth Sciences Division], and Curtis Woodcock [Boston University—Professor and LST Co-Leader] welcomed the participants to the three-day meeting. The group immediately and enthusiastically recognized Woodcock’s receipt of the 2016 Pecora Award. Loveland summarized the primary meeting objectives to identify priorities for future Landsat measurements and to begin identifying next-generation Landsat products. He also discussed USGS’s plans to issue a request for proposals for membership on the 2018-2023 LST (i.e., the next five-year term). Irons stressed Landsat’s bipartisan support but cautioned against complacency when looking toward future capabilities. Meeting presentations are available at https://landsat.usgs.gov/landsat-science-teammeeting-jan-10-12-2017.</p>","language":"English","publisher":"NASA","usgsCitation":"Loveland, T., Wulder, M.A., and Irons, J.R., 2017, Landsat Science Team: 2017 Winter meeting summary: The Earth Observer, v. 29, no. 3, p. 21-25.","productDescription":"5 p.","startPage":"21","endPage":"25","ipdsId":"IP-085721","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":339608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381443,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.nasa.gov/earthobserver/may-jun-2017"}],"volume":"29","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ef3da9e4b0eed1ab8e3bd2","contributors":{"authors":[{"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":686696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulder, Michael A.","contributorId":189990,"corporation":false,"usgs":false,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":686697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":686698,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70186146,"text":"fs20173026 - 2017 - U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data","interactions":[],"lastModifiedDate":"2017-05-31T10:38:36","indexId":"fs20173026","displayToPublicDate":"2017-03-31T00:00:00","publicationYear":"2017","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":"2017-3026","title":"U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data","docAbstract":"<p>A partnership established between the European Space Agency (ESA) and the U.S. Geological Survey (USGS) allows for USGS storage and redistribution of images acquired by the MultiSpectral Instrument (MSI) on the European Union's Sentinel-2 satellite mission. The MSI data are acquired from a pair of satellites, Sentinel-2A and Sentinel-2B, which are part of a larger set of ESA missions focusing on different aspects of Earth observation. The primary purpose of the Sentinel-2 series is to collect multispectral imagery over the Earth’s land surfaces, large islands, and inland and coastal waters. Sentinel-2A was launched in 2015 and Sentinel-2B launched in 2017.</p><p>The collaborative effort between ESA and USGS provides for public access and redistribution of global acquisitions of Sentinel-2 data at no cost, which allows users to download the MSI imagery from USGS access systems such as Earth- Explorer, in addition to the ESA Sentinels Scientific Data Hub. The MSI sensor acquires 13 spectral bands that are highly complementary to data acquired by the USGS Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The product options from USGS include a Full-Resolution Browse (FRB) image&nbsp;product generated by USGS, along with a 100-kilometer (km) by 100-km tile-based Level-1C top-of-atmosphere (TOA) reflectance product that is very similar (but not identical) to the currently (2017) distributed ESA Level 1C product.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173026","usgsCitation":"Pieschke, R.L., 2017, U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data: U.S. Geological Survey Fact Sheet 2017–3026, 2 p., https://doi.org/10.3133/fs20173026.\n","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-082585","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":338860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3026/fs20173026.pdf","text":"Fact Sheet","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3026"},{"id":338859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3026/coverthb.jpg"}],"contact":"<p>Director, Earth Resources Observation and Science (EROS) Center<br>U.S. Geological Survey<br>47914 252nd Street<br>Sioux Falls, SD 57198–0001</p><p><a href=\"https://eros.usgs.gov\" data-mce-href=\"https://eros.usgs.gov\">https://eros.usgs.gov</a></p>","tableOfContents":"<ul><li>Data Characteristics<br></li><li>Access to Data<br></li><li>U.S. Geological Survey Access and Distribution<br></li><li>Additional Resources<br></li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2017-03-31","noUsgsAuthors":false,"publicationDate":"2017-03-31","publicationStatus":"PW","scienceBaseUri":"58df6abfe4b02ff32c6aea29","contributors":{"authors":[{"text":"Pieschke, Renee L. 0000-0002-8366-2231 renee.pieschke.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8366-2231","contributorId":190134,"corporation":false,"usgs":true,"family":"Pieschke","given":"Renee","email":"renee.pieschke.ctr@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":687667,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70185601,"text":"ofr20171034 - 2017 - Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production","interactions":[],"lastModifiedDate":"2017-03-30T12:15:26","indexId":"ofr20171034","displayToPublicDate":"2017-03-29T17:45:00","publicationYear":"2017","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":"2017-1034","title":"Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production","docAbstract":"<h1>Executive Summary</h1><p>The use of Landsat satellite imagery for global agricultural monitoring began almost immediately after the launch of Landsat 1 in 1972, making agricultural monitoring one of the longest-standing operational applications for the Landsat program. More recently, Landsat imagery has been used in domestic agricultural applications as an input for field-level production management. The enactment of the U.S. Geological Survey’s free and open data policy in 2008 and the launch of Landsat 8 in 2013 have both influenced agricultural applications. This report presents two primary sets of case studies on the applications and benefits of Landsat imagery use in agriculture. The first set examines several operational applications within the U.S. Department of Agriculture (USDA) and the second focuses on private sector applications for agronomic management. &nbsp;</p><p>Information on the USDA applications is provided in the U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring section of the report in the following subsections:</p><ul><li><i>Estimating Crop Production</i>.—Provides an overview of how Landsat satellite imagery is used to estimate crop production, including the spectral bands most frequently utilized in this application.</li><li><i>Monitoring Consumptive Water Use</i>.—Highlights the role of Landsat imagery in monitoring consumptive water use for agricultural production. Globally, a significant amount of agricultural production relies on irrigation, so monitoring water resources is a critical component of agricultural monitoring. <br></li><li><i>National Agricultural Statistics Service</i>—Cropland Data Layer.—Highlights the use of Landsat imagery in developing the annual Cropland Data Layer, a crop-specific land cover classification product that provides information on more than 100 crop categories grown in the United States.&nbsp;</li><li><i>Foreign Agricultural Service</i>—Global Agricultural Monitoring.—Highlights Landsat’s role in monitoring global agricultural production. The USDA has been using Landsat imagery to monitor global agricultural production since the launch of Landsat 1 in 1972. Landsat imagery provides objective, global input for a number of USDA agricultural programs and plays an important role in economic and food security forecasting.</li><li><i>U.S. Department of Agriculture</i>—Satellite Imagery Archive.—Highlights a number of the experiences of the USDA in acquiring, sharing, and managing moderate resolution imagery to support the diversity of USDA operational programs.&nbsp;</li></ul><p>Private sector applications using Landsat imagery for agricultural management are discussed in the Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management section of the report in the following subsections:</p><ul><li><i>Field-Level Management</i>.—Provides an introduction to what field-level production management is and how it can be applied to agricultural management. This section explores the concept of zone mapping and how Landsat imagery can be used to identify different conditions within a field. The section also provides a case study of zone-mapping software, developed by GK Technology, Inc., that is used by numerous agricultural consultants.</li><li><i>Putting Zone Maps to Work</i>.—Highlights several case studies of private agricultural consultants who have been using Landsat imagery to develop zone maps for farmers. Landsat imagery is helping consultants and farmers optimize agricultural inputs, including fertilizer and seed, which leads to higher yield and economic return for the farmer.</li><li><i>Increasing Yield</i>.—Highlights the primary benefit of zone mapping using Landsat imagery. Using 5-year market average prices for a number of commodities, this section provides examples of how yield increases translate into higher returns for farmers.</li></ul>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171034","usgsCitation":"Leslie, C.R., Serbina, L.O., and Miller, H.M., 2017, Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production: U.S. Geological Survey Open-File Report 2017–1034, 27 p., https://doi.org/10.3133/ofr20171034. ","productDescription":"vi, 27 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-074917","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":338573,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1034/coverthb.jpg"},{"id":338574,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1034/ofr20171034.pdf","text":"Report","size":"6.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1034"}],"contact":"<p>Director, Fort Collins Science Center&nbsp;<br>U.S. Geological Survey<br>2150 Centre Ave., Bldg. C<br>Fort Collins, CO 80526-8118</p><p><a href=\"http://www.fort.usgs.gov/\" data-mce-href=\"http://www.fort.usgs.gov/\">http://www.fort.usgs.gov/</a></p>","tableOfContents":"<ul><li>Executive Summary</li><li>Introduction</li><li>U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring</li><li>Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management</li><li>Conclusion</li><li>References</li></ul>","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2017-03-29","noUsgsAuthors":false,"publicationDate":"2017-03-29","publicationStatus":"PW","scienceBaseUri":"58dcc7cfe4b02ff32c68565b","contributors":{"authors":[{"text":"Leslie, Colin R.","contributorId":167359,"corporation":false,"usgs":false,"family":"Leslie","given":"Colin","email":"","middleInitial":"R.","affiliations":[{"id":24700,"text":"Student contractor","active":true,"usgs":false}],"preferred":false,"id":686079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Serbina, Larisa O.","contributorId":189807,"corporation":false,"usgs":false,"family":"Serbina","given":"Larisa O.","affiliations":[],"preferred":false,"id":686080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Holly M. 0000-0003-0914-7570 millerh@usgs.gov","orcid":"https://orcid.org/0000-0003-0914-7570","contributorId":29544,"corporation":false,"usgs":true,"family":"Miller","given":"Holly","email":"millerh@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":false,"id":686078,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70185344,"text":"70185344 - 2017 - Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011","interactions":[],"lastModifiedDate":"2022-04-22T16:06:14.62554","indexId":"70185344","displayToPublicDate":"2017-03-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011","docAbstract":"<p><span>Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986–1992, 1992–2001, 2001–2006, and 2006–2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.</span></p>","language":"English","publisher":"Kluwer","publisherLocation":"Dordrecht","doi":"10.1007/s10661-017-5879-5","usgsCitation":"Soulard, C.E., Acevedo, W., Cohen, W.B., Yang, Z., Stehman, S.V., and Taylor, J.L., 2017, Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011: Environmental Monitoring and Assessment, v. 189, 170: 17 p., https://doi.org/10.1007/s10661-017-5879-5.","productDescription":"170: 17 p.","ipdsId":"IP-075245","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":337906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"189","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-18","publicationStatus":"PW","scienceBaseUri":"58d23b90e4b0236b68f828ea","chorus":{"doi":"10.1007/s10661-017-5879-5","url":"http://dx.doi.org/10.1007/s10661-017-5879-5","publisher":"Springer Nature","authors":"Soulard Christopher E., Acevedo William, Cohen Warren B., Yang Zhiqiang, Stehman Stephen V., Taylor Janis L.","journalName":"Environmental Monitoring and Assessment","publicationDate":"3/18/2017","auditedOn":"3/20/2017","publiclyAccessibleDate":"3/18/2017"},"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":685248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acevedo, William wacevedo@usgs.gov","contributorId":2689,"corporation":false,"usgs":true,"family":"Acevedo","given":"William","email":"wacevedo@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":685249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cohen, Warren B.","contributorId":100093,"corporation":false,"usgs":true,"family":"Cohen","given":"Warren","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":685250,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Zhiqiang","contributorId":189584,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","email":"","affiliations":[],"preferred":false,"id":685280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":685252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Taylor, Janis L. 0000-0002-9418-5215 jltaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-9418-5215","contributorId":147371,"corporation":false,"usgs":true,"family":"Taylor","given":"Janis","email":"jltaylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":685253,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70191302,"text":"70191302 - 2017 - Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure","interactions":[],"lastModifiedDate":"2017-10-03T16:34:40","indexId":"70191302","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","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":"Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure","docAbstract":"<p><span>Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r</span><sup>2</sup><span>&nbsp;=&nbsp;0.86, p&nbsp;&lt;&nbsp;0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.12.022","usgsCitation":"McCarley, T.R., Kolden, C.A., Vaillant, N.M., Hudak, A.T., Smith, A., Wing, B.M., Kellogg, B., and Kreitler, J.R., 2017, Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure: Remote Sensing of Environment, v. 191, p. 419-432, https://doi.org/10.1016/j.rse.2016.12.022.","productDescription":"14 p.","startPage":"419","endPage":"432","ipdsId":"IP-076180","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470007,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.12.022","text":"Publisher Index Page"},{"id":346371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Pole Creek Fire","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.75,\n              44.0833\n            ],\n            [\n              -121.5,\n              44.0833\n            ],\n            [\n              -121.5,\n              44.25\n            ],\n            [\n              -121.75,\n              44.25\n            ],\n            [\n              -121.75,\n              44.0833\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"191","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d4a1a9e4b05fe04cc4e0ff","contributors":{"authors":[{"text":"McCarley, T. Ryan","contributorId":196908,"corporation":false,"usgs":false,"family":"McCarley","given":"T.","email":"","middleInitial":"Ryan","affiliations":[],"preferred":false,"id":711897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolden, Crystal A.","contributorId":196909,"corporation":false,"usgs":false,"family":"Kolden","given":"Crystal","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaillant, Nicole M.","contributorId":196237,"corporation":false,"usgs":false,"family":"Vaillant","given":"Nicole","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":711899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hudak, Andrew T.","contributorId":196022,"corporation":false,"usgs":false,"family":"Hudak","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":711900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Alistair","contributorId":196910,"corporation":false,"usgs":false,"family":"Smith","given":"Alistair","email":"","affiliations":[],"preferred":false,"id":711901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wing, Brian M.","contributorId":196911,"corporation":false,"usgs":false,"family":"Wing","given":"Brian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":711902,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kellogg, Bryce","contributorId":196912,"corporation":false,"usgs":false,"family":"Kellogg","given":"Bryce","email":"","affiliations":[],"preferred":false,"id":711903,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":711896,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70184217,"text":"fs20173018 - 2017 - Landsat eyes help guard the world's forests","interactions":[],"lastModifiedDate":"2017-03-06T12:58:39","indexId":"fs20173018","displayToPublicDate":"2017-03-03T16:45:00","publicationYear":"2017","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":"2017-3018","title":"Landsat eyes help guard the world's forests","docAbstract":"<h1>Summary</h1><p>The Landsat program is a joint effort between the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), but the partner agencies have distinct roles. NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance in orbit. The USGS owns and operates Landsat satellites in space and manages their data transmissions, including ground reception, archiving, product generation, and public distribution. In 2008, with support from the U.S. Department of the Interior, the USGS made its Landsat data free to anyone in the world.</p><p>The current satellites in the Landsat program, Landsat 7 (launched in 1999) and Landsat 8 (launched in 2013), provide complete coverage of the Earth every eight days. A Landsat 9 satellite is scheduled for launch in late 2020.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173018","usgsCitation":"Campbell, Jon, 2017, Landsat eyes help guard the world's forests: U.S. Geological Survey Fact Sheet 2017–3018, 2 p., https://doi.org/10.3133/fs20173018.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084384","costCenters":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"links":[{"id":336823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3018/coverthb.jpg"},{"id":336824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3018/fs20173018.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3018"}],"otherGeospatial":"Earth","contact":"<p>Associate Director<br> U.S. Geological Survey<br> Climate and Land Use Change Mission Area<br> 12201 Sunrise Valley Drive<br> Reston, VA 20192<br> Web site: <a href=\"https://www.usgs.gov/science/mission-areas/climate-and-land-use-change?qt-mission_areas_l2_landing_page_ta=0#qt-mission_areas_l2_landing_page_ta\" data-mce-href=\"https://www.usgs.gov/science/mission-areas/climate-and-land-use-change?qt-mission_areas_l2_landing_page_ta=0#qt-mission_areas_l2_landing_page_ta\">Climate and Land Use Change</a></p>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2017-03-03","noUsgsAuthors":false,"publicationDate":"2017-03-03","publicationStatus":"PW","scienceBaseUri":"58ba8ebae4b0bcef64f0b92d","contributors":{"authors":[{"text":"Campbell, Jon","contributorId":35743,"corporation":false,"usgs":true,"family":"Campbell","given":"Jon","affiliations":[],"preferred":false,"id":680697,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70185038,"text":"70185038 - 2017 - Autumn olive (<i>Elaeagnus umbellata</i>) presence and proliferation on former surface coal mines in Eastern USA","interactions":[],"lastModifiedDate":"2017-03-13T16:53:20","indexId":"70185038","displayToPublicDate":"2017-03-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Autumn olive (<i>Elaeagnus umbellata</i>) presence and proliferation on former surface coal mines in Eastern USA","docAbstract":"<p><span>Invasive plants threaten native plant communities. Surface coal mines in the Appalachian Mountains are among the most disturbed landscapes in North America, but information about land cover characteristics of Appalachian mined lands is lacking. The invasive shrub autumn olive (</span><i class=\"EmphasisTypeItalic \">Elaeagnus umbellata</i><span>) occurs on these sites and interferes with ecosystem recovery by outcompeting native trees, thus inhibiting re-establishment of the native woody-plant community. We analyzed Landsat 8 satellite imagery to describe autumn olive’s distribution on post-mined lands in southwestern Virginia within the Appalachian coalfield. Eight images from April 2013 through January 2015 served as input data. Calibration and validation data obtained from high-resolution aerial imagery were used to develop a land cover classification model that identified areas where autumn olive was a primary component of land cover. Results indicate that autumn olive cover was sufficiently dense to enable detection on approximately 12.6&nbsp;% of post-mined lands within the study area. The classified map had user’s and producer’s accuracies of 85.3 and 78.6&nbsp;%, respectively, for the autumn olive coverage class. Overall accuracy was assessed in reference to an independent validation dataset at 96.8&nbsp;%. Autumn olive was detected more frequently on mines disturbed prior to 2003, the last year of known plantings, than on lands disturbed by more recent mining. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in eastern USA can be mapped using Landsat 8 Operational Land Imager imagery; and that autumn olive occurrence is a significant landscape vegetation feature on former surface coal mines in the southwestern Virginia segment of the Appalachian coalfield.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10530-016-1271-6","usgsCitation":"Oliphant, A., Wynne, R., Zipper, C.E., Ford, W., Donovan, P.F., and Li, J., 2017, Autumn olive (<i>Elaeagnus umbellata</i>) presence and proliferation on former surface coal mines in Eastern USA: Biological Invasions, v. 19, no. 1, p. 179-195, https://doi.org/10.1007/s10530-016-1271-6.","productDescription":"17 p.","startPage":"179","endPage":"195","ipdsId":"IP-072884","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":337475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-12","publicationStatus":"PW","scienceBaseUri":"58c7af98e4b0849ce9795e6a","contributors":{"authors":[{"text":"Oliphant, Adam J.","contributorId":189232,"corporation":false,"usgs":false,"family":"Oliphant","given":"Adam J.","affiliations":[],"preferred":false,"id":684165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wynne, R.H.","contributorId":147844,"corporation":false,"usgs":false,"family":"Wynne","given":"R.H.","email":"","affiliations":[],"preferred":false,"id":684166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zipper, Carl E.","contributorId":43683,"corporation":false,"usgs":true,"family":"Zipper","given":"Carl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":684167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":684033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donovan, P. F.","contributorId":189233,"corporation":false,"usgs":false,"family":"Donovan","given":"P.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":684168,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Jing","contributorId":9166,"corporation":false,"usgs":true,"family":"Li","given":"Jing","email":"","affiliations":[],"preferred":false,"id":684169,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70180260,"text":"70180260 - 2017 - Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed","interactions":[],"lastModifiedDate":"2017-01-27T11:10:10","indexId":"70180260","displayToPublicDate":"2017-01-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed","docAbstract":"<p><span>This research highlights development and application of an integrated hydrologic model (GSFLOW) to a semiarid, snow-dominated watershed in the Great Basin to evaluate Pinyon-Juniper (PJ) and temperature controls on mountain meadow shallow groundwater. The work used Google Earth Engine Landsat satellite and gridded climate archives for model evaluation. Model simulations across three decades indicated that the watershed operates on a threshold response to precipitation (P) &gt;400&nbsp;mm/y to produce a positive yield (P-ET; 9%) resulting in stream discharge and a rebound in meadow groundwater levels during these wetter years. Observed and simulated meadow groundwater response to large P correlates with above average predicted soil moisture and with a normalized difference vegetation index threshold value &gt;0.3. A return to assumed pre-expansion PJ conditions or an increase in temperature to mid-21st century shifts yielded by only ±1% during the multi-decade simulation period; but changes of approximately ±4% occurred during wet years. Changes in annual yield were largely dampened by the spatial and temporal redistribution of evapotranspiration across the watershed: Yet the influence of this redistribution and vegetation structural controls on snowmelt altered recharge to control water table depth in the meadow. Even a small-scale removal of PJ (0.5&nbsp;km</span><sup>2</sup><span>) proximal to the meadow will promote a stable, shallow groundwater system resilient to droughts, while modest increases in temperature will produce a meadow susceptible to declining water levels and a community structure likely to move toward dry and degraded conditions.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/eco.1792","usgsCitation":"Carroll, R.W., Huntington, J., Snyder, K.A., Niswonger, R.G., Morton, C., and Stringham, T.K., 2017, Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed: Ecohydrology, v. 10, no. 1, p. 1-18, https://doi.org/10.1002/eco.1792.","productDescription":"e1792; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-072881","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":461779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.1792","text":"Publisher Index Page"},{"id":334058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin","volume":"10","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"588b1976e4b0ad67323f97dc","contributors":{"authors":[{"text":"Carroll, Rosemary W.H.","contributorId":39928,"corporation":false,"usgs":true,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":660972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Justin L.","contributorId":31279,"corporation":false,"usgs":true,"family":"Huntington","given":"Justin L.","affiliations":[],"preferred":false,"id":660973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Keirith A.","contributorId":178786,"corporation":false,"usgs":false,"family":"Snyder","given":"Keirith","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":660974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":152462,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":660975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morton, Charles","contributorId":178787,"corporation":false,"usgs":false,"family":"Morton","given":"Charles","affiliations":[],"preferred":false,"id":660976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stringham, Tamzen K.","contributorId":178788,"corporation":false,"usgs":false,"family":"Stringham","given":"Tamzen","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":660977,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192455,"text":"70192455 - 2017 - Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl","interactions":[],"lastModifiedDate":"2019-06-04T08:40:19","indexId":"70192455","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl","docAbstract":"<p>Wildlife researchers frequently study resource and habitat selection of wildlife to understand their potential habitat requirements and to conserve their populations. Understanding wildlife spatial-temporal distributions related to habitat have other applications such as to model interfaces between wildlife and domestic food animals in order to mitigate disease transmission to food animals. The highly pathogenic avian influenza (HPAI) virus represents a significant risk to the poultry industry. The Central Valley of California offers a unique geographical confluence of commercial poultry and wild waterfowl, which are thought to be a key reservoir of avian influenza (AI). Therefore, understanding spatio-temporal distributions of waterfowl could improve our understanding of potential risk of HPAI exposure from a commercial poultry perspective. Using existing radio-telemetry data on waterfowl (U.S. Geological Survey) in combination with habitat and vegetation data based on Geographic Information Systems (GIS), we are developing GIS-based statistical models that predict the probability of waterfowl presence (Habitat Suitability Mapping). Near-real-time application can be developed using recent habitat data derived from Landsat imagery (acquired by satellites and publicly available through the U.S. Geological Survey) to predict temporally- and spatially-varying distributions of waterfowl in the Central Valley. These results could be used to provide decision support for the poultry industry in addressing potential risk of HPAI exposure related to waterfowl proximity.</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Sixty-Sixth Western Poultry Disease Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Sixty-Sixth Western Poultry Disease Conference","conferenceDate":"March 20-22, 2017","conferenceLocation":"Sacramento, California","language":"English","publisher":"Western Poutlry Disease Conference","usgsCitation":"Matchett, E., Casazza, M.L., Fleskes, J.P., Kelman, T., Cadena, M., and Pitesky, M., 2017, Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl, <i>in</i> Proceedings of the Sixty-Sixth Western Poultry Disease Conference, Sacramento, California, March 20-22, 2017, p. 118-120.","productDescription":"3 p.","startPage":"118","endPage":"120","ipdsId":"IP-083273","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":352033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364313,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://aaap.memberclicks.net/wpdc-proceedings"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee8f7e4b0da30c1bfc4f0","contributors":{"authors":[{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleskes, Joseph P. 0000-0001-5388-6675 joe_fleskes@usgs.gov","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":177154,"corporation":false,"usgs":true,"family":"Fleskes","given":"Joseph","email":"joe_fleskes@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelman, T.","contributorId":198390,"corporation":false,"usgs":false,"family":"Kelman","given":"T.","email":"","affiliations":[],"preferred":false,"id":715918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cadena, M.","contributorId":198391,"corporation":false,"usgs":false,"family":"Cadena","given":"M.","email":"","affiliations":[],"preferred":false,"id":715919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pitesky, M.","contributorId":198392,"corporation":false,"usgs":false,"family":"Pitesky","given":"M.","affiliations":[],"preferred":false,"id":715920,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192594,"text":"70192594 - 2017 - Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas","interactions":[],"lastModifiedDate":"2017-11-17T11:39:08","indexId":"70192594","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas","docAbstract":"<p><span>Since the beginning of the 20th century, the overall occupied range of the lesser prairie-chicken (</span><i>Tympanuchus pallidicinctus</i><span>) has declined by 84% commensurate with population trends. Much of this decline has been attributed to the loss and<span>&nbsp;</span><a title=\"Learn more about Fragmentation (cell biology)\" href=\"http://www.sciencedirect.com/topics/agricultural-and-biological-sciences/fragmentation-cell-biology\" data-mce-href=\"http://www.sciencedirect.com/topics/agricultural-and-biological-sciences/fragmentation-cell-biology\">fragmentation</a><span>&nbsp;</span>of native grasslands throughout the lesser prairie-chicken range. However, quantification of changes in land cover in the distribution of the lesser prairie-chicken is lacking. Our objectives were to (1) document changes in the areal extent and connectivity of grasslands in the identified lesser prairie-chicken range in Kansas, USA, (&gt;60% of extant lesser prairie-chicken population) from the 1950s to 2013 using remotely sensed data and (2) assess the potential of the Conservation Reserve Program (U.S. Department of Agriculture Program converting cropland to permanent cover; CRP) to mitigate grassland loss. Digital land cover maps were generated on a decadal time step through spectral classification of LANDSAT images and visual analysis of aerial photographs (1950s and 1960s). Landscape composition and configuration were assessed using FRAGSTATS to compute a variety of landscape metrics measuring changes in the amount of grassland present as well as changes in the size and configuration of grassland patches. With the exception of a single regional portion of the range, nearly all of the grassland converted to cropland in the lesser prairie-chicken range of Kansas occurred prior to the 1950s. Prior to the implementation of CRP, the amount of grassland decreased 3.6% between the 1950s and 1985 from 18,455 km</span><sup>2</sup><span><span>&nbsp;</span>to 17,788 km</span><sup>2</sup><span>. Since 1985, the overall amount of grassland in the lesser prairie-chicken range has increased 11.9% to 19,898 km</span><sup>2</sup><span><span>&nbsp;</span>due to implementation of CRP, although the area of grassland decreased between 1994 and 2013 as CRP contracts were not renewed by landowners. Since 1986 grassland in Kansas became more connected and less fragmented in response to the CRP. While the CRP has been successful in increasing grassland quantity and connectivity throughout the lesser prairie-chicken range in Kansas, offsetting loss of grassland since the 1950s, abundance and occupied range of lesser prairie-chickens has declined since the 1980s, suggesting that habitat quality is the principal factor influencing population demography of the species. Although the CRP is contributing to conservation actions for lesser prairie-chickens, efforts to improve habitat quality throughout the range of the lesser prairie-chicken are likely necessary to meet management goals. Continuation of the CRP faces an uncertain future in the face of rising commodity prices, energy development, and reduction in program scope, leaving open the possibility that these areas that have created habitat for lesser prairie-chickens could be lost.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2016.11.004","usgsCitation":"Haukos, D.A., Spencer, D., Hagen, C.A., Daniels, M.D., and Goodin, D., 2017, Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas: Global Ecology and Conservation, v. 9, p. 21-38, https://doi.org/10.1016/j.gecco.2016.11.004.","productDescription":"18 p.","startPage":"21","endPage":"38","ipdsId":"IP-078839","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2016.11.004","text":"Publisher Index Page"},{"id":349063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -102.01904296874999,\n              36.96744946416934\n            ],\n            [\n              -97.8662109375,\n              36.96744946416934\n            ],\n            [\n              -97.8662109375,\n              40.027614437486655\n            ],\n            [\n              -102.01904296874999,\n              40.027614437486655\n            ],\n            [\n              -102.01904296874999,\n              36.96744946416934\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc3de4b06e28e9c23c02","contributors":{"authors":[{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":716485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, David","contributorId":200553,"corporation":false,"usgs":false,"family":"Spencer","given":"David","affiliations":[],"preferred":false,"id":722646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":177795,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":722647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniels, Melinda D.","contributorId":166701,"corporation":false,"usgs":false,"family":"Daniels","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":722648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goodin, Doug","contributorId":200554,"corporation":false,"usgs":false,"family":"Goodin","given":"Doug","email":"","affiliations":[],"preferred":false,"id":722649,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70181028,"text":"70181028 - 2017 - Patterns and drivers for wetland connections in the Prairie Pothole Region, United States","interactions":[],"lastModifiedDate":"2017-06-01T10:24:44","indexId":"70181028","displayToPublicDate":"2016-11-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Patterns and drivers for wetland connections in the Prairie Pothole Region, United States","docAbstract":"<p><span>Ecosystem function in rivers, lakes and coastal waters depends on the functioning of upstream aquatic ecosystems, necessitating an improved understanding of watershed-scale interactions including variable surface-water flows between wetlands and streams. As surface water in the Prairie Pothole Region expands in wet years, surface-water connections occur between many depressional wetlands and streams. Minimal research has explored the spatial patterns and drivers for the abundance of these connections, despite their potential to inform resource management and regulatory programs including the U.S. Clean Water Act. In this study, wetlands were identified that did not intersect the stream network, but were shown with Landsat images (1990–2011) to become merged with the stream network as surface water expanded. Wetlands were found to spill into or consolidate with other wetlands within both small (2–10 wetlands) and large (&gt;100 wetlands) wetland clusters, eventually intersecting a stream channel, most often via a riparian wetland. These surface-water connections occurred over a wide range of wetland distances from streams (averaging 90–1400&nbsp;m in different ecoregions). Differences in the spatial abundance of wetlands that show a variable surface-water connection to a stream were best explained by smaller wetland-to-wetland distances, greater wetland abundance, and maximum surface-water extent. This analysis demonstrated that wetland arrangement and surface water expansion are important mechanisms for depressional wetlands to connect to streams and provides a first step to understanding the frequency and abundance of these surface-water connections across the Prairie Pothole Region.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s11273-016-9516-9","usgsCitation":"Vanderhoof, M.K., Christensen, J.R., and Alexander, L., 2017, Patterns and drivers for wetland connections in the Prairie Pothole Region, United States: Wetlands Ecology and Management, v. 25, no. 3, p. 275-297, https://doi.org/10.1007/s11273-016-9516-9.","productDescription":"23 p.","startPage":"275","endPage":"297","ipdsId":"IP-069163","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11273-016-9516-9","text":"Publisher Index Page"},{"id":335162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -100.052490234375,\n              46.475699386607516\n            ],\n            [\n              -100.052490234375,\n              48.785151998043155\n            ],\n            [\n              -97.61352539062499,\n              48.785151998043155\n            ],\n            [\n              -97.61352539062499,\n              46.475699386607516\n            ],\n            [\n              -100.052490234375,\n              46.475699386607516\n            ]\n          ]\n        ]\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -98.23974609375,\n              43.8028187190472\n            ],\n            [\n              -98.23974609375,\n              46.027481852486645\n            ],\n            [\n              -94.8779296875,\n              46.027481852486645\n            ],\n            [\n              -94.8779296875,\n              43.8028187190472\n            ],\n            [\n              -98.23974609375,\n              43.8028187190472\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"25","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-19","publicationStatus":"PW","scienceBaseUri":"589fff06e4b099f50d3e0449","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":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":663374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Jay R.","contributorId":179361,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":663375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":663376,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70182747,"text":"70182747 - 2016 - An automated approach for mapping persistent ice and snow cover over high latitude regions","interactions":[],"lastModifiedDate":"2017-02-28T09:38:05","indexId":"70182747","displayToPublicDate":"2017-02-28T00:00:00","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":"An automated approach for mapping persistent ice and snow cover over high latitude regions","docAbstract":"<p></p><p><span>We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.</span></p>","language":"English","publisher":"MDPI","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs8010016","usgsCitation":"Selkowitz, D.J., and Forster, R.R., 2016, An automated approach for mapping persistent ice and snow cover over high latitude regions: Remote Sensing, v. 8, no. 1, 21 p., https://doi.org/10.3390/rs8010016.","productDescription":"21 p.","ipdsId":"IP-066601","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":461980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8010016","text":"Publisher Index Page"},{"id":336312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Circumpolar Arctic","volume":"8","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-25","publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f86","contributors":{"authors":[{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":673560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forster, Richard R.","contributorId":169008,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","email":"","middleInitial":"R.","affiliations":[{"id":25396,"text":"Department of Geography, University of Utah","active":true,"usgs":false}],"preferred":false,"id":673561,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70178572,"text":"70178572 - 2016 - Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","interactions":[],"lastModifiedDate":"2017-01-03T16:02:50","indexId":"70178572","displayToPublicDate":"2016-11-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1170,"text":"Canadian Journal of Forest Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","docAbstract":"<p><span>High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by (</span><i>i</i><span>) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, (</span><i>ii</i><span>) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and (</span><i>iii</i><span>) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an </span><i>R</i><sup>2</sup><span> of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an </span><i>R</i><sup>2</sup><span> of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m</span><sup>3</sup><span>·ha</span><sup>−1</sup><span>·year</span><sup>−1</sup><span>. Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.</span></p>","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfr-2016-0209","usgsCitation":"Huang, S., Ramirez, C., Conway, S., Kennedy, K., Kohler, T., and Liu, J., 2016, Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA: Canadian Journal of Forest Research, v. 47, no. 1, p. 113-124, https://doi.org/10.1139/cjfr-2016-0209.","productDescription":"12 p.","startPage":"113","endPage":"124","ipdsId":"IP-076665","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfr-2016-0209","text":"External Repository"},{"id":331299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583ea1bfe4b0f0dc05ea54e1","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":654460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramirez, Carlos","contributorId":177061,"corporation":false,"usgs":false,"family":"Ramirez","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":654461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Scott","contributorId":177062,"corporation":false,"usgs":false,"family":"Conway","given":"Scott","email":"","affiliations":[],"preferred":false,"id":654462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Kama","contributorId":177063,"corporation":false,"usgs":false,"family":"Kennedy","given":"Kama","email":"","affiliations":[],"preferred":false,"id":654463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kohler, Tanya","contributorId":177064,"corporation":false,"usgs":false,"family":"Kohler","given":"Tanya","email":"","affiliations":[],"preferred":false,"id":654464,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654465,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70178529,"text":"70178529 - 2016 - Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","interactions":[],"lastModifiedDate":"2017-01-17T19:03:06","indexId":"70178529","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","docAbstract":"The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a\nnew end-to-end capability to continuously track and characterize changes in land cover, use, and condition\nto better support research and applications relevant to resource management and environmental\nchange. Among the LCMAP product suite are annual land cover maps that will be available to the public.\nThis paper describes an approach to optimize the selection of training and auxiliary data for deriving the\nthematic land cover maps based on all available clear observations from Landsats 4–8. Training data were\nselected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest\nclassifier was applied for different classification scenarios based on the Continuous Change Detection and\nClassification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence\nof land cover classes was superior to an equal distribution of training data per class, and suggest using a\ntotal of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced\ntraining data was alleviated by extracting a minimum of 600 training pixels and a maximum of\n8000 training pixels per class. We additionally explored removing outliers contained within the training\ndata based on their spectral and spatial criteria, but observed no significant improvement in classification\nresults. We also tested the importance of different types of auxiliary data that were available for the conterminous\nUnited States, including: (a) five variables used by the National Land Cover Database, (b) three\nvariables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the\nchange detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and\nits derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability,\nand cloud probability improved the accuracy of land cover classification. Compared to the original\nstrategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification\naccuracies substantially (15-percentage point increase in overall accuracy and 4-percentage\npoint increase in minimum accuracy).","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.isprsjprs.2016.11.004","usgsCitation":"Zhu, Z., Gallant, A.L., Woodcock, C., Pengra, B., Olofsson, P., Loveland, T., Jin, S., Dahal, D., Yang, L., and Auch, R.F., 2016, Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative: ISPRS Journal of Photogrammetry and Remote Sensing, v. 122, p. 206-221, https://doi.org/10.1016/j.isprsjprs.2016.11.004.","productDescription":"16 p.","startPage":"206","endPage":"221","numberOfPages":"16","ipdsId":"IP-080672","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2016.11.004","text":"Publisher Index Page"},{"id":331219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5836b8dde4b0d9329c801c53","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":654293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":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":654287,"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":654502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@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":654291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":654290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":121503,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[],"preferred":false,"id":654289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":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":654288,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":654286,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654292,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654285,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70188065,"text":"70188065 - 2016 - Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","interactions":[],"lastModifiedDate":"2017-05-31T16:04:59","indexId":"70188065","displayToPublicDate":"2016-11-23T00:00:00","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":"Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","docAbstract":"<p><span>There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43&nbsp;years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all available clear Landsat 5–8 observations with the new Continuous Change Detection and Classification (CCDC) algorithm was very effective for illuminating vegetation trends. There are a number of potential challenges for assessing gradual changes, including atmospheric impacts, algorithm development and visualization of the changes. One of the biggest challenges for studying gradual change will be the lack of appropriate data for validating results and products.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.02.060","usgsCitation":"Vogelmann, J., Gallant, A.L., Shi, H., and Zhu, Z., 2016, Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data: Remote Sensing of Environment, v. 185, p. 258-270, https://doi.org/10.1016/j.rse.2016.02.060.","productDescription":"13 p.","startPage":"258","endPage":"270","ipdsId":"IP-066052","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470406,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.02.060","text":"Publisher Index Page"},{"id":341856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84b8e4b092b266f10d2c","contributors":{"authors":[{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":192352,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":696380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70178470,"text":"70178470 - 2016 - Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","interactions":[],"lastModifiedDate":"2018-09-13T14:45:17","indexId":"70178470","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","docAbstract":"<p><span>Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30&nbsp;mg/L) was well represented in the main channels (IQR: 29–32&nbsp;mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60&nbsp;yr due to model sensitivity at the marsh edge (80–140&nbsp;cm NAVD88), although at 100&nbsp;yr, elevation forecasts differed less than 10&nbsp;cm across 97% of the marsh surface (150–200&nbsp;cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1582","usgsCitation":"Byrd, K.B., Windham-Myers, L., Leeuw, T., Downing, B.D., Morris, J.T., and Ferner, M.C., 2016, Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model: Ecosphere, v. 7, no. 11, e01582; 27 p., https://doi.org/10.1002/ecs2.1582.","productDescription":"e01582; 27 p.","ipdsId":"IP-073438","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470411,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1582","text":"Publisher Index Page"},{"id":438505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76M34Z1","text":"USGS data release","linkHelpText":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model"},{"id":331164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335610,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F76M34Z1","text":"Data release for journal article titled, \"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model\""}],"country":"United States","state":"California","otherGeospatial":"Rush Ranch Open Space Preserve, Suisun Slough, Suisun Marsh","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.05501556396483,\n              38.17802085110361\n            ],\n            [\n              -122.05501556396483,\n              38.212288054388175\n            ],\n            [\n              -121.99802398681642,\n              38.212288054388175\n            ],\n            [\n              -121.99802398681642,\n              38.17802085110361\n            ],\n            [\n              -122.05501556396483,\n              38.17802085110361\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed81a","chorus":{"doi":"10.1002/ecs2.1582","url":"http://dx.doi.org/10.1002/ecs2.1582","publisher":"Wiley-Blackwell","authors":"Byrd Kristin B., Windham-Myers Lisamarie, Leeuw Thomas, Downing Bryan, Morris James T., Ferner Matthew C.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/29/2016"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leeuw, Thomas","contributorId":176970,"corporation":false,"usgs":false,"family":"Leeuw","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":654115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Bryan D. 0000-0002-2007-5304 bdowning@usgs.gov","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":1449,"corporation":false,"usgs":true,"family":"Downing","given":"Bryan","email":"bdowning@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, James T.","contributorId":29118,"corporation":false,"usgs":true,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":654117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferner, Matthew C.","contributorId":176972,"corporation":false,"usgs":false,"family":"Ferner","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":654118,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70178356,"text":"70178356 - 2016 - An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","interactions":[],"lastModifiedDate":"2017-01-17T19:03:37","indexId":"70178356","displayToPublicDate":"2016-11-15T00:00:00","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":"An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data","docAbstract":"<p><span>Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MAD</span><sub>training</sub><span> = 2.5 and MAD</span><sub>testing</sub><span> = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/rs8110943","usgsCitation":"Gu, Y., Wylie, B.K., Boyte, S.P., Picotte, J.J., Howard, D., Smith, K., and Nelson, K., 2016, An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data: Remote Sensing, v. 8, p. 1-13, https://doi.org/10.3390/rs8110943.","productDescription":"Article 943; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-079805","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110943","text":"Publisher Index Page"},{"id":331008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfa","contributors":{"authors":[{"text":"Gu, Yingxin 0000-0002-3544-1856 ygu@usgs.gov","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":139586,"corporation":false,"usgs":true,"family":"Gu","given":"Yingxin","email":"ygu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":653754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","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":653756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653757,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howard, Danny 0000-0002-7563-7538 danny.howard.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":176610,"corporation":false,"usgs":true,"family":"Howard","given":"Danny","email":"danny.howard.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":653758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":653760,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, Kurtis 0000-0003-4911-4511 knelson@usgs.gov","orcid":"https://orcid.org/0000-0003-4911-4511","contributorId":3602,"corporation":false,"usgs":true,"family":"Nelson","given":"Kurtis","email":"knelson@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":653759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
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