{"pageNumber":"35","pageRowStart":"850","pageSize":"25","recordCount":1869,"records":[{"id":70034296,"text":"70034296 - 2011 - Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data","interactions":[],"lastModifiedDate":"2018-02-23T12:48:07","indexId":"70034296","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data","docAbstract":"<p><span>Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat–lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, “young” forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS–VCT disturbance products. The height of “young” forest is then mapped based on the derived relationships and the LTSS–VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS–VCT method yielded the lowest cross validation error. The R</span><sup>2</sup><span> and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97&nbsp;m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS–VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5&nbsp;m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2011.03.001","issn":"00344257","usgsCitation":"Li, A., Huang, C., Sun, G., Shi, H., Toney, C., Zhu, Z., Rollins, M.G., Goward, S.N., and Masek, J.G., 2011, Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data: Remote Sensing of Environment, v. 115, no. 8, p. 1837-1849, https://doi.org/10.1016/j.rse.2011.03.001.","productDescription":"13 p.","startPage":"1837","endPage":"1849","numberOfPages":"13","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":244747,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":216851,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.rse.2011.03.001"}],"volume":"115","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5c46e4b0c8380cd6fb68","contributors":{"authors":[{"text":"Li, Ainong","contributorId":202742,"corporation":false,"usgs":false,"family":"Li","given":"Ainong","email":"","affiliations":[],"preferred":false,"id":445131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huang, Chengquan 0000-0003-0055-9798","orcid":"https://orcid.org/0000-0003-0055-9798","contributorId":198972,"corporation":false,"usgs":false,"family":"Huang","given":"Chengquan","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":445129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sun, Guoqing","contributorId":202743,"corporation":false,"usgs":false,"family":"Sun","given":"Guoqing","email":"","affiliations":[],"preferred":false,"id":445125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":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":445130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Toney, Chris","contributorId":86598,"corporation":false,"usgs":true,"family":"Toney","given":"Chris","email":"","affiliations":[],"preferred":false,"id":445128,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Zhiliang 0000-0002-6860-6936 zzhu@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-6936","contributorId":150078,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhiliang","email":"zzhu@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":505,"text":"Office of the AD Climate and Land-Use Change","active":true,"usgs":true}],"preferred":true,"id":445126,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rollins, Matthew G.","contributorId":54695,"corporation":false,"usgs":true,"family":"Rollins","given":"Matthew","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":445127,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goward, Samuel N.","contributorId":44459,"corporation":false,"usgs":true,"family":"Goward","given":"Samuel","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":445132,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":445133,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70034238,"text":"70034238 - 2011 - Classifying the hydrologic function of prairie potholes with remote sensing and GIS","interactions":[],"lastModifiedDate":"2017-04-06T13:33:15","indexId":"70034238","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Classifying the hydrologic function of prairie potholes with remote sensing and GIS","docAbstract":"<p><span>A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. We found that wetlands receiving groundwater discharge responded differently over the time period than wetlands that did not. Also, wetlands located within topographically closed discharge basins could be distinguished from discharge basins with overland outlets. Field verification data showed that discharge wetlands with closed basins were most distinct and identifiable with reasonable accuracies (user’s accuracy = 97%, producer’s accuracy = 71%). The classification of other hydrologic function types had lower accuracies reducing the overall accuracy for the four hydrologic function classes to 51%. A simplified classification approach identifying only two hydrologic function classes was 82%. Although this technique has limited success for detecting small wetlands, Landsat-derived multipixel-object clustering can reliably differentiate wetlands receiving groundwater discharge and provides a new approach to quantify wetland dynamics in landscape scale investigations and models.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s13157-011-0146-y","issn":"02775212","usgsCitation":"Rover, J.R., Wright, C., Euliss, N.H., Mushet, D.M., and Wylie, B.K., 2011, Classifying the hydrologic function of prairie potholes with remote sensing and GIS: Wetlands, v. 31, no. 2, p. 319-327, https://doi.org/10.1007/s13157-011-0146-y.","productDescription":"9 p.","startPage":"319","endPage":"327","numberOfPages":"9","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":216944,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s13157-011-0146-y"},{"id":244846,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2011-02-22","publicationStatus":"PW","scienceBaseUri":"5059f632e4b0c8380cd4c5f3","contributors":{"authors":[{"text":"Rover, Jennifer R. 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":2941,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":444842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, C.K.","contributorId":25780,"corporation":false,"usgs":true,"family":"Wright","given":"C.K.","affiliations":[],"preferred":false,"id":444841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Euliss, Ned H. Jr. ceuliss@usgs.gov","contributorId":2916,"corporation":false,"usgs":true,"family":"Euliss","given":"Ned","suffix":"Jr.","email":"ceuliss@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":false,"id":444843,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":444844,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":444840,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70034138,"text":"70034138 - 2011 - Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data","interactions":[],"lastModifiedDate":"2012-03-12T17:21:50","indexId":"70034138","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data","docAbstract":"Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs. ?? 2011 by the authors.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.3390/rs3040816","issn":"20724292","usgsCitation":"Gumma, M., Thenkabail, P., Hideto, F., Nelson, A., Dheeravath, V., Busia, D., and Rala, A., 2011, Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data: Remote Sensing, v. 3, no. 4, p. 816-835, https://doi.org/10.3390/rs3040816.","startPage":"816","endPage":"835","numberOfPages":"20","costCenters":[],"links":[{"id":475249,"rank":10000,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs3040816","text":"Publisher Index Page"},{"id":216515,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.3390/rs3040816"},{"id":244392,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2011-04-15","publicationStatus":"PW","scienceBaseUri":"505a505de4b0c8380cd6b64a","contributors":{"authors":[{"text":"Gumma, M.K.","contributorId":12286,"corporation":false,"usgs":true,"family":"Gumma","given":"M.K.","email":"","affiliations":[],"preferred":false,"id":444275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, P.S.","contributorId":66071,"corporation":false,"usgs":true,"family":"Thenkabail","given":"P.S.","email":"","affiliations":[],"preferred":false,"id":444281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hideto, F.","contributorId":37567,"corporation":false,"usgs":true,"family":"Hideto","given":"F.","email":"","affiliations":[],"preferred":false,"id":444276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, A.","contributorId":50343,"corporation":false,"usgs":true,"family":"Nelson","given":"A.","affiliations":[],"preferred":false,"id":444277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dheeravath, V.","contributorId":55234,"corporation":false,"usgs":true,"family":"Dheeravath","given":"V.","affiliations":[],"preferred":false,"id":444278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Busia, D.","contributorId":60471,"corporation":false,"usgs":true,"family":"Busia","given":"D.","email":"","affiliations":[],"preferred":false,"id":444280,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rala, A.","contributorId":58119,"corporation":false,"usgs":true,"family":"Rala","given":"A.","email":"","affiliations":[],"preferred":false,"id":444279,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70032679,"text":"70032679 - 2011 - Expansion of urban area and wastewater irrigated rice area in Hyderabad, India","interactions":[],"lastModifiedDate":"2012-03-12T17:21:23","indexId":"70032679","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2111,"text":"Irrigation and Drainage Systems","active":true,"publicationSubtype":{"id":10}},"title":"Expansion of urban area and wastewater irrigated rice area in Hyderabad, India","docAbstract":"The goal of this study was to investigate land use changes in urban and peri-urban Hyderabad and their influence on wastewater irrigated rice using Landsat ETM + data and spectral matching techniques. The main source of irrigation water is the Musi River, which collects a large volume of wastewater and stormwater while running through the city. From 1989 to 2002, the wastewater irrigated area along the Musi River increased from 5,213 to 8,939 ha with concurrent expansion of the city boundaries from 22,690 to 42,813 ha and also decreased barren lands and range lands from 86,899 to 66,616 ha. Opportunistic shifts in land use, especially related to wastewater irrigated agriculture, were seen as a response to the demand for fresh vegetables and easy access to markets, exploited mainly by migrant populations. While wastewater irrigated agriculture contributes to income security of marginal groups, it also supplements the food basket of many city dwellers. Landsat ETM + data and advanced methods such as spectral matching techniques are ideal for quantifying urban expansion and associated land use changes, and are useful for urban planners and decision makers alike. ?? 2011 Springer Science+Business Media B.V.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Irrigation and Drainage Systems","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1007/s10795-011-9117-y","issn":"01686291","usgsCitation":"Gumma, K., van, R.D., Nelson, A., Thenkabail, P., Aakuraju, R.V., and Amerasinghe, P., 2011, Expansion of urban area and wastewater irrigated rice area in Hyderabad, India: Irrigation and Drainage Systems, v. 25, no. 3, p. 135-149, https://doi.org/10.1007/s10795-011-9117-y.","startPage":"135","endPage":"149","numberOfPages":"15","costCenters":[],"links":[{"id":241260,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213615,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10795-011-9117-y"}],"volume":"25","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-01-05","publicationStatus":"PW","scienceBaseUri":"505a0db7e4b0c8380cd53168","contributors":{"authors":[{"text":"Gumma, K.M.","contributorId":6266,"corporation":false,"usgs":true,"family":"Gumma","given":"K.M.","email":"","affiliations":[],"preferred":false,"id":437410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van, Rooijen D.","contributorId":46775,"corporation":false,"usgs":true,"family":"van","given":"Rooijen","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":437412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, A.","contributorId":50343,"corporation":false,"usgs":true,"family":"Nelson","given":"A.","affiliations":[],"preferred":false,"id":437413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thenkabail, P.S.","contributorId":66071,"corporation":false,"usgs":true,"family":"Thenkabail","given":"P.S.","email":"","affiliations":[],"preferred":false,"id":437415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aakuraju, Radha V.","contributorId":44359,"corporation":false,"usgs":false,"family":"Aakuraju","given":"Radha","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":437411,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amerasinghe, P.","contributorId":53609,"corporation":false,"usgs":true,"family":"Amerasinghe","given":"P.","email":"","affiliations":[],"preferred":false,"id":437414,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70032445,"text":"70032445 - 2011 - Mapping and monitoring Louisiana's mangroves in the aftermath of the 2010 Gulf of Mexico Oil spill","interactions":[],"lastModifiedDate":"2017-04-06T12:32:38","indexId":"70032445","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping and monitoring Louisiana's mangroves in the aftermath of the 2010 Gulf of Mexico Oil spill","docAbstract":"<p><span>Information regarding the present condition, historical status, and dynamics of mangrove forests is needed to study the impacts of the Gulf of Mexico oil spill and other stressors affecting mangrove ecosystems. Such information is unavailable for Louisiana at sufficient spatial and thematic detail. We prepared mangrove forest distribution maps of Louisiana (prior to the oil spill) at 1&nbsp;m and 30&nbsp;m spatial resolution using aerial photographs and Landsat satellite data, respectively. Image classification was performed using a decision-tree classification approach. We also prepared land-cover change pairs for 1983, 1984, and every 2&nbsp;y from 1984 to 2010 depicting “ecosystem shifts” (e.g., expansion, retraction, and disappearance). This new spatiotemporal information could be used to assess short-term and long-term impacts of the oil spill on mangroves. Finally, we propose an operational methodology based on remote sensing (Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER], hyperspectral, light detection and ranging [LIDAR], aerial photographs, and field inventory data) to monitor the existing and emerging mangrove areas and their disturbance and regrowth patterns. Several parameters such as spatial distribution, ecosystem shifts, species composition, and tree height/biomass could be measured to assess the impact of the oil spill and mangrove recovery and restoration. Future research priorities will be to quantify the impacts and recovery of mangroves considering multiple stressors and perturbations, including oil spill, winter freeze, sea-level rise, land subsidence, and land-use/land-cover change for the entire Gulf Coast.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-11-00028.1","issn":"07490208","usgsCitation":"Giri, S., Long, J., and Tieszen, L., 2011, Mapping and monitoring Louisiana's mangroves in the aftermath of the 2010 Gulf of Mexico Oil spill: Journal of Coastal Research, v. 27, no. 6, p. 1059-1064, https://doi.org/10.2112/JCOASTRES-D-11-00028.1.","productDescription":"6 p.","startPage":"1059","endPage":"1064","numberOfPages":"6","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":241341,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":213689,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.2112/JCOASTRES-D-11-00028.1"}],"volume":"27","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5050e4b0c8380cd6b5d2","contributors":{"authors":[{"text":"Giri, S.","contributorId":102621,"corporation":false,"usgs":true,"family":"Giri","given":"S.","email":"","affiliations":[],"preferred":false,"id":436223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, J.","contributorId":41993,"corporation":false,"usgs":true,"family":"Long","given":"J.","affiliations":[],"preferred":false,"id":436222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tieszen, L.","contributorId":22887,"corporation":false,"usgs":true,"family":"Tieszen","given":"L.","email":"","affiliations":[],"preferred":false,"id":436221,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70032259,"text":"70032259 - 2011 - Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest","interactions":[],"lastModifiedDate":"2017-04-06T12:27:29","indexId":"70032259","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest","docAbstract":"It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can providemultitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.","language":"English","publisher":"Taylor & Francis","publisherLocation":"London, UK","doi":"10.1080/01431161.2010.524678","issn":"01431161","usgsCitation":"Chen, X., Vogelmann, J., Rollins, M., Ohlen, D., Key, C.H., Yang, L., Huang, C., and Shi, H., 2011, Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest: International Journal of Remote Sensing, v. 32, no. 23, p. 7905-7927, https://doi.org/10.1080/01431161.2010.524678.","productDescription":"23 p.","startPage":"7905","endPage":"7927","numberOfPages":"23","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":486672,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1PEKZPH","text":"USGS data release","linkHelpText":"Fuels Data for the 2000 Jasper Fire in the Black Hills of South Dakota, Collected in 2023 and 2024"},{"id":242579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":214827,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161.2010.524678"}],"volume":"32","issue":"23","noUsgsAuthors":false,"publicationDate":"2011-08-09","publicationStatus":"PW","scienceBaseUri":"5059ff62e4b0c8380cd4f164","contributors":{"authors":[{"text":"Chen, Xuexia","contributorId":14213,"corporation":false,"usgs":true,"family":"Chen","given":"Xuexia","affiliations":[],"preferred":false,"id":513930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vogelmann, James E. 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":649,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James E.","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":513927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rollins, Matt mrollins@usgs.gov","contributorId":647,"corporation":false,"usgs":true,"family":"Rollins","given":"Matt","email":"mrollins@usgs.gov","affiliations":[],"preferred":true,"id":513926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ohlen, Donald","contributorId":121016,"corporation":false,"usgs":true,"family":"Ohlen","given":"Donald","affiliations":[],"preferred":false,"id":513932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Key, Carl H. carl_key@usgs.gov","contributorId":4138,"corporation":false,"usgs":true,"family":"Key","given":"Carl","email":"carl_key@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":513928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":513929,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":513931,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":513925,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70043294,"text":"70043294 - 2010 - A self-trained classification technique for producing 30 m percent-water maps from Landsat data","interactions":[],"lastModifiedDate":"2013-02-26T20:04:00","indexId":"70043294","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2010","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":"A self-trained classification technique for producing 30 m percent-water maps from Landsat data","docAbstract":"Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor and Francis","publisherLocation":"Philadelphia, PA","doi":"10.1080/01431161003667455","usgsCitation":"Rover, J.R., Wylie, B.K., and Ji, L., 2010, A self-trained classification technique for producing 30 m percent-water maps from Landsat data: International Journal of Remote Sensing, v. 31, no. 8, p. 2197-2203, https://doi.org/10.1080/01431161003667455.","productDescription":"7 p.","startPage":"2197","endPage":"2203","ipdsId":"IP-017132","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268425,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161003667455"}],"volume":"31","issue":"8","noUsgsAuthors":false,"publicationDate":"2010-04-28","publicationStatus":"PW","scienceBaseUri":"53cd4a8be4b0b290850efd77","contributors":{"authors":[{"text":"Rover, Jennifer R. 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":2941,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":473315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":473313,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":2832,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":473314,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70043234,"text":"70043234 - 2010 - A procedure for radiometric recalibration of Landsat 5 TM reflective-band data","interactions":[],"lastModifiedDate":"2013-02-27T17:49:39","indexId":"70043234","displayToPublicDate":"2013-01-01T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"A procedure for radiometric recalibration of Landsat 5 TM reflective-band data","docAbstract":"From the Landsat program's inception in 1972 to the present, the Earth science user community has been benefiting from a historical record of remotely sensed data. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for the L5 TM imagery used the detectors' response to the internal calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time, causing radiometric calibration errors up to 20%. In May 2003, the L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center through the National Landsat Archive Production System (NLAPS) were updated to use a lifetime lookup-table (LUT) gain model to radiometrically calibrate TM data instead of using scene-specific IC gains. Further modification of the gain model was performed in 2007. The L5 TM data processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing level-1 products. The best recalibration results are obtained if the work-order report that was included in the original standard data product delivery is available. However, if users do not have the original work-order report, the IC trends can be used for recalibration. The IC trends were generated using the radiometric gain trends recorded in the NLAPS database. This paper provides the details of the recalibration procedure for the following: 1) data processed using IC where users have the work-order file; 2) data processed using IC where users do not have the work-order file; 3) data processed using prelaunch calibration parameters; and 4) data processed using the previous version of the LUT (e.g., LUT03) that was released before April 2, 2007.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"IEEE Transactions on Geoscience and Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"IEEE","publisherLocation":"Washington, D.C.","doi":"10.1109/TGRS.2009.2026166","usgsCitation":"Chander, G., Haque, M., Micijevic, E., and Barsi, J., 2010, A procedure for radiometric recalibration of Landsat 5 TM reflective-band data: IEEE Transactions on Geoscience and Remote Sensing, v. 48, no. 1, p. 556-574, https://doi.org/10.1109/TGRS.2009.2026166.","productDescription":"19 p.","startPage":"556","endPage":"574","ipdsId":"IP-010187","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":268420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":268395,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1109/TGRS.2009.2026166"}],"volume":"48","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"512f38f3e4b0cad81a732d8e","contributors":{"authors":[{"text":"Chander, G.","contributorId":51449,"corporation":false,"usgs":true,"family":"Chander","given":"G.","affiliations":[],"preferred":false,"id":473203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haque, M.O. 0000-0002-0914-1446","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":73087,"corporation":false,"usgs":true,"family":"Haque","given":"M.O.","affiliations":[],"preferred":false,"id":473205,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Micijevic, E. 0000-0002-3828-9239","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":59939,"corporation":false,"usgs":true,"family":"Micijevic","given":"E.","affiliations":[],"preferred":false,"id":473204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barsi, J. A.","contributorId":24085,"corporation":false,"usgs":true,"family":"Barsi","given":"J. A.","affiliations":[],"preferred":false,"id":473202,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":9000586,"text":"ds566 - 2010 - Remotely sensed imagery revealing the effects of hurricanes Gustav and Ike on coastal Louisiana","interactions":[],"lastModifiedDate":"2012-02-10T00:10:06","indexId":"ds566","displayToPublicDate":"2011-02-07T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"566","title":"Remotely sensed imagery revealing the effects of hurricanes Gustav and Ike on coastal Louisiana","docAbstract":"Hurricane Gustav, a category 2 storm with 170 kilometers per hour (km/h) winds, approached the Louisiana coast from the south-southeast, making landfall near Cocodrie, La., on September 1, 2008 (Beven and Kimberlain, 2009); Hurricane Ike, a category 2 storm with 175 km/h winds, approached the Texas coast from the southeast, paralleling offshore of the Louisiana coast, before making landfall along the north end of Galveston Island, Tex., on September 13, 2008 (Berg, 2009). Hurricane Ike's large wind field elevated water levels, increasing coastal flooding well before making landfall (Berg, 2009). An initial land area change assessment, based on comparison of Landsat Thematic Mapper (TM) satellite imagery, acquired before 2006 and after the 2008 landfalls of Hurricanes Gustav and Ike and classified to identify land and water, reported that the water area increased by 323 square kilometers (km2) in coastal Louisiana as a result of the storms (Barras, 2009). The land area decrease of 195 km2 was less than the 513 km2 decrease reported between 2004 and 2006 (Barras and others, 2008) after the landfalls of Hurricane Katrina, a strong category 3 storm that made landfall near Buras, La., on August 29, 2005, and Hurricane Rita, a category 3 storm that made landfall just west of Johnsons Bayou, La., on September 29, 2005. The 2004 to 2006 land area decrease is 49 km2 less than the 562 km2 initial change estimate based on satellite imagery obtained two months after the 2005 storms (Barras, 2007a). The comparison area used to identify the 2004 to 2006 land area change matches the extent of historical land and water data used to quantify coastal land loss from 1956 to 2006 (Barras and others, 2008) and is 3,841 km2 less than the 33,457.7 km2 used for Barras (2006) and Barras (2009). The greater comparison area used for the 2006 to 2008 period (Barras, 2009) resulted in a 2004 to 2006 loss estimate of 525.8 km2, 13.0 km2 greater than the 512.8 km2 estimate reported in Barras (2008).","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds566","usgsCitation":"Barras, J., Brock, J., Morton, R., and Travers, L.J., 2010, Remotely sensed imagery revealing the effects of hurricanes Gustav and Ike on coastal Louisiana: U.S. Geological Survey Data Series 566, HTML Page; CD-ROM, https://doi.org/10.3133/ds566.","productDescription":"HTML Page; CD-ROM","additionalOnlineFiles":"Y","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":126205,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_566.bmp"},{"id":19207,"rank":200,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/566/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -94.5,28 ], [ -94.5,31 ], [ -88.75,31 ], [ -88.75,28 ], [ -94.5,28 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e49e2e4b07f02db5e4f1b","contributors":{"authors":[{"text":"Barras, John A. jbarras@usgs.gov","contributorId":2425,"corporation":false,"usgs":true,"family":"Barras","given":"John A.","email":"jbarras@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":344335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brock, John 0000-0002-5289-9332 jbrock@usgs.gov","orcid":"https://orcid.org/0000-0002-5289-9332","contributorId":2261,"corporation":false,"usgs":true,"family":"Brock","given":"John","email":"jbrock@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":344334,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morton, Robert A.","contributorId":88333,"corporation":false,"usgs":true,"family":"Morton","given":"Robert A.","affiliations":[],"preferred":false,"id":344337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Travers, Laurinda J. ltravers@usgs.gov","contributorId":3002,"corporation":false,"usgs":true,"family":"Travers","given":"Laurinda","email":"ltravers@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":344336,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70256004,"text":"70256004 - 2010 - Monitoring the long term stability of the IRS-P6 AWiFS sensor using the Sonoran and RVPN sites","interactions":[],"lastModifiedDate":"2024-07-12T14:21:58.241258","indexId":"70256004","displayToPublicDate":"2010-11-11T09:16:43","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Monitoring the long term stability of the IRS-P6 AWiFS sensor using the Sonoran and RVPN sites","docAbstract":"<p><span>This paper focuses on radiometric and geometric assessment of the Indian Remote Sensing (IRS-P6) Advanced Wide Field Sensor (AWiFS) sensor using the Sonoran desert and Railroad Valley Playa, Nevada (RVPN) ground sites. Imageto- Image (I2I) accuracy and relative band-to-band (B2B) accuracy were measured. I2I accuracy of the AWiFS imagery was assessed by measuring the imagery against Landsat Global Land Survey (GLS) 2000. The AWiFS images were typically registered to within one pixel to the GLS 2000 mosaic images. The B2B process used the same concepts as the I2I, except instead of a reference image and a search image; the individual bands of a multispectral image are tested against each other. The B2B results showed that all the AWiFS multispectral bands are registered to sub-pixel accuracy. Using the limited amount of scenes available over these ground sites, the reflective bands of AWiFS sensor indicate a long-term drift in the top-of-atmosphere (TOA) reflectance. Because of the limited availability of AWiFS scenes over these ground sites, a comprehensive evaluation of the radiometric stability using these sites is not possible. In order to overcome this limitation, a cross-comparison between AWiFS and Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) was performed using image statistics based on large common areas observed by the sensors within 30 minutes. Regression curves and coefficients of determination for the TOA trends from these sensors were generated to quantify the uncertainty in these relationships and to provide an assessment of the calibration differences between these sensors.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SPIE 7862, Earth observing missions and sensors: Development, implementation, and characterization","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Earth Observing Missions and Sensors: Development, Implementation, and Characterization","conferenceDate":"October 11-14, 2010","conferenceLocation":"Incheon, Republic of Korea","language":"English","publisher":"Society of Photo-Optical Instrumentation Engineers (SPIE)","doi":"10.1117/12.869537","usgsCitation":"Chander, G., Sampath, A., Angal, A., Choi, T., and Xiong, X., 2010, Monitoring the long term stability of the IRS-P6 AWiFS sensor using the Sonoran and RVPN sites, <i>in</i> Proceedings of SPIE 7862, Earth observing missions and sensors: Development, implementation, and characterization, v. 7862, Incheon, Republic of Korea, October 11-14, 2010, 78620K, 12 p., https://doi.org/10.1117/12.869537.","productDescription":"78620K, 12 p.","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":431005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7862","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":906329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sampath, Aparajithan 0000-0002-6922-4913 asampath@usgs.gov","orcid":"https://orcid.org/0000-0002-6922-4913","contributorId":3622,"corporation":false,"usgs":true,"family":"Sampath","given":"Aparajithan","email":"asampath@usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":906330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angal, Amit","contributorId":67394,"corporation":false,"usgs":true,"family":"Angal","given":"Amit","email":"","affiliations":[],"preferred":false,"id":906331,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choi, Taeyoung","contributorId":146955,"corporation":false,"usgs":false,"family":"Choi","given":"Taeyoung","email":"","affiliations":[],"preferred":false,"id":906332,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":906333,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":98855,"text":"gip107 - 2010 - Postcard for Ride the Rockies 2010","interactions":[],"lastModifiedDate":"2012-02-02T00:04:04","indexId":"gip107","displayToPublicDate":"2010-11-02T00:00:00","publicationYear":"2010","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":"107","title":"Postcard for Ride the Rockies 2010","docAbstract":"2010 Ride The Rockies route on shaded-relief mosaic of USGS Landsat 7 satellite images, southwestern Colorado.\r\n\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/gip107","usgsCitation":"Van Sistine, D., 2010, Postcard for Ride the Rockies 2010: U.S. Geological Survey General Information Product 107, Postcard, https://doi.org/10.3133/gip107.","productDescription":"Postcard","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"links":[{"id":126087,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/gip_107.jpg"},{"id":14268,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/gip/107/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4ad5e4b07f02db683a6f","contributors":{"authors":[{"text":"Van Sistine, D.R.","contributorId":45250,"corporation":false,"usgs":true,"family":"Van Sistine","given":"D.R.","email":"","affiliations":[],"preferred":false,"id":306724,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70157549,"text":"70157549 - 2010 - Preliminary assessment of several parameters to measure and compare usefulness of the CEOS reference pseudo-invariant calibration sites","interactions":[],"lastModifiedDate":"2017-04-25T16:33:06","indexId":"70157549","displayToPublicDate":"2010-10-13T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary assessment of several parameters to measure and compare usefulness of the CEOS reference pseudo-invariant calibration sites","docAbstract":"<p><span>Test sites are central to any future quality assurance and quality control (QA/QC) strategy. The Committee on Earth Observation Satellites (CEOS) Working Group for Calibration and Validation (WGCV) Infrared Visible Optical Sensors (IVOS) worked with collaborators around the world to establish a core set of CEOS-endorsed, globally distributed, reference standard test sites (both instrumented and pseudo-invariant) for the post-launch calibration of space-based optical imaging sensors. The pseudo-invariant calibration sites (PICS) have high reflectance and are usually made up of sand dunes with low aerosol loading and practically no vegetation. The goal of this paper is to provide preliminary assessment of \"several parameters\" than can be used on an operational basis to compare and measure usefulness of reference sites all over the world. The data from Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Earth Observing-1 (EO-1) Hyperion sensors over the CEOS PICS were used to perform a preliminary assessment of several parameters, such as usable area, data availability, top-of-atmosphere (TOA) reflectance, at-sensor brightness temperature, spatial uniformity, temporal stability, spectral stability, and typical spectrum observed over the sites.</span></p>","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"SPIE Proceedings Volume 7826","conferenceTitle":"Sensors, Systems, and Next-Generation Satellites XIV","conferenceDate":"September 20, 2010","conferenceLocation":"Toulouse, France","language":"English","publisher":"SPIE","doi":"10.1117/12.865166","usgsCitation":"Chander, G., Angal, A., Xiong, X., Helder, D.L., Mishra, N., Choi, T., and Wu, A., 2010, Preliminary assessment of several parameters to measure and compare usefulness of the CEOS reference pseudo-invariant calibration sites, <i>in</i> SPIE Proceedings Volume 7826, v. 7826, Toulouse, France, September 20, 2010, 12 p., https://doi.org/10.1117/12.865166.","productDescription":"12 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-025073","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":308619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7826","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56067036e4b058f706e51947","contributors":{"authors":[{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":573566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angal, Amit","contributorId":67394,"corporation":false,"usgs":true,"family":"Angal","given":"Amit","email":"","affiliations":[],"preferred":false,"id":573567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":573568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helder, Dennis L.","contributorId":105613,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":573569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishra, Nischal nischal.mishra.ctr@usgs.gov","contributorId":148000,"corporation":false,"usgs":false,"family":"Mishra","given":"Nischal","email":"nischal.mishra.ctr@usgs.gov","affiliations":[],"preferred":false,"id":573570,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Choi, Taeyoung","contributorId":146955,"corporation":false,"usgs":false,"family":"Choi","given":"Taeyoung","email":"","affiliations":[],"preferred":false,"id":573571,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wu, Aisheng","contributorId":65362,"corporation":false,"usgs":true,"family":"Wu","given":"Aisheng","email":"","affiliations":[],"preferred":false,"id":573572,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70043115,"text":"70043115 - 2010 - Using Landsat satellite data to support pesticide exposure assessment in California","interactions":[],"lastModifiedDate":"2013-05-28T11:33:18","indexId":"70043115","displayToPublicDate":"2010-10-13T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2050,"text":"International Journal of Health Geographics","active":true,"publicationSubtype":{"id":10}},"title":"Using Landsat satellite data to support pesticide exposure assessment in California","docAbstract":"Background\nThe recent U.S. Geological Survey policy offering Landsat satellite data at no cost provides researchers new opportunities to explore relationships between environment and health. The purpose of this study was to examine the potential for using Landsat satellite data to support pesticide exposure assessment in California.\n\nMethods and Results\nWe collected a dense time series of 24 Landsat 5 and 7 images spanning the year 2000 for an agricultural region in Fresno County. We intersected the Landsat time series with the California Department of Water Resources (CDWR) land use map and selected field samples to define the phenological characteristics of 17 major crop types or crop groups. We found the frequent overpass of Landsat enabled detection of crop field conditions (e.g., bare soil, vegetated) over most of the year. However, images were limited during the winter months due to cloud cover. Many samples designated as single-cropped in the CDWR map had phenological patterns that represented multi-cropped or non-cropped fields, indicating they may have been misclassified.\n\nConclusions\nWe found the combination of Landsat 5 and 7 image data would clearly benefit pesticide exposure assessment in this region by 1) providing information on crop field conditions at or near the time when pesticides are applied, and 2) providing information for validating the CDWR map. The Landsat image time-series was useful for identifying idle, single-, and multi-cropped fields. Landsat data will be limited during the winter months due to cloud cover, and for years prior to the Landsat 7 launch (1999) when only one satellite was operational at any given time. We suggest additional research to determine the feasibility of integrating CDWR land use maps and Landsat data to derive crop maps in locations and time periods where maps are not available, which will allow for substantial improvements to chemical exposure estimation.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Health Geographics","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1186/1476-072X-9-46","usgsCitation":"Maxwell, S.K., Airola, M., and Nuckols, J.R., 2010, Using Landsat satellite data to support pesticide exposure assessment in California: International Journal of Health Geographics, v. 9, no. 46, 14 p., https://doi.org/10.1186/1476-072X-9-46.","productDescription":"14 p.","ipdsId":"IP-015841","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":475653,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/1476-072x-9-46","text":"Publisher Index Page"},{"id":272887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":267002,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1186/1476-072X-9-46"}],"country":"United States","state":"California","county":"Fresno","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.92,35.91 ], [ -120.92,37.58 ], [ -118.36,37.58 ], [ -118.36,35.91 ], [ -120.92,35.91 ] ] ] } } ] }","volume":"9","issue":"46","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51a5d1f0e4b0605bc571f029","contributors":{"authors":[{"text":"Maxwell, Susan K.","contributorId":90198,"corporation":false,"usgs":true,"family":"Maxwell","given":"Susan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":472986,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Airola, Matthew","contributorId":51630,"corporation":false,"usgs":true,"family":"Airola","given":"Matthew","affiliations":[],"preferred":false,"id":472984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nuckols, John R.","contributorId":87037,"corporation":false,"usgs":true,"family":"Nuckols","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":472985,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":98738,"text":"gip111 - 2010 - Earth as art three","interactions":[],"lastModifiedDate":"2012-02-02T00:13:58","indexId":"gip111","displayToPublicDate":"2010-09-25T00:00:00","publicationYear":"2010","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":"111","title":"Earth as art three","docAbstract":"For most of us, deserts, mountains, river valleys, coastlines even dry lakebeds are relatively familiar features of the Earth's terrestrial environment. For earth scientists, they are the focus of considerable scientific research. Viewed from a unique and unconventional perspective, Earth's geographic attributes can also be a surprising source of awe-inspiring art. That unique perspective is space. The artists for the Earth as Art Three exhibit are the Landsat 5 and Landsat 7 satellites, which orbit approximately 705 kilometers (438 miles) above the Earth's surface. While studying the images these satellites beam down daily, researchers are often struck by the sheer beauty of the scenes. Such images inspire the imagination and go beyond scientific value to remind us how stunning, intricate, and simply amazing our planet's features can be. Instead of paint, the medium for these works of art is light. But Landsat satellite sensors don't see light as human eyes do; instead, they see radiant energy reflected from Earth's surface in certain wavelengths, or bands, of red, green, blue, and infrared light. When these different bands are combined into a single image, remarkable patterns, colors, and shapes emerge. The Earth as Art Three exhibit provides fresh and inspiring glimpses of different parts of our planet's complex surface. The images in this collection were chosen solely based on their aesthetic appeal. Many of the images have been manipulated to enhance color variations or details. They are not intended for scientific interpretation only for your viewing pleasure. Enjoy!","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/gip111","usgsCitation":"Water Resources Division, U.S. Geological Survey, 2010, Earth as art three: U.S. Geological Survey General Information Product 111, 20 p., https://doi.org/10.3133/gip111.","productDescription":"20 p.","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":115976,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/gip_111.jpg"},{"id":14148,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/gip/111/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a51e4b07f02db62a2f2","contributors":{"authors":[{"text":"Water Resources Division, U.S. Geological Survey","contributorId":128075,"corporation":true,"usgs":false,"organization":"Water Resources Division, U.S. Geological Survey","id":535041,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":98735,"text":"i2600I - 2010 - Coastal-change and glaciological map of the Ross Island area, Antarctica","interactions":[],"lastModifiedDate":"2012-02-10T00:11:56","indexId":"i2600I","displayToPublicDate":"2010-09-24T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":320,"text":"IMAP","code":"I","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"2600","chapter":"I","title":"Coastal-change and glaciological map of the Ross Island area, Antarctica","docAbstract":"Reduction in the area and volume of Earth?s two polar ice sheets is intricately linked to changes in global climate and to the resulting rise in sea level. Measurement of changes in area and mass balance of the Antarctic ice sheet was given a very high priority in recommendations by the Polar Research Board of the National Research Council. On the basis of these recommendations, the U.S. Geological Survey used its archive of satellite images to document changes in the cryospheric coastline of Antarctica and analyze the glaciological features of the coastal regions. \r\n\r\nThe Ross Island area map is bounded by long 141? E. and 175? E. and by lat 76? S. and 81? S. The map covers the part of southern Victoria Land that includes the northwestern Ross Ice Shelf, the McMurdo Ice Shelf, part of the polar plateau and Transantarctic Mountains, the McMurdo Dry Valleys, northernmost Shackleton Coast, Hillary Coast, the southern part of Scott Coast, and Ross Island. Little noticeable change has occurred in the ice fronts on the map, so the focus is on glaciological features. In the western part of the map area, the polar plateau of East Antarctica, once thought to be a featureless region, has subtle wavelike surface forms (megadunes) and flow traces of glaciers that originate far inland and extend to the coast or into the Ross Ice Shelf. There are numerous outlet glaciers. Glaciers drain into the McMurdo Dry Valleys, through the Transantarctic Mountains into the Ross Sea, or into the Ross Ice Shelf. Byrd Glacier is the largest. West of the Transantarctic Mountains are areas of blue ice, readily identifiable on Landsat images, that have been determined to be prime areas for finding meteorites. Three subglacial lakes have been identified in the map area. Because McMurdo Station, the main U.S. scientific research station in Antarctica, is located on Ross Island in the map area, many of these and other features in the area have been studied extensively. \r\n\r\nThe paper version of this map is available for purchase from the USGS Store.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/i2600I","collaboration":"Prepared in cooperation with the Scott Polar Research Institute, University of Cambridge, United Kingdom ","usgsCitation":"Ferrigno, J.G., Foley, K.M., Swithinbank, C., and Williams, R., 2010, Coastal-change and glaciological map of the Ross Island area, Antarctica: U.S. Geological Survey IMAP 2600, Map: 43.59 inches x 27.34 inches; Pamphlet: iv, 14 p.; Appendices; Tables, https://doi.org/10.3133/i2600I.","productDescription":"Map: 43.59 inches x 27.34 inches; Pamphlet: iv, 14 p.; Appendices; Tables","additionalOnlineFiles":"Y","costCenters":[],"links":[{"id":115972,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/i_2600_i.jpg"},{"id":14145,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/imap/i-2600-i/","linkFileType":{"id":5,"text":"html"}}],"scale":"1","projection":"Polar Sterographic Projection","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 141,-81 ], [ 141,-76 ], [ 175,-76 ], [ 175,-81 ], [ 141,-81 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b24e4b07f02db6ae9ff","contributors":{"authors":[{"text":"Ferrigno, Jane G. jferrign@usgs.gov","contributorId":39825,"corporation":false,"usgs":true,"family":"Ferrigno","given":"Jane","email":"jferrign@usgs.gov","middleInitial":"G.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":306287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":306285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swithinbank, Charles","contributorId":26368,"corporation":false,"usgs":true,"family":"Swithinbank","given":"Charles","email":"","affiliations":[],"preferred":false,"id":306286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Richard S. 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,{"id":98729,"text":"ofr20101198 - 2010 - Land-cover change in the Ozark Highlands, 1973-2000","interactions":[],"lastModifiedDate":"2012-02-10T00:11:56","indexId":"ofr20101198","displayToPublicDate":"2010-09-23T00:00:00","publicationYear":"2010","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":"2010-1198","title":"Land-cover change in the Ozark Highlands, 1973-2000","docAbstract":"Led by the Geographic Analysis and Monitoring Program of the U.S. Geological Survey (USGS) in collaboration with the U.S. Environmental Protection Agency (EPA) and the National Aeronautics and Space Administration (NASA), the Land-Cover Trends Project was initiated in 1999 and aims to document the types, geographic distributions, and rates of land-cover change on a region by region basis for the conterminous United States, and to determine some of the key drivers and consequences of the change (Loveland and others, 2002). For 1973, 1980, 1986, 1992, and 2000 land-cover maps derived from the Landsat series are classified by visual interpretation, inspection of historical aerial photography and ground survey, into 11 land-cover classes. The classes are defined to capture land cover that is discernable in Landsat data. A stratified probability-based sampling methodology undertaken within the 84 Omernik Level III Ecoregions (Omernik, 1987) was used to locate the blocks, with 9 to 48 blocks per ecoregion. The sampling was designed to enable a statistically robust 'scaling up' of the sample-classification data to estimate areal land-cover change within each ecoregion (Loveland and others, 2002; Stehman and others, 2005).\r\n\r\nAt the time of writing, approximately 90 percent of the 84 conterminous United States ecoregions have been processed by the Land-Cover Trends Project. Results from these completed ecoregions illustrate that across the conterminous United States there is no single profile of land-cover/land-use change, rather, there are varying pulses affected by clusters of change agents (Loveland and others, 2002).\r\n\r\nLand-Cover Trends Project results for the conterminous United States to-date are being used for collaborative environmental change research with partners such as; the National Science Foundation, the National Oceanic and Atmospheric Administration, and the U.S. Fish and Wildlife Service. The strategy has also been adapted for use in a NASA global deforestation initiative, and elements of the project design are being used in the North American Carbon Program's assessment of forest disturbance.\r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101198","usgsCitation":"Karstensen, K.A., 2010, Land-cover change in the Ozark Highlands, 1973-2000: U.S. Geological Survey Open-File Report 2010-1198, iv, 13 p., https://doi.org/10.3133/ofr20101198.","productDescription":"iv, 13 p.","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1973-01-01","temporalEnd":"2000-12-31","costCenters":[{"id":383,"text":"Mid-Continent Geographic Science Center","active":true,"usgs":true}],"links":[{"id":126383,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1198.jpg"},{"id":14137,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1198/","linkFileType":{"id":5,"text":"html"}}],"geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -95,35 ], [ -95,40 ], [ -90,40 ], [ -90,35 ], [ -95,35 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b23e4b07f02db6adf0d","contributors":{"authors":[{"text":"Karstensen, Krista A. kkarstensen@usgs.gov","contributorId":286,"corporation":false,"usgs":true,"family":"Karstensen","given":"Krista","email":"kkarstensen@usgs.gov","middleInitial":"A.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":306252,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":98711,"text":"sim3103 - 2010 - Conifer health classification for Colorado, 2008","interactions":[],"lastModifiedDate":"2012-02-10T00:11:56","indexId":"sim3103","displayToPublicDate":"2010-09-17T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3103","title":"Conifer health classification for Colorado, 2008","docAbstract":"Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). \r\n\r\nMedium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. \r\n\r\nA supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. \r\n\r\nSampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009. \r\n","language":"ENGLISH","publisher":"U.S. Geological Survey","doi":"10.3133/sim3103","usgsCitation":"Cole, C.J., Noble, S.M., Blauer, S.L., Friesen, B.A., Curry, S.E., and Bauer, M., 2010, Conifer health classification for Colorado, 2008: U.S. Geological Survey Scientific Investigations Map 3103, iv, 11 p.;, https://doi.org/10.3133/sim3103.","productDescription":"iv, 11 p.;","temporalStart":"2008-01-01","temporalEnd":"2008-12-31","costCenters":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"links":[{"id":115930,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim_3103.jpg"},{"id":14119,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3103/","linkFileType":{"id":5,"text":"html"}}],"scale":"650000","projection":"Albers Conical Equal Area Projection","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -109,37 ], [ -109,41 ], [ -102,41 ], [ -102,37 ], [ -109,37 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4b1ae4b07f02db6a80ed","contributors":{"authors":[{"text":"Cole, Christopher J. cjcole@usgs.gov","contributorId":2163,"corporation":false,"usgs":true,"family":"Cole","given":"Christopher","email":"cjcole@usgs.gov","middleInitial":"J.","affiliations":[{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":306199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noble, Suzanne M. smnoble@usgs.gov","contributorId":3400,"corporation":false,"usgs":true,"family":"Noble","given":"Suzanne","email":"smnoble@usgs.gov","middleInitial":"M.","affiliations":[],"preferred":true,"id":306201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blauer, Steven L.","contributorId":23644,"corporation":false,"usgs":true,"family":"Blauer","given":"Steven","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":306202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friesen, Beverly A. bafriesen@usgs.gov","contributorId":3216,"corporation":false,"usgs":true,"family":"Friesen","given":"Beverly","email":"bafriesen@usgs.gov","middleInitial":"A.","affiliations":[{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":306200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Curry, Stacy E.","contributorId":47060,"corporation":false,"usgs":true,"family":"Curry","given":"Stacy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":306203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bauer, Mark A. mabauer@usgs.gov","contributorId":1409,"corporation":false,"usgs":true,"family":"Bauer","given":"Mark A.","email":"mabauer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":306198,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":98690,"text":"pp1386F - 2010 - Glaciers of Asia","interactions":[{"subject":{"id":98690,"text":"pp1386F - 2010 - Glaciers of Asia","indexId":"pp1386F","publicationYear":"2010","noYear":false,"chapter":"F","title":"Glaciers of Asia"},"predicate":"IS_PART_OF","object":{"id":70042384,"text":"pp1386 - 1988 - Satellite image atlas of glaciers of the world","indexId":"pp1386","publicationYear":"1988","noYear":false,"title":"Satellite image atlas of glaciers of the world"},"id":1}],"isPartOf":{"id":70042384,"text":"pp1386 - 1988 - Satellite image atlas of glaciers of the world","indexId":"pp1386","publicationYear":"1988","noYear":false,"title":"Satellite image atlas of glaciers of the world"},"lastModifiedDate":"2024-10-02T16:19:37.591014","indexId":"pp1386F","displayToPublicDate":"2010-09-11T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1386","chapter":"F","title":"Glaciers of Asia","docAbstract":"<p>This chapter is the ninth to be released in U.S. Geological Survey Professional Paper 1386, Satellite Image Atlas of Glaciers of the World, a series of 11 chapters. In each of the geographic area chapters, remotely sensed images, primarily from the Landsat 1, 2, and 3 series of spacecraft, are used to analyze the specific glacierized region of our planet under consideration and to monitor glacier changes. Landsat images, acquired primarily during the middle to late 1970s and early 1980s, were used by an international team of glaciologists and other scientists to study various geographic regions and (or) to discuss related glaciological topics. In each glacierized geographic region, the present areal distribution of glaciers is compared, wherever possible, with historical information about their past extent. The atlas provides an accurate regional inventory of the areal extent of glacier ice on our planet during the 1970s as part of a growing international scientific effort to measure global environmental change on the Earth’s surface.</p><p>The chapter is divided into seven geographic parts and one topical part: Glaciers of the Former Soviet Union (F–1), Glaciers of China (F–2), Glaciers of Afghanistan (F–3), Glaciers of Pakistan (F–4), Glaciers of India (F–5), Glaciers of Nepal (F–6), Glaciers of Bhutan (F–7), and the Paleoenvironmental Record Preserved in Middle-Latitude, High-Mountain Glaciers (F–8). Each geographic section describes the glacier extent during the 1970s and 1980s, the benchmark time period (1972–1981) of this volume, but has been updated to include more recent information.</p><p>Glaciers of the Former Soviet Union are located in the Russian Arctic and various mountain ranges of Russia and the Republics of Georgia, Kyrgyzstan, Tajikistan, and Kazakstun. The Glacier Inventory of the USSR and the World Atlas of Ice and Snow Resources recorded a total of 28,881 glaciers covering an area of 78,938 square kilometers (km<sup>2</sup>).</p><p>China includes many of the mountain-glacier systems of the world including the Himalaya, Karakorum, Tien Shan and Altay mountain ranges. The glaciers are widely scattered and cover an area of about 59,425 km<sup>2</sup>. The mountain glaciers may be classified as maritime, subcontinental or extreme continental.</p><p>In Afghanistan, more than 3,000 small glaciers occur in the Hindu Kush and Pamir mountains. Most glaciers occur on north-facing slopes shaded by mountain peaks and on east and southeast slopes that are shaded by monsoon clouds. The glaciers provide vital water resources to the region and cover an area of about 2,700 km<sup>2</sup>.</p><p>Glaciers of northern Pakistan are some of the largest and longest mid-latitude glaciers on Earth. They are located in the Hindu Kush, Himalaya, and Karakoram mountains and cover an area of about 15,000 km<sup>2</sup>. Glaciers here are important for their role in providing water resources and their hazard potential.</p><p>The glaciers in India are located in the Himalaya and cover about 8,500 km<sup>2</sup>. The Himalaya contains one of the largest reservoirs of snow and ice outside the polar regions. The glaciers are a major source of fresh water and supply meltwater to all the rivers in northern India, thereby affecting the quality of life of millions of people.</p><p>In Nepal, the glaciers are located in the Himalaya as individual glaciers; the glacierized area covers about 5,324 km<sup>2</sup>. The region is the highest mountainous region on Earth and includes the Mt. Everest region.</p><p>Glaciers in the Bhutan Himalaya have a total area of about 1,317 km<sup>2</sup>. Many recent glacier studies are focused on glacier lakes that have the potential of generating dangerous glacier lake outburst floods.</p><p>Research on the glaciers of the middle-latitude, high-mountain glaciers of Asia has also focused on the information contained in the ice cores from the glaciers. This information helps in the reconstruction of paleoclimatic records, and the computer modeling of global climate change.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Satellite image atlas of glaciers of the world (Professional Paper 1386)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/pp1386F","usgsCitation":"Kotlyakov, V.M., Dyakova, A., Koryakin, V., Kravtsova, V., Osipova, G., Varnakova, G., Vinogradov, V., Vinogradov, O., Zverkova, N., Rototaeva, O., Nosenko, G., Tsvetkov, D., Dowdeswell, J.A., Dowdeswell, E., Williams, M., Glazovskii, A., Shi, Y., Mi, D., Yao, T., Zeng, Q., Liu, C., Schroder, J., Bishop, M.P., Vohra, C.P., Hasnain, S.I., Kumar, R., Ahmad, S., Tayal, S., Higuchi, K., Watanabe, O., Fushimi, H., Takenaka, S., Nagoshi, A., Ageta, Y., Iwata, S., Cecil, L.D., Naftz, D.L., Schuster, P.F., Susong, D.D., and Green, J.R., 2010, Glaciers of Asia: U.S. Geological Survey Professional Paper 1386, 349 p., https://doi.org/10.3133/pp1386F.","productDescription":"349 p.","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":116014,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/pp_1386_f.jpg"},{"id":14096,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/pp/p1386f/","linkFileType":{"id":5,"text":"html"}}],"otherGeospatial":"Asia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              104.77499576972167,\n              -12.472139764103787\n            ],\n            [\n              130.57926391435893,\n              -10.344557578812726\n            ],\n            [\n              146.32142520315824,\n              -11.505875271901857\n            ],\n            [\n              156.07576720568426,\n              -6.725462113887772\n            ],\n            [\n              127.28135366010918,\n              14.57883559017256\n            ],\n            [\n              142.79876862379848,\n              35.983763638584634\n            ],\n            [\n              164.77249022732286,\n              54.16792679459681\n            ],\n            [\n              179.9,\n              63.13612479392796\n            ],\n            [\n              179.9,\n              76\n            ],\n            [\n              154.15139503137533,\n              76.34070151898257\n            ],\n            [\n              83.99483204069526,\n              83.01046548671911\n            ],\n            [\n              42.20247117419774,\n              68.50185497361912\n            ],\n            [\n              50.708285716149476,\n              46.31516148026691\n            ],\n            [\n              54.28911944109049,\n              46.20942794666661\n            ],\n            [\n              53.87965719130523,\n              37.60949869230042\n            ],\n            [\n              49.228770442742274,\n              37.271154448927106\n            ],\n            [\n              49.883458677515705,\n              41.66380732964174\n            ],\n            [\n              46.15448340337315,\n              43.99359526560494\n            ],\n            [\n              38.72193599772453,\n              43.78251181695401\n            ],\n            [\n              40.742280114981924,\n              41.47715204183362\n            ],\n            [\n              28.266960141818856,\n              41.32463960519422\n            ],\n            [\n              26.915588823196998,\n              37.2602556256214\n            ],\n            [\n              34.92467660454554,\n              26.8144416610712\n            ],\n            [\n              43.82153141299577,\n              12.357301165947518\n            ],\n            [\n              56.28986867337966,\n              16.834757502053364\n            ],\n            [\n              77.41354244167826,\n              6.540046997190345\n            ],\n            [\n              104.77499576972167,\n              -12.472139764103787\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -179.9,\n              72.05897835182662\n            ],\n            [\n              -179.9,\n              62.84885132362555\n            ],\n            [\n              -170,\n              62.84885132362555\n            ],\n            [\n              -170,\n              72.05897835182662\n            ],\n            [\n              -179.9,\n              72.05897835182662\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4abee4b07f02db67500c","contributors":{"editors":[{"text":"Williams, Richard S. 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,{"id":70157297,"text":"70157297 - 2010 - The use of the Sonoran Desert as a pseudo-invariant site for optical sensor cross-calibration and long-term stability monitoring","interactions":[],"lastModifiedDate":"2017-04-25T16:32:13","indexId":"70157297","displayToPublicDate":"2010-07-30T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The use of the Sonoran Desert as a pseudo-invariant site for optical sensor cross-calibration and long-term stability monitoring","docAbstract":"<p><span>The Sonoran Desert is a large, flat, pseudo-invariant site near the United States-Mexico border. It is one of the largest and hottest deserts in North America, with an area of 311,000 square km. This site is particularly suitable for calibration purposes because of its high spatial and spectral uniformity and reasonable temporal stability. This study uses measurements from four different sensors, Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Aqua MODIS, and Landsat 5 (L5) Thematic Mapper (TM), to assess the suitability of this site for long-term stability monitoring and to evaluate the &ldquo;radiometric calibration differences&rdquo; between spectrally matching bands of all four sensors. In general, the drift in the top-of-atmosphere (TOA) reflectance of each sensor over a span of nine years is within the specified calibration uncertainties. Monthly precipitation measurements of the Sonoran Desert region were obtained from the Global Historical Climatology Network (GHCN), and their effects on the retrieved TOA reflectances were evaluated. To account for the combined uncertainties in the TOA reflectance due to the surface and atmospheric Bi-directional Reflectance Distribution Function (BRDF), a semi-empirical BRDF model has been adopted to monitor and reduce the impact of illumination geometry differences on the retrieved TOA reflectances. To evaluate calibration differences between the MODIS and Landsat sensors, correction for spectral response differences using a hyperspectral sensor is also demonstrated.</span></p>","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International","conferenceTitle":"2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","conferenceDate":"July 25-30, 2010","conferenceLocation":"Honolulu, Hawaii","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2010.5652812","usgsCitation":"Angal, A., Chander, G., Choi, T., Wu, A., and Xiong, X., 2010, The use of the Sonoran Desert as a pseudo-invariant site for optical sensor cross-calibration and long-term stability monitoring, <i>in</i> Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, Honolulu, Hawaii, July 25-30, 2010, p. 1656-1659, https://doi.org/10.1109/IGARSS.2010.5652812.","productDescription":"4 p.","startPage":"1656","endPage":"1659","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-022547","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":308259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fbe44ee4b05d6c4e502913","contributors":{"authors":[{"text":"Angal, A.","contributorId":52716,"corporation":false,"usgs":true,"family":"Angal","given":"A.","affiliations":[],"preferred":false,"id":572620,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":572621,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choi, Taeyoung","contributorId":146955,"corporation":false,"usgs":false,"family":"Choi","given":"Taeyoung","email":"","affiliations":[],"preferred":false,"id":572622,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Aisheng","contributorId":65362,"corporation":false,"usgs":true,"family":"Wu","given":"Aisheng","email":"","affiliations":[],"preferred":false,"id":572623,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xiong, Xiaoxiong","contributorId":15088,"corporation":false,"usgs":true,"family":"Xiong","given":"Xiaoxiong","email":"","affiliations":[],"preferred":false,"id":572624,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70159150,"text":"70159150 - 2010 - Use of EO-1 Hyperion data to calculate spectral band adjustment factors (SBAF) between the L7 ETM+ and Terra MODIS sensors","interactions":[],"lastModifiedDate":"2017-05-10T15:50:14","indexId":"70159150","displayToPublicDate":"2010-07-30T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Use of EO-1 Hyperion data to calculate spectral band adjustment factors (SBAF) between the L7 ETM+ and Terra MODIS sensors","docAbstract":"<p><span>Different applications and technology developments in Earth observations necessarily require different spectral coverage. Thus, even for the spectral bands designed to look at the same region of the electromagnetic spectrum, the relative spectral responses (RSR) of different sensors may be different. In this study, spectral band adjustment factors (SBAF) are derived using hyperspectral Earth Observing-1 (EO-1) Hyperion measurements to adjust for the spectral band differences between the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance measurements from 2000 to 2009 over the pseudo-invariant Libya 4 reference standard test site.</span></p>","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2010 : 25 - 30 July 2010, Honolulu, Hawaii, USA","conferenceTitle":"2010 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 25-30, 2010","conferenceLocation":"Piscataway, N.J.","language":"English","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","doi":"10.1109/IGARSS.2010.5652746","usgsCitation":"Chander, G., Mishra, N., Helder, D.L., Aaron, D., Choi, T., Angal, A., and Xiong, X., 2010, Use of EO-1 Hyperion data to calculate spectral band adjustment factors (SBAF) between the L7 ETM+ and Terra MODIS sensors, <i>in</i> IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2010 : 25 - 30 July 2010, Honolulu, Hawaii, USA, Piscataway, N.J., July 25-30, 2010, p. 1667-1670, https://doi.org/10.1109/IGARSS.2010.5652746.","productDescription":"4 p,","startPage":"1667","endPage":"1670","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-022546","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":309971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5620cec6e4b06217fc478b3d","contributors":{"authors":[{"text":"Chander, Gyanesh gchander@usgs.gov","contributorId":3013,"corporation":false,"usgs":true,"family":"Chander","given":"Gyanesh","email":"gchander@usgs.gov","affiliations":[],"preferred":true,"id":577709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mishra, N.","contributorId":67379,"corporation":false,"usgs":true,"family":"Mishra","given":"N.","email":"","affiliations":[],"preferred":false,"id":577710,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Helder, Dennis L.","contributorId":105613,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":577711,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aaron, David","contributorId":83809,"corporation":false,"usgs":false,"family":"Aaron","given":"David","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":577712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Choi, T.","contributorId":48698,"corporation":false,"usgs":true,"family":"Choi","given":"T.","affiliations":[],"preferred":false,"id":577713,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Angal, A.","contributorId":52716,"corporation":false,"usgs":true,"family":"Angal","given":"A.","affiliations":[],"preferred":false,"id":577714,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xiong, X.","contributorId":37885,"corporation":false,"usgs":true,"family":"Xiong","given":"X.","affiliations":[],"preferred":false,"id":577715,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70159151,"text":"70159151 - 2010 - Operational calibration and validation of landsat data continuity mission (LDCM) sensors using the image assessment system (IAS)","interactions":[],"lastModifiedDate":"2017-04-25T16:26:59","indexId":"70159151","displayToPublicDate":"2010-07-30T00:00:00","publicationYear":"2010","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Operational calibration and validation of landsat data continuity mission (LDCM) sensors using the image assessment system (IAS)","docAbstract":"<p><span>Systematic characterization and calibration of the Landsat sensors and the assessment of image data quality are performed using the Image Assessment System (IAS). The IAS was first introduced as an element of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) ground segment and recently extended to Landsat 4 (L4) and 5 (L5) Thematic Mappers (TM) and Multispectral Sensors (MSS) on-board the Landsat 1-5 satellites. In preparation for the Landsat Data Continuity Mission (LDCM), the IAS was developed for the Earth Observer 1 (EO-1) Advanced Land Imager (ALI) with a capability to assess pushbroom sensors. This paper describes the LDCM version of the IAS and how it relates to unique calibration and validation attributes of its on-board imaging sensors. The LDCM IAS system will have to handle a significantly larger number of detectors and the associated database than the previous IAS versions. An additional challenge is that the LDCM IAS must handle data from two sensors, as the LDCM products will combine the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) spectral bands.</span></p>","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International","conferenceTitle":"Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International","conferenceDate":"July 25-30 2010","conferenceLocation":"Honolulu, Hawaii","language":"English","publisher":"Institute of Electrical and Electronics Engineers, Inc.","doi":"10.1109/IGARSS.2010.5652207","usgsCitation":"Micijevic, E., and Morfitt, R., 2010, Operational calibration and validation of landsat data continuity mission (LDCM) sensors using the image assessment system (IAS), <i>in</i> Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, Honolulu, Hawaii, July 25-30 2010, p. 2291-2294, https://doi.org/10.1109/IGARSS.2010.5652207.","productDescription":"4 p.","startPage":"2291","endPage":"2294","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-022394","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":309972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5620ce8ce4b06217fc478b02","contributors":{"authors":[{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":577716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morfitt, Ron 0000-0002-4777-4877 rmorfitt@usgs.gov","orcid":"https://orcid.org/0000-0002-4777-4877","contributorId":4097,"corporation":false,"usgs":true,"family":"Morfitt","given":"Ron","email":"rmorfitt@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":577717,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70237833,"text":"70237833 - 2010 - A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska","interactions":[],"lastModifiedDate":"2022-10-26T11:47:36.243469","indexId":"70237833","displayToPublicDate":"2010-07-15T06:45:34","publicationYear":"2010","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 comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"aep-abstract-id11\" class=\"abstract author\"><div id=\"aep-abstract-sec-id12\"><p>Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (&gt;&nbsp;0.5&nbsp;m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30&nbsp;m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250&nbsp;m and 500&nbsp;m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275&nbsp;m spatial resolution for a 1067&nbsp;km<sup>2</sup><span>&nbsp;</span>study area in Arctic Alaska. The study area is centered at 69&nbsp;°N, ranges in elevation from 130 to 770&nbsp;m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs &gt;&nbsp;0.5&nbsp;m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from<span>&nbsp;</span><i>in situ</i><span>&nbsp;</span>shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250&nbsp;m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000–2009.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2010.01.012","usgsCitation":"Selkowitz, D.J., 2010, A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska: Remote Sensing of Environment, v. 114, no. 7, p. 1338-1352, https://doi.org/10.1016/j.rse.2010.01.012.","productDescription":"15 p.","startPage":"1338","endPage":"1352","ipdsId":"IP-014402","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":408739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -141.38130176598017,\n              68.19430047782723\n            ],\n            [\n              -141.38130176598017,\n              72.2825482136995\n            ],\n            [\n              -161.95663358999144,\n              72.2825482136995\n            ],\n            [\n              -161.95663358999144,\n              68.19430047782723\n            ],\n            [\n              -141.38130176598017,\n              68.19430047782723\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"114","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855817,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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,{"id":98398,"text":"ofr20101076 - 2010 - Distribution of potential hydrothermally altered rocks in central Colorado derived from Landsat Thematic Mapper data: A geographic information system data set","interactions":[],"lastModifiedDate":"2022-06-08T20:55:51.969595","indexId":"ofr20101076","displayToPublicDate":"2010-05-18T00:00:00","publicationYear":"2010","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":"2010-1076","title":"Distribution of potential hydrothermally altered rocks in central Colorado derived from Landsat Thematic Mapper data: A geographic information system data set","docAbstract":"As part of the Central Colorado Mineral Resource Assessment Project, the digital image data for four Landsat Thematic Mapper scenes covering central Colorado between Wyoming and New Mexico were acquired and band ratios were calculated after masking pixels dominated by vegetation, snow, and terrain shadows. Ratio values were visually enhanced by contrast stretching, revealing only those areas with strong responses (high ratio values). A color-ratio composite mosaic was prepared for the four scenes so that the distribution of potentially hydrothermally altered rocks could be visually evaluated. To provide a more useful input to a Geographic Information System-based mineral resource assessment, the information contained in the color-ratio composite raster image mosaic was converted to vector-based polygons after thresholding to isolate the strongest ratio responses and spatial filtering to reduce vector complexity and isolate the largest occurrences of potentially hydrothermally altered rocks.","language":"English","publisher":"U.S. Geological Survey","doi":"10.3133/ofr20101076","usgsCitation":"Knepper, D.H., 2010, Distribution of potential hydrothermally altered rocks in central Colorado derived from Landsat Thematic Mapper data: A geographic information system data set: U.S. Geological Survey Open-File Report 2010-1076, iv, 14 p., https://doi.org/10.3133/ofr20101076.","productDescription":"iv, 14 p.","onlineOnly":"Y","costCenters":[{"id":170,"text":"Central Mineral and Environmental","active":false,"usgs":true}],"links":[{"id":125552,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2010_1076.jpg"},{"id":401951,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_93235.htm"},{"id":13649,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2010/1076/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.617,\n              37\n            ],\n            [\n              -105.8972,\n              37\n            ],\n            [\n              -105.8972,\n              41\n            ],\n            [\n              -106.617,\n              41\n            ],\n            [\n              -106.617,\n              37\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"4f4e4a7fe4b07f02db648753","contributors":{"authors":[{"text":"Knepper, Daniel H. dknepper@usgs.gov","contributorId":1242,"corporation":false,"usgs":true,"family":"Knepper","given":"Daniel","email":"dknepper@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":305203,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
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