Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
","language":"English","publisher":"JoVE","doi":"10.3791/54578","usgsCitation":"West, A.M., Evangelista, P.H., Jarnevich, C.S., Young, N.E., Stohlgren, T.J., Talbert, C., Talbert, M., Morisette, J., and Anderson, R., 2016, Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM): Journal of Visualized Experiments, v. 116, e54578, https://doi.org/10.3791/54578.","productDescription":"e54578","ipdsId":"IP-070978","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5092193","text":"Publisher Index Page"},{"id":330808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"116","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-11","publicationStatus":"PW","scienceBaseUri":"5821a0dbe4b02f1a881de95a","contributors":{"authors":[{"text":"West, Amanda M.","contributorId":176705,"corporation":false,"usgs":false,"family":"West","given":"Amanda","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":653200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evangelista, Paul H.","contributorId":14747,"corporation":false,"usgs":true,"family":"Evangelista","given":"Paul","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":653201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Nicholas E.","contributorId":58572,"corporation":false,"usgs":true,"family":"Young","given":"Nicholas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":653203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stohlgren, Thomas J. 0000-0001-9696-4450 stohlgrent@usgs.gov","orcid":"https://orcid.org/0000-0001-9696-4450","contributorId":2902,"corporation":false,"usgs":true,"family":"Stohlgren","given":"Thomas","email":"stohlgrent@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":653204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbert, Colin talbertc@usgs.gov","contributorId":4668,"corporation":false,"usgs":true,"family":"Talbert","given":"Colin","email":"talbertc@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":653205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Talbert, Marian mtalbert@usgs.gov","contributorId":5180,"corporation":false,"usgs":true,"family":"Talbert","given":"Marian","email":"mtalbert@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"preferred":false,"id":653206,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morisette, Jeffrey","contributorId":100739,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","affiliations":[],"preferred":false,"id":653207,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, Ryan","contributorId":106029,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","affiliations":[],"preferred":false,"id":653208,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70178081,"text":"70178081 - 2016 - Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico","interactions":[],"lastModifiedDate":"2016-11-02T10:55:52","indexId":"70178081","displayToPublicDate":"2016-11-02T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico","docAbstract":"We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C4) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI2) data from Landsat to develop the NDPI, and MSAVI2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI2 and summer maximum MSAVI2), and two simple, conventional VI metrics (summer average MSAVI2, summer maximum MSAVI2). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C4 grass cover as estimated from landscape photographs at 92 sites (R2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110889","usgsCitation":"Dye, D.G., Middleton, B.R., Vogel, J.M., Wu, Z., and Velasco, M.G., 2016, Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico: Remote Sensing, v. 8, no. 11, p. 1-33, https://doi.org/10.3390/rs8110889.","productDescription":"Article 889; 33 p.","startPage":"1","endPage":"33","ipdsId":"IP-069667","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470445,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110889","text":"Publisher Index Page"},{"id":330648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112,\n 31\n ],\n [\n -112,\n 33\n ],\n [\n -110,\n 33\n ],\n [\n -110,\n 31\n ],\n [\n -112,\n 31\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-28","publicationStatus":"PW","scienceBaseUri":"581afb64e4b0bb36a4ca664b","contributors":{"authors":[{"text":"Dye, Dennis G. 0000-0002-7100-272X ddye@usgs.gov","orcid":"https://orcid.org/0000-0002-7100-272X","contributorId":4233,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","email":"ddye@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652713,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogel, John M. 0000-0002-8226-1188 jvogel@usgs.gov","orcid":"https://orcid.org/0000-0002-8226-1188","contributorId":3167,"corporation":false,"usgs":true,"family":"Vogel","given":"John","email":"jvogel@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":652715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Velasco, Miguel G. 0000-0003-2559-7934 mvelasco@usgs.gov","orcid":"https://orcid.org/0000-0003-2559-7934","contributorId":2103,"corporation":false,"usgs":true,"family":"Velasco","given":"Miguel","email":"mvelasco@usgs.gov","middleInitial":"G.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":652716,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191147,"text":"70191147 - 2016 - Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive","interactions":[],"lastModifiedDate":"2017-09-27T17:15:13","indexId":"70191147","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive","docAbstract":"Groundwater dependent ecosystems (GDEs) rely on near-surface groundwater. These systems are receiving more attention with rising air temperature, prolonged drought, and where groundwater pumping captures natural groundwater discharge for anthropogenic use. Phreatophyte shrublands, meadows, and riparian areas are GDEs that provide critical habitat for many sensitive species, especially in arid and semi-arid environments. While GDEs are vital for ecosystem services and function, their long-term (i.e. ~ 30 years) spatial and temporal variability is poorly understood with respect to local and regional scale climate, groundwater, and rangeland management. In this work, we compute time series of NDVI derived from sensors of the Landsat TM, ETM +, and OLI lineage for assessing GDEs in a variety of land and water management contexts. Changes in vegetation vigor based on climate, groundwater availability, and land management in arid landscapes are detectable with Landsat. However, the effective quantification of these ecosystem changes can be undermined if changes in spectral bandwidths between different Landsat sensors introduce biases in derived vegetation indices, and if climate, and land and water management histories are not well understood. The objective of this work is to 1) use the Landsat 8 under-fly dataset to quantify differences in spectral reflectance and NDVI between Landsat 7 ETM + and Landsat 8 OLI for a range of vegetation communities in arid and semiarid regions of the southwestern United States, and 2) demonstrate the value of 30-year historical vegetation index and climate datasets for assessing GDEs. Specific study areas were chosen to represent a range of GDEs and environmental conditions important for three scenarios: baseline monitoring of vegetation and climate, riparian restoration, and groundwater level changes. Google's Earth Engine cloud computing and environmental monitoring platform is used to rapidly access and analyze the Landsat archive along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.07.004","usgsCitation":"Huntington, J., McGwire, K.C., Morton, C., Snyder, K.A., Peterson, S., Erickson, T., Niswonger, R., Carroll, R.W., Smith, G., and Allen, R., 2016, Assessing the role of climate and resource management on groundwater dependent ecosystem changes in arid environments with the Landsat archive: Remote Sensing of Environment, v. 185, p. 186-197, https://doi.org/10.1016/j.rse.2016.07.004.","productDescription":"12 p.","startPage":"186","endPage":"197","ipdsId":"IP-072882","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470547,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.07.004","text":"Publisher Index Page"},{"id":346143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ccb8a6e4b017cf314383de","contributors":{"authors":[{"text":"Huntington, Justin","contributorId":33413,"corporation":false,"usgs":true,"family":"Huntington","given":"Justin","affiliations":[],"preferred":false,"id":711359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGwire, Kenneth C.","contributorId":140699,"corporation":false,"usgs":false,"family":"McGwire","given":"Kenneth","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":711360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morton, Charles","contributorId":178787,"corporation":false,"usgs":false,"family":"Morton","given":"Charles","affiliations":[],"preferred":false,"id":711361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snyder, Keirith A.","contributorId":178786,"corporation":false,"usgs":false,"family":"Snyder","given":"Keirith","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711362,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peterson, Sarah","contributorId":196734,"corporation":false,"usgs":false,"family":"Peterson","given":"Sarah","affiliations":[],"preferred":false,"id":711363,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Erickson, Tyler","contributorId":196735,"corporation":false,"usgs":false,"family":"Erickson","given":"Tyler","affiliations":[],"preferred":false,"id":711364,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Niswonger, Richard G. rniswon@usgs.gov","contributorId":140377,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard G.","email":"rniswon@usgs.gov","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":711365,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carroll, Rosemary W.H.","contributorId":39928,"corporation":false,"usgs":true,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":711366,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Guy","contributorId":196736,"corporation":false,"usgs":false,"family":"Smith","given":"Guy","email":"","affiliations":[],"preferred":false,"id":711367,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Allen, Richard","contributorId":86694,"corporation":false,"usgs":true,"family":"Allen","given":"Richard","affiliations":[],"preferred":false,"id":711368,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70170679,"text":"70170679 - 2016 - Landsat 8: The plans, the reality, and the legacy","interactions":[],"lastModifiedDate":"2017-04-07T13:53:20","indexId":"70170679","displayToPublicDate":"2016-11-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 8: The plans, the reality, and the legacy","docAbstract":"Landsat 8, originally known as the Landsat Data Continuity Mission (LDCM), is a National Aeronautics and Space Administration (NASA)-U.S. Geological Survey (USGS) partnership that continues the legacy of continuous moderate resolution observations started in 1972. The conception of LDCM to the reality of Landsat 8 followed an arduous path extending over nearly 13 years, but the successful launch on February 11, 2013 ensures the continuity of the unparalleled Landsat record. The USGS took over mission operations on May 30, 2013 and renamed LCDM to Landsat 8. Access to Landsat 8 data was opened to users worldwide. Three years following launch we evaluate the science and applications impact of Landsat 8. With a mission objective to enable the detection and characterization of global land changes at a scale where differentiation between natural and human-induced causes of change is possible, LDCM promised incremental technical improvements in capabilities needed for Landsat scientific and applications investigations. Results show that with Landsat 8, we are acquiring more data than ever before, the radiometric and geometric quality of data are generally technically superior to data acquired by past Landsat missions, and the new measurements, e.g., the coastal aerosol and cirrus bands, are opening new opportunities. Collectively, these improvements are sparking the growth of science and applications opportunities. Equally important, with Landsat 7 still operational, we have returned to global imaging on an 8-day cycle, a capability that ended when Landsat 5 ceased operational Earth imaging in November 2011. As a result, the Landsat program is on secure footings and planning is underway to extend the record for another 20 or more years.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.07.033","usgsCitation":"Loveland, T.R., and Irons, J.R., 2016, Landsat 8: The plans, the reality, and the legacy: Remote Sensing of Environment, v. 185, p. 1-6, https://doi.org/10.1016/j.rse.2016.07.033.","productDescription":"6 p.","startPage":"1","endPage":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074490","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470463,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.07.033","text":"Publisher Index Page"},{"id":331816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584bd0dce4b077fc20250e04","contributors":{"authors":[{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":628069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":628070,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212562,"text":"70212562 - 2016 - Mapping annual forest cover in sub-humid and semi-arid regions through analysis of landsat and PALSAR imagery","interactions":[],"lastModifiedDate":"2020-08-21T13:59:59.041494","indexId":"70212562","displayToPublicDate":"2016-10-21T08:59:40","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping annual forest cover in sub-humid and semi-arid regions through analysis of landsat and PALSAR imagery","docAbstract":"Accurately mapping the spatial distribution of forests in sub-humid to semi-arid regions over years is a challenging task and causes difficulty to forest management. Relatively large uncertainties still exist in the spatial distribution of forests and deforestation in the sub-humid and semi-arid regions. Numerous publications have used either optical or synthetic aperture radar (SAR) remote sensing imagery, but the resultant forest cover maps often have large errors. In this study, we proposed a pixel- and rule-based algorithm to identify and map annual forests from 2007 to 2010 in Oklahoma, USA, a transition region with various climate and landscapes, using the integration of the L-band ALOS PALSAR Fine Beam Dual Polarization (FBD) mosaic dataset and Landsat images. The overall accuracy and Kappa coefficient of the PALSAR/Landsat forest map were about 88.2% and 0.75 in 2010, with the user and producer accuracy about 93.4% and 75.7%, based on the 3,270 random ground plots collected in 2012 and 2013. Compared with the forest products from JAXA, NLCD, OKESM and OKFRA, the PALSAR/Landsat forest map showed great improvement. The area of the PALSAR/Landsat forest was about 40,149 km2 in 2010, which was close to the area from OKFRA (40,468 km2), but much larger than those from JAXA (32,403 km2) and NLCD (37,628 km2). We analyzed annual forest cover dynamics, and the results show extensive deforestation (2,761 km2, 6.9% of the total forest area in 2010) and reforestation (3,630 km2, 9.0%) in the southeast and central Oklahoma, and the total area of forests increased by 684 km2 from 2007 to 2010. This study clearly demonstrates the potential of data fusion between PALSAR and Landsat images for mapping annual forest cover dynamics in sub-humid to semi-arid regions, and the resultant forest maps would be helpful to forest management.","language":"English","publisher":"MDPI","doi":"10.3390/rs8110933","usgsCitation":"Qin, Y., Xiao, X., Wang, J., Dong, J., Ewing, K., Hoagland, B., Hough, D.J., Fagin, T.D., Zou, Z., Geissler, G.L., Xian, G.Z., and Loveland, T., 2016, Mapping annual forest cover in sub-humid and semi-arid regions through analysis of landsat and PALSAR imagery: Remote Sensing, v. 8, no. 11, 933, 19 p., https://doi.org/10.3390/rs8110933.","productDescription":"933, 19 p.","ipdsId":"IP-080582","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110933","text":"Publisher Index 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George L.","contributorId":239086,"corporation":false,"usgs":false,"family":"Geissler","given":"George","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":797002,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796880,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":797003,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70176714,"text":"70176714 - 2016 - Multi-index time series monitoring of drought and fire effects on desert grasslands","interactions":[],"lastModifiedDate":"2017-04-27T10:48:56","indexId":"70176714","displayToPublicDate":"2016-10-04T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Multi-index time series monitoring of drought and fire effects on desert grasslands","docAbstract":"The Western United States is expected to undergo both extended periods of drought and longer wildfire seasons under forecasted global climate change and it is important to understand how these disturbances will interact and affect recovery and composition of plant communities in the future. In this research paper we describe the temporal response of grassland communities to drought and fire in southern Arizona, where land managers are using repeated, prescribed fire as a habitat restoration tool. Using a 25-year atlas of fire locations, we paired sites with multiple fires to unburned control areas and compare satellite and field-based estimates of vegetation cover over time. Two hundred and fifty Landsat TM images, dating from 1985–2011, were used to derive estimates of Total Vegetation Fractional Cover (TVFC) of live and senescent grass using the Soil-Adjusted Total Vegetation Index (SATVI) and post-fire vegetation greenness using the Normalized Difference Vegetation Index (NDVI). We also implemented a Greenness to Cover Index that is the difference of time-standardized SATVI-TVFC and NDVI values at a given time and location to identify post-fire shifts in native, non-native, and annual plant cover. The results highlight anomalous greening and browning during drought periods related to amounts of annual and non-native plant cover present. Results suggest that aggressive application of prescribed fire may encourage spread of non-native perennial grasses and annual plants, particularly during droughts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.05.026","usgsCitation":"Villarreal, M.L., Norman, L.M., Buckley, S., Wallace, C., and Coe, M.A., 2016, Multi-index time series monitoring of drought and fire effects on desert grasslands: Remote Sensing of Environment, v. 183, p. 186-197, https://doi.org/10.1016/j.rse.2016.05.026.","productDescription":"12 p.","startPage":"186","endPage":"197","ipdsId":"IP-067559","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470517,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.05.026","text":"Publisher Index Page"},{"id":329262,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c639e4b0bc0bec09c81a","contributors":{"authors":[{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":650109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":650110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buckley, Steven","contributorId":175122,"corporation":false,"usgs":false,"family":"Buckley","given":"Steven","affiliations":[],"preferred":false,"id":650111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallace, Cynthia S.A. cwallace@usgs.gov","contributorId":139089,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia S.A.","email":"cwallace@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":650112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coe, Michelle A.","contributorId":175123,"corporation":false,"usgs":false,"family":"Coe","given":"Michelle","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":650113,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192912,"text":"70192912 - 2016 - Landsat-7 ETM+ radiometric calibration status","interactions":[],"lastModifiedDate":"2017-12-20T10:56:58","indexId":"70192912","displayToPublicDate":"2016-09-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Landsat-7 ETM+ radiometric calibration status","docAbstract":"Now in its 17th year of operation, the Enhanced Thematic Mapper + (ETM+), on board the Landsat-7 satellite, continues to systematically acquire imagery of the Earth to add to the 40+ year archive of Landsat data. Characterization of the ETM+ on-orbit radiometric performance has been on-going since its launch in 1999. The radiometric calibration of the reflective bands is still monitored using on-board calibration devices, though the Pseudo-Invariant Calibration Sites (PICS) method has proven to be an effective tool as well. The calibration gains were updated in April 2013 based primarily on PICS results, which corrected for a change of as much as -0.2%/year degradation in the worst case bands. A new comparison with the SADE database of PICS results indicates no additional degradation in the updated calibration. PICS data are still being tracked though the recent trends are not well understood. The thermal band calibration was updated last in October 2013 based on a continued calibration effort by NASA/Jet Propulsion Lab and Rochester Institute of Technology. The update accounted for a 0.036 W/m2 sr μm or 0.26K at 300K bias error. The updated lifetime trend is now stable to within +/- 0.4K.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings Volume 9972, Earth Observing Systems XXI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"SPIE","doi":"10.1117/12.2238625","usgsCitation":"Barsi, J.A., Markham, B.L., Czapla-Myers, J.S., Helder, D.L., Hook, S., Schott, J.R., and Haque, O., 2016, Landsat-7 ETM+ radiometric calibration status, in Proceedings Volume 9972, Earth Observing Systems XXI, v. 9972, https://doi.org/10.1117/12.2238625.","ipdsId":"IP-079294","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470629,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://doi.org/10.1117/12.2238625","text":"External Repository"},{"id":350125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9972","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fcd4e4b06e28e9c24390","contributors":{"authors":[{"text":"Barsi, Julia A.","contributorId":71822,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","middleInitial":"A.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":725247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Markham, Brian L.","contributorId":90482,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","email":"","middleInitial":"L.","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":725248,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czapla-Myers, J. 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The goal of the initiative is to identify a collection of consistently geolocated and radiometrically calibrated images across the entire Landsat archive that is readily suitable for time-series analyses. In order to perform an accurate land change analysis, the data from all Landsat sensors must be on the same radiometric scale. Landsat 7 Enhanced Thematic Mapper Plus (ETM+) is calibrated to a radiance standard and all previous sensors are cross-calibrated to its radiometric scale. Landsat 8 Operational Land Imager (OLI) is calibrated to both radiance and reflectance standards independently. The Landsat 8 OLI reflectance calibration is considered to be most accurate. To improve radiometric calibration accuracy of historical data, Landsat 1-7 sensors also need to be cross-calibrated to the OLI reflectance scale. Results of that effort, as well as other calibration updates including the absolute and relative radiometric calibration and saturated pixel replacement for Landsat 8 OLI and absolute calibration for Landsat 4 and 5 Thematic Mappers (TM), will be implemented into Landsat products during the archive reprocessing campaign planned within the new collection management strategy. This paper reports on the planned radiometric calibration updates to the solar reflective bands of the new Landsat collection.
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Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
In his 1963 book, “The Quiet Crisis,” former Interior Secretary Stewart Udall lamented what he called the decline of natural resources in the United States under the advancements of industrialization and urbanization.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The United Nations’ Department of Economic and Social Affairs predicts 9.7 billion people will sit down every day to the global dinner table by 2050. If this prediction is correct, the world is going to need more crops, more livestock, and more efficient and sustainable agricultural practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163059","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"U.S. Geological Survey, 2016, Landsat plays a key role in reducing hunger on Earth (ver. 1.1, September 2019): U.S. Geological Survey Fact Sheet 2016–3059, 2 p., https://doi.org/10.3133/fs20163059.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-077596","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":327580,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3059/coverthb2.jpg"},{"id":367514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3059/fs20163059_2.pdf","text":"Report","size":"654 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3059"},{"id":367515,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3059/versionHist.txt","text":"Version History","size":"1.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"edition":"Version 1.0: August 24, 2016; Version 1.1 September 18, 2019","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
One of the cruelest, most complex narratives in the world today (2019) is written in the hunger of sub-Saharan Africa. When famine is the only yield from the scorched Earth, survival often depends on a heart-rending calculation—how far is the distant feeding center and how close is the nearest well?
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163060","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"U.S. Geological Survey, 2016, Landsat helps bolster food security (ver. 1.1, September 2019): U.S. Geological Survey Fact Sheet 2016–3060, 2 p., https://doi.org/10.3133/fs20163060.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-077595","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":367517,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3060/fs20163060_2.pdf","text":"Report","size":"718 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3060"},{"id":367518,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3060/versionHist.txt","text":"Version History","description":"Version History"},{"id":327590,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3060/coverthb2.jpg"}],"edition":"Version 1.0: August 24, 2016; Version 1.1 September 18, 2019","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The study described in this report represents the first time that a satellite model has been used to identify potential Southwestern Willow Flycatcher (Empidonax traillii extimus) (hereinafter referred to as “flycatcher”) breeding habitat rangewide for 2013–15. Fifty-seven Landsat scenes were required to map the entire range of the flycatcher, encompassing parts of six States and more than 1 billion 30-meter pixels. Predicted flycatcher habitat was summarized in a hierarchical fashion from largest to smallest: regionwide, State, U.S. Fish and Wildlife Service (FWS) management unit, 7.5-minute quadrangle, and critical-habitat reach. The term “predicted habitat” is used throughout this report to distinguish areas the satellite model predicts as suitable flycatcher habitat from what may actually exist on the ground. A rangewide accuracy assessment was done with 758 territories collected in 2014, and change detection was done with yearly habitat maps to identify how and where habitat changed over time. Additionally, effects of tamarisk leaf beetles (Diorhabda spp.) on flycatcher habitat were summarized for the lower Virgin River from 2010 to 2015, and simulations of how tamarisk leaf beetles may affect flycatcher habitat in the lower Colorado and upper Gila Rivers were done for 2015. Model results indicated that the largest areas of predicted flycatcher habitat at elevations below 1,524 meters were in New Mexico and Arizona, areas followed in descending order by California, Texas, Nevada, Utah, and Colorado. By FWS management unit, the largest area of flycatcher habitat during all 3 years were the Middle Rio Grande (New Mexico), followed by the Upper Gila (Arizona and New Mexico) and Middle Gila/San Pedro (Arizona) management units. The area of predicted flycatcher habitat varied considerably in 7.5-minute quadrangles, ranging from 0 to1,398 hectares (ha). Averaged across 3 years, the top three producing quadrangles were Paraje Well (New Mexico), San Marcial (New Mexico), and San Carlos Reservoir (Arizona). The top three FWS critical-habitat reaches in 2015 were Rio Grande-middle (9,544 ha), San Pedro River (1,779 ha), and Gila River-mid San Carlos (1,356 ha); this ranking did not change in 2013 or 2014. Change detection among years showed a large shift in predicted flycatcher habitat influenced by drought patterns, with California habitat decreasing and New Mexico habitat increasing. An accuracy assessment indicated that 88 percent of territories were correctly classified at a 40 percent probability threshold, with an exponential relationship between territory densities and five probability classes. A spatially explicit analysis indicated that beetles decreased predicted flycatcher habitat 94.2 percent from 2010 to 2015 along the lower Virgin River, with only 5.8 percent persisting. In contrast, beetle simulations indicated that 64.1 percent of habitat will persist along the lower Colorado River and 45 percent will persist along the upper Gila River. This project shows that the satellite model adequately predicts flycatcher habitat rangewide, but it lacks the ability to predict which patches will be occupied in a given year. The next logical step is the development of an occupancy model that ties the habitat predictions of the satellite model to patch occupancy so managers can better allocate their resources for survey and restoration activities. Finally, the methods presented in this report seem well suited for automated mapping applications and cloud-based resources.
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U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Climate change coupled with an intensifying wildfire regime is becoming an important driver of permafrost loss and ecosystem change in the northern boreal forest. There is a growing need to understand the effects of fire on the spatial distribution of permafrost and its associated ecological consequences. We focus on the effects of fire a decade after disturbance in a rocky upland landscape in the interior Alaskan boreal forest. Our main objectives were to (1) map near-surface permafrost distribution and drainage classes and (2) analyze the controls over landscape-scale patterns of post-fire permafrost degradation. Relationships among remote sensing variables and field-based data on soil properties (temperature, moisture, organic layer thickness) and vegetation (plant community composition) were analyzed using correlation, regression, and ordination analyses. The remote sensing data we considered included spectral indices from optical datasets (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI)), the principal components of a time series of radar backscatter (Advanced Land Observing Satellite—Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR)), and topographic variables from a Light Detection and Ranging (LiDAR)-derived digital elevation model (DEM). We found strong empirical relationships between the normalized difference infrared index (NDII) and post-fire vegetation, soil moisture, and soil temperature, enabling us to indirectly map permafrost status and drainage class using regression-based models. The thickness of the insulating surface organic layer after fire, a measure of burn severity, was an important control over the extent of permafrost degradation. According to our classifications, 90% of the area considered to have experienced high severity burn (using the difference normalized burn ratio (dNBR)) lacked permafrost after fire. Permafrost thaw, in turn, likely increased drainage and resulted in drier surface soils. Burn severity also influenced plant community composition, which was tightly linked to soil temperature and moisture. Overall, interactions between burn severity, topography, and vegetation appear to control the distribution of near-surface permafrost and associated drainage conditions after disturbance.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8080654","usgsCitation":"Brown, D.R., Jorgenson, M., Kielland, K., Verbyla, D.L., Prakash, A., and Koch, J.C., 2016, Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing: Remote Sensing, v. 8, no. 8, p. 1-22, https://doi.org/10.3390/rs8080654.","productDescription":"Article 654; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-077121","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":470694,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8080654","text":"Publisher Index Page"},{"id":337471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"8","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-12","publicationStatus":"PW","scienceBaseUri":"58c7afa1e4b0849ce9795ea2","contributors":{"authors":[{"text":"Brown, Dana R. N.","contributorId":140386,"corporation":false,"usgs":false,"family":"Brown","given":"Dana","email":"","middleInitial":"R. N.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":684126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M. Torre","contributorId":127675,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M. Torre","affiliations":[],"preferred":false,"id":684127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kielland, Knut","contributorId":189214,"corporation":false,"usgs":false,"family":"Kielland","given":"Knut","email":"","affiliations":[],"preferred":false,"id":684128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verbyla, David L.","contributorId":84611,"corporation":false,"usgs":true,"family":"Verbyla","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":684129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prakash, Anupma","contributorId":189216,"corporation":false,"usgs":false,"family":"Prakash","given":"Anupma","email":"","affiliations":[{"id":13662,"text":"Geophysical Institute, University of Alaska, Fairbanks","active":true,"usgs":false}],"preferred":false,"id":684130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":684125,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70185006,"text":"70185006 - 2016 - Damage and recovery assessment of the Philippines' mangroves following Super Typhoon Haiyan","interactions":[],"lastModifiedDate":"2017-05-31T16:05:49","indexId":"70185006","displayToPublicDate":"2016-08-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Damage and recovery assessment of the Philippines' mangroves following Super Typhoon Haiyan","docAbstract":"We quantified mangrove disturbance resulting from Super Typhoon Haiyan using a remote sensing approach. Mangrove areas were mapped prior to Haiyan using 30 m Landsat imagery and a supervised decision-tree classification. A time sequence of 250 m eMODIS data was used to monitor mangrove condition prior to, and following, Haiyan. Based on differences in eMODIS NDVI observations before and after the storm, we classified mangrove into three damage level categories: minimal, moderate, or severe. Mangrove damage in terms of extent and severity was greatest where Haiyan first made landfall on Eastern Samar and Western Samar provinces and lessened westward corresponding with decreasing storm intensity as Haiyan tracked from east to west across the Visayas region of the Philippines. However, within 18 months following Haiyan, mangrove areas classified as severely, moderately, and minimally damaged decreased by 90%, 81%, and 57%, respectively, indicating mangroves resilience to powerful typhoons.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpolbul.2016.06.080","usgsCitation":"Long, J., Giri, C., Primavera, J., and Trivedi, M., 2016, Damage and recovery assessment of the Philippines' mangroves following Super Typhoon Haiyan: Marine Pollution Bulletin, v. 109, no. 2, p. 734-743, https://doi.org/10.1016/j.marpolbul.2016.06.080.","productDescription":"10 p.","startPage":"734","endPage":"743","ipdsId":"IP-059352","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":337442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","volume":"109","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c7afa2e4b0849ce9795eaa","contributors":{"authors":[{"text":"Long, Jordan 0000-0002-4814-464X jlong@usgs.gov","orcid":"https://orcid.org/0000-0002-4814-464X","contributorId":3609,"corporation":false,"usgs":true,"family":"Long","given":"Jordan","email":"jlong@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":683914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giri, Chandra cgiri@usgs.gov","contributorId":189128,"corporation":false,"usgs":true,"family":"Giri","given":"Chandra","email":"cgiri@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":683915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Primavera, Jurgene H.","contributorId":56151,"corporation":false,"usgs":true,"family":"Primavera","given":"Jurgene H.","affiliations":[],"preferred":false,"id":683916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trivedi, Mandar","contributorId":189130,"corporation":false,"usgs":false,"family":"Trivedi","given":"Mandar","email":"","affiliations":[],"preferred":false,"id":683917,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174832,"text":"70174832 - 2016 - Estimating carbon sequestration in the piedmont ecoregion of the United States from 1971 to 2010","interactions":[],"lastModifiedDate":"2017-04-07T13:54:11","indexId":"70174832","displayToPublicDate":"2016-07-18T12:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1183,"text":"Carbon Balance and Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating carbon sequestration in the piedmont ecoregion of the United States from 1971 to 2010","docAbstract":"Background: Human activities have diverse and profound impacts on ecosystem carbon cycles. The Piedmont ecoregion in the eastern United States has undergone significant land use and land cover change in the past few decades. The purpose of this study was to use newly available land use and land cover change data to quantify carbon changes within the ecoregion. Land use and land cover change data (60-m spatial resolution) derived from sequential remotely sensed Landsat imagery were used to generate 960-m resolution land cover change maps for the Piedmont ecoregion. These maps were used in the Integrated Biosphere Simulator (IBIS) to simulate ecosystem carbon stock and flux changes from 1971 to 2010. Results: Results show that land use change, especially urbanization and forest harvest had significant impacts on carbon sources and sinks. From 1971 to 2010, forest ecosystems sequestered 0.25 Mg C ha−1 yr−1, while agricultural ecosystems sequestered 0.03 Mg C ha−1 yr−1. The total ecosystem C stock increased from 2271 Tg C in 1971 to 2402 Tg C in 2010, with an annual average increase of 3.3 Tg C yr−1. Conclusions: Terrestrial lands in the Piedmont ecoregion were estimated to be weak net carbon sink during the study period. The major factors contributing to the carbon sink were forest growth and afforestation; the major factors contributing to terrestrial emissions were human induced land cover change, especially urbanization and forest harvest. An additional amount of carbon continues to be stored in harvested wood products. If this pool were included the carbon sink would be stronger. Keywords: Land-use change, Carbon change, Piedmont ecoregion, IBIS model
","language":"English","publisher":"Springer","doi":"10.1186/s13021-016-0052-y","usgsCitation":"Liu, J., Sleeter, B.M., Zhu, Z., Heath, L., Tan, Z., Wilson, T., Sherba, J.T., and Zhou, D., 2016, Estimating carbon sequestration in the piedmont ecoregion of the United States from 1971 to 2010: Carbon Balance and Management, v. 11, no. 10, p. 1-13, https://doi.org/10.1186/s13021-016-0052-y.","productDescription":"13 p.","startPage":"1","endPage":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075244","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13021-016-0052-y","text":"Publisher Index Page"},{"id":325355,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-13","publicationStatus":"PW","scienceBaseUri":"578defa3e4b0f1bea0e03bc9","chorus":{"doi":"10.1186/s13021-016-0052-y","url":"http://dx.doi.org/10.1186/s13021-016-0052-y","publisher":"Springer Nature","authors":"Liu Jinxun, Sleeter Benjamin M., Zhu Zhiliang, Heath Linda S., Tan Zhengxi, Wilson Tamara S., Sherba Jason, Zhou Decheng","journalName":"Carbon Balance and Management","publicationDate":"6/13/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"6/13/2016"},"contributors":{"authors":[{"text":"Liu, Jinxun 0000-0003-0561-8988 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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":505,"text":"Office of the AD Climate and Land-Use Change","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}],"preferred":true,"id":642688,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heath, Linda S.","contributorId":84207,"corporation":false,"usgs":true,"family":"Heath","given":"Linda S.","affiliations":[],"preferred":false,"id":642692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tan, Zhengxi 0000-0002-4136-0921 ztan@usgs.gov","orcid":"https://orcid.org/0000-0002-4136-0921","contributorId":2945,"corporation":false,"usgs":true,"family":"Tan","given":"Zhengxi","email":"ztan@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":642689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":642690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sherba, Jason T. jsherba@usgs.gov","contributorId":5972,"corporation":false,"usgs":true,"family":"Sherba","given":"Jason","email":"jsherba@usgs.gov","middleInitial":"T.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":false,"id":642691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhou, Decheng","contributorId":172941,"corporation":false,"usgs":false,"family":"Zhou","given":"Decheng","email":"","affiliations":[{"id":27124,"text":"Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China","active":true,"usgs":false}],"preferred":false,"id":642693,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70174010,"text":"fs20163045 - 2016 - Landsat—The watchman that never sleeps","interactions":[],"lastModifiedDate":"2019-09-20T11:00:35","indexId":"fs20163045","displayToPublicDate":"2016-07-12T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3045","displayTitle":"Landsat—The Watchman that Never Sleeps","title":"Landsat—The watchman that never sleeps","docAbstract":"In western North America, where infestations of mountain pine beetles continue to ravage thousands of acres of forest lands, Landsat satellites bear witness to the onslaught in a way that neither humans nor most other satellites can see.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163045","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"U.S. Geological Survey, 2016, Landsat—The watchman that never sleeps (ver. 1.1, September 2019): U.S. Geological Survey Fact Sheet 2016–3045, 2 p., https://doi.org/10.3133/fs20163045.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075237","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":325029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3045/coverthb2.jpg"},{"id":367509,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3045/versionHist.txt","size":"1.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":367508,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3045/fs20163045_2.pdf","text":"Report","size":"998 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2016–3045"}],"edition":"Version 1.0: July 12, 2016; Version 1.1 September 18, 2019","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
A wildfire’s devastation of forest and rangeland seldom ends when the last embers die. In the western United States, rain on a scorched mountainside can turn ash into mudslides. Debris flows unleashed by rainstorms can put nearby homes into harm’s way and send people scrambling for safety. The infrared capabilities of Landsat satellite imagery provide vita information about potential dangers after a wildfire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163044","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"U.S. Geological Survey, 2016, When wildfire damage threatens humans, Landsat provides answers (ver. 1.1, September 2019): U.S. Geological Survey Fact Sheet 2016–3044, 2 p., https://doi.org/10.3133/fs20163044.\n","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075238","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":367506,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3044/fs20163044_2.pdf","text":"Report","size":"931 kB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2016–3044"},{"id":367507,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3044/versionHist.txt","text":"Version History","size":"1.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":325038,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3044/coverthb2.jpg"}],"edition":"Version 1.0: July 12, 2016; Version 1.1 September 18, 2019","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.
No abstract available.
","language":"English","publisher":"NASA","usgsCitation":"Schroeder, T., Loveland, T., Wulder, M., and Irons, J., 2016, Landsat Science Team: 2016 Winter meeting summary: The Earth Observer, v. 3, no. 28, p. 19-23.","productDescription":"5 p.","startPage":"19","endPage":"23","ipdsId":"IP-080431","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":421609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421567,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.nasa.gov/sites/default/files/eo_pdfs/May_June_2016_color%20508.pdf#page=19"}],"volume":"3","issue":"28","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schroeder, Todd","contributorId":330543,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":885267,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Mike","contributorId":330544,"corporation":false,"usgs":false,"family":"Wulder","given":"Mike","email":"","affiliations":[{"id":13540,"text":"Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":885269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irons, James","contributorId":330545,"corporation":false,"usgs":false,"family":"Irons","given":"James","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":885270,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170970,"text":"70170970 - 2016 - Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US","interactions":[],"lastModifiedDate":"2017-01-17T19:16:04","indexId":"70170970","displayToPublicDate":"2016-06-29T15:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US","docAbstract":"Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/17538947.2016.1158876","usgsCitation":"Schleeweis, K., Goward, S.N., Huang, C., Dwyer, J.L., Dungan, J.L., Lindsey, M.A., Michaelis, A., Rishmawi, K., and Masek, J.G., 2016, Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US: International Journal of Digital Earth, v. 9, no. 10, p. 963-980, https://doi.org/10.1080/17538947.2016.1158876.","productDescription":"18 p.","startPage":"963","endPage":"980","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073476","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":324639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-20","publicationStatus":"PW","scienceBaseUri":"5774e34fe4b07dd077c5fcf9","contributors":{"authors":[{"text":"Schleeweis, Karen","contributorId":169308,"corporation":false,"usgs":false,"family":"Schleeweis","given":"Karen","email":"","affiliations":[{"id":6679,"text":"US Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":629277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goward, Samuel N.","contributorId":44459,"corporation":false,"usgs":true,"family":"Goward","given":"Samuel","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":629278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Chengquan","contributorId":25378,"corporation":false,"usgs":true,"family":"Huang","given":"Chengquan","affiliations":[],"preferred":false,"id":629279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":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":629276,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dungan, Jennifer L.","contributorId":172579,"corporation":false,"usgs":false,"family":"Dungan","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":641329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindsey, Mary A.","contributorId":169309,"corporation":false,"usgs":false,"family":"Lindsey","given":"Mary","email":"","middleInitial":"A.","affiliations":[{"id":25467,"text":"Climate Program Office, NOAA","active":true,"usgs":false}],"preferred":false,"id":629280,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michaelis, Andrew","contributorId":169311,"corporation":false,"usgs":false,"family":"Michaelis","given":"Andrew","email":"","affiliations":[{"id":25468,"text":"University Corporation Monterey Bay / NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":629282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rishmawi, Khaldoun","contributorId":169310,"corporation":false,"usgs":false,"family":"Rishmawi","given":"Khaldoun","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":629281,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":629283,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70171084,"text":"70171084 - 2016 - Comparison of remote sensing indices for monitoring of desert cienegas","interactions":[],"lastModifiedDate":"2016-07-28T10:34:07","indexId":"70171084","displayToPublicDate":"2016-06-29T15:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":904,"text":"Arid Land Research and Management","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of remote sensing indices for monitoring of desert cienegas","docAbstract":"This research considers the applicability of different vegetation indices at 30 m resolution for mapping and monitoring desert wetland (cienega) health and spatial extent through time at Cienega Creek in southeastern Arizona, USA. Multiple stressors including the risk of decadal-scale drought, the effects of current and predicted global warming, and continued anthropogenic pressures threaten aquatic habitats in the southwest and cienegas are recognized as important sites for conservation and restoration efforts. However, cienegas present a challenge to satellite-imagery based analysis due to their small size and mixed surface cover of open water, exposed soils, and vegetation. We created time series of five well-known vegetation indices using annual Landsat Thematic Mapper (TM) images retrieved during the April–June dry season, from 1984 to 2011 to map landscape-level distribution of wetlands and monitor the temporal dynamics of individual sites. Indices included the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII). One topographic index, the Topographic Wetness Index (TWI), was analyzed to examine the utility of topography in mapping distribution of cienegas. Our results indicate that the NDII, calculated using Landsat TM band 5, outperforms the other indices at differentiating cienegas from riparian and upland sites, and was the best means to analyze change. As such, it offers a critical baseline for future studies that seek to extend the analysis of cienegas to other regions and time scales, and has broader applicability to the remote sensing of wetland features in arid landscapes.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15324982.2016.1170076","usgsCitation":"Wilson, N.R., Norman, L.M., Villarreal, M.L., Gass, L., Tiller, R., and Salywon, A., 2016, Comparison of remote sensing indices for monitoring of desert cienegas: Arid Land Research and Management, v. 30, no. 4, p. 460-478, https://doi.org/10.1080/15324982.2016.1170076.","productDescription":"19 p.","startPage":"460","endPage":"478","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068692","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470807,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15324982.2016.1170076","text":"Publisher Index Page"},{"id":324640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cienega Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.5667495727539,\n 31.894319802510566\n ],\n [\n -110.5667495727539,\n 31.970512683093744\n ],\n [\n -110.5063247680664,\n 31.970512683093744\n ],\n [\n -110.5063247680664,\n 31.894319802510566\n ],\n [\n -110.5667495727539,\n 31.894319802510566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-18","publicationStatus":"PW","scienceBaseUri":"5774e32fe4b07dd077c5fbff","contributors":{"authors":[{"text":"Wilson, Natalie R. 0000-0001-5145-1221 nrwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-5145-1221","contributorId":5770,"corporation":false,"usgs":true,"family":"Wilson","given":"Natalie","email":"nrwilson@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Laura M. 0000-0002-3696-8406 lnorman@usgs.gov","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":967,"corporation":false,"usgs":true,"family":"Norman","given":"Laura","email":"lnorman@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":629794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tiller, Ron","contributorId":169496,"corporation":false,"usgs":false,"family":"Tiller","given":"Ron","email":"","affiliations":[{"id":25532,"text":"Arizona Department of Transportation, Environmental Planning Group","active":true,"usgs":false}],"preferred":false,"id":629795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Salywon, Andrew","contributorId":169497,"corporation":false,"usgs":false,"family":"Salywon","given":"Andrew","email":"","affiliations":[{"id":25533,"text":"Desert Botanical Garden","active":true,"usgs":false}],"preferred":false,"id":629796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70177751,"text":"70177751 - 2016 - The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","interactions":[],"lastModifiedDate":"2018-08-08T10:25:00","indexId":"70177751","displayToPublicDate":"2016-06-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2676,"text":"Marine Pollution Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)","docAbstract":"Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.
","language":"English","publisher":"Pergamon Press","doi":"10.1016/j.marpolbul.2016.02.076","usgsCitation":"Slonecker, E.T., Jones, D.K., and Pellerin, B.A., 2016, The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM): Marine Pollution Bulletin, v. 107, no. 2, p. 518-527, https://doi.org/10.1016/j.marpolbul.2016.02.076.","productDescription":"10 p.","startPage":"518","endPage":"527","ipdsId":"IP-069654","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":470815,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpolbul.2016.02.076","text":"Publisher Index Page"},{"id":438605,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7125QQM","text":"USGS data release","linkHelpText":"CDOM/fDOM and Landsat 8 Comparisons"},{"id":330242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5809d7c4e4b0f497e78fca62","chorus":{"doi":"10.1016/j.marpolbul.2016.02.076","url":"http://dx.doi.org/10.1016/j.marpolbul.2016.02.076","publisher":"Elsevier BV","authors":"Slonecker E. Terrence, Jones Daniel K., Pellerin Brian A.","journalName":"Marine Pollution Bulletin","publicationDate":"6/2016","auditedOn":"3/21/2016","publiclyAccessibleDate":"3/4/2016"},"contributors":{"authors":[{"text":"Slonecker, E. Terrence 0000-0002-5793-0503 tslonecker@usgs.gov","orcid":"https://orcid.org/0000-0002-5793-0503","contributorId":168591,"corporation":false,"usgs":true,"family":"Slonecker","given":"E.","email":"tslonecker@usgs.gov","middleInitial":"Terrence","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":651634,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Daniel K. 0000-0003-0724-8001 dkjones@usgs.gov","orcid":"https://orcid.org/0000-0003-0724-8001","contributorId":4959,"corporation":false,"usgs":true,"family":"Jones","given":"Daniel","email":"dkjones@usgs.gov","middleInitial":"K.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. bpeller@usgs.gov","contributorId":1451,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":651653,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170462,"text":"70170462 - 2016 - Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)","interactions":[],"lastModifiedDate":"2019-12-14T06:31:12","indexId":"70170462","displayToPublicDate":"2016-06-28T16:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014)","docAbstract":"Remote sensing has proven a useful way of evaluating long-term trends in vegetation “greenness” through the use of vegetation indices like Normalized Differences Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). In particular, analyses of greenness trends have been performed for large areas (continents, for example) in an attempt to understand vegetation response to climate. These studies have been most often used coarse resolution sensors like Moderate Resolution Image Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, trends in greenness are also important at more local scales, particularly in and around cities as vegetation offers a variety of valuable ecosystem services ranging from minimizing air pollution to mitigating urban heat island effects. To explore the ability to monitor greenness trends in and around cities, this paper presents a new way for analyzing greenness trends based on all available Landsat 5, 7, and 8 images and applies it to Guangzhou, China. This method is capable of including the effects of land cover change in the evaluation of greenness trends by separating the effects of abrupt and gradual changes, and providing information on the timing of greenness trends.
\nAn assessment of the consistency of surface reflectance from Landsat 8 with past Landsat sensors indicates biases in the visible bands of Landsat 8, especially the blue band. Landsat 8 NDVI values were found to have a larger bias than the EVI values; therefore, EVI was used in the analysis of greenness trends for Guangzhou. In spite of massive amounts of development in Guangzhou from 2000 to 2014, greenness was found to increase, mostly as a result of gradual change. Comparison of the greening magnitudes estimated from the approach presented here and a Simple Linear Trend (SLT) method indicated large differences for certain time intervals as the SLT method does not include consideration for abrupt land cover changes. Overall, this analysis demonstrates the importance of considering land cover change when analyzing trends in greenness from satellite time series in areas where land cover change is common.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.03.036","usgsCitation":"Zhu, Z., Fu, Y., Woodcock, C., Olofsson, P., Vogelmann, J., Holden, C., Wang, M., Dai, S., and Yu, Y., 2016, Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014): Remote Sensing of Environment, v. 185, p. 243-257, https://doi.org/10.1016/j.rse.2016.03.036.","productDescription":"15 p.","startPage":"243","endPage":"257","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068963","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470818,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.03.036","text":"Publisher Index Page"},{"id":324550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","city":"Guangzhou","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 112.5,\n 21.453068633086783\n ],\n [\n 116.71874999999999,\n 21.453068633086783\n ],\n [\n 116.71874999999999,\n 26.86328062676624\n ],\n [\n 112.5,\n 26.86328062676624\n ],\n [\n 112.5,\n 21.453068633086783\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"185","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"577391a6e4b07657d1a88bd0","contributors":{"authors":[{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":627309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fu, Yingchun","contributorId":172520,"corporation":false,"usgs":false,"family":"Fu","given":"Yingchun","email":"","affiliations":[],"preferred":false,"id":641108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":641109,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olofsson, Pontus","contributorId":131007,"corporation":false,"usgs":false,"family":"Olofsson","given":"Pontus","email":"","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":641110,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":127752,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":595,"text":"U.S. Geological Survey","active":false,"usgs":true}],"preferred":false,"id":641111,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holden, Christopher","contributorId":172521,"corporation":false,"usgs":false,"family":"Holden","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":641112,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Min","contributorId":145692,"corporation":false,"usgs":false,"family":"Wang","given":"Min","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":641113,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dai, Shu","contributorId":172522,"corporation":false,"usgs":false,"family":"Dai","given":"Shu","email":"","affiliations":[],"preferred":false,"id":641114,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yu, Yang","contributorId":172524,"corporation":false,"usgs":false,"family":"Yu","given":"Yang","email":"","affiliations":[],"preferred":false,"id":641115,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}