The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index (R2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index (R2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100–1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.
","language":"English","publisher":"MDPI","doi":"10.3390/f8080287","usgsCitation":"Humagain, K., Portillo-Quintero, C., Cox, R.D., and Cain, J.W., 2017, Mapping tree density in forests of the southwestern USA using Landsat 8 data: Forests, v. 8, no. 8, p. 1-15, https://doi.org/10.3390/f8080287.","productDescription":"Article 287; 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-087221","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8080287","text":"Publisher Index Page"},{"id":347412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.78092956542969,\n 35.621023736228004\n ],\n [\n -106.40396118164062,\n 35.621023736228004\n ],\n [\n -106.40396118164062,\n 36.00134056648952\n ],\n [\n -106.78092956542969,\n 36.00134056648952\n ],\n [\n -106.78092956542969,\n 35.621023736228004\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-09","publicationStatus":"PW","scienceBaseUri":"59f1a2a5e4b0220bbd9d9f44","contributors":{"authors":[{"text":"Humagain, Kamal","contributorId":198375,"corporation":false,"usgs":false,"family":"Humagain","given":"Kamal","email":"","affiliations":[],"preferred":false,"id":715906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Portillo-Quintero, Carlos","contributorId":198384,"corporation":false,"usgs":false,"family":"Portillo-Quintero","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":715907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cox, Robert D.","contributorId":26240,"corporation":false,"usgs":true,"family":"Cox","given":"Robert","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":715908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":714002,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189870,"text":"70189870 - 2017 - Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data","interactions":[],"lastModifiedDate":"2017-08-27T18:10:36","indexId":"70189870","displayToPublicDate":"2017-07-27T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data","docAbstract":"In the southwestern U.S., many riparian ecosystems have been altered by dams, water diversions, and other anthropogenic activities. This is particularly true of the Colorado River, where numerous dams and agricultural diversions have affected this water course, especially south of the U.S.–Mexico border. In the spring of 2014, 130 million cubic meters of water was released to the lower Colorado River Delta in Mexico. To understand the impact of this pulse flow release on vegetation in the delta’s riparian corridor, we analyzed a modified form of Landsat 8 Operational Land Imager (OLI) Normalized Difference Vegetation Index (NDVI*) data. We assessed greenup during the growing period and estimated actual evapotranspiration (ETa) for the period prior to (yr. 2013) and following (i.e., yr. 2014 and 2015) the pulse flow. We found a significant increase in NDVI* from 2013 to 2014 (P < 0.05) and a decrease from 2014 to 2015; however, 2015 levels were still significantly higher than in 2013. ETa was also higher in 2014 vs. 2013, with an estimated 74.5 million cubic meters in 2013 and 88.9 in 2014. The most intense greening occurred in the zone of inundation but also extended into the non-flooded part of the riparian zone, indicating replenishment of groundwater. These findings suggest the peak response by vegetation to the flow lasted about one year, followed by a decrease in NDVI*. As a long term solution to the declining condition of vegetation, additional pulse releases are likely needed for restoration and survival of riparian plant communities in the Colorado River Delta.
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B, p. 776-783, https://doi.org/10.1016/j.ecoleng.2016.08.007.","productDescription":"8 p.","startPage":"776","endPage":"783","ipdsId":"IP-074636","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2016.08.007","text":"Publisher Index Page"},{"id":344415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.11886596679688,\n 32.132594234149906\n ],\n [\n -114.67941284179688,\n 32.132594234149906\n ],\n [\n -114.67941284179688,\n 32.72375394304274\n ],\n [\n -115.11886596679688,\n 32.72375394304274\n ],\n [\n -115.11886596679688,\n 32.132594234149906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"B","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"597afba2e4b0a38ca2750b36","contributors":{"authors":[{"text":"Jarchow, Christopher J. 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":5813,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":706599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":706600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":706601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189277,"text":"70189277 - 2017 - Mapping burned areas using dense time-series of Landsat data","interactions":[],"lastModifiedDate":"2022-04-22T15:43:05.359179","indexId":"70189277","displayToPublicDate":"2017-07-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Mapping burned areas using dense time-series of Landsat data","docAbstract":"Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.06.027","usgsCitation":"Hawbaker, T., Vanderhoof, M.K., Beal, Y.G., Takacs, J., Schmidt, G.L., Falgout, J.T., Williams, B., Brunner, N.M., Caldwell, M., Picotte, J.J., Howard, S.M., Stitt, S., and Dwyer, J.L., 2017, Mapping burned areas using dense time-series of Landsat data: Remote Sensing of Environment, v. 198, p. 504-522, https://doi.org/10.1016/j.rse.2017.06.027.","productDescription":"19 p.","startPage":"504","endPage":"522","ipdsId":"IP-077532","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":469690,"rank":1,"type":{"id":40,"text":"Open Access Publisher 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0000-0002-0663-2696","orcid":"https://orcid.org/0000-0002-0663-2696","contributorId":194383,"corporation":false,"usgs":false,"family":"Stitt","given":"Susan","affiliations":[],"preferred":false,"id":703880,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":703881,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70192089,"text":"70192089 - 2017 - Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product","interactions":[],"lastModifiedDate":"2017-10-23T15:52:11","indexId":"70192089","displayToPublicDate":"2017-07-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product","docAbstract":"Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2017.03.008","usgsCitation":"Wang, Z., Schaaf, C.B., Sun, Q., Kim, J., Erb, A.M., Gao, F., Roman, M.O., Yang, Y., Petroy, S., Taylor, J., Masek, J.G., Morisette, J.T., Zhang, X., and Papuga, S.A., 2017, Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product: International Journal of Applied Earth Observation and Geoinformation, v. 59, p. 104-117, https://doi.org/10.1016/j.jag.2017.03.008.","productDescription":"14 p.","startPage":"104","endPage":"117","ipdsId":"IP-083678","costCenters":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true}],"links":[{"id":469719,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2017.03.008","text":"Publisher Index 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Jeffrey","contributorId":197724,"corporation":false,"usgs":false,"family":"Taylor","given":"Jeffrey","email":"","affiliations":[],"preferred":false,"id":714141,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Masek, Jeffrey G.","contributorId":197725,"corporation":false,"usgs":false,"family":"Masek","given":"Jeffrey","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":714142,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morisette, Jeffrey T. 0000-0002-0483-0082 morisettej@usgs.gov","orcid":"https://orcid.org/0000-0002-0483-0082","contributorId":307,"corporation":false,"usgs":true,"family":"Morisette","given":"Jeffrey","email":"morisettej@usgs.gov","middleInitial":"T.","affiliations":[{"id":477,"text":"North Central Climate Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science 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They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here we present, a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM,ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (-0.69%), Western Alaska (-2.82%), and Kolyma Lowland (-0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e. upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.","language":"English","publisher":"Multidisciplinary Digital Publishing Institute (MDPI)","doi":"10.3390/rs9070640","usgsCitation":"Nitze, I., Grosse, G., Jones, B.M., Arp, C.D., Ulrich, M., Federov, A., and Veremeeva, A., 2017, Landsat-based trend analysis of lake dynamics across northern permafrost regions: Remote Sensing, v. 9, no. 7, 640, 28 p., https://doi.org/10.3390/rs9070640.","productDescription":"640, 28 p.","ipdsId":"IP-087096","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":469730,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9070640","text":"Publisher Index 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Geography","active":true,"usgs":true}],"preferred":true,"id":702948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":702951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ulrich, Mathias","contributorId":194029,"corporation":false,"usgs":false,"family":"Ulrich","given":"Mathias","email":"","affiliations":[],"preferred":false,"id":702952,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Federov, Alexander","contributorId":194027,"corporation":false,"usgs":false,"family":"Federov","given":"Alexander","email":"","affiliations":[],"preferred":false,"id":702953,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Veremeeva, Alexandra","contributorId":194028,"corporation":false,"usgs":false,"family":"Veremeeva","given":"Alexandra","email":"","affiliations":[],"preferred":false,"id":702954,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192992,"text":"70192992 - 2017 - A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011","interactions":[],"lastModifiedDate":"2018-03-08T13:03:59","indexId":"70192992","displayToPublicDate":"2017-06-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011","docAbstract":"Monitoring and mapping land cover changes are important ways to support evaluation of the status and transition of ecosystems. The Alaska National Land Cover Database (NLCD) 2001 was the first 30-m resolution baseline land cover product of the entire state derived from circa 2001 Landsat imagery and geospatial ancillary data. We developed a comprehensive approach named AKUP11 to update Alaska NLCD from 2001 to 2011 and provide a 10-year cyclical update of the state's land cover and land cover changes. Our method is designed to characterize the main land cover changes associated with different drivers, including the conversion of forests to shrub and grassland primarily as a result of wildland fire and forest harvest, the vegetation successional processes after disturbance, and changes of surface water extent and glacier ice/snow associated with weather and climate changes. For natural vegetated areas, a component named AKUP11-VEG was developed for updating the land cover that involves four major steps: 1) identify the disturbed and successional areas using Landsat images and ancillary datasets; 2) update the land cover status for these areas using a SKILL model (System of Knowledge-based Integrated-trajectory Land cover Labeling); 3) perform decision tree classification; and 4) develop a final land cover and land cover change product through the postprocessing modeling. For water and ice/snow areas, another component named AKUP11-WIS was developed for initial land cover change detection, removal of the terrain shadow effects, and exclusion of ephemeral snow changes using a 3-year MODIS snow extent dataset from 2010 to 2012. The overall approach was tested in three pilot study areas in Alaska, with each area consisting of four Landsat image footprints. The results from the pilot study show that the overall accuracy in detecting change and no-change is 90% and the overall accuracy of the updated land cover label for 2011 is 86%. The method provided a robust, consistent, and efficient means for capturing major disturbance events and updating land cover for Alaska. The method has subsequently been applied to generate the land cover and land cover change products for the entire state of Alaska.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.04.021","usgsCitation":"Jin, S., Yang, L., Zhu, Z., and Homer, C.G., 2017, A land cover change detection and classification protocol for updating Alaska NLCD 2001 to 2011: Remote Sensing of Environment, v. 195, p. 44-55, https://doi.org/10.1016/j.rse.2017.04.021.","productDescription":"12 p.","startPage":"44","endPage":"55","ipdsId":"IP-082390","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347728,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","volume":"195","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f83a36e4b063d5d30980dc","contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717549,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187550,"text":"sir20175044 - 2017 - Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013","interactions":[],"lastModifiedDate":"2017-05-30T12:46:29","indexId":"sir20175044","displayToPublicDate":"2017-05-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5044","title":"Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013","docAbstract":"The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175044","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management","usgsCitation":"Hartley, S.B., Couvillion, B.R., and Enwright, N.M., 2017, Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013: U.S. Geological Survey Scientific Investigations Report 2017–5044, 6 p., https://doi.org/10.3133/20175044.","productDescription":"Report: vi, 6 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084395","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":341819,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5044/coverthb.jpg"},{"id":341820,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5044/sir20175044.pdf","text":"Report","size":"499 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5044"},{"id":341821,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7474820","text":"USGS - 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U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature (Ts) normalized difference vegetation index (NDVI) and daily maximum air temperature (Ta). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using Ts and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the Ts from Landsat relative to the Ts from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.
Remote sensing-based field-scale evapotranspiration (ET) maps are useful for characterizing water use patterns and assessing crop performance. The relative impact of climate variability and water management decisions are better studied and quantified using historical data that are derived using a set of consistent datasets and methodology. Historical (1984–2014) Landsat-based ET maps were generated for major irrigation districts in California, i.e., Palo Verde and eight other sub-basins in parts of the middle and lower Central Valley. A total of 3396 Landsat images were processed using the Operational Simplified Surface Energy Balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study sites over the 31 years. Model output evaluation and validation using gridded-flux data and water balance ET approaches indicated relatively good correspondence (R2 up to 0.88, root mean square error as low as 14 mm/month) between SSEBop ET and validation datasets. In a pairwise comparison, annual variability of agro-hydrologic parameters of actual evapotranspiration (ETa), land surface temperature (Ts), and runoff (Q) were found to be more variable than their corresponding climatic counterparts of atmospheric water demand (ETo), air temperature (Ta), and precipitation (P), revealing process differences between regional climatic drivers and localized agro-hydrologic responses. However, only Ta showed a consistent increase (up to 1.2 K) over study sites during the 31 years, whereas other climate variables such as ETo and P showed a generally neutral trend. This study demonstrates a useful application of “Big Data” science where large volumes of historical Landsat and weather datasets were used to quantify and understand the relative importance of water management and climate variability in crop water use dynamics in regards to the linkages among water management decisions, hydrologic processes and economic transactions. Irrigation district-wide ETa estimates were used to compute historical crop water use volumes and monetary equivalents of water savings for the Palo Verde Irrigation District (PVID). During the peak crop fallowing year in PVID, the water saved reached a maximum of ~ 107,200 acre-feet in 2011 with an estimated monetary payout value of $20.5 million. A significant decreasing trend in actual ET despite an increasing atmospheric demand in PVID highlights the role of management decisions in affecting local hydrologic processes. This study has importance for planning water resource allocation, managing water rights, sustaining agricultural production, and quantifying impacts of climate and land use/land cover changes on water resources. With increased computational efficiency, similar studies can be conducted in other parts of the world to help policy and decision makers understand and quantify various aspects of water resources management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.05.005","usgsCitation":"Senay, G.B., Schauer, M., Friedrichs, M., Velpuri, N., and Singh, R., 2017, Satellite-based water use dynamics using historical Landsat data (1984–2014) in the southwestern United States: Remote Sensing of Environment, v. 202, p. 98-112, https://doi.org/10.1016/j.rse.2017.05.005.","productDescription":"15 p.","startPage":"98","endPage":"112","ipdsId":"IP-084512","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493296,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.05.005","text":"Publisher Index Page"},{"id":493191,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.40977836532282,\n 42.04933677309765\n ],\n [\n -123.16675091874134,\n 36.46673710104686\n ],\n [\n -118.58307879273526,\n 32.705501119947826\n ],\n [\n -113.73717874196483,\n 32.12914120007623\n ],\n [\n -113.88408063137007,\n 35.268837865873046\n ],\n [\n -114.8258516238531,\n 35.43740401775197\n ],\n [\n -117.48081678552326,\n 37.20400442230724\n ],\n [\n -120.03564230020528,\n 39.18700095919894\n ],\n [\n -120.18220374157768,\n 41.98136883976758\n ],\n [\n -125.40977836532282,\n 42.04933677309765\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"202","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schauer, Matthew 0000-0002-4198-3379","orcid":"https://orcid.org/0000-0002-4198-3379","contributorId":216909,"corporation":false,"usgs":true,"family":"Schauer","given":"Matthew","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedrichs, MacKenzie 0000-0002-9602-321X mfriedrichs@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-321X","contributorId":5847,"corporation":false,"usgs":true,"family":"Friedrichs","given":"MacKenzie","email":"mfriedrichs@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velpuri, Naga Manohar 0000-0002-6370-1926","orcid":"https://orcid.org/0000-0002-6370-1926","contributorId":216911,"corporation":false,"usgs":true,"family":"Velpuri","given":"Naga Manohar ","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singh, Ramesh 0000-0002-8164-3483","orcid":"https://orcid.org/0000-0002-8164-3483","contributorId":210983,"corporation":false,"usgs":true,"family":"Singh","given":"Ramesh","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":944429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184212,"text":"gip172 - 2017 - Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research","interactions":[],"lastModifiedDate":"2017-05-03T10:03:12","indexId":"gip172","displayToPublicDate":"2017-05-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"172","title":"Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research","docAbstract":"When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting.
Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields.
In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information.
Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand.
Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip172","usgsCitation":"Young, S.M., 2017, Land Change Monitoring, Assessment, and Projection (LCMAP) revolutionizes land cover and land change research: U.S. Geological Survey General Information Product 172, 4 p., https://doi.org/10.3133/gip172.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-083103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":340452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0172/coverthb.jpg"},{"id":340453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0172/gip172.pdf","size":"7.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 172"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Climate change effects on vegetation will likely be strong in the southwestern U.S., which is projected to experience large increases in temperature and changes in precipitation. Plant communities in the southwestern U.S. may be particularly vulnerable to climate change as the productivity of many plant species is strongly water-limited. This study examines the relationship between climate and vegetation condition using a time-series of Landsat imagery across grassland, shrubland, and woodland communities on the Colorado Plateau, USA. We improve on poorly understood inter-annual climate-vegetation relationships by exploring how the responses of different plant communities depend on climate legacies (>12 months) and lag behind shorter-term (3–12 month) changes in water availability. Our results show a prolonged drying trend on the Colorado Plateau since the early 1990s that was punctuated in several years by intense droughts. In areas that experienced sustained dry conditions or a drying trend, vegetation greenness (a proxy for production) increased linearly when conditions were interrupted by wetting events. In contrast, in areas that experienced sustained wet conditions or a wetting trend, vegetation greenness was weakly or not related to wetting events, indicating that production may saturate if vegetation experiences sufficient water availability. Shrubland and woodland communities had stronger relationships with climate at long lags (6–12 months) and many maintained greenness under sustained water deficit, whereas grassland communities had stronger relationships at short lags (3–6 months) and lost greenness even in periods of short-term drought. The results of our study show the importance of identifying climate legacies and lags when assessing indicators of ecological drought, which can be used to improve forecasts of which plant communities will be vulnerable under future climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2016.10.024","usgsCitation":"Bunting, E., Munson, S.M., and Villarreal, M.L., 2017, Climate legacy and lag effects on dryland plant communities in the southwestern U.S.: Ecological Indicators, v. 74, p. 216-229, https://doi.org/10.1016/j.ecolind.2016.10.024.","productDescription":"14 p.","startPage":"216","endPage":"229","ipdsId":"IP-080256","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438355,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90CRK5N","text":"USGS data release","linkHelpText":"Dataset for climate legacy and lag effects on dryland plant communities in the southwestern U.S."},{"id":341940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.115234375,\n 35.8356283888737\n ],\n [\n -106.94091796875,\n 35.8356283888737\n ],\n [\n -106.94091796875,\n 40.96330795307353\n ],\n [\n -113.115234375,\n 40.96330795307353\n ],\n [\n -113.115234375,\n 35.8356283888737\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592fd63ce4b0e9bd0ea896e6","contributors":{"authors":[{"text":"Bunting, Erin 0000-0001-9103-6065 ebunting@usgs.gov","orcid":"https://orcid.org/0000-0001-9103-6065","contributorId":168488,"corporation":false,"usgs":true,"family":"Bunting","given":"Erin","email":"ebunting@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":696776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":696777,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70186881,"text":"70186881 - 2017 - Cloud detection algorithm comparison and validation for operational Landsat data products","interactions":[],"lastModifiedDate":"2017-04-13T09:40:27","indexId":"70186881","displayToPublicDate":"2017-04-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Cloud detection algorithm comparison and validation for operational Landsat data products","docAbstract":"Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.03.026","usgsCitation":"Foga, S.C., Scaramuzza, P., Guo, S., Zhu, Z., Dilley, R., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M., and Laue, B., 2017, Cloud detection algorithm comparison and validation for operational Landsat data products: Remote Sensing of Environment, v. 194, p. 379-390, https://doi.org/10.1016/j.rse.2017.03.026.","productDescription":"12 p.","startPage":"379","endPage":"390","ipdsId":"IP-076780","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469926,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2017.03.026","text":"Publisher Index Page"},{"id":339659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"194","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58f08e5ee4b06911a29fa842","contributors":{"authors":[{"text":"Foga, Steven Curtis 0000-0003-1835-1987 sfoga@usgs.gov","orcid":"https://orcid.org/0000-0003-1835-1987","contributorId":5703,"corporation":false,"usgs":true,"family":"Foga","given":"Steven","email":"sfoga@usgs.gov","middleInitial":"Curtis","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scaramuzza, Pat 0000-0002-2616-8456 pscar@usgs.gov","orcid":"https://orcid.org/0000-0002-2616-8456","contributorId":3970,"corporation":false,"usgs":true,"family":"Scaramuzza","given":"Pat","email":"pscar@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guo, Song 0000-0001-8823-188X sguo@usgs.gov","orcid":"https://orcid.org/0000-0001-8823-188X","contributorId":5245,"corporation":false,"usgs":true,"family":"Guo","given":"Song","email":"sguo@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":690808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dilley, Ronald 0000-0002-6960-1125 ronald.dilley.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-6960-1125","contributorId":190829,"corporation":false,"usgs":true,"family":"Dilley","given":"Ronald","email":"ronald.dilley.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beckmann, Tim 0000-0002-2557-0638 tim.beckmann.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-2557-0638","contributorId":190830,"corporation":false,"usgs":true,"family":"Beckmann","given":"Tim","email":"tim.beckmann.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690811,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidt, Gail L. 0000-0002-9684-8158 gschmidt@usgs.gov","orcid":"https://orcid.org/0000-0002-9684-8158","contributorId":3475,"corporation":false,"usgs":true,"family":"Schmidt","given":"Gail","email":"gschmidt@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":690810,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":690812,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hughes, MJ","contributorId":190831,"corporation":false,"usgs":false,"family":"Hughes","given":"MJ","email":"","affiliations":[],"preferred":false,"id":690813,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Laue, Brady 0000-0002-4559-3618 brady.laue.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4559-3618","contributorId":190832,"corporation":false,"usgs":true,"family":"Laue","given":"Brady","email":"brady.laue.ctr@usgs.gov","affiliations":[],"preferred":false,"id":690814,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70185763,"text":"70185763 - 2017 - Landsat Science Team: 2017 Winter meeting summary","interactions":[],"lastModifiedDate":"2020-12-17T17:35:38.83827","indexId":"70185763","displayToPublicDate":"2017-04-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3555,"text":"The Earth Observer","active":true,"publicationSubtype":{"id":10}},"title":"Landsat Science Team: 2017 Winter meeting summary","docAbstract":"The winter meeting of the NASA-U.S. Geological Survey (USGS) Landsat Science Team (LST) was held January 10-12, 2017, at Boston University. LST co-chairs Tom Loveland [USGS’s Earth Resources Observation and Science Center (EROS)—Senior Scientist], Jim Irons [NASA’s Goddard Space Flight Center (GSFC)—Deputy Director, Earth Sciences Division], and Curtis Woodcock [Boston University—Professor and LST Co-Leader] welcomed the participants to the three-day meeting. The group immediately and enthusiastically recognized Woodcock’s receipt of the 2016 Pecora Award. Loveland summarized the primary meeting objectives to identify priorities for future Landsat measurements and to begin identifying next-generation Landsat products. He also discussed USGS’s plans to issue a request for proposals for membership on the 2018-2023 LST (i.e., the next five-year term). Irons stressed Landsat’s bipartisan support but cautioned against complacency when looking toward future capabilities. Meeting presentations are available at https://landsat.usgs.gov/landsat-science-teammeeting-jan-10-12-2017.
","language":"English","publisher":"NASA","usgsCitation":"Loveland, T., Wulder, M.A., and Irons, J.R., 2017, Landsat Science Team: 2017 Winter meeting summary: The Earth Observer, v. 29, no. 3, p. 21-25.","productDescription":"5 p.","startPage":"21","endPage":"25","ipdsId":"IP-085721","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":339608,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381443,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.nasa.gov/earthobserver/may-jun-2017"}],"volume":"29","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ef3da9e4b0eed1ab8e3bd2","contributors":{"authors":[{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":686696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulder, Michael A.","contributorId":189990,"corporation":false,"usgs":false,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":686697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irons, James R.","contributorId":59284,"corporation":false,"usgs":false,"family":"Irons","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":686698,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70186146,"text":"fs20173026 - 2017 - U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data","interactions":[],"lastModifiedDate":"2017-05-31T10:38:36","indexId":"fs20173026","displayToPublicDate":"2017-03-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3026","title":"U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data","docAbstract":"A partnership established between the European Space Agency (ESA) and the U.S. Geological Survey (USGS) allows for USGS storage and redistribution of images acquired by the MultiSpectral Instrument (MSI) on the European Union's Sentinel-2 satellite mission. The MSI data are acquired from a pair of satellites, Sentinel-2A and Sentinel-2B, which are part of a larger set of ESA missions focusing on different aspects of Earth observation. The primary purpose of the Sentinel-2 series is to collect multispectral imagery over the Earth’s land surfaces, large islands, and inland and coastal waters. Sentinel-2A was launched in 2015 and Sentinel-2B launched in 2017.
The collaborative effort between ESA and USGS provides for public access and redistribution of global acquisitions of Sentinel-2 data at no cost, which allows users to download the MSI imagery from USGS access systems such as Earth- Explorer, in addition to the ESA Sentinels Scientific Data Hub. The MSI sensor acquires 13 spectral bands that are highly complementary to data acquired by the USGS Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The product options from USGS include a Full-Resolution Browse (FRB) image product generated by USGS, along with a 100-kilometer (km) by 100-km tile-based Level-1C top-of-atmosphere (TOA) reflectance product that is very similar (but not identical) to the currently (2017) distributed ESA Level 1C product.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173026","usgsCitation":"Pieschke, R.L., 2017, U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data: U.S. Geological Survey Fact Sheet 2017–3026, 2 p., https://doi.org/10.3133/fs20173026.\n","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-082585","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":338860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3026/fs20173026.pdf","text":"Fact Sheet","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3026"},{"id":338859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3026/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198–0001
The use of Landsat satellite imagery for global agricultural monitoring began almost immediately after the launch of Landsat 1 in 1972, making agricultural monitoring one of the longest-standing operational applications for the Landsat program. More recently, Landsat imagery has been used in domestic agricultural applications as an input for field-level production management. The enactment of the U.S. Geological Survey’s free and open data policy in 2008 and the launch of Landsat 8 in 2013 have both influenced agricultural applications. This report presents two primary sets of case studies on the applications and benefits of Landsat imagery use in agriculture. The first set examines several operational applications within the U.S. Department of Agriculture (USDA) and the second focuses on private sector applications for agronomic management.
Information on the USDA applications is provided in the U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring section of the report in the following subsections:
Private sector applications using Landsat imagery for agricultural management are discussed in the Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management section of the report in the following subsections:
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986–1992, 1992–2001, 2001–2006, and 2006–2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.
","language":"English","publisher":"Kluwer","publisherLocation":"Dordrecht","doi":"10.1007/s10661-017-5879-5","usgsCitation":"Soulard, C.E., Acevedo, W., Cohen, W.B., Yang, Z., Stehman, S.V., and Taylor, J.L., 2017, Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011: Environmental Monitoring and Assessment, v. 189, 170: 17 p., https://doi.org/10.1007/s10661-017-5879-5.","productDescription":"170: 17 p.","ipdsId":"IP-075245","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":337906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"189","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-18","publicationStatus":"PW","scienceBaseUri":"58d23b90e4b0236b68f828ea","chorus":{"doi":"10.1007/s10661-017-5879-5","url":"http://dx.doi.org/10.1007/s10661-017-5879-5","publisher":"Springer Nature","authors":"Soulard Christopher E., Acevedo William, Cohen Warren B., Yang Zhiqiang, Stehman Stephen V., Taylor Janis L.","journalName":"Environmental Monitoring and Assessment","publicationDate":"3/18/2017","auditedOn":"3/20/2017","publiclyAccessibleDate":"3/18/2017"},"contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science 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Zhiqiang","contributorId":189584,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","email":"","affiliations":[],"preferred":false,"id":685280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":685252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Taylor, Janis L. 0000-0002-9418-5215 jltaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-9418-5215","contributorId":147371,"corporation":false,"usgs":true,"family":"Taylor","given":"Janis","email":"jltaylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":685253,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191302,"text":"70191302 - 2017 - Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure","interactions":[],"lastModifiedDate":"2017-10-03T16:34:40","indexId":"70191302","displayToPublicDate":"2017-03-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure","docAbstract":"Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r2 = 0.86, p < 0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.12.022","usgsCitation":"McCarley, T.R., Kolden, C.A., Vaillant, N.M., Hudak, A.T., Smith, A., Wing, B.M., Kellogg, B., and Kreitler, J.R., 2017, Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure: Remote Sensing of Environment, v. 191, p. 419-432, https://doi.org/10.1016/j.rse.2016.12.022.","productDescription":"14 p.","startPage":"419","endPage":"432","ipdsId":"IP-076180","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470007,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.12.022","text":"Publisher Index Page"},{"id":346371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Pole Creek Fire","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75,\n 44.0833\n ],\n [\n -121.5,\n 44.0833\n ],\n [\n -121.5,\n 44.25\n ],\n [\n -121.75,\n 44.25\n ],\n [\n -121.75,\n 44.0833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59d4a1a9e4b05fe04cc4e0ff","contributors":{"authors":[{"text":"McCarley, T. Ryan","contributorId":196908,"corporation":false,"usgs":false,"family":"McCarley","given":"T.","email":"","middleInitial":"Ryan","affiliations":[],"preferred":false,"id":711897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolden, Crystal A.","contributorId":196909,"corporation":false,"usgs":false,"family":"Kolden","given":"Crystal","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vaillant, Nicole M.","contributorId":196237,"corporation":false,"usgs":false,"family":"Vaillant","given":"Nicole","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":711899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hudak, Andrew T.","contributorId":196022,"corporation":false,"usgs":false,"family":"Hudak","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":711900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Alistair","contributorId":196910,"corporation":false,"usgs":false,"family":"Smith","given":"Alistair","email":"","affiliations":[],"preferred":false,"id":711901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wing, Brian M.","contributorId":196911,"corporation":false,"usgs":false,"family":"Wing","given":"Brian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":711902,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kellogg, Bryce","contributorId":196912,"corporation":false,"usgs":false,"family":"Kellogg","given":"Bryce","email":"","affiliations":[],"preferred":false,"id":711903,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":711896,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70184217,"text":"fs20173018 - 2017 - Landsat eyes help guard the world's forests","interactions":[],"lastModifiedDate":"2017-03-06T12:58:39","indexId":"fs20173018","displayToPublicDate":"2017-03-03T16:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3018","title":"Landsat eyes help guard the world's forests","docAbstract":"The Landsat program is a joint effort between the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), but the partner agencies have distinct roles. NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance in orbit. The USGS owns and operates Landsat satellites in space and manages their data transmissions, including ground reception, archiving, product generation, and public distribution. In 2008, with support from the U.S. Department of the Interior, the USGS made its Landsat data free to anyone in the world.
The current satellites in the Landsat program, Landsat 7 (launched in 1999) and Landsat 8 (launched in 2013), provide complete coverage of the Earth every eight days. A Landsat 9 satellite is scheduled for launch in late 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173018","usgsCitation":"Campbell, Jon, 2017, Landsat eyes help guard the world's forests: U.S. Geological Survey Fact Sheet 2017–3018, 2 p., https://doi.org/10.3133/fs20173018.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084384","costCenters":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"links":[{"id":336823,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3018/coverthb.jpg"},{"id":336824,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3018/fs20173018.pdf","text":"Report","size":"2.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3018"}],"otherGeospatial":"Earth","contact":"Associate Director
U.S. Geological Survey
Climate and Land Use Change Mission Area
12201 Sunrise Valley Drive
Reston, VA 20192
Web site: Climate and Land Use Change
Invasive plants threaten native plant communities. Surface coal mines in the Appalachian Mountains are among the most disturbed landscapes in North America, but information about land cover characteristics of Appalachian mined lands is lacking. The invasive shrub autumn olive (Elaeagnus umbellata) occurs on these sites and interferes with ecosystem recovery by outcompeting native trees, thus inhibiting re-establishment of the native woody-plant community. We analyzed Landsat 8 satellite imagery to describe autumn olive’s distribution on post-mined lands in southwestern Virginia within the Appalachian coalfield. Eight images from April 2013 through January 2015 served as input data. Calibration and validation data obtained from high-resolution aerial imagery were used to develop a land cover classification model that identified areas where autumn olive was a primary component of land cover. Results indicate that autumn olive cover was sufficiently dense to enable detection on approximately 12.6 % of post-mined lands within the study area. The classified map had user’s and producer’s accuracies of 85.3 and 78.6 %, respectively, for the autumn olive coverage class. Overall accuracy was assessed in reference to an independent validation dataset at 96.8 %. Autumn olive was detected more frequently on mines disturbed prior to 2003, the last year of known plantings, than on lands disturbed by more recent mining. These results indicate that autumn olive growing on reclaimed coal mines in Virginia and elsewhere in eastern USA can be mapped using Landsat 8 Operational Land Imager imagery; and that autumn olive occurrence is a significant landscape vegetation feature on former surface coal mines in the southwestern Virginia segment of the Appalachian coalfield.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-016-1271-6","usgsCitation":"Oliphant, A., Wynne, R., Zipper, C.E., Ford, W., Donovan, P.F., and Li, J., 2017, Autumn olive (Elaeagnus umbellata) presence and proliferation on former surface coal mines in Eastern USA: Biological Invasions, v. 19, no. 1, p. 179-195, https://doi.org/10.1007/s10530-016-1271-6.","productDescription":"17 p.","startPage":"179","endPage":"195","ipdsId":"IP-072884","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":337475,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-12","publicationStatus":"PW","scienceBaseUri":"58c7af98e4b0849ce9795e6a","contributors":{"authors":[{"text":"Oliphant, Adam J.","contributorId":189232,"corporation":false,"usgs":false,"family":"Oliphant","given":"Adam J.","affiliations":[],"preferred":false,"id":684165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wynne, R.H.","contributorId":147844,"corporation":false,"usgs":false,"family":"Wynne","given":"R.H.","email":"","affiliations":[],"preferred":false,"id":684166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zipper, Carl E.","contributorId":43683,"corporation":false,"usgs":true,"family":"Zipper","given":"Carl","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":684167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":684033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donovan, P. F.","contributorId":189233,"corporation":false,"usgs":false,"family":"Donovan","given":"P.","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":684168,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Jing","contributorId":9166,"corporation":false,"usgs":true,"family":"Li","given":"Jing","email":"","affiliations":[],"preferred":false,"id":684169,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70180260,"text":"70180260 - 2017 - Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed","interactions":[],"lastModifiedDate":"2017-01-27T11:10:10","indexId":"70180260","displayToPublicDate":"2017-01-26T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed","docAbstract":"This research highlights development and application of an integrated hydrologic model (GSFLOW) to a semiarid, snow-dominated watershed in the Great Basin to evaluate Pinyon-Juniper (PJ) and temperature controls on mountain meadow shallow groundwater. The work used Google Earth Engine Landsat satellite and gridded climate archives for model evaluation. Model simulations across three decades indicated that the watershed operates on a threshold response to precipitation (P) >400 mm/y to produce a positive yield (P-ET; 9%) resulting in stream discharge and a rebound in meadow groundwater levels during these wetter years. Observed and simulated meadow groundwater response to large P correlates with above average predicted soil moisture and with a normalized difference vegetation index threshold value >0.3. A return to assumed pre-expansion PJ conditions or an increase in temperature to mid-21st century shifts yielded by only ±1% during the multi-decade simulation period; but changes of approximately ±4% occurred during wet years. Changes in annual yield were largely dampened by the spatial and temporal redistribution of evapotranspiration across the watershed: Yet the influence of this redistribution and vegetation structural controls on snowmelt altered recharge to control water table depth in the meadow. Even a small-scale removal of PJ (0.5 km2) proximal to the meadow will promote a stable, shallow groundwater system resilient to droughts, while modest increases in temperature will produce a meadow susceptible to declining water levels and a community structure likely to move toward dry and degraded conditions.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1792","usgsCitation":"Carroll, R.W., Huntington, J., Snyder, K.A., Niswonger, R.G., Morton, C., and Stringham, T.K., 2017, Evaluating mountain meadow groundwater response to Pinyon-Juniper and temperature in a great basin watershed: Ecohydrology, v. 10, no. 1, p. 1-18, https://doi.org/10.1002/eco.1792.","productDescription":"e1792; 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-072881","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":461779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.1792","text":"Publisher Index Page"},{"id":334058,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin","volume":"10","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"588b1976e4b0ad67323f97dc","contributors":{"authors":[{"text":"Carroll, Rosemary W.H.","contributorId":39928,"corporation":false,"usgs":true,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":660972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huntington, Justin L.","contributorId":31279,"corporation":false,"usgs":true,"family":"Huntington","given":"Justin L.","affiliations":[],"preferred":false,"id":660973,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Keirith A.","contributorId":178786,"corporation":false,"usgs":false,"family":"Snyder","given":"Keirith","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":660974,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":152462,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":false,"id":660975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morton, Charles","contributorId":178787,"corporation":false,"usgs":false,"family":"Morton","given":"Charles","affiliations":[],"preferred":false,"id":660976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stringham, Tamzen K.","contributorId":178788,"corporation":false,"usgs":false,"family":"Stringham","given":"Tamzen","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":660977,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192455,"text":"70192455 - 2017 - Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl","interactions":[],"lastModifiedDate":"2019-06-04T08:40:19","indexId":"70192455","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl","docAbstract":"Wildlife researchers frequently study resource and habitat selection of wildlife to understand their potential habitat requirements and to conserve their populations. Understanding wildlife spatial-temporal distributions related to habitat have other applications such as to model interfaces between wildlife and domestic food animals in order to mitigate disease transmission to food animals. The highly pathogenic avian influenza (HPAI) virus represents a significant risk to the poultry industry. The Central Valley of California offers a unique geographical confluence of commercial poultry and wild waterfowl, which are thought to be a key reservoir of avian influenza (AI). Therefore, understanding spatio-temporal distributions of waterfowl could improve our understanding of potential risk of HPAI exposure from a commercial poultry perspective. Using existing radio-telemetry data on waterfowl (U.S. Geological Survey) in combination with habitat and vegetation data based on Geographic Information Systems (GIS), we are developing GIS-based statistical models that predict the probability of waterfowl presence (Habitat Suitability Mapping). Near-real-time application can be developed using recent habitat data derived from Landsat imagery (acquired by satellites and publicly available through the U.S. Geological Survey) to predict temporally- and spatially-varying distributions of waterfowl in the Central Valley. These results could be used to provide decision support for the poultry industry in addressing potential risk of HPAI exposure related to waterfowl proximity.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the Sixty-Sixth Western Poultry Disease Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Sixty-Sixth Western Poultry Disease Conference","conferenceDate":"March 20-22, 2017","conferenceLocation":"Sacramento, California","language":"English","publisher":"Western Poutlry Disease Conference","usgsCitation":"Matchett, E., Casazza, M.L., Fleskes, J.P., Kelman, T., Cadena, M., and Pitesky, M., 2017, Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl, in Proceedings of the Sixty-Sixth Western Poultry Disease Conference, Sacramento, California, March 20-22, 2017, p. 118-120.","productDescription":"3 p.","startPage":"118","endPage":"120","ipdsId":"IP-083273","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":352033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364313,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://aaap.memberclicks.net/wpdc-proceedings"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee8f7e4b0da30c1bfc4f0","contributors":{"authors":[{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleskes, Joseph P. 0000-0001-5388-6675 joe_fleskes@usgs.gov","orcid":"https://orcid.org/0000-0001-5388-6675","contributorId":177154,"corporation":false,"usgs":true,"family":"Fleskes","given":"Joseph","email":"joe_fleskes@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":715917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelman, T.","contributorId":198390,"corporation":false,"usgs":false,"family":"Kelman","given":"T.","email":"","affiliations":[],"preferred":false,"id":715918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cadena, M.","contributorId":198391,"corporation":false,"usgs":false,"family":"Cadena","given":"M.","email":"","affiliations":[],"preferred":false,"id":715919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pitesky, M.","contributorId":198392,"corporation":false,"usgs":false,"family":"Pitesky","given":"M.","affiliations":[],"preferred":false,"id":715920,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192594,"text":"70192594 - 2017 - Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas","interactions":[],"lastModifiedDate":"2017-11-17T11:39:08","indexId":"70192594","displayToPublicDate":"2017-01-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas","docAbstract":"Since the beginning of the 20th century, the overall occupied range of the lesser prairie-chicken (Tympanuchus pallidicinctus) has declined by 84% commensurate with population trends. Much of this decline has been attributed to the loss and fragmentation of native grasslands throughout the lesser prairie-chicken range. However, quantification of changes in land cover in the distribution of the lesser prairie-chicken is lacking. Our objectives were to (1) document changes in the areal extent and connectivity of grasslands in the identified lesser prairie-chicken range in Kansas, USA, (>60% of extant lesser prairie-chicken population) from the 1950s to 2013 using remotely sensed data and (2) assess the potential of the Conservation Reserve Program (U.S. Department of Agriculture Program converting cropland to permanent cover; CRP) to mitigate grassland loss. Digital land cover maps were generated on a decadal time step through spectral classification of LANDSAT images and visual analysis of aerial photographs (1950s and 1960s). Landscape composition and configuration were assessed using FRAGSTATS to compute a variety of landscape metrics measuring changes in the amount of grassland present as well as changes in the size and configuration of grassland patches. With the exception of a single regional portion of the range, nearly all of the grassland converted to cropland in the lesser prairie-chicken range of Kansas occurred prior to the 1950s. Prior to the implementation of CRP, the amount of grassland decreased 3.6% between the 1950s and 1985 from 18,455 km2 to 17,788 km2. Since 1985, the overall amount of grassland in the lesser prairie-chicken range has increased 11.9% to 19,898 km2 due to implementation of CRP, although the area of grassland decreased between 1994 and 2013 as CRP contracts were not renewed by landowners. Since 1986 grassland in Kansas became more connected and less fragmented in response to the CRP. While the CRP has been successful in increasing grassland quantity and connectivity throughout the lesser prairie-chicken range in Kansas, offsetting loss of grassland since the 1950s, abundance and occupied range of lesser prairie-chickens has declined since the 1980s, suggesting that habitat quality is the principal factor influencing population demography of the species. Although the CRP is contributing to conservation actions for lesser prairie-chickens, efforts to improve habitat quality throughout the range of the lesser prairie-chicken are likely necessary to meet management goals. Continuation of the CRP faces an uncertain future in the face of rising commodity prices, energy development, and reduction in program scope, leaving open the possibility that these areas that have created habitat for lesser prairie-chickens could be lost.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2016.11.004","usgsCitation":"Haukos, D.A., Spencer, D., Hagen, C.A., Daniels, M.D., and Goodin, D., 2017, Conservation Reserve Program mitigates grassland loss in the lesser prairie-chicken range of Kansas: Global Ecology and Conservation, v. 9, p. 21-38, https://doi.org/10.1016/j.gecco.2016.11.004.","productDescription":"18 p.","startPage":"21","endPage":"38","ipdsId":"IP-078839","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2016.11.004","text":"Publisher Index Page"},{"id":349063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.01904296874999,\n 36.96744946416934\n ],\n [\n -97.8662109375,\n 36.96744946416934\n ],\n [\n -97.8662109375,\n 40.027614437486655\n ],\n [\n -102.01904296874999,\n 40.027614437486655\n ],\n [\n -102.01904296874999,\n 36.96744946416934\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc3de4b06e28e9c23c02","contributors":{"authors":[{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":716485,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, David","contributorId":200553,"corporation":false,"usgs":false,"family":"Spencer","given":"David","affiliations":[],"preferred":false,"id":722646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":177795,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":722647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniels, Melinda D.","contributorId":166701,"corporation":false,"usgs":false,"family":"Daniels","given":"Melinda","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":722648,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goodin, Doug","contributorId":200554,"corporation":false,"usgs":false,"family":"Goodin","given":"Doug","email":"","affiliations":[],"preferred":false,"id":722649,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70181028,"text":"70181028 - 2017 - Patterns and drivers for wetland connections in the Prairie Pothole Region, United States","interactions":[],"lastModifiedDate":"2017-06-01T10:24:44","indexId":"70181028","displayToPublicDate":"2016-11-18T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Patterns and drivers for wetland connections in the Prairie Pothole Region, United States","docAbstract":"Ecosystem function in rivers, lakes and coastal waters depends on the functioning of upstream aquatic ecosystems, necessitating an improved understanding of watershed-scale interactions including variable surface-water flows between wetlands and streams. As surface water in the Prairie Pothole Region expands in wet years, surface-water connections occur between many depressional wetlands and streams. Minimal research has explored the spatial patterns and drivers for the abundance of these connections, despite their potential to inform resource management and regulatory programs including the U.S. Clean Water Act. In this study, wetlands were identified that did not intersect the stream network, but were shown with Landsat images (1990–2011) to become merged with the stream network as surface water expanded. Wetlands were found to spill into or consolidate with other wetlands within both small (2–10 wetlands) and large (>100 wetlands) wetland clusters, eventually intersecting a stream channel, most often via a riparian wetland. These surface-water connections occurred over a wide range of wetland distances from streams (averaging 90–1400 m in different ecoregions). Differences in the spatial abundance of wetlands that show a variable surface-water connection to a stream were best explained by smaller wetland-to-wetland distances, greater wetland abundance, and maximum surface-water extent. This analysis demonstrated that wetland arrangement and surface water expansion are important mechanisms for depressional wetlands to connect to streams and provides a first step to understanding the frequency and abundance of these surface-water connections across the Prairie Pothole Region.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-016-9516-9","usgsCitation":"Vanderhoof, M.K., Christensen, J.R., and Alexander, L., 2017, Patterns and drivers for wetland connections in the Prairie Pothole Region, United States: Wetlands Ecology and Management, v. 25, no. 3, p. 275-297, https://doi.org/10.1007/s11273-016-9516-9.","productDescription":"23 p.","startPage":"275","endPage":"297","ipdsId":"IP-069163","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11273-016-9516-9","text":"Publisher Index Page"},{"id":335162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.052490234375,\n 46.475699386607516\n ],\n [\n -100.052490234375,\n 48.785151998043155\n ],\n [\n -97.61352539062499,\n 48.785151998043155\n ],\n [\n -97.61352539062499,\n 46.475699386607516\n ],\n [\n -100.052490234375,\n 46.475699386607516\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.23974609375,\n 43.8028187190472\n ],\n [\n -98.23974609375,\n 46.027481852486645\n ],\n [\n -94.8779296875,\n 46.027481852486645\n ],\n [\n -94.8779296875,\n 43.8028187190472\n ],\n [\n -98.23974609375,\n 43.8028187190472\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-19","publicationStatus":"PW","scienceBaseUri":"589fff06e4b099f50d3e0449","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":663374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Jay R.","contributorId":179361,"corporation":false,"usgs":false,"family":"Christensen","given":"Jay","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":663375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Laurie C.","contributorId":138989,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":663376,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182747,"text":"70182747 - 2016 - An automated approach for mapping persistent ice and snow cover over high latitude regions","interactions":[],"lastModifiedDate":"2017-02-28T09:38:05","indexId":"70182747","displayToPublicDate":"2017-02-28T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An automated approach for mapping persistent ice and snow cover over high latitude regions","docAbstract":"We developed an automated approach for mapping persistent ice and snow cover (glaciers and perennial snowfields) from Landsat TM and ETM+ data across a variety of topography, glacier types, and climatic conditions at high latitudes (above ~65°N). Our approach exploits all available Landsat scenes acquired during the late summer (1 August–15 September) over a multi-year period and employs an automated cloud masking algorithm optimized for snow and ice covered mountainous environments. Pixels from individual Landsat scenes were classified as snow/ice covered or snow/ice free based on the Normalized Difference Snow Index (NDSI), and pixels consistently identified as snow/ice covered over a five-year period were classified as persistent ice and snow cover. The same NDSI and ratio of snow/ice-covered days to total days thresholds applied consistently across eight study regions resulted in persistent ice and snow cover maps that agreed closely in most areas with glacier area mapped for the Randolph Glacier Inventory (RGI), with a mean accuracy (agreement with the RGI) of 0.96, a mean precision (user’s accuracy of the snow/ice cover class) of 0.92, a mean recall (producer’s accuracy of the snow/ice cover class) of 0.86, and a mean F-score (a measure that considers both precision and recall) of 0.88. We also compared results from our approach to glacier area mapped from high spatial resolution imagery at four study regions and found similar results. Accuracy was lowest in regions with substantial areas of debris-covered glacier ice, suggesting that manual editing would still be required in these regions to achieve reasonable results. The similarity of our results to those from the RGI as well as glacier area mapped from high spatial resolution imagery suggests it should be possible to apply this approach across large regions to produce updated 30-m resolution maps of persistent ice and snow cover. In the short term, automated PISC maps can be used to rapidly identify areas where substantial changes in glacier area have occurred since the most recent conventional glacier inventories, highlighting areas where updated inventories are most urgently needed. From a longer term perspective, the automated production of PISC maps represents an important step toward fully automated glacier extent monitoring using Landsat or similar sensors.
","language":"English","publisher":"MDPI","publisherLocation":"Basel, Switzerland","doi":"10.3390/rs8010016","usgsCitation":"Selkowitz, D.J., and Forster, R.R., 2016, An automated approach for mapping persistent ice and snow cover over high latitude regions: Remote Sensing, v. 8, no. 1, 21 p., https://doi.org/10.3390/rs8010016.","productDescription":"21 p.","ipdsId":"IP-066601","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":461980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8010016","text":"Publisher Index Page"},{"id":336312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Circumpolar Arctic","volume":"8","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-25","publicationStatus":"PW","scienceBaseUri":"58b69a3fe4b01ccd54ff3f86","contributors":{"authors":[{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":673560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forster, Richard R.","contributorId":169008,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","email":"","middleInitial":"R.","affiliations":[{"id":25396,"text":"Department of Geography, University of Utah","active":true,"usgs":false}],"preferred":false,"id":673561,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178572,"text":"70178572 - 2016 - Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","interactions":[],"lastModifiedDate":"2017-01-03T16:02:50","indexId":"70178572","displayToPublicDate":"2016-11-29T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1170,"text":"Canadian Journal of Forest Research","active":true,"publicationSubtype":{"id":10}},"title":"Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA","docAbstract":"High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by (i) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, (ii) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and (iii) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R2 of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an R2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m3·ha−1·year−1. Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfr-2016-0209","usgsCitation":"Huang, S., Ramirez, C., Conway, S., Kennedy, K., Kohler, T., and Liu, J., 2016, Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA: Canadian Journal of Forest Research, v. 47, no. 1, p. 113-124, https://doi.org/10.1139/cjfr-2016-0209.","productDescription":"12 p.","startPage":"113","endPage":"124","ipdsId":"IP-076665","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470401,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfr-2016-0209","text":"External Repository"},{"id":331299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583ea1bfe4b0f0dc05ea54e1","contributors":{"authors":[{"text":"Huang, Shengli shuang@usgs.gov","contributorId":1926,"corporation":false,"usgs":true,"family":"Huang","given":"Shengli","email":"shuang@usgs.gov","affiliations":[],"preferred":true,"id":654460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramirez, Carlos","contributorId":177061,"corporation":false,"usgs":false,"family":"Ramirez","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":654461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Scott","contributorId":177062,"corporation":false,"usgs":false,"family":"Conway","given":"Scott","email":"","affiliations":[],"preferred":false,"id":654462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Kama","contributorId":177063,"corporation":false,"usgs":false,"family":"Kennedy","given":"Kama","email":"","affiliations":[],"preferred":false,"id":654463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kohler, Tanya","contributorId":177064,"corporation":false,"usgs":false,"family":"Kohler","given":"Tanya","email":"","affiliations":[],"preferred":false,"id":654464,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654465,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}