Satellite-derived phenology metrics are valuable tools for understanding broad-scale patterns and changes in vegetated landscapes over time. However, the extraction and interpretation of phenology in ecosystems with subtle growth dynamics can be challenging. US National Park Service monitoring of evergreen pinyon-juniper ecosystems in the western US revealed an unexpected winter-peaking phenological pattern in normalized difference vegetation index (NDVI) time-series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. In this paper, we assess the validity of the winter peaks through ground-based observation of phenology and examination of solar and satellite geometry effects. To test the premise of a true vegetation response, we analyzed NDVI values extracted from a time series of ground-based digital camera (‘phenocam’) images collected September 2017 to December 2018 in a pinyon-juniper woodland in Arizona, US. Results show pinyon and juniper growth peaked in the warm season, as did the other species in the phenocam field of view. NDVI time series from four other sensors (Landsat 7, Sentinel-2, VIIRS, and Proba-V) confirmed that winter peaks in this ecosystem are not limited to MODIS products. Examination of NDVI time series (2003–2018) derived from daily 250-m MODIS data in the broader pinyon-juniper ecosystem revealed that solar-to-sensor angle, sensor zenith angle, and forward/back-scatter reflectance explained >80% of intra-annual variability. Solar-to-sensor angle exerted the greatest control, and the direction of its correlation (positive) was the opposite of that which would be expected if it were driven by vegetation greenness. Solar-to-sensor angle is controlled seasonally by solar zenith angle and daily by variations in satellite overpass geometry. We mapped winter peaks across the western US in Google Earth Engine using 500-m MODIS MCD43A4 data, which correct for reflectance differences caused by view angle. In areas where winter vegetation peaks are ecologically improbable (i.e., locations with sub-freezing December temperatures), consistent winter peaks (≥ 14 years in 2003 to 2018) are widespread in both pinyon-juniper and non-pinyon-juniper conifer ecosystems; winter peaks are common (≥ 5 years in 2003 to 2018) across areas of shrubland. We attribute winter peaks to the positive correlation of NDVI with solar-to-sensor angle and solar zenith angle in combination with sparse, vertically oriented evergreen vegetation canopies. Increasing shadow visibility has been shown to increase overall NDVI, and the prevalence of the winter peaking in evergreen western sparse canopy ecosystems is consistent with this hypothesis. The extent of winter peaking patterns may have been previously overlooked due to temporal compositing, curve fitting, and incomplete snow screening.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.112013","usgsCitation":"Norris, J.R., and Walker, J.J., 2020, Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States: Remote Sensing of Environment, v. 249, 112013, 19 p.; Data Release, https://doi.org/10.1016/j.rse.2020.112013.","productDescription":"112013, 19 p.; Data Release","ipdsId":"IP-115826","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455570,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.112013","text":"Publisher Index Page"},{"id":377823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404210,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNQL6L"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.984375,\n 31.316101383495624\n ],\n [\n -108.28125,\n 31.316101383495624\n ],\n [\n -108.1494140625,\n 31.690781806136822\n ],\n [\n -106.25976562499999,\n 31.765537409484374\n ],\n [\n -106.12792968749999,\n 32.02670629333614\n ],\n [\n -103.1396484375,\n 32.0639555946604\n ],\n [\n -103.0078125,\n 36.80928470205937\n ],\n [\n -102.1728515625,\n 36.98500309285596\n ],\n [\n -102.041015625,\n 41.07935114946899\n ],\n [\n -104.0185546875,\n 40.94671366508002\n ],\n [\n -104.0185546875,\n 49.095452162534826\n ],\n [\n -122.958984375,\n 49.009050809382046\n ],\n [\n -122.9150390625,\n 48.19538740833338\n ],\n [\n -124.71679687499999,\n 48.48748647988415\n ],\n [\n -124.62890625,\n 47.81315451752768\n ],\n [\n -124.1455078125,\n 46.6795944656402\n ],\n [\n -124.541015625,\n 43.389081939117496\n ],\n [\n -124.93652343749999,\n 42.32606244456202\n ],\n [\n -124.1455078125,\n 41.409775832009565\n ],\n [\n -124.93652343749999,\n 40.34654412118006\n ],\n [\n -123.92578125,\n 38.41055825094609\n ],\n [\n -121.11328124999999,\n 35.17380831799959\n ],\n [\n -120.84960937499999,\n 34.30714385628804\n ],\n [\n -117.68554687499999,\n 32.69486597787505\n ],\n [\n -117.2900390625,\n 32.54681317351514\n ],\n [\n -114.7412109375,\n 32.731840896865684\n ],\n [\n -114.7412109375,\n 32.43561304116276\n ],\n [\n -111.005859375,\n 31.353636941500987\n ],\n [\n -108.984375,\n 31.316101383495624\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"249","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Norris, Jodi R.","contributorId":190196,"corporation":false,"usgs":false,"family":"Norris","given":"Jodi","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":797242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":797243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70214303,"text":"70214303 - 2020 - Landsat 9: Empowering open science and applications through continuity","interactions":[],"lastModifiedDate":"2020-09-25T14:25:32.132942","indexId":"70214303","displayToPublicDate":"2020-07-23T09:25:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 9: Empowering open science and applications through continuity","docAbstract":"The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111968","usgsCitation":"Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C., Storey, J.C., and Jenstrom, D., 2020, Landsat 9: Empowering open science and applications through continuity: Remote Sensing of Environment, v. 248, 111968, 13 p., https://doi.org/10.1016/j.rse.2020.111968.","productDescription":"111968, 13 p.","ipdsId":"IP-118603","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":378748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":799592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":799593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markham, Brian 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":139286,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":799594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCorkel, Joel","contributorId":192459,"corporation":false,"usgs":false,"family":"McCorkel","given":"Joel","email":"","affiliations":[],"preferred":false,"id":799595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":799596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","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":799597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenstrom, Del","contributorId":241119,"corporation":false,"usgs":false,"family":"Jenstrom","given":"Del","email":"","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":799598,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211221,"text":"70211221 - 2020 - Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine","interactions":[],"lastModifiedDate":"2020-07-20T13:33:45.743932","indexId":"70211221","displayToPublicDate":"2020-07-18T07:32:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine","docAbstract":"Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global CroplandsWinter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (>26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass (p ≤0.01, R2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit.
To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly (
Monitoring rangelands by identifying the departure of contemporary conditions from long-term ecological potential allows for the disentanglement of natural biophysical gradients driving change from changes associated with land uses and other disturbance types. We developed maps of ecological potential (EP) for shrub, sagebrush (Artemisia spp.), perennial herbaceous, litter, and bare ground fractional cover in Wyoming, USA. EP maps correspond to the potential natural vegetation cover expected by environmental conditions in the absence of anthropogenic and natural disturbance as represented by the greenest and least disturbed period of the Landsat archive. EP was predicted using regression tree models with inputs of soil maps and spectral data associated with the 75th percentile of the Normalized Difference Vegetation Index in the Landsat archive. We trained our EP models with 2015 component cover maps on ecologically intact sites with relatively lower bare ground than expected. We generated departure of vegetation cover by comparing the EP and 2015 fractional cover. The departures represent land cover change from potential land cover and/or within-state changes in 2015. Next, we converted EP and 2015 fractional cover maps into thematic land cover and evaluated departure to determine if it was great enough to result in land cover change. The 2015 conditions showed reduced shrub, sagebrush, litter, and perennial herbaceous cover and increased bare ground relative to EP. Known disturbances, such as energy development, fires, and vegetation treatments, are clearly visible on the departure maps, but not on EP component maps. The most frequent departure from EP land cover was shrubland conversion to grassland. Land cover departures can be explained only in small part by known disturbance, and instead are ostensibly related to climate and land management practices. These drivers result in land cover departures that broadened the ecotone between shrubland and grassland relative to EP.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2020.03.009","usgsCitation":"Rigge, M.B., Homer, C.G., Shi, H., and Wylie, B., 2020, Departures of rangeland fractional component cover and land cover from landsat-based ecological potential in Wyoming USA: Rangeland Ecology and Management, v. 73, no. 6, p. 856-870, https://doi.org/10.1016/j.rama.2020.03.009.","productDescription":"15 p.","startPage":"856","endPage":"870","ipdsId":"IP-114686","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":456635,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2020.03.009","text":"Publisher Index Page"},{"id":436954,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IKI4XV","text":"USGS data release","linkHelpText":"Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 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Surface soil temperature plays a significant role in agricultural drought monitoring, ecosystem energy transfer modeling, and global carbon cycle evaluation. Studies have been proposed to estimate surface soil temperature, but surface soil temperature monitoring within forested areas still poses a significant challenge. In this study, we proposed a surface soil temperature retrieval method using combined satellite observations and in-situ measurements for the Great Dismal Swamp (GDS). The GDS is a U.S. protected area managed and protected by the U.S. Fish and Wildlife Service. It is located along the boundary of Virginia and North Carolina, with maple gum, Atlantic white cedar, and pine pocosin as the main forest cover types. Ground-based surface soil temperature measurements were collected for these forest types from May 2015 to April 2017. Both the Land Remote Sensing Satellite (Landsat) Thermal Infrared Sensor (TIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) carry two thermal infrared (TIR) channels. The TIR channels with similar corresponding wavelengths were first fused using an improved fusing model to generate high resolution TIR measurements. Then the enterprise algorithm was applied to calculate land surface temperature (LST) from the fused TIR bands. An improved soil temperature retrieval method was applied to generate surface soil temperature based on LST and vegetation index (VI) within the study area for the three forest types. In-situ measurements were used to build the surface soil temperature retrieval method, and results were then validated. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated separately as VIs in the model to monitor surface soil temperature. The R2 for retrieved surface soil temperature through satellite observations was 0.76, and the RMSE was 1.96
Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km2 year-1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km2 over the four-year period, and 186 km2, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12091464","usgsCitation":"Vanderhoof, M.K., Christensen, J.R., Beal, Y.G., DeVries, B., Lang, M.W., Hwang, N., Mazzarella, C., and Jones, J., 2020, Isolating anthropogenic wetland loss by concurrently tracking inundation and land cover disturbance across the Mid-Atlantic Region, U.S.: Remote Sensing, v. 12, no. 9, 1464, 29 p., https://doi.org/10.3390/rs12091464.","productDescription":"1464, 29 p.","ipdsId":"IP-116446","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":35993,"text":"Hydrologic Investigations and Research Section","active":true,"usgs":true}],"links":[{"id":456841,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12091464","text":"Publisher Index Page"},{"id":437000,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ODILGN","text":"USGS data release","linkHelpText":"Tracking disturbance and inundation to identify wetland loss"},{"id":377459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, MarylandPennsylvania, Virginia, West Virginia","otherGeospatial":"Mid-Atlantic Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.70703125,\n 41.44272637767212\n ],\n [\n -75.05859375,\n 41.77131167976407\n ],\n [\n -75.41015624999999,\n 42.09822241118974\n ],\n [\n -79.5849609375,\n 42.06560675405716\n ],\n [\n -79.9365234375,\n 42.293564192170095\n ],\n [\n -80.6396484375,\n 41.672911819602085\n ],\n [\n -80.6396484375,\n 40.1452892956766\n ],\n [\n -81.474609375,\n 39.232253141714885\n ],\n [\n -81.8701171875,\n 38.92522904714054\n ],\n [\n -82.5732421875,\n 38.44498466889473\n ],\n [\n -82.2216796875,\n 37.43997405227057\n ],\n [\n -83.5400390625,\n 36.63316209558658\n ],\n [\n -76.2451171875,\n 36.56260003738545\n ],\n [\n -73.47656249999999,\n 34.30714385628804\n ],\n [\n -70.6640625,\n 35.137879119634185\n ],\n [\n -72.333984375,\n 40.212440718286466\n ],\n [\n -73.8720703125,\n 40.48038142908172\n ],\n [\n -74.6630859375,\n 39.027718840211605\n ],\n [\n -75.6298828125,\n 39.470125122358176\n ],\n [\n -75.5859375,\n 39.90973623453719\n ],\n [\n -74.92675781249999,\n 40.1452892956766\n ],\n [\n -75.234375,\n 40.48038142908172\n ],\n [\n -74.70703125,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-05-05","publicationStatus":"PW","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 - 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Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796087,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70213227,"text":"70213227 - 2020 - Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces","interactions":[],"lastModifiedDate":"2020-09-15T12:56:38.466452","indexId":"70213227","displayToPublicDate":"2020-05-02T07:41:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces","docAbstract":"Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high‐, medium‐ and coarse‐spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI‐based ET, over a 780‐ha urban park in Adelaide, Australia. We validated ET with the ground‐based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long‐term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET‐MODIS and ET‐Landsat (R2 > 0.99 for all VIs). Landsat‐VIs captured the seasonal changes of greenness better than MODIS‐VIs. We used artificial neural network (ANN) to relate the RS‐ET and ground data, and ET‐MODIS (EVI2) showed the highest correlation (R2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS‐ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water‐limited cities.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13790","usgsCitation":"Nouri, H., Nagler, P.L., Borujeni, S.C., Munez, A.B., Alaghmand, S., Noori, B., Galindo, A., and Didan, K., 2020, Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces: Hydrological Processes, v. 34, no. 15, p. 3183-3199, https://doi.org/10.1002/hyp.13790.","productDescription":"17 p.","startPage":"3183","endPage":"3199","ipdsId":"IP-110995","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":456880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13790","text":"Publisher Index Page"},{"id":378390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","city":"Adelaide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 138.4716796875,\n -35.06597313798418\n ],\n [\n 138.955078125,\n -35.06597313798418\n ],\n [\n 138.955078125,\n -34.70549341022545\n ],\n [\n 138.4716796875,\n -34.70549341022545\n ],\n [\n 138.4716796875,\n -35.06597313798418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nouri, Hamideh 0000-0002-7424-5030","orcid":"https://orcid.org/0000-0002-7424-5030","contributorId":16327,"corporation":false,"usgs":true,"family":"Nouri","given":"Hamideh","email":"","affiliations":[],"preferred":false,"id":798683,"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":798645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borujeni, Sattar Chavoshi","contributorId":240671,"corporation":false,"usgs":false,"family":"Borujeni","given":"Sattar","email":"","middleInitial":"Chavoshi","affiliations":[],"preferred":false,"id":798684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munez, Armando Barreto","contributorId":240672,"corporation":false,"usgs":false,"family":"Munez","given":"Armando","email":"","middleInitial":"Barreto","affiliations":[],"preferred":false,"id":798685,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alaghmand, Sina","contributorId":172388,"corporation":false,"usgs":false,"family":"Alaghmand","given":"Sina","email":"","affiliations":[{"id":27031,"text":"School of Natural and Built Environments, U. 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Aus and Discipline of Civil Engineering, School Of Engineering, Monash University Malaysia","active":true,"usgs":false}],"preferred":false,"id":798686,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Noori, Behnaz","contributorId":172392,"corporation":false,"usgs":false,"family":"Noori","given":"Behnaz","email":"","affiliations":[],"preferred":false,"id":798687,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Galindo, Alejandro","contributorId":240673,"corporation":false,"usgs":false,"family":"Galindo","given":"Alejandro","email":"","affiliations":[],"preferred":false,"id":798688,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":798689,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70214673,"text":"70214673 - 2020 - Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","interactions":[],"lastModifiedDate":"2020-10-02T13:24:58.347144","indexId":"70214673","displayToPublicDate":"2020-04-29T08:21:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","docAbstract":"Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2153","usgsCitation":"Byrd, K.B., Lorenz, A., Anderson, J., Wallace, C., Kara Moore-O'Leary, Isola, J., Ortega, R., and Reiter, M., 2020, Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing: Ecological Applications, v. 30, no. 7, e02153, 20 p., https://doi.org/10.1002/eap.2153.","productDescription":"e02153, 20 p.","ipdsId":"IP-112842","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":378986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16796875,\n 40.48038142908172\n ],\n [\n -122.431640625,\n 40.713955826286046\n ],\n [\n -123.00292968749999,\n 40.34654412118006\n ],\n [\n -122.958984375,\n 39.26628442213066\n ],\n [\n -122.431640625,\n 38.58252615935333\n ],\n [\n -121.9482421875,\n 37.33522435930639\n ],\n [\n -120.5419921875,\n 36.06686213257888\n ],\n [\n -119.4873046875,\n 35.02999636902566\n ],\n [\n -119.00390625,\n 34.994003757575776\n ],\n [\n -118.564453125,\n 35.209721645221386\n ],\n [\n -118.95996093749999,\n 36.35052700542763\n ],\n [\n -120.0146484375,\n 37.055177106660814\n ],\n [\n -121.201171875,\n 38.89103282648846\n ],\n [\n -122.16796875,\n 40.48038142908172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, James","contributorId":242025,"corporation":false,"usgs":false,"family":"Anderson","given":"James","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":800395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallace, Cynthia 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":149179,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kara Moore-O'Leary","contributorId":242031,"corporation":false,"usgs":false,"family":"Kara Moore-O'Leary","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isola, Jennifer","contributorId":242027,"corporation":false,"usgs":false,"family":"Isola","given":"Jennifer","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ortega, Ricardo","contributorId":242028,"corporation":false,"usgs":false,"family":"Ortega","given":"Ricardo","email":"","affiliations":[{"id":48476,"text":"Grassland Water District","active":true,"usgs":false}],"preferred":false,"id":800399,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reiter, Matt","contributorId":242029,"corporation":false,"usgs":false,"family":"Reiter","given":"Matt","email":"","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":800400,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210161,"text":"70210161 - 2020 - The Landsat Burned Area algorithm and products for the conterminous United States","interactions":[],"lastModifiedDate":"2022-04-14T19:23:11.991912","indexId":"70210161","displayToPublicDate":"2020-04-20T10:12:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"The Landsat Burned Area algorithm and products for the conterminous United States","docAbstract":"Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. Here, we present the BA algorithm and products, changes relative to the BAECV algorithm and products, and updated validation metrics. We also present spatial and temporal patterns of burned area across the conterminous U.S., how burned area varies in relation to the number of operational Landsat sensors, and a comparison with other burned area datasets, including the BAECV, Monitoring Trends in Burn Severity (MTBS), GeoMAC, and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1.006 data. The BA algorithm identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984 through 2018 using machine learning, thresholding, and image segmentation. Validation with reference data from high-resolution commercial satellite imagery resulted in omission and commission error rates averaging 19% and 41%, respectively. In comparison, validation with Landsat reference data had omission and commission error rates averaging 40% and 28%, respectively when burned areas in cultivated crops and pasture/hay land-cover types were excluded. Both validation tests documented lower commission error rates relative to the BAECV products. The amount of burned area detected varies not only in response to climate but also with the number of operational sensors and scenes collected. The combined amount of burned area detected by multiple sensors was larger than from any individual sensor, but there was no significant difference between individual sensors. Therefore, we used BA products from individual sensors to assess trends over time and all available sensors to compare with other existing BA products. From 1984 through 2018, annual burned area averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. Compared to existing burned area products, the new Landsat BA products identified 29% more burned area than the BAECV products (1984–2015), 183% more than the MTBS/GeoMAC products (1984–2018), and 56% more than the MCD64A1.006 products (2003–2018). The products had similar patterns of year-to-year variability; the R2 values of linear regressions between annual burned area were >0.70 with the BAECV products and the MTBS/GeoMAC products, but somewhat lower for the MCD64A1.006 product (R2 = 0.66). The BA products are routinely produced as new Landsat data are collected and provide a unique data source to monitor and assess the spatial and temporal patterns and the impacts of fire.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111801","usgsCitation":"Hawbaker, T., Vanderhoof, M.K., Schmidt, G.L., Beal, Y.G., Picotte, J.J., Takacs, J., Falgout, J.T., and Dwyer, J.L., 2020, The Landsat Burned Area algorithm and products for the conterminous United States: Remote Sensing of Environment, v. 244, 111801, 24 p., https://doi.org/10.1016/j.rse.2020.111801.","productDescription":"111801, 24 p.","ipdsId":"IP-111890","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"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":5044,"text":"National Research Program - Central 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Joshua 0000-0003-1509-5498 jdtakacs@usgs.gov","orcid":"https://orcid.org/0000-0003-1509-5498","contributorId":194380,"corporation":false,"usgs":true,"family":"Takacs","given":"Joshua","email":"jdtakacs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":789353,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Falgout, Jeff T. 0000-0002-7108-477X jfalgout@usgs.gov","orcid":"https://orcid.org/0000-0002-7108-477X","contributorId":4957,"corporation":false,"usgs":true,"family":"Falgout","given":"Jeff","email":"jfalgout@usgs.gov","middleInitial":"T.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":789354,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dwyer, John L. 0000-0002-8281-0896 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Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Gap fill of Land surface temperature and reflectance products in Analysis Ready Data","docAbstract":"The recently released Landsat Analysis Ready Data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data acquisition strategy. We present a new algorithm that focuses on data gap filling using clear observations from orbit overlap regions. Multiple linear regression models were established for each pixel time series to estimate stable predictions and uncertainties. The model's training data came from stratified random samples based on the time series similarity between the pixel and data from the overlap regions. The algorithm was evaluated using four tiles (5,000 × 5,000 30-m pixels for each tile) from 2018 land surface temperature data (LST) in Atlanta, Georgia. The accuracy was assessed using 1,000 randomly masked pixels and daily air temperature from eight ground stations. Both assessments showed the r2 value above 0.75, except two stations with mixed Landsat pixels. We also compared our results with the eMODIS LST product in terms of annual mean temperature. The two maps showed a similar spatial pattern at the region level, but our results showed more spatial detail in the urban area that matched the pattern of impervious surface. We also applied the method on ARD surface reflectance bands at Fairbanks, Alaska, to illustrate its improvements in surface reflectance products and in land change modeling. This approach can also be applied to other datasets, vegetation indexes, or spectral reflectance bands of other sensors.","language":"English","publisher":"MDPI","doi":"10.3390/rs12071192","collaboration":"","usgsCitation":"Zhou, Q., Xian, G.Z., and Shi, H., 2020, Gap fill of Land surface temperature and reflectance products in Analysis Ready Data: Remote Sensing, v. 12, no. 7, 1192, 16 p., https://doi.org/10.3390/rs12071192.","productDescription":"1192, 16 p.","ipdsId":"IP-113228","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457126,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12071192","text":"Publisher Index Page"},{"id":374000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":787088,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213220,"text":"70213220 - 2020 - Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data","interactions":[],"lastModifiedDate":"2020-09-15T13:10:28.365993","indexId":"70213220","displayToPublicDate":"2020-04-08T08:00:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data","docAbstract":"Because groundwater recharge in dry regions is generally low, arid and semiarid environments have been considered well‐suited for long‐term isolation of hazardous materials (e.g., radioactive waste). In these dry regions, water lost (transpired) by plants and evaporated from the soil surface, collectively termed evapotranspiration (ET), is usually the primary discharge component in the water balance. Therefore, vegetation can potentially affect groundwater flow and contaminant transport at waste disposal sites. We studied vegetation health and ET dynamics at a Uranium Mill Tailings Radiation Control Act (UMTRCA) disposal site in Shiprock, New Mexico, where a floodplain alluvial aquifer was contaminated by mill effluent. Vegetation on the floodplain was predominantly deep‐rooted, non‐native tamarisk shrubs (Tamarix sp.). After the introduction of the tamarisk beetle (Diorhabda sp.) as a biocontrol agent, the health of the invasive tamarisk on the Shiprock floodplain declined. We used Landsat normalized difference vegetation index (NDVI) data to measure greenness and a remote sensing algorithm to estimate landscape‐scale ET along the floodplain of the UMTRCA site in Shiprock prior to (2000–2009) and after (2010–2018) beetle establishment. Using groundwater level data collected from 2011 to 2014, we also assessed the role of ET in explaining seasonal variations in depth to water of the floodplain. Growing season scaled NDVI decreased 30% (p < .001), while ET decreased 26% from the pre‐ to post‐beetle period and seasonal ET estimates were significantly correlated with groundwater levels from 2011 to 2014 (r2 = .71; p = .009). Tamarisk greenness (a proxy for health) was significantly affected by Diorhabda but has partially recovered since 2012. Despite this, increased ET demand in the summer/fall period might reduce contaminant transport to the San Juan River during this period.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13772","usgsCitation":"Jarchow, C.J., Waugh, W.J., Didan, K., Barreto-Munoz, A., Herrmann, S.M., and Nagler, P.L., 2020, Vegetation‐groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data: Hydrological Processes, v. 34, no. 12, p. 2739-2749, https://doi.org/10.1002/hyp.13772.","productDescription":"11 p.","startPage":"2739","endPage":"2749","ipdsId":"IP-112673","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489705,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13772","text":"Publisher Index Page"},{"id":378391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Shiprock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.71383666992188,\n 36.766667073939736\n ],\n [\n -108.66302490234375,\n 36.766667073939736\n ],\n [\n -108.66302490234375,\n 36.806261006694555\n ],\n [\n -108.71383666992188,\n 36.806261006694555\n ],\n [\n -108.71383666992188,\n 36.766667073939736\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-04-29","publicationStatus":"PW","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":798690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waugh, William J.","contributorId":196107,"corporation":false,"usgs":false,"family":"Waugh","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":798691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Didan, Kamel","contributorId":130999,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":798692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":798693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herrmann, Stefanie M. 0000-0002-4069-2019","orcid":"https://orcid.org/0000-0002-4069-2019","contributorId":20234,"corporation":false,"usgs":true,"family":"Herrmann","given":"Stefanie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":798694,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":798634,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209153,"text":"70209153 - 2020 - Implementation of a surface water extent model in Cambodia using cloud-based remote sensing","interactions":[],"lastModifiedDate":"2020-03-20T06:38:20","indexId":"70209153","displayToPublicDate":"2020-03-19T18:59:44","publicationYear":"2020","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":"Implementation of a surface water extent model in Cambodia using cloud-based remote sensing","docAbstract":"Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe.","language":"English","publisher":"MDPI","doi":"10.3390/rs12060984","usgsCitation":"Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a surface water extent model in Cambodia using cloud-based remote sensing: Remote Sensing, v. 12, no. 6, 984, https://doi.org/10.3390/rs12060984.","productDescription":"984","ipdsId":"IP-115688","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":457313,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12060984","text":"Publisher Index Page"},{"id":437053,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LH9YYF","text":"USGS data release","linkHelpText":"Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps"},{"id":373394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cambodia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[103.49728,10.63256],[103.09069,11.15366],[102.58493,12.18659],[102.3481,13.39425],[102.98842,14.22572],[104.28142,14.41674],[105.21878,14.27321],[106.04395,13.88109],[106.49637,14.57058],[107.38273,14.20244],[107.61455,13.53553],[107.4914,12.33721],[105.81052,11.56761],[106.24967,10.96181],[105.19991,10.88931],[104.33433,10.48654],[103.49728,10.63256]]]},\"properties\":{\"name\":\"Cambodia\"}}]}","volume":"12","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":785150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":785151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petrakis, Roy E. 0000-0001-8932-077X","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":219707,"corporation":false,"usgs":false,"family":"Petrakis","given":"Roy","email":"","middleInitial":"E.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":785152,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210163,"text":"70210163 - 2020 - A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery","interactions":[],"lastModifiedDate":"2020-05-19T15:05:04.146927","indexId":"70210163","displayToPublicDate":"2020-03-19T09:58:05","publicationYear":"2020","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 within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery","docAbstract":"Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111752","usgsCitation":"Gao, F., Anderson, M., Daughtry, C.S., Karnieli, A., Hively, W.D., and Kustas, W.P., 2020, A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery: Remote Sensing of Environment, v. 242, 111752, 19 p., https://doi.org/10.1016/j.rse.2020.111752.","productDescription":"111752, 19 p.","ipdsId":"IP-113523","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.111752","text":"Publisher Index Page"},{"id":374923,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Beltsville Agricultural Research Center (BARC), Choptank River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.94412231445312,\n 38.756225137839074\n ],\n [\n -76.38381958007812,\n 38.756225137839074\n ],\n [\n -76.38381958007812,\n 39.29392267616436\n ],\n [\n -76.94412231445312,\n 39.29392267616436\n ],\n [\n -76.94412231445312,\n 38.756225137839074\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"242","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng","contributorId":197297,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","affiliations":[],"preferred":false,"id":789358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":210925,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[],"preferred":false,"id":789359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daughtry, Craig S. T.","contributorId":211093,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S. T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":789360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karnieli, Arnon 0000-0001-8065-9793","orcid":"https://orcid.org/0000-0001-8065-9793","contributorId":224743,"corporation":false,"usgs":false,"family":"Karnieli","given":"Arnon","email":"","affiliations":[{"id":40930,"text":"Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel","active":true,"usgs":false}],"preferred":false,"id":789361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kustas, William P.","contributorId":29962,"corporation":false,"usgs":false,"family":"Kustas","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":789363,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209057,"text":"70209057 - 2020 - Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database","interactions":[],"lastModifiedDate":"2020-03-12T12:52:37","indexId":"70209057","displayToPublicDate":"2020-03-03T12:46:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database","docAbstract":"The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001-2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001-2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this time. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4,778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2020.02.019","usgsCitation":"Homer, C.G., Dewitz, J., Jin, S., Xian, G.Z., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R.F., and Riitters, K.H., 2020, Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184-199, https://doi.org/10.1016/j.isprsjprs.2020.02.019.","productDescription":"16 p.","startPage":"184","endPage":"199","ipdsId":"IP-113469","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457514,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":784664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Riitters, Kurt H. 0000-0003-3901-4453","orcid":"https://orcid.org/0000-0003-3901-4453","contributorId":139788,"corporation":false,"usgs":false,"family":"Riitters","given":"Kurt","email":"","middleInitial":"H.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":784665,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70204123,"text":"70204123 - 2020 - Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","interactions":[],"lastModifiedDate":"2024-05-17T15:49:38.223294","indexId":"70204123","displayToPublicDate":"2020-03-01T11:07:27","publicationYear":"2020","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":"Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","docAbstract":"The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as \"duplicate interpretations\"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111261","usgsCitation":"Pengra, B., Stehman, S.V., Horton, J., Dockter, D., Schroeder, T.A., Yang, Z., Cohen, W.B., Healey, S.P., and Loveland, T., 2020, Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program: Remote Sensing of Environment, v. 238, 111261, 10 p., https://doi.org/10.1016/j.rse.2019.111261.","productDescription":"111261, 10 p.","ipdsId":"IP-101422","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457550,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111261","text":"Publisher Index Page"},{"id":437077,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QA5Q25","text":"USGS data release","linkHelpText":"LCMAP CONUS Intensification Reference Data Product 1984–2019 land cover, land use and change process attributes"},{"id":414788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":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":765622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":765623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":216813,"corporation":false,"usgs":true,"family":"Horton","given":"Josephine","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schroeder, Todd A. taschroeder@fs.fed.us","contributorId":190802,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","email":"taschroeder@fs.fed.us","middleInitial":"A.","affiliations":[],"preferred":false,"id":765626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Zhiqiang","contributorId":189584,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","email":"","affiliations":[],"preferred":false,"id":765627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cohen, Warren B 0000-0003-3144-9532","orcid":"https://orcid.org/0000-0003-3144-9532","contributorId":216815,"corporation":false,"usgs":false,"family":"Cohen","given":"Warren","email":"","middleInitial":"B","affiliations":[{"id":39525,"text":"USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331","active":true,"usgs":false}],"preferred":false,"id":765628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Healey, Sean P.","contributorId":216816,"corporation":false,"usgs":false,"family":"Healey","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":39526,"text":"USDA Forest Service, Rocky Mountain Research Station, 507 25th Street, Ogden, UT 84401","active":true,"usgs":false}],"preferred":false,"id":765629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loveland, Thomas 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":202518,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":765630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249358,"text":"70249358 - 2020 - Transitioning from change detection to monitoring with remote sensing: A paradigm shift","interactions":[],"lastModifiedDate":"2023-10-04T23:41:21.337275","indexId":"70249358","displayToPublicDate":"2020-03-01T09:55:47","publicationYear":"2020","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":"Transitioning from change detection to monitoring with remote sensing: A paradigm shift","docAbstract":"The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111558","usgsCitation":"Woodcock, C.E., Loveland, T., Herold, M., and Bauer, M.E., 2020, Transitioning from change detection to monitoring with remote sensing: A paradigm shift: Remote Sensing of Environment, v. 238, 111558, 5 p., https://doi.org/10.1016/j.rse.2019.111558.","productDescription":"111558, 5 p.","ipdsId":"IP-113612","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457553,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111558","text":"Publisher Index Page"},{"id":421598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodcock, Curtis E.","contributorId":294423,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","email":"","middleInitial":"E.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":885300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herold, Martin","contributorId":330558,"corporation":false,"usgs":false,"family":"Herold","given":"Martin","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":885302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Marvin E.","contributorId":330559,"corporation":false,"usgs":false,"family":"Bauer","given":"Marvin","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":885303,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208644,"text":"70208644 - 2020 - Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","interactions":[],"lastModifiedDate":"2022-03-31T18:52:42.924432","indexId":"70208644","displayToPublicDate":"2020-02-22T06:42:15","publicationYear":"2020","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":"Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony","docAbstract":"Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040725","usgsCitation":"Pastick, N., Dahal, D., Wylie, B.K., Parajuli, S., Boyte, S.P., and Wu, Z., 2020, Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony: Remote Sensing, v. 12, no. 4, 725, 17 p.; Data release, https://doi.org/10.3390/rs12040725.","productDescription":"725, 17 p.; Data release","ipdsId":"IP-114798","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457631,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040725","text":"Publisher Index Page"},{"id":437093,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91NJ2PD","text":"USGS data release","linkHelpText":"Near real time estimation of annual exotic herbaceous fractional cover in the sagebrush ecosystem 30m, USA, July 2020"},{"id":437092,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KKPT07","text":"USGS data release","linkHelpText":"Weekly cloud free Harmonized Landsat Sentinel-2 (HLS) Normalized Difference Vegetation Index (NDVI) data for western United States (2016 &amp;ndash; 2019)."},{"id":437091,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT1BV2","text":"USGS data release","linkHelpText":"Fractional estimates of exotic annual grass cover in dryland ecosystems of western United States (2016 - 2019)"},{"id":372534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":397944,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZZSX5Q","text":"USGS data release","description":"USGS data release","linkHelpText":"Early estimates of Annual Exotic Herbaceous Fractional Cover in the Sagebrush Ecosystem, USA, May 2020"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.728515625,\n 40.97989806962013\n ],\n [\n -114.7412109375,\n 40.97989806962013\n ],\n [\n -114.7412109375,\n 44.18220395771566\n ],\n [\n -121.728515625,\n 44.18220395771566\n ],\n [\n -121.728515625,\n 40.97989806962013\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":782880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":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":782883,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parajuli, Sujan 0000-0002-1652-3063","orcid":"https://orcid.org/0000-0002-1652-3063","contributorId":222684,"corporation":false,"usgs":true,"family":"Parajuli","given":"Sujan","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":782882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":782884,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":782885,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209599,"text":"70209599 - 2020 - Training data selection for annual land cover classification for the LCMAP initiative","interactions":[],"lastModifiedDate":"2020-04-15T11:55:08.292617","indexId":"70209599","displayToPublicDate":"2020-02-20T06:53:08","publicationYear":"2020","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":"Training data selection for annual land cover classification for the LCMAP initiative","docAbstract":"The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative characterizes changes in land cover, use, and condition with the goal of producing land change information that improves understanding of the earth system and provides insight into the impacts of land change on society. For LCMAP, all available high-quality data from the Landsat archive is used in a time series approach to detect land surface change. Annual thematic land cover maps are produced by classifying time series models. In this paper, we describe optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). Results were evaluated and validated based on independent data from the training dataset. We found that refining auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability (i.e., SLC-off). The classification accuracy and stability were improved considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class. Finally, the NLCD 2001 training data delivered the best classification accuracy. Comparing to the original LCMAP classification strategy (Trends training data, 20,000 samples), the optimized classification strategy considerably improved the annual land cover map accuracy.","language":"English","publisher":"MDPI","doi":"10.3390/rs12040699","collaboration":"","usgsCitation":"Zhou, Q., Tollerud, H.J., Barber, C., Smith, K., and Zelenak, D.J., 2020, Training data selection for annual land cover classification for the LCMAP initiative: Remote Sensing, v. 12, no. 4, 699, 16 p., https://doi.org/10.3390/rs12040699.","productDescription":"699, 16 p.","ipdsId":"IP-114747","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12040699","text":"Publisher Index Page"},{"id":374001,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barber, Christopher P. 0000-0003-0570-1140","orcid":"https://orcid.org/0000-0003-0570-1140","contributorId":223102,"corporation":false,"usgs":true,"family":"Barber","given":"Christopher","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":787084,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zelenak, Daniel J. 0000-0003-3457-0960","orcid":"https://orcid.org/0000-0003-3457-0960","contributorId":224118,"corporation":false,"usgs":true,"family":"Zelenak","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":787085,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208404,"text":"70208404 - 2020 - Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing","interactions":[],"lastModifiedDate":"2020-03-11T15:23:08","indexId":"70208404","displayToPublicDate":"2020-02-07T13:35:19","publicationYear":"2020","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":"Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing","docAbstract":"Sediment budgets are a critical metric to assess coastal marsh vulnerability to sea-level rise and declining riverine sediment inputs. However, calculating accurate sediment budgets is challenging in tidal marsh-influenced estuaries where suspended sediment concentrations (SSC) typically vary on scales of hours and meters, and where SSC dynamics are driven by a complex and often site-specific interplay of hydrodynamic and meteorological conditions. The mapping of SSC using ocean-color remote sensing is well established and can help capture the spatio-temporal variability needed to determine the dominant drivers regulating sediment budgets. However, the coarse spatial resolution of traditional ocean-color sensors (1-km) generally precludes their use in coastal-marsh estuaries. Here, using the Plum Island Estuary (Massachusetts, USA) as an example, we demonstrate that high-spatial-resolution maps of SSC derived from Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/B Multispectral Instruments (MSI) can be used to determine the main drivers of SSC dynamics in tidal marsh-influenced estuaries, despite the long revisit time of these sensors. Local empirical algorithms between SSC and remote sensing reflectance were derived and applied to a total of 46 clear-sky scenes collected by the OLI and the MSI between 2013 and 2018. The analysis revealed that this 5-year record was sufficient to capture a representative range of meteorological and tidal conditions required to determine the main drivers of SSC dynamics in this mid-latitude system. The interplay between river and tidal flows dominated SSC dynamics in this estuary, whereas wind-driven resuspension had more moderate effects. The SSC were higher during spring because of increased river discharge due to snowmelt. Tidal asymmetry also enhanced sediment resuspension during flood tides, possibly favoring deposition on marsh platforms. Together, water level, water-level rate of change, river discharge and wind speed were able to explain > 60% of the variability in the main-channel thalweg-averaged SSC, thereby facilitating future prediction of SSC from these readily available variables. This study demonstrates that the existing multi-year records of high-resolution remote sensing can provide a representative depiction of SSC dynamics in hydrodynamically-complex and small-scale estuaries that moderate-resolution ocean color remote sensing and in situ measurements are unable to capture.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111682","usgsCitation":"Zhang, X., Fichot, C., Baracco, C., Guo, R., Neugebauer, S., Bengtsson, Z., Ganju, N., and Fagherazzi, S., 2020, Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing: Remote Sensing, v. 240, 111682, 14 p., https://doi.org/10.1016/j.rse.2020.111682.","productDescription":"111682, 14 p.","ipdsId":"IP-109014","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457785,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.111682","text":"Publisher Index Page"},{"id":372176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Plum Island Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.9771728515625,\n 42.72683914955442\n ],\n [\n -70.68328857421875,\n 42.72683914955442\n ],\n [\n -70.68328857421875,\n 42.871938424448466\n ],\n [\n -70.9771728515625,\n 42.871938424448466\n ],\n [\n -70.9771728515625,\n 42.72683914955442\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"240","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Xiaohe","contributorId":213308,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaohe","email":"","affiliations":[],"preferred":false,"id":781753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fichot, Cedric","contributorId":222269,"corporation":false,"usgs":false,"family":"Fichot","given":"Cedric","affiliations":[{"id":40511,"text":"Department of Earth and Environment, Boston University, Boston, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":781754,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baracco, Carly","contributorId":222270,"corporation":false,"usgs":false,"family":"Baracco","given":"Carly","email":"","affiliations":[{"id":40511,"text":"Department of Earth and Environment, Boston University, Boston, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":781755,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guo, Ruizhe","contributorId":222271,"corporation":false,"usgs":false,"family":"Guo","given":"Ruizhe","email":"","affiliations":[{"id":40512,"text":"NASA DEVELOP National Program, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":781756,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Neugebauer, Sydney","contributorId":222272,"corporation":false,"usgs":false,"family":"Neugebauer","given":"Sydney","email":"","affiliations":[{"id":40512,"text":"NASA DEVELOP National Program, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":781757,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bengtsson, Zachary","contributorId":222273,"corporation":false,"usgs":false,"family":"Bengtsson","given":"Zachary","email":"","affiliations":[{"id":40512,"text":"NASA DEVELOP National Program, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":781758,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781752,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fagherazzi, Sergio","contributorId":207153,"corporation":false,"usgs":false,"family":"Fagherazzi","given":"Sergio","email":"","affiliations":[{"id":37465,"text":"Boston University, Earth and Environment, Boston, 02215, USA.","active":true,"usgs":false}],"preferred":false,"id":781759,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}