Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program
Bruce Pengra, Stephen V. Stehman, Josephine Horton, Daryn (Contractor) Dockter, Todd A. Schroeder, Zhiqiang Yang, Warren B Cohen, Sean P. Healey, Thomas Loveland
2020, Remote Sensing of Environment (238)
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...
Transitioning from change detection to monitoring with remote sensing: A paradigm shift
Curtis E. Woodcock, Thomas Loveland, Martin Herold, Marvin E. Bauer
2020, Remote Sensing of Environment (238)
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...
Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony
Neal Pastick, Devendra Dahal, Bruce K. Wylie, Sujan Parajuli, Stephen P. Boyte, Zhuoting Wu
2020, Remote Sensing (12)
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...
Training data selection for annual land cover classification for the LCMAP initiative
Qiang Zhou, Heather J. Tollerud, Christopher Barber, Kelcy Smith, Daniel J. Zelenak
2020, Remote Sensing (12)
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...
Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing
Xiaohe Zhang, Cedric Fichot, Carly Baracco, Ruizhe Guo, Sydney Neugebauer, Zachary Bengtsson, Neil K. Ganju, Sergio Fagherazzi
2020, Remote Sensing (240)
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...
Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska
Gerald Frost, Rachel A. Loehman, Lisa Saperstein, Matthew J. Macander, Peter Nelson, David Paradis, Sue M. Natali
2020, Environmental Research Letters
Alaska’s Yukon-Kuskokwim Delta (YKD) is one of the warmest parts of the Arctic tundra biome and tundra fires are common in its upland areas. Here we combine field measurements, Landsat observations, and quantitative cover maps for tundra plant functional types (PFTs) to characterize multi-decadal succession and landscape change after fire in lichen-dominated upland tundra...
Evaluation of hydrologic impact of an irrigation curtailment program in the Upper Klamath Lake Basin using Landsat satellite data
Naga Manohar Velpuri, Gabriel Senay, Matthew Schauer, C. Amanda Garcia, Ramesh Singh, MacKenzie Friedrichs, Stefanie Bohms, Jonathan V. Haynes, Terrence D. Conlon
2020, Hydrological Processes (34) 1697-1713
Upper Klamath Lake (UKL) is the source of the Klamath river that flows through southern Oregon and northern California. The UKL basin is home to two endangered species and provides water for 81,000+ ha (200,000+ acres) of irrigation on the United States Bureau of Reclamation (USBR) Klamath Project located downstream...
Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data
Matthew Rigge, Collin Homer, Lauren Cleeves, Deb Meyer, Brett Bunde, Hua Shi, George Z. Xian, Matthew R Bobo
2020, Remote Sensing (12)
Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine rangeland ecosystem components, including percent shrub, sagebrush (Artemisia), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were...
Potential underestimation of satellite fire radiative power retrievals over gas flares and wildland fires
Sanath S. Kumar, John Edward Hult, Joshua J. Picotte, Birgit Peterson
2020, Remote Sensing (12)
Fire Radiative Power (FRP) is related to fire combustion rates and is used to quantify the atmospheric emissions of greenhouse gases and aerosols. FRP over gas flares and wildfires can be retrieved remotely using satellites that observe in shortwave infrared (SWIR) to middle infrared (MIR) wavelengths. Heritage techniques to retrieve...
Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics
Martha M. Gomez-Sapiens, Christopher Jarchow, Karl W. Flessa, Patrick B. Shafroth, Edward P. Glenn, Pamela L. Nagler
2020, Hydrological Processes (34) 1682-1696
Understanding the effectiveness of environmental flow deliveries along rivers requires monitoring vegetation. Monitoring data are often collected at multiple spatial scales. For riparian vegetation, optical remote sensing methods can estimate growth responses at the riparian corridor scale, and field‐based measures can quantify species composition; however, the extent to which these...
Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud
Murali Krishna Gumma, Prasad Thenkabail, Pardhasaradhi Teluguntla, Adam Oliphant
2020, GIScience and Remote Sensing (57) 302-322
The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps...
Using full and partial unmixing algorithms to estimate the inundation extent of small, isolated stock ponds in an arid landscape
Christopher Jarchow, Brent H. Sigafus, Erin L. Muths, Blake R. Hossack
2020, Wetlands (40) 563-575
Many natural wetlands around the world have disappeared or been replaced, resulting in the dependence of many wildlife species on small, artificial earthen stock ponds. These ponds provide critical wildlife habitat, such that the accurate detection of water and assessment of inundation extent is required. We applied a full (linear...
Earth as art 6
U.S. Geological Survey
2019, General Information Product 194
Earth has a stunning variety of landscapes. The colors, patterns, textures, and shapes all make for intriguing artwork as seen from the perspective of space.Earth As Art shows not only what satellites capture in the visible wavelengths of light you and I can see, but also what’s hiding in the...
Validating a landsat time-series of fractional component cover across western U.S. Rangelands
Matthew B. Rigge, Collin G. Homer, Hua Shi, Debra K. Meyer
2019, Remote Sensing (11)
Western U.S. rangelands have been quantified as six fractional cover (0%–100%) components over the Landsat archive (1985–2018) at a 30 m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images...
Overall methodology design for the United States National Land Cover Database 2016 products
Suming Jin, Collin Homer, Limin Yang, Patrick Danielson, Jon Dewitz, Congcong Li, Zhe Zhu, George Z. Xian, Danny Howard
2019, Remote Sensing (11)
The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand...
Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires
Jessica J. Walker, Christopher E. Soulard
2019, Remote Sensing (11)
Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the US Southwest are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We...
The U. S. Geological Survey’s approach to analysis ready data
Cody Anderson, Steven Labahn, Dennis Helder, Gregory L. Stensaas, Christopher Engebretson, Christopher J. Crawford, Calli B. Jenkerson, Christopher Barnes
2019, Conference Paper, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Analysis Ready Data (ARD) is a recent concept in Earth observing remote sensing which encompasses many different initiatives by individual imagery providers and collaborative international organizations working towards easing/minimizing data preprocessing required by users. This allows users to spend more time on analysis and less time on downloading, formatting, and...
Pre‐fire vegetation drives post‐fire outcomes in sagebrush ecosystems: Evidence from field and remote sensing data
Brittany S. Barker, David Pilliod, Matthew Rigge, Collin G. Homer
2019, Ecosphere (10)
Understanding the factors that influence vegetation responses to disturbance is important because vegetation is the foundation of food resources, wildlife habitat, and ecosystem properties and processes. We integrated vegetation cover data derived from field plots and remotely sensed Landsat images in two focal areas over a 37‐yr period (1979–2016) to...
Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping
George Z. Xian, Hua Shi, Cody Anderson, Zhuoting Wu
2019, Remote Sensing (11)
Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for...
Landsat time series assessment of invasive annual grasses following energy development
Miguel L. Villarreal, Christopher E. Soulard, Eric Waller
2019, Remote Sensing (11) 1-18
Invasive annual grasses are of concern in many areas of the Western United States because they tolerate resource variability and have high reproductive capacity, with propagules that are readily dispersed in disturbed areas like those created and maintained for energy development. Early-season invasive grasses “green up” earlier than the most...
Influence of multi-decadal land use, irrigation practices and climate on riparian corridors across the Upper Missouri River Headwaters Basin, Montana
Melanie K. Vanderhoof, J.R. Christensen, Laurie C. Alexander
2019, Hydrology and Earth System Sciences (23) 4269-4292
The Upper Missouri River Headwaters Basin (36,400 km2) depends on its river corridors to support irrigated agriculture and world-class trout fisheries. We evaluated trends (1984-2016) in riparian wetness, an indicator of riparian condition, in peak irrigation months (June, July, August) for 158 km2 of riparian area across the basin using...
Assessing plant production responses to climate across water-limited regions using Google Earth Engine
Erin L. Bunting, Seth M. Munson, John B. Bradford
2019, Remote Sensing of Environment (233) 1-15
(Munson) Climate variability and change acting at broad scales can lead to divergent changes in plant production at local scales. Quantifying how production responds to variation in climate at local scales is essential to understand underlying ecological processes and inform land management decision-making, but has historically been limited in spatiotemporal...
Economic valuation of landsat imagery
Crista L. Straub, Stephen R. Koontz, John B. Loomis
2019, Open-File Report 2019-1112
Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). A previous USGS study estimated that Landsat imagery provided users an annual benefit of $2.19 billion in 2011, with...
Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California
Jessica J. Walker, Christopher E. Soulard, Roy E. Petrakis
2019, International Journal of Applied Earth Observation and Geoinformation (84)
Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study...
Landsat 1-5 Multispectral Scanner System (MSS) sensors radiometric calibration update
Cibele Teixeira Pinto, Md Obaidul Haque, Esad Micijevic, Dennis Helder
2019, IEEE Transactions on Geoscience and Remote Sensing (57) 7378-7394
First launched in 1972, the Landsat satellite sensors have provided the longest continuous record of high quality images of the Earth’s surface that are used in both civilian and military applications. The Landsat Multispectral Scanner (MSS) sensor was on-board Landsat-1 through Landsat-5. In fact, the MSS sensors provide the only...