Publications

An assessment of annual land cover on publicly and privately managed lands across the conterminous United States (CONUS) from 1985–2018 was performed, including land cover conversions within their management category, to inform future policy and land-use decision-making in natural resource management. Synthesizing land cover data with land management delineations aids our ability to...

The vertical structure of forests provides important parameters for estimating aboveground biomass (AGB) and it can be measured by LiDAR sensors. The Global Ecosystem Dynamics Investigation (GEDI) full-waveform LiDAR sensor collects data systematically over the Earth’s surface from the International Space Station. Since GEDI became operational, it has collected billions of ~25 m diameter

Executive Summary This report addresses system characterization of the BlackSky Global satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and...

Elevation data are critical for assessments of coastal hazards, including sea-level rise (SLR), flooding, storm surge, tsunami impacts, and wave run-up. Previous research has demonstrated that the quality of data used in elevation-based hazard assessments must be well documented and applied properly to assess potential impacts. Global digital elevation models (DEMs), at 30- to 90-meter

The availability of reliable gridded precipitation datasets is limited around the world, especially in arid regions. In this study, we utilized observations from satellite-based precipitation data and in situ rain gauge observations to determine a suitable precipitation dataset in the Middle East & North Africa (MENA) region. First, we evaluated seven different precipitation products...

Executive Summary The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat...

Context: Changes in disturbance regimes, including reductions in flooding and geomorphic dynamism from dam construction and flow regulation, have facilitated invasion by eastern redcedar (Juniperus virginiana L.), an upland tree species, in the understory of floodplain forests along the Missouri National Recreational River (MNRR). Objectives: Our aim was to determine the spatiotemporal...

The High Plains aquifer (HPA) is the primary water source for agricultural irrigation in the US Great Plains. The water levels in many locations of the aquifer have declined steadily over the past several decades because the rate of water withdrawals exceeds recharge, which has been a serious concern to the water resources management in the region. We evaluated temporal trends and...

Having highly accurate and reliable Digital Elevation Models (DEMs) of the Earth’s surface is critical to orthorectify Landsat imagery. Without such accuracy, pixel locations reported in the data are difficult to assure as accurate, especially in more mountainous landscapes, where the orthorectification process is the most challenging. To this end, the Landsat Calibration and Validation...

High interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information...

Current machine learning applications and algorithms have developed promise to produce autonomous systems that automatically perceive, learn, predict, and act on their own. However, the effectiveness of these systems is limited by the machine's current inability to explain their decisions, algorithmic paths, and actions to human users. The purpose of this chapter is to apply explainable...

Landsat Collection 2 (C2) U.S. Analysis Ready Data (U.S. ARD) are bundles of tiled Landsat data that make the Landsat archive easier to analyze and reduce the amount of time users spend on data processing for time-series analysis. Landsat C2 was released in 2020 and includes improvements over Landsat Collection 1 data, including better geometric accuracy, which increases the number of...