A5002768259- Remote Sensing and LiDAR Applications
- Forest ecology and management
- Remote Sensing in Agriculture
- Soil Geostatistics and Mapping
- Forest Management and Policy
- Statistical Methods and Inference
- Spatial and Panel Data Analysis
- Species Distribution and Climate Change
- Atmospheric and Environmental Gas Dynamics
- Diverse Scientific and Economic Studies
- Geographic Information Systems Studies
- Fire effects on ecosystems
South Dakota School of Mines and Technology
2024-2025
University of Maryland, College Park
2022-2024
Observations from the NASA Global Ecosystem Dynamics Investigation (GEDI) provide global information on forest structure and biomass. Footprint-level predictions of aboveground biomass density (AGBD) in GEDI mission are based training data sourced sparsely distributed field plots coincident with airborne laser scanning surveys. National Forest Inventories (NFI) rarely used to calibrate footprint models because their sampling positional accuracy prevent accurate colocation or ALS. This...
Atmospheric CO 2 concentrations are dependent on land-atmosphere carbon fluxes resultant from forest dynamics and land-use changes. These not well-constrained, in part because reliable baseline estimates of stocks the associated uncertainties lacking. NASA's Global Ecosystem Dynamics Investigation (GEDI) produces aboveground biomass density (AGBD) that unique GEDI's hybrid estimation framework enables formal uncertainty calculations accompany estimates. However, without issue; a recent...
Spaceborne lidar provides a unique opportunity to supplement the field plot measurements of national forest inventories (NFIs) by providing dense vertical canopy structure. For full waveform instruments such as Global Ecosystem Dynamics Investigation (GEDI), take form reflected energy function height within an observed footprint. Many attributes cannot be directly computed from waveforms, and thus statistical models relating target must trained using data. Because discrete footprint samples...
Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in format of Intergovernmental Panel (IPCC) Tier 1 values for natural forests, sourced National Aeronautics and Space Administration's (NASA's) Global Ecosystem Dynamics Investigation (GEDI) Ice, Cloud land Elevation Satellite (ICESat-2), European Agency's...
The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover small fraction the land surface. Converting into complete predictive maps practical importance for many ecological studies. A complicating factor over forested non-forested alike, with no automatic labeling type. Such classification important, as it categorically influences...
Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in format of Intergovernmental Panel (IPCC) Tier 1 values for natural forests, sourced National Aeronautics and Space Administration’s (NASA’s) Global Ecosystem Dynamics Investigation (GEDI) Ice, Cloud land Elevation Satellite (ICESat-2), European Agency’s...
Spatially explicit quantification of forest biomass is important for forest-health monitoring and carbon accounting. Direct field measurements are laborious expensive, typically limiting their spatial temporal sampling density therefore the precision resolution resulting inference. Satellites can provide predictions at a far greater density, but these often biased relative to exhibit heterogeneous errors. We developed implemented coregionalization model between sparse predictive satellite...
National Forest Inventory (NFI) programs can provide vital information on the status, trend, and change in forest parameters. These are being increasingly asked to parameter estimates for spatial temporal extents smaller than their current design accompanying design-based methods deliver with desired levels of uncertainty. Many NFI designs estimation focus status not well equipped acceptable trend parameters, especially over small domains and/or short time periods. Fine-scale space-time...
ABSTRACT Spatially explicit quantification of forest biomass is important for forest‐health monitoring and carbon accounting. Direct field measurements are laborious expensive, typically limiting their spatial temporal sampling density therefore the precision resolution resulting inference. Satellites can provide predictions at a far greater density, but these often biased relative to exhibit heterogeneous errors. We developed implemented coregionalization model between sparse predictive...
Abstract National forest inventory (NFI) programs provide vital information on parameters' status, trend, and change. Most NFI designs estimation methods are tailored to estimate status over large areas but not well suited trend change, especially small spatial and/or short time periods (e.g., annual estimates). Fine-scale space-time indexed estimates critical a variety of environmental, ecological, economic monitoring efforts. In the United States, for example, data used carbon change...
Dimension reduction is an important tool for analyzing high-dimensional data. The predictor envelope a method of dimension regression that assumes certain linear combinations the predictors are immaterial to regression. can result in substantial gains estimation efficiency and prediction accuracy over traditional maximum likelihood least squares estimates. While envelopes have been developed studied independent data, no work has done adapting spatial In this work, adapted popular model form...