Lucas Johnson- PhD
- Postdoctoral Scholar at Oregon State University
Lucas Johnson
- PhD
- Postdoctoral Scholar at Oregon State University
Investigating how the warming impacts of surface albedo change in forests effect management for climate mitigation.
About
22
Publications
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Introduction
I’m a Postdoctoral Scholar at Oregon State University working with the USFS Forest Inventory and Analysis unit in Riverdale, Utah. In this role, I will investigate how the warming effects of surface albedo change resulting from forest growth, reforestation, and afforestation impact decisions around forest management for climate mitigation. Additionally, I will assist in operationalizing forest carbon reporting tools that leverage data from NASA’s GEDI mission.
Current institution
Education
August 2019 - May 2024
August 2013 - May 2017
Publications
Publications (22)
Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to...
Evaluating models fit to data with internal spatial structure requires specific cross-validation (CV) approaches, because randomly selecting assessment data may produce assessment sets that are not truly independent of data used to train the model. Many spatial CV methodologies have been proposed to address this by forcing models to extrapolate spa...
Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or shrublands, instead of secondary forests in post-agricultural landscapes is well documented by field studies, but poorly und...
Estimating forest aboveground biomass (AGB) at large scales and fine spatial resolutions has become increasingly important for greenhouse gas accounting, monitoring, and verification efforts to mitigate climate change. Airborne LiDAR is highly valuable for modeling attributes of forest structure including AGB, yet most LiDAR collections take place...
Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple heig...
Reforestation is generally regarded as having the most substantial climate mitigation potential among a suite of available natural climate solutions which have focused almost exclusively on the benefits of carbon sequestration and storage. However, these reforestation studies have not accounted for the adverse warming impacts resulting from corresp...
Remote change detection algorithms are emerging tools to observe and quantify patterns of vegetation and land use change at fine-grained spatiotemporal resolution across broad landscape scales. Here we applied the LandTrendr algorithm and Landsat time-series imagery to map canopy disturbance regimes across ∼1 M hectares of unmanaged reserve forests...
Fine-resolution maps of forest aboveground biomass (AGB) effectively represent spatial patterns and can be flexibly aggregated to map subregions by computing spatial averages or totals of pixel-level predictions. However, generalized model-based uncertainty estimation for spatial aggregates requires computationally expensive processes like iterativ...
The United States national forest inventory (NFI) serves as the foundation for forest aboveground biomass (AGB) and carbon accounting across the nation. These data enable design-based estimates of forest carbon stocks and stock-changes at state and regional levels, but also serve as inputs to model-based approaches for characterizing forest carbon...
Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to...
Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, whi...
Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (Li...
Estimating forest aboveground biomass at fine spatial scales has become increasingly important for greenhouse gas estimation, monitoring, and verification efforts to mitigate climate change. Airborne LiDAR continues to be a valuable source of remote sensing data for estimating aboveground biomass. However airborne LiDAR collections may take place a...
Context: Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or 'shrublands', instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but...
The need for reliable landscape-scale monitoring of forest disturbance has grown with increased policy and regulatory attention to promoting the climate benefits of forests. Change detection algorithms based on satellite imagery can address this need but are largely untested for the forest types and disturbance regimes of the US Northeast, includin...
Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height threshol...
