Chad Babcock

Chad Babcock
University of Minnesota Twin Cities | UMN · Forest Resources

PhD

About

16
Publications
3,293
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244
Citations
Introduction
I am an assistant professor in the Department of Forest Resources at the University of Minnesota. My primary interests are in developing approaches to improve forest inventory efforts by incorporating remote sensing information, including lidar and spectroscopy data. Much of my work involves building models that link air- and space-borne data with field-based measurements to increase precision of forest inventory estimates. By using hierarchical Bayesian estimation techniques to fit geostatistical models we are able to fuse field and remote sensing data to produce spatially explicit maps of forest variables with associated uncertainty.
Additional affiliations
August 2014 - May 2017
University of Washington Seattle
Position
  • Research Assistant

Publications

Publications (16)
Article
We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel-2 (HLS) dataset. Building on previous work, we propose a double logistic function that, once couched within a Bayesian model, yields posterior distributions for all LSP...
Article
Full-text available
Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can...
Preprint
Full-text available
We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel-2 (HLS) dataset. Building on previous work, we propose a double logistic function that, once couched within a Bayesian model, yields posterior distributions for all LSP...
Preprint
Full-text available
Systematic sampling is often used to select plot locations for forest inventory estimation. However, it is not possible to derive a design-unbiased variance estimator for a systematic sample using one random start. As a result, many forest inventory analysts resort to applying variance estimators that are design-unbiased following simple random sam...
Article
Full-text available
Research related to object-based image analysis has typically relied on data inputs that provide information on the spectral and spatial characteristics of objects, but the temporal domain is far less explored. For some objects, which are spectrally similar to other landscape features, their temporal pattern may be their sole defining characteristi...
Article
Full-text available
Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial domains is infeasible. Next generation collection initiatives of remotely sensed Light Detection and Ranging (LiDAR) data are specifically aimed at producing complete-co...
Article
Use of data from airborne laser scanning (ALS) is a well-established practice for enhancing the accuracy of forest inventories in combination with ground-based observations. For regular monitoring of large areas, wall-to-wall ALS data is economically prohibitive. However, when data are acquired in a strip-sampling mode, ALS can support the estimati...
Article
Full-text available
The goal of this research was to develop and examine the performance of a geostatistical coregionalization modeling approach for combining field inventory measurements, strip samples of airborne lidar and Landsat-based remote sensing data products to predict aboveground biomass (AGB) in interior Alaska's Tanana Valley. The proposed modeling strateg...
Article
Full-text available
Fire in the boreal region is the dominant agent of forest disturbance with direct impacts on ecosystem structure, carbon cycling, and global climate. Global and biome-scale impacts are mediated by burn severity, measured as loss of forest canopy and consumption of the soil organic layer. To date, knowledge of the spatial variability in burn severit...
Article
Full-text available
Recent advancements in remote sensing technology, specifically Light Detection and Ranging (LiDAR) sensors, provide the data needed to quantify forest characteristics at a fine spatial resolution over large geographic domains. From an inferential standpoint, there is interest in prediction and interpolation of the often sparsely sampled and spatial...
Article
Full-text available
Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of A...
Article
Full-text available
Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that...
Article
Many forest management planning decisions are based on information about the number of trees by species and diameter per unit area. This information is commonly summarized in a stand table, where a stand is defined as a group of forest trees of sufficiently uniform species composition, age, condition, or productivity to be considered a homogeneous...
Article
Full-text available
This study assesses univariate and multivariate spatial regression models for predicting individual tree structure variables using Light Detection And Ranging (LiDAR) covariates. Many studies have used covariates derived from LiDAR to help explain the variability in tree, stand, or forest variables at a fine spatial resolution across a specified do...