John W. Coulston

This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.

Foundational forest and land-use change monitoring programs, with their statistical rigor and long-term baselines, are increasingly important to inform emerging global agricultural and forest commodity trade policies such as the European Union regulation on deforestation-free products (EUDR). The EUDR requires national-scale information on deforestation prevalence as one component of deforestation risk assessments, with additional traceability and risk assessment requirements at the parcel scale for exports to the EU to comply with the regulation. To better understand potential implications of this policy and evaluate relevant monitoring data, we estimate area converted from forest to agricultural land use in the United States (US) using three authoritative data sources from US Department of Agriculture statistical programs: the Nationwide Forest Inventory, National Resources Inventory, and Crop Sequence Boundaries Data. Results from all three datasets consistently indicate that agriculture-driven gross deforestation has been minimal over the past decade in the US, averaging 0.01%–0.04% of forested area annually. Most of this land-use change is occurring in the Southeastern US with conversion to pasture being the most common agricultural conversion subcategory. The rarity of agriculture-driven deforestation results in high levels of uncertainty in county-scale estimates due to the low sample size and potential misclassification errors in modeled data. Nevertheless, comparing results from these three complementary datasets can provide greater confidence in fine-scale spatial patterns of past deforestation. This analysis highlights several challenges associated with implementing and enforcing trade policy based on land use categories. Given the low rate of agriculture-driven deforestation and potential difficulties with identifying land-use change at small spatial scales, trade policies focused solely on land use categories may have a limited benefit in countries such as the US where agriculture is not a major driver of deforestation.

Forest conversions are an important consideration of the forest products sector. Both third-party certifications, such as the Sustainable Forestry Initiative, and government regulation, such as the European Union Regulation on Deforestation Free Supply Chains, require knowledge of conversions in fiber supply regions. Here we develop a new approach to estimate some relevant forest conversion metrics for economic regions of the United States. Across economic regions, forest conversion rates were small. For example, gross annual forest loss to agriculture was <0.044%, gross annual natural forest loss to planted forest was <0.86%, net annual loss in natural forest was <0.41%, and net 10-year forest loss was <1.86%. Our results suggest three major conclusions. First, forest conversions to agriculture are not currently an issue in roundwood producing regions of the United States. Second, natural forest conversions to planted forest are offset by landowners choosing to use natural regeneration methods. Third, there are several economic regions where 10-year net forest loss approaches but does not exceed 1% net loss at p = 0.95. Finer-scale analyses, in these economic regions, will likely be necessary for the forest products sector to ensure compliance with forest certification standards.

The forest carbon sink of the United States offsets emissions in other sectors. Recently passed US laws include important climate legislation for wildfire reduction, forest restoration, and forest planting. In this study, we examine how wildfire reduction strategies and planting might alter the forest carbon sink. Our results suggest that wildfire reduction strategies reduce carbon sequestration potential in the near term but provide a longer term benefit. Planting initiatives increase carbon sequestration but at levels that do not offset lost sequestration from wildfire reduction strategies. We conclude that recent legislation may increase near-term carbon emissions due to fuel treatments and reduced wildfire frequency and intensity, and expand long-term US carbon sink strength.

The majority of the aboveground biomass on the Earth’s land surface is stored in forests. Thus, forest biomass plays a critical role in the global carbon cycle. Yet accurate estimate of forest aboveground biomass (FAGB) remains elusive. This study proposed a new conceptual model to map FAGB using remotely sensed data from multiple sensors. The conceptual model, which provides guidance for selecting remotely sensed data, is based on the principle of estimating FAGB on the ground using allometry, which needs species, diameter at breast height (DBH), and tree height as inputs. Based on the conceptual model, we used multiseasonal Landsat images to provide information about species composition for the forests in the study area, LiDAR data for canopy height, and the image texture and image texture ratio at two spatial resolutions for tree crown size, which is related to DBH. Moreover, we added RaDAR data to provide canopy volume information to the model. All the data layers were fed to a Random Forest (RF) regression model. The study was carried out in eastern North Carolina. We used biomass from the USFS Forest Inventory and Analysis plots to train and test the model performance. The best model achieved an R² of 0.625 with a root mean squared error (RMSE) of 18.8 Mg/ha (47.6%) with the “out-of-bag” samples at 30 × 30 m spatial resolution. The top five most important variables include the 95th, 85th, 75th, and 50th percentile heights of the LiDAR points and their standard deviations of 85th heights. Numerous features from multiseasonal Sentinel-1 C-Band SAR, multiseasonal Landsat 8 imagery along with image texture features from very high-resolution imagery were selected. But the importance of the height metrics dwarfed all other variables. More tests of the conceptual model in places with a broader range of biomass and more diverse species composition are needed to evaluate the importance of other input variables.

Understanding the extent and timing of forest disturbances and their impacts is critical to formulating effective management and policy responses. Broad-scale inventory programs provide key estimates of forest parameters that indicate the extent and severity of disturbance impacts. Here, we review the use of a post-stratified estimator in a panelized design, in the context of disturbance observations that are collected retrospectively. We further develop a sample weight adjustment that is requisite for proper estimation of the extent and timing of disturbances. Using populations from areas of Arkansas, California, and Maine in the US, the weight adjustment technique was tested in a Monte Carlo simulation. We found that the estimated area of disturbance using the weight adjustment technique had satisfactory agreement with the true population values and performed considerably better than the conventional post-stratified estimation approach. The proliferation of panelized forest inventory designs globally suggests that accurate estimates of areal extent and timing of disturbances will often require that weighting adjustment techniques be employed in the estimation process.