Modeling Land use Change and Forest Carbon Stock Changes in Temperate Forests in the United States

Abstract

Background:Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stock’s dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine.Results: During the study period (2000-2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Forest disturbances were not a major predictor of land use change.Conclusions:Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

“…In order to understand the viability of reforestation for carbon sequestration, or to conduct better estimates of deforestation impacts, it is necessary to understand the actual emissions or sequestration of changes in forestation [24]. However, doing so requires forest inventory data that provides the basis of carbon, ecological, and land use modeling [9], which is often constrained in either geographical availability or data resolution due to surveying difficulties. Due to the impracticalities of scaling manual measurements to large areas, survey programs usually make use of sampling sites and inferring the population data [2], or remote sensing via aerial photography or satellite imagery.…”

“…Due to the impracticalities of scaling manual measurements to large areas, survey programs usually make use of sampling sites and inferring the population data [2], or remote sensing via aerial photography or satellite imagery. The resulting forest inventory data imprecision limits efforts in forest carbon modeling [9].…”