Modeling land use change and forest carbon stock changes in temperate forests in the United States

• Published in: Carbon balance and management 2021-07, Vol.16 (1), p.1-16, Article 20
• Main Authors: Fitts, Lucia A., Russell, Matthew B., Domke, Grant M., Knight, Joseph K.
• Format: Article
• Language: English
• Subjects: Algorithms; Carbon; Carbon dynamics; Climate change; Climate change mitigation; Coastal zone; Conversion; Critical components; Decision making; Decision trees; Disturbances; Earth and Environmental Science; Ecological effects; Ecosystem services; Ecosystems; Emissions; Environment; Environmental Management; Fluxes; Forest inventory; Forest loss drivers; Forest management; Forestry; Growth rate; Housing; Human populations; Land use; Land use management; Land use planning; Landsat; Landsat satellites; Learning algorithms; Machine learning; Modelling; Remote sensing; Risk; Temperate forests; USDA Forest Inventory and Analysis (FIA) data
• ISSN: 1750-0680; 1750-0680
• DOI: 10.1186/s13021-021-00183-6

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 stocks 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. Natural 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.