High-resolution forest canopy cover estimation in ecodiverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment

Abstract

The Forest Canopy Cover (FCC) is an important factor in the health and functioning of forests, as it plays a role in ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Techniques for accurately and efficiently mapping and extracting FCC information are evolving quickly, and it is of interest to evaluate their validity and reliability. The primary objectives of this study are to: 1) develop a large-scale FCC dataset with a spatial resolution of 1-m, 2) assess the spatial distribution of the FCC at a regional scale, and 3) examine the discrepancies in the FCC areas within the existing data from the Tree Canopy Cover (TCC) percent data by Hansen et al. (2013) and U.S. Forest Service TCC products at different spatial scales within the state of Arkansas (i.e., county and city levels). The FCC dataset was generated using high-resolution aerial imagery and a random forest (RF) machine learning algorithm, which were processed and analyzed using the Google Earth Engine cloud computing platform. The produced FCC dataset was validated using one-third of the observations (i.e., reference locations) obtained from the TCC percent data by Hansen et al. (2013) dataset and the 0.6-m spatial resolution National Agriculture Imagery Program (NAIP) aerial imagery. The results showed that the dataset successfully identified the FCC at a 1-m resolution in the study area, with an overall accuracy ranging between 83.31% and 94.35% per county. The spatial comparison results between the produced FCC dataset and both the Hansen et al. (2013) and USFS products also indicate a strong positive correlation, with the R2 values ranging between 0.94 and 0.98 for the county and the city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and other regions. Furthermore, the results of this study provide evidence that the use of machine learning and cloud computing technologies can produce high-resolution forest cover datasets and can be applied to other areas around the world.