Annual national tree canopy cover mapping: A novel workflow with temporal transferability and improved uncertainty quantification

Abstract

In 2023, a new generation of National Land Cover Database (NLCD) Tree Canopy Cover (TCC) data were produced that include annual 30m gridded time-series products for the Conterminous United States (CONUS). These NLCD TCC v2021.4 products have more frequent time steps, inter-annual coherency, lower product release latency, improved accuracy, uncertainty metrics, and documentation over previous versions. Our methodology leveraged Google Earth Engine (GEE) to generate annual time-series composites from Landsat and Sentinel-2 imagery that were used to predict annual pixel-wise TCC. Next, we used a moving window approach with a 5x5 window of equal-area tiles (480 × 480 km) to calibrate 54 random forest models on 2011 Forest Inventory and Analysis (FIA) reference data. Then we used model temporal transfer to apply each 2011 model to the window’s center tile, with annual time-series predictors, to predict per pixel annual TCC and standard error (SE). To quantify model uncertainty, we simulated uncertainty for each TCC model to derive an uncertainty metric, Tau (τ), which enables users to incorporate confidence boundaries into environmental and ecological applications using the NLCD TCC v2021.4 data. An independent statistical assessment of the 2011 TCC map, conducted using 16,607 FIA observations, yielded a weighted Root Mean Square Deviation (RMSDw) of 12.8 percent TCC and weighted Mean Absolute Error (MAEw) of 8.0 percent TCC at the CONUS scale. This paper provides a detailed description of the methodology and example use cases of the NLCD TCC and United States Forest Service Science v2021.4 products, paving the way for robust and repeatable future TCC updates.

Citation

Heyer, Joshua; Schleeweis, Karen; Ruefenacht, Bonnie; Housman, Ian; Zhiqiang, Yang; Ryerson, Daniel; Reischmann, Jaclyn; Megown, Kevin; Bogle, M. Seth. 2025. Annual national tree canopy cover mapping: A novel workflow with temporal transferability and improved uncertainty quantification. Science of Remote Sensing. 12: 100301.