Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia.

Abstract

Continuous mapping of the height and canopy cover of forests is vital for measuring forest biomass, monitoring forest degradation and restoration. In this regard, the contribution of Light Detection and Ranging (LiDAR) sensors, which were developed to obtain detailed data on forest composition across large geographical areas, is immense. Accordingly, this study aims to predict forest canopy cover and height in tropical forest areas utilizing Global Ecosystem Dynamics Investigation (GEDI) LIDAR, multisensor images, and random forest regression. To achieve this, we gathered predictor variables from the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Sentinel-2 multispectral datasets, and Sentinel-1 synthetic aperture radar (SAR) backscatters. The model's accuracy was evaluated based on a validation dataset of GEDI Level 2A and Level 2B. The random forest method was used the combination of data layers from Sentinel-1, Sentinel-2, and topographic measurements to model forest canopy cover and height. The produced canopy height and cover maps had a resolution of 30m with R2 = 0.86 and an RMSE of 3.65m for forest canopy height and R2 = 0.87 and an RMSE of 0.15 for canopy cover for the year 2022. These results suggest that combining multiple variables and data sources improves canopy cover and height prediction accuracy compared to relying on a single data source. The output of this study could be helpful in creating forest management plans that support sustainable utilization of the forest resources.