Comparison Between Traditional Forest Inventory and Remote Sensing with Random Forest for Estimating the Periodic Annual Increment in a Dry Tropical Forest

This study evaluates the effectiveness of remote sensing combined with the Random Forest algorithm compared to traditional forest inventory methods for estimating the Annual Periodic Increment (API) in a dry tropical forest within the Caatinga biome. Data were collected from permanent plots monitored between 2011 and 2019 and integrated with Landsat imagery processed in Google Earth Engine. The results show that both surface reflectance and vegetation indices significantly improves the accuracy of API estimates while reducing fieldwork costs and efforts. The Random Forest model demonstrated robustness (R²= 0.8867, RMSE=0.87), further enhanced with post-processing correction factors, offering a sustainable approach for forest management in ecosystems vulnerable to climate change.