The first Above-ground Biomass map of the Philippines produced using Remote sensing and Machine learning

Abstract

Maps of above-ground biomass (AGB) using remote sensing (RS) are valuable to countries like the Philippines for multi-purposes including national greenhouse gas reporting, carbon accounting and even reforestation monitoring. As RS data increase, both optical and radar satellite data have been combined to produce higher quality AGB maps, rather than using individual satellites alone. AGB is then produced after establishing a statistical relationship between combined RS data and plot-based AGB often using machine learning (ML) regression tasks. Here we model a country-wide AGB map of the Philippines while assessing the effects of combining freely and easily accessible satellite data as AGB predictors: Landsat-8 (optical RS), and ALOS-2 PALSAR-2 and Sentinel-1 (radar RS). Using annual composites of each satellite data along with other ecological variables and the National Forest Inventory (NFI) 2014–2015, we trained, cross-validated and compared ML models: Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN). Model evaluation results were found similar between RF and SVM models i.e., 63 and 65 Mg ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> RMSE respectively; while the NN model showed the lowest accuracy i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{RMSE} =84$</tex> Mg ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , likely a consequence of limited training data. The effect of combining Landsat-8 and ALOS-2 PALSAR-2 was indicated by better AGB predictions in the upland forests, while the inclusion of Sentinel-1 improved the AGB estimates in the agricultural lowland. Using the three satellite data and an RF model not only provides the map with the least bias, but also complements the ridge-to-reef topography of the country where woody vegetations exist from forested mountains down to agricultural lands and mangroves. This study is helpful also to other tropical countries in addressing the problem on expensive forest inventories as well as inaccessible forest lands and conflict areas, while providing spatially explicit and reliable AGB estimates.