Mapping Tropical Dry Forest Succession With CHRIS/PROBA Hyperspectral Images Using Nonparametric Decision Trees

Abstract

Most satellites for environmental monitoring point ground targets from a perpendicular position to the terrain, also known as nadir. The CHRIS/PROBA platform provides four extra angles of observation, plus nadir pointing: -55°, -36°, 0°, +36°, and +55°. The purpose of this study is to investigate the best season (wet or dry season) and angle of observation to map tropical dry forest succession in Brazil. Nonparametric decision trees were used to build up classification maps based on principal component analysis (PCA) inputs. Our results indicate that the use of off-nadir data improves the map accuracy of successional stages of tropical dry forests and riparian forests. Particularly, extreme and negative angles of observation generate higher map accuracies, suggesting that tree shadows are enhancing spectral differences between the studied vegetation classes. Images from the dry season provide better total and classes' map accuracies for late and intermediate stages of tropical dry forests. On the other hand, some classes, such as riparian forests and early stage of tropical forests need the use of off-nadir angles of observation to reach a minimum accuracy and best scores are reached using wet season's images.