Spectral Variability and Discrimination Assessment in a Tropical Peat Swamp Landscape Using CHRIS/PROBA Data

In this study, we examine seasonal aspects and the potential of multi-angle CHRIS/PROBA data, acquired at two different dates, to improve forest classification. The test site is a typical peat swamp landscape located in Central Kalimantan, Indonesia. We focus on eight specific land use/cover categories from a single view angle and from a multi-angular perspective. We show that: (I) reflectance changes from the end of the monsoon to the beginning of the dry season in the visible were small and slightly positive for the forestry classes, whereas slightly negative for grassland classes; (2) reflectance increases according to the successional stages for a given angle and were higher in the beginning of the dry season; (3) reflectance values increase in the near-infrared with decreasing leaf area index (LAI); and (4) classification results using a multi-angular approach were statistically better at a 5% level of significance from a single view approach on both selected dates, showing that anisotropy information can improve differentiation between peatland landscape classes.