In this study we examine the classification accuracy of a CHRIS (Compact High Resolution Imaging Spectrometer) data, acquired on May 18, 2004 (monsoon). The test site is a typical peat swamp landscape located in South Borneo (Central Kalimantan, Indonesia). We focus on eight specific land use/cover categories from a single view angle (at nadir as a reference) and from a multi-angular perspective in four view angles with 18 spectral bands. We show that (1) the reflectance increases accordingly to the successional stages for a given angle and (2) that reflectance values increase in the near-infrared with decreasing leaf area index (LAI). From the single (nadir) to the multi-angular approach, classification overall accuracy increased from 77.1% to 90.4%. Kappa statistics increased from 0.74 to 0.89 and confirmed that the classification performances were statistically different at a 5% level of significance. Our results demonstrate that multi-angular data improve the differentiation between different Peatland landscape classes.
['Veraldo Liesenberg', 'Hans-Dieter Viktor Boehm', 'Richard Gloaguen']
The contribution of CHRIS/PROBA data for tropical peat swamp landscape discrimination purposes