MAPPING LAND COVER IN THE TAITA HILLS, SE KENYA, USING AIRBORNE LASER SCANNING AND IMAGING SPECTROSCOPY DATA FUSION
- Department of Geosciences and Geography, P. O. Box 64, 00014 University of Helsinki, Finland
Keywords: GEOBIA, LCCS, Hyperspectral data, LiDAR, Data fusion, Object-based classification
Abstract. The Taita Hills, located in south-eastern Kenya, is one of the world’s biodiversity hotspots. Despite the recognized ecological importance of this region, the landscape has been heavily fragmented due to hundreds of years of human activity. Most of the natural vegetation has been converted for agroforestry, croplands and exotic forest plantations, resulting in a very heterogeneous landscape. Given this complex agro-ecological context, characterizing land cover using traditional remote sensing methods is extremely challenging. The objective of this study was to map land cover in a selected area of the Taita Hills using data fusion of airborne laser scanning (ALS) and imaging spectroscopy (IS) data. Land Cover Classification System (LCCS) was used to derive land cover nomenclature, while the height and percentage cover classifiers were used to create objective definitions for the classes. Simultaneous ALS and IS data were acquired over a 10 km × 10 km area in February 2013 of which 1 km × 8 km test site was selected. The ALS data had mean pulse density of 9.6 pulses/m2, while the IS data had spatial resolution of 1 m and spectral resolution of 4.5–5 nm in the 400–1000 nm spectral range. Both IS and ALS data were geometrically co-registered and IS data processed to at-surface reflectance. While IS data is suitable for determining land cover types based on their spectral properties, the advantage of ALS data is the derivation of vegetation structural parameters, such as tree height and crown cover, which are crucial in the LCCS nomenclature. Geographic object-based image analysis (GEOBIA) was used for segmentation and classification at two scales. The benefits of GEOBIA and ALS/IS data fusion for characterizing heterogeneous landscape were assessed, and ALS and IS data were considered complementary. GEOBIA was found useful in implementing the LCCS based classification, which would be difficult to map using pixel-based methods.