Abstract
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.
Highlights
The land cover has changed rapidly in the Taita Hills, in southeastern Kenya
4.1 Land Cover Classification System (LCCS) class definitions and their implementation based on Geographic object-based image analysis (GEOBIA) approach
As agricultural land and other low vegetation targets were not separated at level-1, the presence of buildings was used as criteria to separate agroforestry and woodland classes
Summary
Large areas of forests, woodlands and shrublands have been converted into agricultural use (Clark and Pellikka, 2009; Pellikka et al, 2009). Mapping these changes using remote sensing (RS) is a challenging task as even the class definitions are based on heuristic views of given classification system. Mapping heterogeneous classes using L-resolution satellite data is difficult, since individual components (e.g. single trees) that form agroforestry or woodland classes cannot be distinguished (Zomer et al 2009; Blinn et al 2013). Lresolution data refers to situations where the scene objects are smaller than the pixel size of the data, while in H-resolution data the scene objects are larger than the pixel size (Strahler et al, 1986)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
