Auxiliary datasets improve accuracy of object based land use land cover classi fi cation in heterogeneous savanna landscapes
Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel based classifications, and the coverage, accuracy and resolution of free and open access auxiliary datasets have been poor until recent years. We evaluated how recent global coverage open access geospatial datasets improve object based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral temporal metrics from Landsat 8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands,