Auxiliary datasets improve accuracy of object-based land use/land cover classification 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,
and settlements.
The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation
of four Formosat-2 scenes with 8m spatial resolution using 1370 ground reference points for training, validation,
and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object
features in each. The baseline model having only spectral and texture features was compared with five other
models supplemented with auxiliary features.
Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model
gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5
percentage points. The best OA was achieved with a model including all features of which elevation was the most
important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree.
Applying object-based classification to geospatial information on topography, soil, settlement patterns and
vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved.
We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping
across a multitude of fragmented landscapes worldwide.