Projects per year
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,
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.
Fields of Science
- 1171 Geosciences
- Image segmentation
- Land use/land cover mapping
- Auxiliary data
- Random Forest
- Satellite Time Series
- RANDOM FOREST
- MT. KILIMANJARO
- SURFACE TEMPERATURE
- SOUTHERN SLOPES
- ANCILLARY DATA
Kilimanjaro ecosystems under global change: Linking biodiversity, biotic interactions and biogeochemical ecosystem processes
01/10/2012 → …
Project: Research project
TAITAGIS: Improving capacity, quality and access of Geoinformatics teaching, research and daily use in Taita Taveta, Kenya
01/03/2017 → 29/02/2020
Project: Other project
GIERI: Strengthening geoinformatics teaching and research capacity in Eritrea higher education institutions.
01/10/2015 → 31/12/2017
Project: Other project