Abstract
D.Phil.
Land cover changes associated with growing human populations and expected
changes in climatic conditions are likely to accelerate alterations in hydrological
phenomena and processes on various scales. Subsequently, these changes could
significantly influence the quantity and quality of water resources for both nature and
human society. Documenting the distribution of land cover types within the Shire
River catchment is the foundation for applications in this study of the hydrology of the
Shire catchment.
The aim of this study is to investigate the relationships between the measured land
cover changes and hydrological regimes in the Shire River Catchment in Malawi.
Maps depicting land cover dynamics for 1989 and 2002 were derived from multispectral
and multi-temporal Landsat 5 (1989) and Landsat 7 ETM+ (2002) satellite
remote sensing data for this catchment. Other spectral-independent data sets included
the 90-m resolution Shuttle Radar Topographic Mission (SRTM) digital elevation
model (DEM), Geographical Information System (GIS) layers of soils, geology and
archived land cover. Core image-derived data sets such as individual Landsat bands,
Normalized Difference Vegetation Index (NDVI), Principal Components Analysis and
Tasseled Cap transformations were computed. From generated composite images,
land cover classes were identified using a maximum likelihood algorithm. Eight land
cover classes were mapped.
A hierarchical multispectral shape classifier with an object conditional approach
determined by the Food and Agriculture Organisation (FAO) Land Cover
Classification System (LCCS) legend structure was used to map land cover variables.
LCCS was used as a basis for classification to achieve legend harmonization within
Africa and on a global scale. Flexibility of the hierarchical system allowed
incorporation of digital elevation objects, soil and underlying geological features as
well as other available geographical data sets. This approach improved classification
accuracy and can be adopted to discriminate land cover features at several scales,
which are internally relatively homogeneous.In addition to compatibility with the
FAO/LCCS classification system, the derived land cover maps have provided recent
and improved classification accuracy, and added thematic detail compared to the
existing 1992 land cover maps.
Fieldwork was conducted to validate the land cover classes identified during
classification. Accuracy assessment was based on the correlation between ground
reference samples collected during field exercise and the satellite image classification.
The overall mapping accuracy was 87%, with individual classes being mapped at
accuracies of above 77% for both user and producer accuracy. The combination of
Landsat images, vector data and detailed ground truthing information was used
successfully to classify land cover of the Shire River catchment for years 1989 and
2002.