Olaf Kranz

Assessment of environmental changes induced by internally displaced person (IDP) camps in the Darfur region, Sudan, based on multitemporal MODIS data

Darfur’s complex conflict situation led to large-scale internal displacement of people with significant implications for the environment as well as for the conflict situation itself. Time series analyses of medium-resolution satellite data for monitoring large regions were carried out over a longer period to detect the impact of camps for internally displaced persons (IDPs) on their surrounding environment. Four regions of interest were defined in the Darfur region, Sudan, which cover the main and most dynamic IDP camps of the region and are characterized by different environmental conditions. Hotspots of anthropogenic impacts on the vegetation are revealed based on the Seasonal Kendall test applied to quality-enhanced enhanced vegetation index and normalized difference vegetation index Moderate Resolution Imaging Spectroradiometer time series. Overlaying the resulting trend pattern with the locations of IDP camps suggests significant impact on the vegetation, especially – but not exclusively – in those areas with very high population densities. A comparison of the temporal development of the detected hotspots with precipitation and population figures over the investigated period help differentiate between natural and human-induced impacts. Subsequent to the analysis of the medium resolution data, the resulting trends in vegetation cover were correlated with changes detected with very-high-resolution satellite imagery for the two areas around Zalingei and Zam Zam. Areas of decreased vegetation cover in the resulting trend pattern can be correlated with logging of trees as well as removal of shrubs and grass, while positive changes in vegetation cover are related to agricultural land expansion.

Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo

Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to identify hot spots of mining activities and provide reliable information about the situation in and around two selected mining sites, Mumba-Bibatama and Bisie. The first is the test case for the approach and the detection of unknown mining sites, whereas the second acts as reference case since it is the largest and most well-known location for cassiterite extraction in eastern Congo. Thus it plays a key-role within the context of the conflicts in this region. Detailed multi-temporal analyses of very high-resolution (VHR) satellite data demonstrates the capabilities of Geographic Object-Based Image Analysis (GEOBIA) techniques for providing information about the situation during a mining ban announced by the Congolese President between September 2010 and March 2011. Although the opening of new surface patches can serve as an indication for activities in the area, the pure change between the two satellite images does not in itself produce confirming evidence. However, in combination with observations on the ground, it becomes evident that mining activities continued in Bisie during the ban, even though the production volume went down considerably.

Earth observation-based multi-scale impact assessment of internally displaced person (IDP) camps on wood resources in Zalingei, Darfur

This study describes the development of a semi-automatic object-based image analysis approach for the detection and quantification of deforestation in Zalingei, Darfur, in consequence of the increasing concentration of refugees or internally displaced persons (IDPs) in the region. The classification workflow is based on a multi-scale approach, ranging from the analysis of high resolution SPOT-4 to very high resolution IKONOS and QuickBird satellite imagery between 2003 and 2008. The overall accuracy rates for the classification of the SPOT 4 data ranged from 92% up to 95%, while those for the QuickBird and IKONOS classification have shown values of 88 and 87%, respectively. The resulting trends in woody vegetation cover were compared with the development of the local population and the variability of precipitation. The results show that the strong increase in human population in the Zalingei IDP camps can be associated with considerable decrease in woody vegetation in the camp vicinity.