@article{AsareKyeiForkuorVenus2015, author = {Asare-Kyei, Daniel and Forkuor, Gerald and Venus, Valentijn}, title = {Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches}, series = {Water}, volume = {7}, journal = {Water}, doi = {10.3390/w7073531}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-151581}, pages = {3531 -- 3564}, year = {2015}, abstract = {Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77\% to 81\%. This was supported with local expert knowledge which accurately classified 79\% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized.}, language = {en} } @article{DossoYeoKonateetal.2012, author = {Dosso, Kanvaly and Yeo, Kolo and Konate, Souleymane and Linsenmair, Karl Eduard}, title = {Importance of protected areas for biodiversity conservation in central Cote d'Ivoire: Comparison of termite assemblages between two neighboring areas under differing levels of disturbance}, series = {Journal of Insect Science}, volume = {12}, journal = {Journal of Insect Science}, number = {131}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-133218}, year = {2012}, abstract = {To highlight human impact on biodiversity in the Lamto region, termites were studied with regard to their use as bio-indicators of habitat change in the tropics. Using a standardized method, termites were sampled in the three most common habitat types, i.e., in semi-deciduous forest, savanna woodland, and annually burned savanna, all inside Lamto Reserve and its surrounding rural domain. Termite species richness fell from 25 species in the Lamto forest to 13 species in the rural area, involving strong modification in the species composition (species turnover = 59 \%). In contrast, no significant change in diversity was found between the Lamto savannas and the rural ones. In addition, the relative abundance of termites showed a significantly greater decline in the rural domain, even in the species Ancistrotermes cavithorax (Sjostedt) (Isoptera: Termitidae), which is known to be ecologically especially versatile. Overall, the findings of this study suggest further investigation around Lamto Reserve on the impact of human activities on biodiversity, focusing on forest conversion to land uses (e.g. agricultural and silvicultural systems).}, language = {en} } @article{ErmertFinkMorseetal.2012, author = {Ermert, Volker and Fink, Andreas H. and Morse, Andrew P. and Paeth, Heiko}, title = {The Impact of Regional Climate Change on Malaria Risk due to Greenhouse Forcing and Land-Use Changes in Tropical Africa}, series = {Environmental Health Perspectives}, volume = {120}, journal = {Environmental Health Perspectives}, number = {1}, doi = {10.1289/ehp.1103681}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-135562}, pages = {77-84}, year = {2012}, abstract = {BACKGROUND: Climate change will probably alter the spread and transmission intensity of malaria in Africa. OBJECTIVES: In this study, we assessed potential changes in the malaria transmission via an integrated weather disease model. METHODS: We simulated mosquito biting rates using the Liverpool Malaria Model (LMM). The input data for the LMM were bias-corrected temperature and precipitation data from the regional model (REMO) on a 0.5 degrees latitude longitude grid. A Plasmodium falciparum infection model expands the LMM simulations to incorporate information on the infection rate among children. Malaria projections were carried out with this integrated weather disease model for 2001 to 2050 according to two climate scenarios that include the effect of anthropogenic land-use and land-cover changes on climate. RESULTS: Model-based estimates for the present climate (1960 to 2000) are consistent with observed data for the spread of malaria in Africa. In the model domain, the regions where malaria is epidemic are located in the Sahel as well as in various highland territories. A decreased spread of malaria over most parts of tropical Africa is projected because of simulated increased surface temperatures and a significant reduction in annual rainfall. However, the likelihood of malaria epidemics is projected to increase in the southern part of the Sahel. In most of East Africa, the intensity of malaria transmission is expected to increase. Projections indicate that highland areas that were formerly unsuitable for malaria will become epidemic, whereas in the lower-altitude regions of the East African highlands, epidemic risk will decrease. CONCLUSIONS: We project that climate changes driven by greenhouse-gas and land-use changes will significantly affect the spread of malaria in tropical Africa well before 2050. The geographic distribution of areas where malaria is epidemic might have to be significantly altered in the coming decades.}, language = {en} } @article{ForkuorConradThieletal.2014, author = {Forkuor, Gerald and Conrad, Christopher and Thiel, Michael and Ullmann, Tobias and Zoungrana, Evence}, title = {Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa}, doi = {10.3390/rs6076472}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113070}, year = {2014}, abstract = {Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10\%-15\% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75\% are achievable.}, language = {en} } @article{KnauerGessnerFensholtetal.2016, author = {Knauer, Kim and Gessner, Ursula and Fensholt, Rasmus and Kuenzer, Claudia}, title = {An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {5}, doi = {10.3390/rs8050425}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180712}, pages = {425}, year = {2016}, abstract = {Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\(^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture}, language = {en} } @article{LandmannSchrammColditzetal.2010, author = {Landmann, Tobias and Schramm, Matthias and Colditz, Rene R. and Dietz, Andreas and Dech, Stefan}, title = {Wide Area Wetland Mapping in Semi-Arid Africa Using 250-Meter MODIS Metrics and Topographic Variables}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68628}, year = {2010}, abstract = {Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km2 large study area in western Burkina Faso and southern Mali (West Africa). A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class "Wetland") is 9,350 km2, corresponding to 4.3\% of the total study area size. The classes "No wetland"/"Wetland" could be separated with very high certainty; the overall agreement (KHAT) was 84.2\% (0.67) and 97.9\% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments.}, subject = {Geologie}, language = {en} } @article{PaethPaxianSeinetal.2017, author = {Paeth, Heiko and Paxian, Andreas and Sein, Dimitry V. and Jacob, Daniela and Panitz, Hans-J{\"u}rgen and Warscher, Michael and Fink, Andreas H. and Kunstmann, Harald and Breil, Marcus and Engel, Thomas and Krause, Andreas and Toedter, Julian and Ahrens, Bodo}, title = {Decadal and multi-year predictability of the West African monsoon and the role of dynamical downscaling}, series = {Meteorologische Zeitschrift}, volume = {26}, journal = {Meteorologische Zeitschrift}, number = {4}, doi = {10.1127/metz/2017/0811}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172018}, pages = {363-377}, year = {2017}, abstract = {West African summer monsoon precipitation is characterized by distinct decadal variability. Due to its welldocumented link to oceanic boundary conditions in various ocean basins it represents a paradigm for decadal predictability. In this study, we reappraise this hypothesis for several sub-regions of sub-Saharan West Africa using the new German contribution to the coupled model intercomparison project phase 5 (CMIP5) near-term prediction system. In addition, we assume that dynamical downscaling of the global decadal predictions leads to an enhanced predictive skill because enhanced resolution improves the atmospheric response to oceanic forcing and landsurface feedbacks. Based on three regional climate models, a heterogeneous picture is drawn: none of the regional climate models outperforms the global decadal predictions or all other regional climate models in every region nor decade. However, for every test case at least one regional climate model was identified which outperforms the global predictions. The highest predictive skill is found in the western and central Sahel Zone with correlation coefficients and mean-square skill scores exceeding 0.9 and 0.8, respectively.}, language = {en} } @article{ZoungranaConradAmekudzietal.2015, author = {Zoungrana, Benewinde Jean-Bosco and Conrad, Christopher and Amekudzi, Leonard K. and Thiel, Michael and Dapola Da, Evariste and Forkuor, Gerald and L{\"o}w, Fabian}, title = {Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {9}, doi = {10.3390/rs70912076}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125866}, pages = {12076-12102}, year = {2015}, abstract = {Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92\%. Natural vegetation loss was estimated to be 17.9\% ± 2.5\% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.}, language = {en} }