A number of general circulation model (GCM) experiments have shown that changes in vegetation in the Sahel can cause substantial reductions in rainfall. In some studies, the climate sensitivity is large enough to trigger drought of the severity observed since the late 1960s. The extent and intensity of vegetation changes are crucial in determining the magnitude of the atmospheric response in the models. However, there is no accurate historical record of regional vegetation changes extending back to before the drought began. One important driver of vegetation change is land use practice. In this paper the hypothesis that recent changes in land use have been large enough to cause the observed drought is tested. Results from a detailed land use model are used to generate realistic maps of vegetation changes linked to land use. The land use model suggests that cropland coverage in the Sahel has risen from 5% to 14% in the 35 yr prior to 1996. It is estimated that this process of agricultural extensification, coupled with deforestation and other land use changes, translates to a conversion of 4% of the land from tree cover to bare soil over this period. The model predicts further changes in the composition of the land surface by 2015 based on changes in human population (rural and urban), livestock population, rainfall, cereals imports, and farming systems. The impact of land use change on Sahelian climate is assessed using a GCM, forced by the estimates of land use in 1961, 1996, and 2015. Relative to 1961 conditions, simulated rainfall decreases by 4.6% (1996) and 8.7% (2015). The decreases are closely linked to a later onset of the wet season core during July. Once the wet season is well developed, however, the sensitivity of total rainfall to the land surface is greatly reduced, and depends on the sensitivity of synoptic disturbances to the land surface. The results suggest that while the climate of the region is rather sensitive to small changes in albedo and leaf area index, recent historical land use changes are not large enough to have been the principal cause of the Sahel drought. However, the climatic impacts of land use change in the region are likely to increase rapidly in the coming years.
This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. The processing chain is implemented in Python and relies on existing open-source software GRASS GIS and R. The complete tool chain is available in open access and is adaptable to specific user needs. For automation purposes, we developed two GRASS GIS add-ons enabling users (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual machine learning classifiers or their prediction combinations through voting-schemes. We tested the performance of the processing chain using sub-metric multispectral and height data on two very different urban environments: Ouagadougou, Burkina Faso in sub-Saharan Africa and Liège, Belgium in Western Europe. Using a hierarchical classification scheme, the overall accuracy reached 93% at the first level (5 classes) and about 80% at the second level (11 and 9 classes, respectively).
Eléonore Wolff, "Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data," J. Appl. Remote Sens. 11(3), 036011 (2017), doi: 10.1117/1.JRS.11.036011. Abstract. Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule-and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m 2 . Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Dynamic simulation models allow an integrated representation of human and biophysical driving forces, to test their influence on land use. Dynamic models emphasise the interactions among the components of the system and take into account feedback loops and threshold effects. In this paper, the SALU model was used to generate ''whatif'' scenarios to explore hypotheses on the relative role of driving forces of land-use change in the Sudano-sahelian countries of Africa. The model simulations provided useful insights to better understand the processes of land-use change. Rural population growth represents a larger stimuli for land-use change than urban population growth. Demographic variables have a greater impact on land use than recurring droughts. The demographic driving forces are slow variables while rainfall is a fast variable. Recurring droughts could be viewed as trigger events, and urban population growth and consumption as mediating factors, while rural population growth defines long-term trends. Technological change defines thresholds in land use. Land-use change results from interactions between driving forces. The timing of occurrence of drought with respect to transitions in land use has a major impact on land-use change. Polices aimed at protecting pastoral land and supporting agricultural intensification both contribute to maintain pastoral activities. Simulating a conservative carrying capacity has a major impact on land use predictions. By examining environmental, social and economic implications of various land-use scenarios, the modelling approach adopted in SALU can provide support for decision-making.
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