GIS based Watershed Classification in Lao PDR

Every inclined land surface has a potential for soil and water degradation, the seriousness depends on a multitude of parameters such as slope, soil type, geomorphology, rainfall, land use and natural vegetation cover. In Laos this intensified land use leads to reduced vegetation cover, to increased soil erosion, decreasing yield, and finally is likely to influence the hydrological regime. Against this background the Mekong River Commission (MRC) elaborated a spatial explicit Watershed Classification (WSC) for the Lower Mekong Basin. Based on topographic factors derived from a high-resolution Digital Terrain Model, five watershed classes are calculated, giving indication about the sensitivity to resource degradation by soil erosion. The WSC allows spatial priority setting for watershed management and generally supports informed decision making on reconnaissance level. In the conclusions focus is laid on general considerations when GIS techniques are used for spatial decision support in a development context.

Quantitative morphometric analysis of lakes using GIS: rectangularity R , ellipticity E , orientation O , and the rectangularity vs. ellipticity index, REi

Quantitative measures of polygon shapes and orientation are important elements of geospatial analysis. These kinds of measures are particularly valuable in the case of lakes, where shape and orientation patterns can help identifying the geomorphological agents behind lake formation and evolution. However, the lack of built-in tools in commercial geographic information system (GIS) software packages designed for this kind of analysis has meant that many researchers often must rely on tools and workarounds that are not always accurate. Here, an easy-to-use method to measure rectangularity R, ellipticity E, and orientation O is developed. In addition, a new rectangularity vs. ellipticity index, REi, is defined. Following a step-by-step process, it is shown how these measures and index can be easily calculated using a combination of GIS built-in functions. The identification of shapes and estimation of orientations performed by this method is applied to the case study of the geometric and oriented lakes of the Llanos de Moxos, in the Bolivian Amazon, where shape and orientation have been the two most important elements studied to infer possible formation mechanisms. It is shown that, thanks to these new indexes, shape and orientation patterns are unveiled, which would have been hard to identify otherwise.

GIS-Karte des Südsudans gewinnt Kartografie-Preis

Eine vom Centre for Develpment and Environment (CDE) der Universität Bern gestaltete Karte der südsudanesischen Region East Equatoria hat den „New Mapmaker Award 2009“ gewonnen. Der Preis wird gemeinsam von der britischen Kartografiegesellschaft und der amerikanischen National Geographic Society vergeben. Die im Rahmen eines Capacity-Development- Programms für Geoinformationsmanagement entworfene Karte wurde vollumfänglich in ArcMap aufgebaut, ohne Einsatz von Graphiksoftware.

GIS layers on Human-Elephant conflicts in Laikipia District

GIS layers on Human-Elephant conflicts in Laikipia District: aerial counts, wildlife distribution, land-use, Human-Elephant conflicts hotspots and temporal patterns, and conflict deterrence activities

An automated method for mapping physical soil and water conservation structures on cultivated land using GIS and remote sensing techniques

An efficient and reliable automated model that can map physical Soil and Water Conservation (SWC) structures on cultivated land was developed using very high spatial resolution imagery obtained from Google Earth and ArcGIS, ERDAS IMAGINE, and SDC Morphology Toolbox for MATLAB and statistical techniques. The model was developed using the following procedures: (1) a high-pass spatial filter algorithm was applied to detect linear features, (2) morphological processing was used to remove unwanted linear features, (3) the raster format was vectorized, (4) the vectorized linear features were split per hectare (ha) and each line was then classified according to its compass direction, and (5) the sum of all vector lengths per class of direction per ha was calculated. Finally, the direction class with the greatest length was selected from each ha to predict the physical SWC structures. The model was calibrated and validated on the Ethiopian Highlands. The model correctly mapped 80% of the existing structures. The developed model was then tested at different sites with different topography. The results show that the developed model is feasible for automated mapping of physical SWC structures. Therefore, the model is useful for predicting and mapping physical SWC structures areas across diverse areas.