ரிமோட் சென்சிங் & ஜிஐஎஸ் ஜர்னல்

சுருக்கம்

Hazard Mapping of Landmines and ERW Using Geo-Spatial Techniques

Aura Cecilia Alegria, Esteban Zimanyi, Jan Cornelis and Hichem Sahli

Landmines and Explosive Remnants of War (ERW) continue to represent a significant nuisance for society in affected countries. Coping with humanitarian and development activities, mine action aims at both, reducing the impacts of the presence of landmines/ERW on the population, and ultimately returning cleared land to the communities. These are the main tasks of mine action decision makers. This study combines landmine/ERW contamination data with explanatory variables that contain information about underlying targets. They are integrated into a risk mapping framework using Geographic Information Systems with other information sources, such as remote sensing. The aim of this paper is to provide insights into the populations and/or locations at risk caused by landmine and ERW impacts on a broad and local scale. Thus, the concept of ‘hotspots’ is particularly useful because it provides a visual representation of exposure, aided by a geo-spatial representation of ‘priority areas for mine action planners to focus on. We apply the Kernel Density Estimator (KDE) to derive such ‘hotspots’. KDE is proposed as the basis to define landmine and ERW hazard, vulnerability, and element-at-risk maps, which enable producing a final output, the landmine/ERW risk map. This is accomplished by using an adaptive kernel bandwidth for datasets with highly heterogeneous spatial distributions, and a problem-specific method for generating point samples from polygon data, before using them as inputs for KDE. The geo-statistical model presented here is a time-and-cost-efficient method to construct a landmine risk map, that is as representative as those produced by mine action actors. It can be used as a complement to the risk area maps made by these actors because they are slightly different but show a large degree of overlap. Moreover, the method helps revealing the variables which are the most linked to landmine/ERW-related events in the study area.