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Hazard Mapping of Landmines and ERW Using Geo-Spatial Techniques This publication appears in: Journal of Remote Sensing & GIS Authors: A. Alegria, E. Zimanyi, J. Cornelis and H. Sahli Volume: 6 Issue: 2 Pages: 1-11 Publication Date: Jun. 2017
Abstract: Landmines and Explosive Remnants of War (ERW) continue to represent a significant nuisance for society inaffected countries. Coping with humanitarian and development activities, mine action aims at both, reducing theimpacts 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 datawith explanatory variables that contain information about underlying targets. They are integrated into a risk mappingframework using Geographic Information Systems with other information sources, such as remote sensing. The aim ofthis paper is to provide insights into the populations and/or locations at risk caused by landmine and ERW impacts on abroad and local scale. Thus, the concept of ‘hotspots’ is particularly useful because it provides a visual representationof exposure, aided by a geo-spatial representation of ‘priority areas for mine action planners to focus on. We apply theKernel Density Estimator (KDE) to derive such ‘hotspots’. KDE is proposed as the basis to define landmine and ERWhazard, vulnerability, and element-at-risk maps, which enable producing a final output, the landmine/ERW risk map. Thisis accomplished by using an adaptive kernel bandwidth for datasets with highly heterogeneous spatial distributions, anda problem-specific method for generating point samples from polygon data, before using them as inputs for KDE. Thegeo-statistical model presented here is a time-and-cost-efficient method to construct a landmine risk map, that is asrepresentative as those produced by mine action actors. It can be used as a complement to the risk area maps madeby these actors because they are slightly different but show a large degree of overlap. Moreover, the method helpsrevealing the variables which are the most linked to landmine/ERW-related events in the study area
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