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Publication
Hazard Mapping of Landmines and ERW Using Geo-Spatial Techniques
Journal Contribution - Journal Article
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
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
Journal: Journal of Remote Sensing & GIS
ISSN: 2469-4134
Issue: 2
Volume: 6
Pages: 1-11
Publication year:2017
Keywords:Humanitarian mine action, Landmine risk, Risk indicators, Risk mapping, Geo-Spatial models, KDE
Authors:International
Accessibility:Open