The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 777–782, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-777-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 777–782, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-777-2015

  29 Apr 2015

29 Apr 2015

Combined use of SAR and optical data for environmental assessments around refugee camps in semiarid landscapes

A. Braun and V. Hochschild A. Braun and V. Hochschild
  • Institute for Geography, University of Tübingen, 72070, Tübingen, Germany

Keywords: SAR, refugee camps, landscape capacity, biomass, machine learning

Abstract. Over 15 million people were officially considered as refugees in the year 2012 and another 28 million as internally displaced people (IDPs). Natural disasters, climatic and environmental changes, violent regional conflicts and population growth force people to migrate in all parts of this world. This trend is likely to continue in the near future, as political instabilities increase and land degradation progresses.

EO4HumEn aims at developing operational services to support humanitarian operations during crisis situations by means of dedicated geo-spatial information products derived from Earth observation and GIS data. The goal is to develop robust, automated methods of image analysis routines for population estimation, identification of potential groundwater extraction sites and monitoring the environmental impact of refugee/IDP camps.

This study investigates the combination of satellite SAR data with optical sensors and elevation information for the assessment of the environmental conditions around refugee camps. In order to estimate their impact on land degradation, land cover classifications are required which target dynamic landscapes. We performed a land use / land cover classification based on a random forest algorithm and 39 input prediction rasters based on Landsat 8 data and additional layers generated from radar texture and elevation information. The overall accuracy was 92.9 %, while optical data had the highest impact on the final classification. By analysing all combinations of the three input datasets we additionally estimated their impact on single classification outcomes and land cover classes.