Petra Füreder

Earth observation (EO)-based ex post assessment of internally displaced person (IDP) camp evolution and population dynamics in Zam Zam, Darfur

During humanitarian crises, when population figures are often urgently required but very difficult to obtain, remote sensing is able to provide evidence of both present and past population numbers. This research, conducted on QuickBird time-series imagery of the Zam Zam internally displaced person (IDP) camp in Northern Darfur, investigates automated analysis of the camps evolution between 2002 and 2008, including delineation of the camps outlines and inner structure, employment of rule-based extraction for two categories of dwelling units and derivation of population estimates for the time of image capture. Reference figures for dwelling occupancy were obtained from estimates made by aid agencies. Although validation of such ‘on-demand’ census techniques is still continuing, the benefits of a fast, efficient and objective information source are obvious. Spatial, as well thematic, accuracy was, in this instance, assessed against visual interpretation of eight 200 m × 200 m grid cells and accuracy statistics calculated. Total users and producers accuracy rates ranged from 71.6% up to 94.9%. While achieving promising results with respect to accuracy, transferability and usability, the remaining limitations of automated population estimation in dynamic crisis situations will provide a stimulus for future research.

A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories

Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM), including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km² was detected as landslide-affected area, 1.68 km² were classified as flow-like landslides and 2.09 km² as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI) and an interpretation of PSI (Persistent Scatterer Interferometry) measures derived from ERS1/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI) and 50.4% (PAI), respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data.

Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study

For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation. In cases where detailed field assessments are not available, Earth observation (EO) data can provide important information to get a better overview about the general situation on the ground. In this study, different approaches for dwelling detection were tested using the example of a highly complex camp site in Somalia. On the basis of GeoEye-1 imagery, semi-automatic object-based and manual image analysis approaches were applied, compared and evaluated regarding their analysis results (absolute numbers, population estimation, spatial pattern), statistical correlations and production time. Although even the results of the visual image interpretation vary considerably between the interpreters, there is a similar pattern resulting from all methods, which shows same tendencies for dense and sparse populated areas. The statistical analyses revealed that all approaches have problems in the more complex areas, whereas there is a higher variance in manual interpretations with increasing complexity. The application of advanced rule sets in an object-based environment allowed a more consistent feature extraction in the area under investigation that can be obtained at a fraction of the time compared to visual image interpretation if large areas have to be observed.

Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows

Accurate and reliable information about the situation in refugee or internally displaced person camps is very important for planning any kind of help like health care, infrastructure, or vaccination campaigns. The number and spatial distribution of single dwellings extracted semi-automatically from very high-resolution (VHR) satellite imagery as an indicator for population estimations can provide such important information. The accuracy of the extracted dwellings can vary quite a lot depending on various factors. To enhance established single dwelling extraction approaches, we have tested the integration of stratified template matching methods in object-based image analysis (OBIA) workflows. A template library for various dwelling types (template samples are taken from ten different sites using 16 satellite images), incorporating the shadow effect of dwellings, was established. Altogether, 18 template classes were created covering typically occurring dwellings and their cast shadows. The created template library aims to be generally applicable in similar conditions. Compared to pre-existing OBIA classifications, the approach could increase the producer’s accuracy by 11.7 percentage points on average and slightly increase the user’s accuracy. These results show that the stratified integration of template matching approaches in OBIA workflows is a possibility to further improve the results of semi-automated dwelling extraction, especially in complex situations.

Earth Observation for Humanitarian Operations

Large-scale population displacements have ever increased the need for more effective humanitarian assistance. Humanitarian organizations require up-to-date reliable information about the situation on-site. Field-based surveys are often limited in crisis situations due to time or accessibility constraints. Geospatial and Earth observation (EO) technologies have increasingly become popular in the humanitarian community. An EO-based information service was set up for Medecins Sans Frontieres (MSF) which provides dedicated geospatial information products in support of their operations. The core of the service portfolio is population monitoring using dwelling extraction from (multi-temporal) very high-resolution (VHR) satellite imagery. The service is mainly requested for refugee and IDP (internally displaced people) camps. Additional services on environmental resources, including groundwater, are provided on demand. As of mid-2017, over 350 maps at 60 locations in over 20 countries have been produced.

Land Use/Land Cover Classification of the Natural Environment

Land use/land cover (LULC) information is one of the most important spatial input for environmental modelling and a crucial indicator to identify and quantify natural and socioeconomic impacts triggered by LULC changes. Such impacts are related to glacier, snow cover, and permafrost melting, the forming of GLOFs, erosion by land sides, discharge and sediment transport dynamics of alpine rivers, and the socioeconomic regional urban and rural development to name some of them.