Towards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions

The exploitation of resources, if not properly managed, can lead to spoiling natural habitats as well as to threatening people’s health, livelihoods and security. The paper discusses a multi-scale Earth observation-based approach to provide independent information related to exploitation activities of natural resources for countries which are experiencing armed conflict. The analyses are based on medium to very high spatial resolution optical satellite data. Object-based image analysis is used for information extraction at these different scales. On a subnational level, conflict-related land cover changes as an indication of potential hot spots for exploitation activities are classified. The regional assessment provides information about potential activity areas of resource exploitation, whereas on a local scale, a site-specific assessment of exploitation areas is performed. The study demonstrates the potential of remote sensing for supporting the monitoring and documentation of natural resource exploitation in conflict regions

Doubly Charged Dimers and Trimers of Heavy Noble Gases

Many doubly charged heteronuclear dimers are metastable or even thermodynamically stable with respect to charge separation. Homonuclear dicationic dimers, however, are more difficult to form. He22+ was the first noble gas dimer predicted to be metastable and, decades later, observed. Ne22+ is the only other dicationic noble gas dimer that has been detected so far. Here, we present a novel approach to form fragile dicationic species, by post-ionization of singly charged ions that are embedded in helium nanodroplets (HNDs). Bare ions are then extracted by colliding the HNDs with helium gas. We detect homonuclear doubly charged dimers and trimers of krypton and xenon, but not argon. Our multi-reference ab initio calculations confirm the stability of Kr22+, Kr32+, Xe22+, Xe32+, and Ar22+, but put the stability of Ar32+ towards dissociation to Ar+ + Ar2+ into question

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid- and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA). Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time

Wetland Monitoring in Semi-arid African Regions Using MODIS Time Series

Monitoring of wetlands and water bodies in semi-arid African regions is of high importance for the local population and for wetland ecology. The study area is situated in Burkina Faso, West Africa, and extends over an area of 500 km north-southwards, ranging from Sahelien to Savannah climates, and showing a gradient of different rainfall and land use characteristics. Surface water is an important resource for different livelihoods such as farmers or herders, as well as for water extraction by the local population, and can be scarce particularly in seasons of drought. This study demonstrates the applicability of time series of optical remote sensing data for dynamic water body mapping on a high temporal and medium resolution spatial scale. The main dataset is a 15-year time series (2000-2014) from the Moderate Resolution Imaging Spectrometer (MODIS) with temporal intervals of eight days and 250 m spatial resolution bands in the red and near infrared range (MOD09Q1 product), supported by further information from the 500 m resolution MOD09A1 product, and the digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM). RapidEye and Landsat high resolution optical images are available for different seasons in different years, and serve for exploring trends on a higher resolution scale, as well as for validation of the results derived from MODIS. Spatio-temporal dynamics of water covered areas have been obtained, showing intra-annual variations as well as dynamics throughout the 15-year time series. Trends reveal changing flooding regimes in terms of larger water coverage while the flooding duration decreased. These findings correspond to information gained during field work, and is connected to siltation on the bottom of the water bodies. Caused by dam constructions 21 newly created water bodies larger than 0.5 km², and a number of smaller water bodies were discovered, as well as a few water bodies that have vanished. Secondary applications such as detecting irrigation trends around wetlands from MODIS vegetation indices could be verified using high resolution RapidEye and Landsat data, and vegetation indices can aid for wetland delineation of vegetation covered wetlands. The negative anomalies of both, surface water dynamics of wetlands and vegetation of the environment, were found to coincide with the occurrence of drought seasons (2000-01, 2004-05 and 2011-12). Possibilities and limitations for extending this approach over larger areas are discussed, and challenges such as mapping sediment rich waters, vegetation covered waters, or misclassifications due to cloud shadows or burnt areas are addressed. This study demonstrates the potential to detect and monitor water bodies and wetlands in semi-arid African regions, and contributes to the understanding of inter- and intra-yearly dynamics, based on a time series of medium resolution MODIS data. Additionally, the role of wetland monitoring for both, estimating water availability and indicating drought in semi-arid areas is discussed. An outlook on an adapted concept using Sentinel-3 data is presented. The study shows that the use of remote sensing time series is a suitable tool to monitor wetlands in semi-arid regions

Monitoring of Oil Exploitation Infrastructure by Combining Unsupervised Pixel-Based Classification of Polarimetric SAR and Object-Based Image Analysis

In developing countries, there is a high correlation between the dependence of oil exports and violent conflicts. Furthermore, even in countries which experienced a peaceful development of their oil industry, land use and environmental issues occur. Therefore, an independent monitoring of oil field infrastructure may support problem solving. Earth observation data enables a fast monitoring of large areas which allows comparing the real amount of land used by the oil exploitation and the companies’ contractual obligations. The target feature of this monitoring is the infrastructure of the oil exploitation, oil well pads – rectangular features of bare land covering an area of approximately 50–60 m x 100 m. This article presents an automated feature extraction procedure based on the combination of a pixel-based unsupervised classification of polarimetric synthetic aperture radar data (PolSAR) and an object-based post-classification. The method is developed and tested using dual-polarimetric TerraSAR-X imagery acquired over the Doba basin in south Chad. The advantages of PolSAR are independence of the cloud coverage (vs. optical imagery) and the possibility of detailed land use classification (vs. single-pol SAR). The PolSAR classification uses the polarimetric Wishart probability density function based on the anisotropy/entropy/alpha decomposition. The object-based post-classification refinement, based on properties of the feature targets such as shape and area, increases the user’s accuracy of the methodology by an order of a magnitude. The final achieved user’s and producer’s accuracy is 59–71% in each case (area based accuracy assessment). Considering only the numbers of correctly/falsely detected oil well pads, the user’s and producer’s accuracies increase to even 74–89%. In an iterative training procedure the best suited polarimetric speckle filter and processing parameters of the developed feature extraction procedure are determined. The high transferability of the methodology is proved by an application to a second SAR acquisition

Monitoring of critical water and vegetation anomalies of sub-Saharan West-African wetlands

Surface water is a critical resource in semi-arid west-African regions that are frequently exposed to droughts. The application of time series from the Moderate Resolution Imaging Spectrometer (MODIS) to derive spatio-temporal changes of water and vegetation in and around West-African wetlands is demonstrated for the years 2000-2012. A near infrared (NIR) based gradient threshold and calculation of the Normalized Difference Vegetation Index (NDVI) is applied on the time series using the MOD09Q1 surface reflectance product. Surface water dynamics and vegetation anomalies of surrounding regions were found to coincide with the occurrence of drought seasons. This study demonstrates the successful application of remote sensing time series for wetland monitoring

Earth observation supported monitoring of oil field development in conflict-prone regions in South Sudan

A case study on monitoring oil-related developments in Melut County, located in the Upper Nile region, in South Sudan for the period from 1999 to 2011 is presented

From crisis management to humanitarian technology - a European perspective. Proceedings IEEE Global Humanitarian Technology

The European Union (EU) Member States and the European Commission (EC) are investing substantial funds in research and development (R&D) on technologies and innovative solutions for European and international disaster management, risk reduction as well as general crisis preparedness and response. The German Aerospace Center (DLR) has intensively been working in these R&D programs for many years and has developed its own research agenda in support of crisis and disaster management. In recent years, R&D activities within DLR are beginning to increasingly address also technological and operational needs of humanitarian relief actors who are providing assistance to people most in need. In this paper we report how major EC funded R&D programs and projects, including the current DRIVER project, the Copernicus Emergency Management Service (EMS) as well as DLR cooperation activities with the World Food Program (WFP), SOS Children’s Villages International, the German Agency for Technical Relief (THW), the Red Cross and others are increasingly leading to a humanitarian technology support. With these activities DLR is aiming to help bridging the operational gap between laboratory scale and humanitarian field operations

Towards Semi-Automated Satellite Mapping for Humanitarian Situational Awareness

Very high resolution satellite imagery used to be a rare commodity, with infrequent satellite pass-over times over a specific area-of-interest obviating many useful applications. Today, more and more such satellite systems are available, with visual analysis and interpretation of imagery still important to derive relevant features and changes from satellite data. In order to allow efficient, obust and routine image analysis for humanitarian purposes, emi-automated feature extraction is of increasing importance or operational emergency mapping tasks. In the frame f the European Earth Observation program COPERNICUS and elated research activities under the European Union’s eventh ramework Program, substantial scientific developments and apping services are dedicated to satellite based humanitarian apping and monitoring. In this paper, recent results in methodological esearch and development of routine services in satellite mapping for humanitarian situational awareness are eviewed nd discussed. Ethical aspects of sensitivity and security of humanitarian apping are deliberated. Furthermore methods for onitoring and analysis of refugee/internally displaced persons amps in humanitarian settings are assessed. Advantages and imitations of object-based image analysis, sample supervised egmentation and feature extraction are presented and discussed