Harald Mehl

Urbanization in India – Spatiotemporal analysis using remote sensing data

Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.

Detecting unknown coal fires: synergy of automated coal fire risk area delineation and improved thermal anomaly extraction

This paper presents two complementing algorithms for remote sensing based coal fire research and the results derived thereof. Both are applicable on Landsat, ASTER and MODIS data. The first algorithm automatically delineates coal fire risk areas from multispectral satellite data. The second automatically extracts local coal fire related thermal anomalies from thermal data. The presented methods aim at the automated, unbiased retrieval of coal fire related information. The delineation of coal fire risk areas is based on land cover extraction through a knowledge based spectral test sequence. This sequence has been proven to extract coal fire risk areas not only in time series of the investigated study areas in China, but also in transfer regions of India and Australia. The algorithm for the extraction of thermal anomalies is based on a moving window approach analysing sub‐window histograms. It allows the extraction of thermally anomalous pixels with regard to their surrounding background and therefore supports the extraction of very subtle, local thermal anomalies of different temperature. It thus shows clear advantages to anomaly extraction via simple thresholding techniques. Since the thermal algorithm also does extract thermal anomalies, which are not related to coal fires, the derived risk areas can help to eliminate false alarms. Overall, 50% of anomalies derived from night‐time data can be rejected, while even 80% of all anomalies extracted from daytime data are likely to be false alarms. However, detection rates are very good. Over 80% of existing coal fires in our first study area were extracted correctly and all fires (100%) in study area two were extracted from Landsat data. In MODIS data extraction depends on coal fire types and reaches 80% of all fires in our study area with hot coal fires of large spatial extent, while in another region with smaller and ‘colder’ coal fires only the hottest ones (below 20%) can be extracted correctly. The success of the synergetic application of the two methods has been proven through our detection of so far unknown coal fires in Landsat 7 ETM+ remote sensing data. This is the first time in coal fire research that unknown coal fires were detected in satellite remote sensing data exclusively and were validated later subsequently during in situ field checks.

Detecting coal fires using remote sensing techniques

This paper gives an overview of the theory and case studies of detecting coal fires by using remote sensing techniques. Coal fires, either man-made or spontaneous combustion, not only cause losses of natural resources, but also cause environmental problems. The surface feature and by-products of coal fires include pyro-metamorphic rocks, fumarolic minerals, burnt pits and trench, subsidence and cracks, and surface thermal anomalies. These features can be detected from visible, near infrared, short-wave infrared, radar and thermal infrared remote sensing images. The ability to detect these features is limited by the spectral, spatial and temporal resolution of the remote sensing data. The advances of new remote sensing systems will enhance the capability to detect coal fire related features.

Urban structure analysis of mega city Mexico City using multisensoral remote sensing data

Mega city Mexico City is ranked the third largest urban agglomeration to date around the globe. The large extension as well as dynamic urban transformation and sprawl processes lead to a lack of up-to-date and area-wide data and information to measure, monitor, and understand the urban situation. This paper focuses on the capabilities of multisensoral remotely sensed data to provide a broad range of products derived from one scientific field - remote sensing - to support urban managing and planning. Therefore optical data sets from the Landsat and Quickbird sensors as well as radar data from the Shuttle Radar Topography Mission (SRTM) and the TerraSAR-X sensor are utilised. Using the multi-sensoral data sets the analysis are scale-dependent. On the one hand change detection on city level utilising the derived urban footprints enables to monitor and to assess spatiotemporal urban transformation, areal dimension of urban sprawl, its direction, and the built-up density distribution over time. On the other hand, structural characteristics of an urban landscape - the alignment and types of buildings, streets and open spaces - provide insight in the very detailed physical pattern of urban morphology on higher scale. The results show high accuracies of the derived multi-scale products. The multi-scale analysis allows quantifying urban processes and thus leading to an assessment and interpretation of urban trends.

ARES: A new reflective / emissive Imaging Spectrometer for Terrestrial Applications

A new airborne imaging spectrometer introduced: the ARES (Airborne Reflective Emissive Spectrometer) to be built by Integrated Spectronics, Sydney, Australia, financed by DLR German Aerospace Center and the GFZ GeoResearch Center Potsdam, Germany, and will be available to the scientific community from 2003/2004 on. The ARES sensor will provide 160 channels in the solar reflective region (0.45-2.45 μm) and the thermal region (8-13 μm). It will consists of two separate coregistered optical systems for the reflective and thermal part of the spectrum. The spectral resolution is intended to be between 12 and 15 nm in the solar wavelength range and should reach 150nm in the thermal. ARES will be used mainly for environmental applications in terrestrial ecosystems. The thematic focus is thought to be on soil sciences, geology, agriculture and forestry. Limnologic applications should be possible but will not play a key role in the thematic applications. For all above mentioned key application scenarios the spectral response of soils, rocks, and vegetation as well as their mixtures contain the valuable information to be extracted and quantified. The radiometric requirements for the instrument have been modelled based on realistic application scenarios and account for the most demanding requirements of the three application fields: a spectral bandwidth of 15 nm in the 0.45-1.8 μm region, and 12 nm in the 2 - 2.45 μm region. The required noise equivalent radiance is 0.005, 0.003, and 0.003 mWcm-2sr-1μm-1 for the spectral regions 0.45-1 μm, 1 - 1.8 μm, and 2 - 2.45 μm, respectively.

Experience and Perspective of Providing Satellite Based Crisis Information, Emergency Mapping & Disaster Monitoring Information to Decision Makers and Relief Workers

Recognizing an increasing demand for up-to-date and precise information on disaster and crisis situations the German Remote Sensing Data Center (DFD) of DLR has set up a dedicated interface for linking the available and comprehensive remote sensing and analysis capacities with national and international civil protection, humanitarian relief actors and political decision makers. This so called “Center for Satellite Based Crisis Information” (ZKI) is engaged in the acquisition, analysis and provision of satellite based information products on natural disasters, humanitarian crisis situation, and civil security. Besides response and assessment activities, DFDZKI also focuses on the provision of geoinformation for medium term rehabilitation, reconstruction and prevention activities. DFD-ZKI operates in national, European and international contexts, closely networking with public authorities (civil security), non-governmental organizations (humanitarian relief organizations), satellite operators and other space agencies. ZKI supports the “International Charter on Space and Major Disasters”, which is a major cooperative activity among international space agencies in the context of natural and man-made disasters.

Spaceborne remote sensing for detection and impact assessment of coal fires in North China

Abstract China has tremendous coal fields in its Northern regions. Not all of them are accessible for mining yet despite the fact, that China is the worlds largest coal producer (about 1 billion tons annually). Many coal seams are reaching the surface and show self-ignited fires of considerable extension. Furthermore, fires occur in mines or in the underground. 56 areas of large fires are known to the Chinese authorities and numerous small ones exist in addition. Due to the vast dimensions of some burning coal fields, extinguishing the fires requires a huge effort. Besides the economic losses caused by burning coal (more than 20 million tons p.a.); 3 to 5 times of this amount is heavily affected by the fire and is therefore of no economic use. The environmental impact regionally and globally must not be neglected. It has been estimated by Chinese scientists that the carbon dioxide produced by these uncontrolled fires contributes with approximately 3 % to the Chinese CO2 production. Spaceborne remote sensing offers important information such as digital elevation models (DEMs) as basic data for geologic formations and routes for access to burning areas, such as hot spot detection for fire assessment, such as land use classification and deposit estimation, such as estimation of environmentally harmful gases. Multitemporal measurements (e.g. differential SAR interferometry) offer a measure for ground subsidence and estimations of burned coal volumes. Furthermore, it is an efficient means for an early warning system of new fires. In the paper, examples of burning areas will be shown and some relevant computed DEMs. Furthermore, some land use data and some infrared data will be presented. The application of these data for other involved disciplines like modelling of the geologic vicinity surrounding the burning coal seams, analysis and modelling of the fire and its 3-dimensional propagation also based on ground and underground air (oxygen) supply (chimneys) will be mentioned. Based on these models, the best method for extinguishing the fire under observation may be derived as well as methods to prevent new self ignition by oxidation processes. But this will not be covered in the paper.

Modeling of impervious surface in Germany using Landsat images and topographic vector data

The constant expansion of urban agglomerations in most countries is closely associated with a significant increase of impervious surface (IS). In Europe, robust and cost-effective methods for detection and quantification of IS on a continental or national scale are still rare. Thus, our study focuses on determining the percentage of impervious surface (PIS) for whole Germany based on a combined analysis of Landsat images and vector data on roads and railway networks, using Support Vector Machines (SVM) and GIS functionalities. We developed a procedure which provides functionalities for 1) the modeling of IS for built-up areas (PISB) based on optical earth observation data, 2) the combination of PISB with vector data providing additional information on small-scale infrastructure (PIST) and 3) the spatial aggregation of the combined product (PISBT) to the administrative units of municipalities. Compared to reference data sets of four cities, the results showed a mean absolute error of 19.4 % and a mean standard deviation of 17.3 %. The mean PIS of the total of residential, industrial and transportation-related areas in Germany comes up to 43.0 %, with a minimum in the federal state of Brandenburg (39.3 %) and a maximum in Hessen (46.1 %).

Beiträge der Satellitenfernerkundung für ein nachhaltiges und grenzüberschreitendes Wassermanagement in Zentralasien

Das Problem des schrumpfenden Aralsees ist hinlanglich bekannt, wesentliche Auswirkungen sind beschrieben und extensiver Bewasserungsfeldbau in Zentralasien wurde als Hauptursache identifiziert. Zur agrarindustriellen Produktion von Baumwolle wurde seit den fruhen 1960er Jahren entlang der beiden grosen Flusse Amudarja und Syrdarja ein extensives Kanalnetzwerk etabliert. Damit sollte der stetig steigende Wasserbedarf der kontinuierlich wachsenden landwirtschaftlichen Nutzflachen in Zentralasien gedeckt werden. Bis Ende der 1980er Jahre wurde dieses System bis an die moglichen Grenzen der Tragfahigkeit erweitert. Nicht angepasster Wasserverbrauch fuhrt zunehmend zu schweren okologischen und okonomischen Problemen in den Bewasserungsgebieten.