Satellite based monitoring of agricultural activities requires a very high temporal resolution, due to the highly dynamic processes on viewed surfaces. The solitary use of optical data is restricted by its dependency on weather conditions. Hence, the synergetic use of SAR and optical data has a very high potential for agricultural applications such as biomass monitoring or yield estimation.
Synthetic Aperture Radar data of the ERS-2 offer the chance of bi-weekly data acquisitions. Additionally, Landsat-5 Thematic Mapper (TM) and high-resolution optical data from the Quickbird satellite shall help to verify the derived information. The Advanced Synthetic Aperture Radar (ASAR) of the European environmental satellite (ENVISAT) enables several acquisitions per week, due to the availability of different incidence angles. Moreover, the ASAR sensor offers the possibility to acquire alternating polarization data, providing HH/HV and VV/VH images. This will help to fill time gaps and bring an additional information gain in further studies.
In the present study the temporal development of biomass from two winter wheat fields is modeled based on multitemporal and multisensoral satellite data. For this purpose comprehensive ground truth information (e.g. biomass, LAI, vegetation height) was recorded in weekly intervals for the vegetation period of 2005. A positive relationship between the normalized difference vegetation index (NDVI) of optical data and biomass could be shown. The backscatter of SAR data is negatively related to the biomass. Regression coefficients of models for biomass based on satellite data and the collected biomass vary between r2=0.49 for ERS-2 and r2=0.86 for Quickbird.
The study is a first step in the synergetic use of optical and SAR data for biomass modeling and yield estimation over agricultural sites in Central Europe.
Speckle - appearing in SAR Images as random noise - hampers image processing techniques like segmentation and classification. Several algorithms have been developed to suppress the speckle effect. One disadvantage, even with optimized speckle reduction algorithms, is a blurring of the image. This effect, which appears especially along the edges of structures, is leading to further problems in subsequent image interpretation. To prevent a loss of information, the knowledge of structures in the image could be an advantage. Therefore the proposed methodology combines common filtering techniques with results from a segmentation of optical images for an object-based speckle filtering. The performance of the adapted algorithm is compared to those of common speckle filters. The accuracy assessment is based on statistical criteria and visual interpretation of the images. The results show that the efficiency of the speckle filter algorithm can be increased while a loss of information can be reduced using the boundary during the filtering process.
Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.
Information about imperviousness surface distributions is essential for several environmental applications and the planning and management of sustainable development of urban areas. Satellite remote sensing based mapping of imperviousness has shown important potentials to acquire such information in great spatial detail but the actual mapping process has been challenged by the heterogeneity of urban environment and limited spatial and spectral sensor capabilities. This study explores and compares two methods based on the vegetation fraction from linear spectral unmixing and the NDVI to map the degree of imperviousness in the urban agglomeration of Cologne/Bonn in Western Germany. The study employed data from the ASTER satellite sensor with improved spatial and spectral resolution. Fieldwork was carried out in the area of Bonn to obtain a comprehensive set of reference data with estimated degrees of imperviousness for different types of urban areas. Rural areas were excluded using data from the governmental land information system (ATKIS). The applied simple linear spectral unmixing approach revealed less suitable results for the built area fraction due to the heterogeneity of the spectral response from urban targets. The vegetation fraction and the NDVI provided sufficient results in estimating the impervious surface fraction that were used to derive related maps for the study areas.
Increasing population growth and growing ecological problems in urban areas require advanced remote sensing technology for the acquisition of detailed and accurate land-use information for urban management and planning issues. Surface consumption of 120 ha per day (2003) for traffic and settlement areas in Germany is far away from the 30 ha per day of the sustainability-strategy intended for the year 2020 by the Federal Environmental Ministry. With regard to the 50ies, imperviousness and sealing almost doubled. The presented study is embedded in a project in North Rhine-Westphalia (NRW), the most densely populated federal state in Germany. During the last decades, industrial transformation processes as well as strong economic and socio-structural changes have taken place, making NRW most suitable as an exemplary region to study and visualize dynamic developments in Europe. The examined time period of this work includes intense urban development and expansion in the suburban regions. LANDSAT data of three time slices (1975, 1984 & 2001) build the backbone to detect the changes taken place. Applying a multisensoral approach with improved spatial and even spectral resolution the focus is on the urban development of certain “hot spots” in NRW. CORONA, IKONOS as well as ASTER satellite data is used to allow a further characterization of urban land-use types and changes in more detail over the last four decades. Classical change detection methods as PCA are combined with classification of segmented urban land-use areas when evaluating the type of change.
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