The advent of very high resolution (e.g., 0.6 - 1.0 m) satellite programs and, in addition, digital airborne cameras with ultra high resolution (e.g., 5 - 20 cm) offers new possibilities for very accurate mapping of the environment. With these sensors of improved spatial resolution, however, the user community faces a new problem in the analysis of this type of image data. Standard classification techniques have to be augmented for an appropriate analysis because the necessary homogeneity of landuse/landcover classes can no longer be achieved by the integration effect of large pixel sizes (e.g., 20 - 80 m). New intelligent techniques will have to be developed that make use of multisensor approaches, GIS integration and context based interpretation schemes. This paper will present an overview on recent developments in remote sensing and the challenges presented to the user community.

Understanding the dynamics of land cover change has increasingly been recognized as one of the key research imperatives in global environmental change research. Scientists have developed and applied various methods in order to find and propose solutions for many environmental world problems. From 1986-1995 changes in Kenya coastal zone landcover, derived from the post-classification TM images, were significant with arid areas growing from 3% to 10%, woody areas decreased from 4% to 2%, herbaceous areas decreased from 25% to 20%, developed land increased from 2% to 3%. In order to generate the change probability map as a continuous surface using geostatistical method-ArcGIS, we used as an input the Generalized Linear Model (GLM) probability result. The results reveal the efficiency of the Probability-of-Change map (POC), especially if reference data are lacking, in indicating the possibility of having a change and its type in a determined area, taking advantage of the layer transparency of the GIS systems. Thus, the derived information supplies a good tool for the interpretation of the magnitude of the land cover changes and guides the final user directly to the areas of changes to understand and derive the possible interactions of human or natural processes.

In this work, we proposed a method to detect roads in aerial imagery. In our approach, we applied the wavelet transform in a multiresolution sense by forming the products of wavelet coefficients at the different scales to locate and identify roads. After detecting possible road pixels, we used a graph searching algorithm to identify roads. We found that our approach leads to an effective method to form the basis of a road extraction approach.

This paper focuses on the issues of categorical database generalization and emphasizes the roles of supporting data model, integrated data model, spatial analysis and semantic analysis in database generalization. The framework contents of categorical database generalization transformation are defined. The paper presents an integrated spatial supporting data structure, a semantic supporting model and similarity model for categorical database generalization. The concept of transformation unit is proposed in generalization. The paper concludes with an application of landuse database generalization.

The 3D model of interchanges is one of the fundamental components of the city models and has got researcher’s extensive concern in recent years. However, solution to automatic extraction of 3D man-made complicated objects is still unavailable up to now because automatic interpretation of spatial image lacks required performance for practical applications. In this paper, an integrated method involving stereo image pair, CAD, DPW and VR technology for 3D reconstruction of the interchange is put forward and various solutions are presented to meet the demands of the Cyber City according to application requirements. Besides, the semantics of interchange as a whole is used to control and to evaluate the quality of interchange model extraction in all the reconstruction process. Finally, a software platform for 3D reconstruction of the interchange using OpenGL and VC++ is built up and the efficiency of suggested method is examined through practical case studies.

The emphasis of this paper is on an improvement of the classification procedure for change detection purposes using remotely sensed imagery as well as various data sources combined in a GIS. Our contributions will be on an advanced knowledge-based methodology for the training phase within the change detection process, as well as on the evaluation of the applied software package (Erdas Imagine Expert Classifier) and of the used image data source, namely Landsat TM 7 data. Firstly, we will demonstrate an improved and flexible methodology for defining and describing training areas in the course of a change detection process using knowledge-based image analysis techniques. Using the GIS-database which comprises several data sources at point of time t0 the outlines of the desired object classes will be determined and rated according to their accuracy respectively reliability. Combining this information with the image data of the first phase (t1), we are entering the first training stage. Here, not only a single standard object signature like a multispectral reflectance but a large amount of additional parameters are checked. For each parameter the inference mechanism automatically checks the separability for different object classes and evaluates the suitability of each signature for further use within the first classification stage. The reliability of an object is derived from all information stored in the initial database. As an output of the classification stage -- again applying knowledge-based rules -- we obtain probability vectors which decide in favor of a confirmation, a modification or an elimination of the given outlines for the specific class. The new outlines together with the imagery of the next acquisition phase and up-dated ancillary data are put into the next training phase. Due to possibly changed image properties it is meaningful to test all signatures for the given outlines again, and proceed as described above. The implementation of this knowledge-based approach is performed by means of the "Expert Classifier" from Erdas Imagine (version 8.6). In conclusion it can be stated that the proposed knowledge-based method and its implementation has been proved to be a very valuable and reliable method for environmental change detection purposes.

Remote sensing technology provides a cost-effective tool for monitoring changes in land-cover. The effective use of satellite remote sensing data and a suitable blend with socio-economic data helps in achieving a local specific prescription to achieve sustainable development of a region. This paper presents the results obtained from using remote sensing and GIS techniques to map land-cover changes in Skiathos Island for a period of 13 years. A set of three multidate Landsat TM images were used for the detection and iventory of disturbance and other changes that occur in land use, cover type, and cover condition in areas of research interest. The burnt areas during the 13-years period were well defined showing the changes in the landscape. It is shown that the use of satellite remote sensing can be used not only to improve the understanding of the significant land-cover changes that have been occurred over the past 13 years but also to enable better management decisions to be made.

The detection of accidentally or illegal marine oil discharges in the German territorial waters of the North Sea and Baltic Sea is of great importance for combating of oil spills and protection of the marine ecosystem. Therefore the German Federal Ministry of Transport set up an airborne surveillance system consisting of two Dornier DO 228-212 aircrafts equipped with a Side-Looking Airborne Radar (SLAR), a IR/UV sensor, a Microwave Radiometer (MWR) for quantification and a Laser-Flurosensor (LFS) for classification purposes of the oil spills. The flight parameters and the remote sensing data are stored in a database during the flight. A Pollution Observation Log is completed by the operator consisting of information about the detected oil spill (e.g. position, length, width) and several other information about the flight (e.g. name of navigator, name of observer). The objective was to develop an oil spill information system which integrates the described data, metadata and includes visualization and spatial analysis capabilities. The metadata are essential for further statistical analysis in spatial and temporal domains of oil spill occurrences and of the surveillance itself. It should facilitate the communication and distribution of metadata between the administrative bodies and partners of the German oil spill surveillance system. A connection between a GIS and the database allows to use the powerful visualization and spatial analysis functionality of the GIS in conjunction with the oil spill database.

Take the Dawenhe River for an example, based on the dynamic analysis of the suspended mudsand flow where the river pushes into the Dongpinghu Lake, according to different sensors and multi-temporal remote sensing images, this paper discusses its impacts on the lake deposition near the Dawenhe River estuary, and points out the cause of forming and development of the delta. On the foundation of the data from experiments at lab and the field test outdoors, this paper analyses the relation between the content of mudsand and max wavelength by the high spectrum, and together with the relativity between the consistency of the mudsand and the satellitic remote sensing image, more precisely quantitative formula is achieved.

The Romanian North Western coastal and shelf zones of the Black Sea and Danube delta are a mosaic of complex, interacting ecosystems, rich natural resources and socio-economic activity. Dramatic changes in the Black Sea's ecosystem and resources are due to natural and anthropogenic causes (increase in the nutrient and pollutant load of rivers input, industrial and municipal wastewater pollution along the coast, and dumping on the open sea). A scientific management system for protection, conservation and restoration must be based on reliable information on bio-geophysical and geomorphologic processes, coastal erosion, sedimentation dynamics, mapping of macrophyte fields, water quality, climatic change effects. A multitemporal data set consisting of LANDSAT MSS, TM and SAR ERS-1 images was used for comparing and mapping landcover change via change detection. Synergetic use of quasi-simultaneously acquired multi-sensor data may therefore allow for a better approach of change detection of coastal area. The main aim of this paper is to conduct a comprehensive analysis based on existing historical and more recent in situ and remote sensing data to establish the link between phytoplankton bloom development, increasing erosion and diminishing of beaches and related coastal zone harmful phenomena.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) launched on NASA’s Terra satellite ranks lately among potential tools for Earth Observation. ASTER measures reflected radiations in 3 bands between 0.52 to 0.86 μm (VNIR) and in 6 bands between 1.6 to 2.43 μm (SWIR). An ASTER image has been acquired on the Southern margin of the Damara orogen (Namibia, northern margin of the Nama marine sedimentary sequence) essentially composed by limestone, sandstone and shale. Furthermore, a program initiated by the Geological Survey of Namibia has permitted to obtain a radiometric data set (Thorium, Uranium and Potassium) of Rehoboth region in Namibia. The combined ASTER and airborne geophysical data have been processed and interpreted for improving the existing geological map. First, a Principal Component Analysis (PCA) on the 9 ASTER bands is realized and only the first five components are kept. Then, the Maximum Likelihood approach is used to identify lithological formations from radiometric data and PCA components. Importance of random training data has been pointed out. Finally the coupling of ASTER and radiometric data is the best approach for the improvement of geological map with a proportion of correctly mapped surface of 63.4% from initial geological map.

Larson in 1991 put forward a hypothesis on the extremely high development of diamond-bearing kimberlites in the Mid-Cretaceous period. In Israel, the Cretaceous magmatic activity is well known in the central Negev. The first microdiamond in Israel was found in northern Negev and the authors associated its origin with an extraterrestrial event. An integrated analysis of several geological and geophysical factors enables us to select for detailed investigations the area of Makhtesh Ramon canyon situated near the town of Mizpe-Ramon (nothern Negev). Data of aero- and land magnetic surveys as well as self-potential method were analyzed using modern interpreting methods. Application of geochemical/geophysical ion-selective analysis testifies to presence of kimberlite-like bodies located at a small depth. Performed mineralogical analyses of subsurface geological associations allowed to identifying a variety of minerals of diamondiferous association: chrome-diopside, orange garnet, bright-crimson pyrope, picroilmenite, black spinel, olivine, anatase, tourmaline, aggregates of perovskite, yttrium phosphate, moissanite and corundum. The recent geochemical analyses signify to discovering of quasi-kimberlite rock -- meimechite, traces of REE and some platinum group elements also testify presence of diamondiferous associations. Finally, five diamonds (with a size exceeding 1 mm) and more than 400 microdiamonds (< 1 mm) have been discovered in this area. Thus, we can unambiguously concluding that the Makhtesh Ramon area contains typical products of kimberlite pipe destruction.

We propose a three stage algorithm to build all 3D-horizons simultaneously in volume seismic data. To improve reliability, this algorithm takes into consideration the relative positions of all horizons, and uses globally self consistent connectivity criteria which respect the temporal order of horizon creation. The first stage consists of the preliminary estimation of the local direction of each horizon at each point of the 3D-space. The second is smoothing the signal along the detected layer structure to reduce the level of noise in the signal. The basic processing is realized at the last stage of the algorithm. The processed 3D-seismic data are used for the simultaneous building of all 3D horizons. The output of the processing is a set of 3D-horizons represented by a series of triangulated surfaces.

Alfios is the biggest river of the Peloponnese and the ninth biggest river in Greece. It drains an area of almost 2575 km² in Western Peloponnese and discharges at the Kiparissiakos Gulf. Due to its extent Alfios basin presents complex physiography and geomorphology. In order to detect the changes of the Alfios river channel we used the following multitemporal and multisensor satellite images: A Landsat 5 TM subscene, acquired on July 27 1984, A Landsat 5 TM subscene, acquired on September 18 1986, Two KFA-1000 images of September 1986, A Landsat 7 ETM cloud free subscene, acquired on July 28 1999. As the images have been acquired from different sensors and at different dates we used absolute atmospheric correction algorithms in order to reduce the phenomena of atmospheric attenuation. We also used fusion techniques in order to fuse multispectral ETM data with panchromatic data as well as TM multispectral data of 1984 & 1986 with high-resolution data of the Russian camera KFA-1000. The PCA method and GIS techniques have been applied, at the resulting atmospheric corrected multispectral images with 15m resolution, in order to map the changes of the Alfios river channel during the 1984-1999 period with very good results. During the period 1984-1999 the seventh order Channel of Alfios River has been subject to big changes. The main cause for these changes is the human activity in the area.

This study investigates the usefulness of satellite images for the study of moutain lake ecosystems and develops an effective approach to extract landscape features of interest for the determination of the lake characteristics. A methodology is proposed taking advantage of the unique spectral features of the land cover classes defined for this study along to their statistical information to determine different band pairs which contain key information for every class. The land cover classes are extracted separately from the selected pair of bands and the class images obtained are later added together in a final classified image. This methodology is applied to ten european sites representing glacial lake districts. The GIS ArcInfo is used to integrate the information obtained from the satellite images with the lake catchments thus the final classified images are merged with the lake catchment boundary vectors of the study areas therefore every pixel is assigned to the corresponding lake catchment. Moreover different patterns were observed in the spatial distribution of the land cover classes. These patterns were analyzed and classified as different lithological classes and every lake catchment was assigned to the corresponding lithological class. At the end every studied pixel has two attributes: land cover and lithology which together give a more detailed perception of the terrain.

The area under study is located in the southern part of the Central Iranian Volcanic Sedimentary belt and covers an area of about 488 sq. kms. Sar Cheshmeh and Darrehzar areas with known mineralization and alteration are chosen as control areas. Airborne geophysical data -- radiometry and megnetometry -- and ETM+ data has been integrated and analyzed using fuzzy classification. This type of classification is suggested for remote sensing data, but it can also be used for classification of combined airborne geophysical and satellite data. After defining the training areas (Sar Cheshmeh and Darrehzar areas), the entire region is classified into altered and unaltered aras. This analysis is found useful for exploration of porphyry type deposits in the Central Iranian Volcanic Belt, where most parts of this belt is surveyed by airborne geophysics.

With the growing development of the computer, Remote Sensing (RS) and Geographical Information System (GIS), multi-data fusion is becoming more and more important in data processing. In this paper, we are trying to fuse RS images of different time, different resolutions with geophysical data such as aero magnetic data and gravitational data and geochemical data of Au, Ag, Cu, As, Pb and Zn elements. By processing RS images, we get the surface lithological and structural information relating to the gold forming. By processing the geophysical data and geochemical data, we get the information about the gold distribution as well as the environment and geological factors controlling the gold under the ground. By the fusion of all these data, we get both the gold information hiding in the surface and under the ground. After all these work, we use GIS to manage, analyze and display all these results, whether they are raster data, vector data or attribute data. On the basis of all these, we finally define the hopeful areas of the gold that serve as valuable bases for gold exploration.

This paper explores the development of an idea conceived a number of years ago for the integration of three diverse technologies into a system capable of supporting a wide variety of applications. The concept of Virtual Geographic Information System (VGIS) was developed in the early 1990's even though no technology base existed that would support real time implementation of the idea. The VGIS concept grew out of the integration of Geographic Information Systems (GIS), Remote Sensing (RS), and Visualization (Viz) into a comprehensive tool that is now used at Georgia Tech in terrain analysis, environmental analysis, weather radar visualization, weather model understanding, situational visualization for emergency response, etc. The implementation of the concept culminated in the development of a system called the Georgia Tech Virtual GIS System (GTVGIS). This paper will discuss the evolution of the system, its applications at Georgia Tech, and the new directions and commercial utilization of similar concepts.

The perceptual problems of viewing topography on geo-images are caused by illumination from the southeast during data collecion. This problem affects the majority of satellite images. The aim f this work was to obtain a stereoscopic effect of shaded relief in such images. Techniques available in commercial digital processing programs were used in the absence of a digital elevation model. The images used were taken by the Landsat TM and SPOT P satellites; the software used was the EASI-PACE and ACE programs (Canadian PCI Geomatics Group). The pseudoscopic effect was solved by using the first principal component obtained in principle components analysis of the three channels resulting from the weighted merging of the Landsat and SPOT data. The map obtained provides the observer a view with shaded relief.

In sonar imaging for seafloor remote sensing, research activities are more and more oriented on the use of data fusion approaches. Nowadays, it is well established that using sonar images, the Digital Elevation Maps (DEMs), can be generated by exploiting either the amplitude information or the phase information of the acoustic signal. In this paper, the main interest consists on the generation of a complete Digital Elevation Map (DEM) by the use of a data fusion approach of two existing DEMs issued from two different techniques. The aim of the proposed approach is to elaborate a general interpretation system that coherently links works on data selection and fusion leading to improve DEMs generation and to exploit it in the seafloor remote sensing applications (particularly for the inhomogeneous scenes with a variety terrain). In this paper, shape from shading and the interferometry techniques are considered. Then, the manner of the DEMs fusion proposed, has been based on fuzzy logic and some fuzzy propositions, which defined using experts a priori knowledge source. This promising idea enables information to be managed through the consideration of the imprecision and ambiguity information and the benefit provide by the injection of the a priori knowledge in the decision taken system.

This paper proposes a new approach to image matching by epipolar constraint and local reliability constraint for Remote Sensing Image. We define a new measure of matching support according to the local reliability constraint. A new search strategy -- Self-Diagnosis is developed for robust image matching. This strategy only selects those matches having both high matching support and low matching ambiguity. The proposed algorithm has been tested and works remarkably well in remote sensing imagery stereo pairs.

The use of High Resolution Satellite Imagery (HRSI) has opened up opportunities for exploiting their high spatial resolution in mapping. Many remote sensing satellite sensors exist that can be effectively used for map production, including SPOT, IRS-1C/D, Ikonos, and recently QuickBird. Successful exploitation of the high accuracy potential of these systems depends on the accuracy of the mathematical models used for sensor geometry. However, in the absence of sensor calibration and satellite orbit information for most HRSI, practical approaches have to be adopted. A number of different sensor models are available in most software packages, among them polynomial models are especially popular due to their simplicity and reasonable accuracy. This paper presents a comparative analysis and evaluation of the use of different polynomial models, as opposed to satellite rigorous models, with different HRSI. Experiments were performed using different real data sets of IRS-1D, Ikonos, and QuickBird. Our analyses suggest that approximate mathematical models can be effectively used for rectification and 3D determination process after taking into consideration different factors that may affect the results such as the satellite inclination angles, differences in terrain elevations and sensor geometry.

The High Resolution Stereo Camera (HRSC) is a multiple line "pushbroom" instrument developed by the German Aerospace Center (DLR). Nine super-imposed image tracks are acquired simultaneously by nine parallel CCD-linesensors behind one single optic. Five are panchromatic sensors arranged at specific viewing angles. They provide the multiple-stereo and photometric capabilities of the instrument. Four of the nine CCD-lines are covered with color-filters for the acquisition of multispectral images. The resolution is 10 cm from a flight altitude of 2500 m. The airborne HRSC-A cameras have acquired data for a large number of targets since 1997. Most targets are covered in overlapping flightlines with alternating heading for commercial and photogrammetric considerations. The multi-angular character and directional effects can be observed and differentiated in many flight campaigns: (1) View angle dependent effects in the 5 panchromatic sensors within an individual flightline. (2) Effects due to changes in flight direction (heading alternating 180°, usually one flightline flown perpendicular). (3) Effects due to illumination conditions and changes during the acquisition of the image data (sun elevation, azimuth). (4) Examples and a summary of observed effects are presented. Some potentials and problems of these effects for systematic image processing (e.g. radiometric correction and mosaicking) and interpretation are discussed.

In 1988, the detail information on land resource was investigated in China. Fourteen years later, it has changed a lot. It is necessary that the second land resource detailed investigation should be implemented. On this condition, the New National Land and Resources Investigation Project in China, which will last 12 years, has been started since 1999. The project is directly under the administration of the Ministry of Land and Resource (MLR). It was organized and implemented By China Geological, China Land Surveying and Planning Institute (CLSPI) and Information Center of MLR. It is a grand and cross century project supported by the Central Finance, based on State and public interests and strategic characteristics. Up to now, "Land Use Dynamic Monitoring By Remote Sensing," "Arable Land Resource Investigation," "Rural Collective Land Property Right Investgiation," "Establishment of Public Consulting Standardization of Cadastral Information," "Land Resource Fundamental Maps and Data Updating," "Urban Land Price Investigation and Intensive Utilization Potential Capacity Evaluation," "Farmland Classification, Gradation, and Evaluation," "Land Use Database Construction at City or County Level" 8 subprojects have had the preliminary achievements. In this project, SPOT-1/2/4 and Landsat-7 TM data were always applied to monitor land use dynamic change as the main data resource. Certainly, IRS, CBERS-2, and IKONOS data also were tested in small areas. In 2002, the SPOT-5 data, whose spatial resolution of the panchromatic image is 2.5 meters and the spectral one is 10 meters, were applied into update the land use base map at the 1:10000 scale in 26 Chinese cities. The purpose in this paper is to communicate the experience of SPOT-5 image processing with the colleagues.

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.

Very High Resolution (VHR) satellite imagery offers a great potential for extracting land-use and land-cover related information for urban areas, but do they meet the requirements of present day urban planners? Assessing user needs for urban land use/land cover data, and investigating the potential of VHR data to better meet these needs is therefore essential. These two parts lead to an interactive definition of remote sensing products in Belgium. This paper presents the background of our analysis (previous surveys at European and French level), the methods that we use to assess the urban users needs (questionnaire and survey), how these can be met by VHR data (classification results) and some preliminary results of the Belgian survey obtained for both the Walloon and Brussels region. Among these results, the survey reports the preference on ortho-rectified aerial photographs when this product is available, a scarce use of remote sensing data explained by spatial resolution and cost reasons, and the lack of awareness of the new VHR images capabilities. As results for the ongoing survey become complete, we hope to better understand what data products derived from VHR imagery can potentially be of interest to users of LU/LC data in Belgium. This will enable us to propose image processing methods that better fulfil the needs of local and regional authorities in Belgium.

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.

Data of Landsat-7 Enhanced Thematic Mapper (ETM+) and Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have been examined to reveal differences and similarities concerning urban change analysis. A mosaic from two ASTER scenes and a Landsat ETM+ scene were compared on the basis of invariant targets. A sensitivity analysis was conducted to determine the influence of geometric and spectral differences between these two sensors. It could be shown that - despite comparable sensor concepts - dissimilarities in geometric and spectral properties lead to measurable variations in statistical and thematic indicators. However, it is believed that notwithstanding these differences, a multi-temporal change analysis based on Landsat-TM, ETM+ and Terra-ASTER is a feasible approach to monitor urban change, if divergences are correctly understood and the related uncertainties can be quantified.

The restoration and maintenance of architecturally complex monuments need advanced tools for helping the definition of the working plan and for storing analysing and updating all the data produced. In the case of the Gothic Milan Cathedral a three-dimensional metric support has been developed. It comprises several oriented and connected stereoscopic models which makes it possible, through the stereoscopic vision, to navigate through several photograms, to accurately measure the dimension of architectural details, to draw structures with a millimeter precision. In this way a 3D-CAD model of the facade and of the internal walls of the Milan Cathedral have been created. On those vectorial models, it is possible to insert photos, documents, characterisation data and even to draw thematic maps. For instance, the load bearing structures maps have been realised after a GPR (Ground Penetrating Radar) structural survey. These maps provide structural information (e.g. fractures, block thickness and status, lessons, etc.) extremely useful for planning the restoration and maintenance work. The photogrammetric survey has been proceeded by a 3D laser scanning survey, necessary for providing a preliminary model for planning the work until the complete elaboration of the stereoscopic model. All the data have been updated in the georeferenced and integrated 3D data base of the Cathedral, which now constitutes the necessary support for defining the specific operations.

The motivation for data fusion is to reduce the limitations and uncertainties associated with single sensor data. In the context of remotely sensed data this is often performed by combining images of high spatial resolution with those of high spectral resolution at different levels. In contrast to pixel-based approaches like Intensity-Hue-Saturation (IHS) or Principal Component (PC) we will focus on image fusion at feature level. The research of this paper was conducted within within the "HyScan" project which goal is to develop a GIS based analysis and mapping of surface characteristics in urban areas using hyperspectral images in combination with remote sensing data of very high spatial resolution. In most cases the classification of hyperspectral data is performed using methods like Spectral Angle Mapper (SAM) or Mixture Tuned Matched Filtering (MTMF). Reference spectra for those algorithms are stored in libraries which contain the spectra of pure materials so called endmembers. The problem is that endmembers that represent urban surface types often display a mixture of spectral pure materials and thus show flat spectra. As a result, those thematic endmembers can hardly be detected by standard algorithms like the Pixel Purity Index (PPI). As a consequence standard classification procedures fail. In order to improve the quality of results, we fuse hyperspectral data recorded by the DAIS sensor with high spatial resolution imagery (e.g. HRSC) for a combined endmember selection, classification, and structural analysis. After segmentation of the high spatial resolution data, appropriate thematic classes are manually defined. The resulting segments are used to detect a set of pure pixels in the hyperspectral data which represent thematic endmembers. The segments resulting from the spatial high resolution data are processed using the endmember abundances of the hyperspectral data through a combined classification. Method and initial results of our fusion method are presented for endmember selection and classification of urban surface characteristics.

Linear unmixing decomposes an hyperspectral image into a collection of reflectance spectra, called endmember signatures, and a set corresponding abundance fractions from the respective spatial coverage. This paper introduces vertex component analysis, an unsupervised algorithm to unmix linear mixtures of hyperpsectral data. VCA exploits the fact that endmembers occupy vertices of a simplex, and assumes the presence of pure pixels in data. VCA performance is illustrated using simulated and real data. VCA competes with state-of-the-art methods with much lower computational complexity.

Emerged areas around open-system lakes developing marshes are sensitive environments to climate changes. Under a semi-arid climate the sediments oxidize and dehydrate developing red colors due to iron bearing minerals. Mineral climate-dependent mixtures are spatially traced using hyperspectral imagery. Iron oxide mixtures have been mapped along differentially dehydrated units in the past 2000 years using DAIS spectrometer data. Spectral behavior interactions and masking from iron and carbonate mixtures suffering desiccation on the sands are described on the imagery and laboratory spectra. Four morphological sandy units can be distinguished, located at different height from the lake coast-line. These units are related to terraces, eolian deposits and desiccated areas, and appear as both continuous and remnant sparse encased surfaces showing different stages of landscape development. Mineralogical variations on iron oxides and hydroxides developed when sediments are exposed to the atmosphere are easily recorded in the visible and thermal infrared wavelength range in the imagery. Quantitative evaluation of soil color and related mineralogy is attempted.

Vegetation mapping by use of satellite data are often divided into two main operations, the pre- and post-classification processes. Experience from producing vegetation maps based on spectral-only classifications, has shown that misclassifications occurs. The aim of the post-classification process is to improve the pre-classified product by use of ancillary data. The mountain areas of Norway are characterized by complex topography. Vegetation maps are though difficult to produce for these areas. In this study two Landsat 5/TM image from 1986 and 1998, covering parts of the Dovre mountain massif in Norway, are classified using unsupervised classification methods. The spectrally classified product is thereafter corrected using several ancillary data layers. Based on the ancillary data the delineation of forest vegetation and the heather vegetation above the woodland limit is more precisely defined. Bogs and mires are easily differentiated from snow-bed communities. The grass- and herb-rich communities in the mountain areas are spectrally much similar to agricultural areas in the lowland; even the floristical composition and content are totally different. By use of digital elevation models the alpine meadows and cultivated land in the lowland are separated into different classes by the use of an altitude threshold. The cost of, and types of corrections we can do in the post-classification process, largely depends on what additional information is available and the quality of this information.

Desertification is reported to be intensifying and spreading in Kenya dry lands, threatening millions of inhabitants and severely reducing productivity of the land. Concern over desertification acceleration status in the country has been raised and measures to address the problem called upon. Among these measures is assessment of desertification using available data and technological tools. Vegetation cover was used as a land degradation indicator to determine land degradation and rate of change using spectral change detection technique based on pixel-wise operation. In combination with ancillary data, vegetation degradation occurrence and areas at risk of desertification were assessed. The study area is located in Northwestern Kenya, one of the dry land areas. Multi-spectral and multi-temporal analysis was applied to NOAA/AVHRR 1km and Landsat TM/ETM 30 meter resolution for periods covering wet and dry season of 1986 to 2001. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were used to detect change. The results show desertification is apparent and increased vegetation degradation. Arid areas were found to be increasingly degraded and at high risk of further degradation at a rate of 1.8% per year.

The primary purpose of this study was to estimate the boundary between vegetated and non-vegetated areas and to assess the condition of desertification in central Asia and western China located in arid and semiarid regions. Remote sensing data used in this study are a time-series of 10-day maximum Normalized Difference Vegetation Index (NDVI) composites derived from Global Area Coverage of Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2000. Taking place and development of desertification in the arid and semiarid regions directly influence the density and growth status of vegetation, making surface vegetation a most important indicator to desertification assessment. Vegetation is very sparse in desert and therefore onset of green-up in the desert was undetectable with AVHRR NDVI data. The occurrence of onset of green-up, as determined with time series NDVI data was used to identify desert and non-desert areas. The coefficient of variation (CoV) of the monthly NDVI (maximum-value composite) is used as a parameter to characterize the changes of vegetation in this work. The CoV can be used to compare the amount of variation in different sets of samples data. Changes in the value of the pixel-level CoV over time can be interpreted as a measure of vegetative biomass change over that time. The method to detect and quantify changes in CoV values for each pixel over 20-year period for which data was available is based on linear regression. If the CoV values exhibit a statistically significant decrease over time, it is possible to conclude that the area imaged in that pixel is under desertification.

Mining activities are very often influencing the surface. Ongoing hard coal mining in Germany results in high subsidence rates within an area of relatively vast extent. Beside these well understood and predicted consequences, relicts of former mining are in Germany present, too. These relicts are causing risk for people and infrastructure. The main risk is induced by sudden falls of the surface due to the collapse of still existing cavities in the ground, justifying the development of a monitoring concept for historical mining areas. This contribution shows a general approach to realize a monitoring system by the integrated use of Differential Interferometric Synthetic Aperture Radar (DInSAR) and additional abandoned mining related data using a Geographic Information System (GIS). First, qualitative DInSAR results showing surface deformations in a specific area of investigation are presented. Second, these results are classified into potentially abandoned mining induced surface deformations and others. This classification is done by using mining related background information and standard GIS functionality.

Many scientists believe that the earthquake cannot be predicted. But, the fact that there were positive infrared (IR) anomalies in satellite remote sensing images 15 - 60 days before some moderate-strong tectonic earthquakes in Asia is changing the situation. To reveal the mechanism and to seek for the spatio-temporal laws of IR anomaly evaluation before tectonic earthquakes, a series of fundamental experiments detecting the surface IR radiation images on loaded rock samples, which were designed to model the incubation of tectonic earthquakes, were conducted. This article introduced the spatio-temporal evaluation laws of IR anomaly from rock surface for the condition of compressively sheared rock, bi-axial loaded en echelon faults, compressively-sheared sliding faults and compressively loaded intersected faults, respectively simulating the four gestation mechanisms of tectonic earthquake. The experiments concluded that the tectonic earthquake is predictable, and the satellite IR remote sensing is a prospecting technique for earthquake prediction, especially in the condition that remote sensing is assisted with analyzing to rock properties of regional crust and locations of active faults.

To emphasize geomorphological features for detection of faulting zones responsible of seismic events generation, this paper present the results of LANDSAT TM and SAR-ERS satellite data application for Vrancea (Romania) seismic area by data fusion technique. Remote sensing and field studies of active faults provide a geologic history that better than instrumental and historic records. Vrancea area, the most risky seismic in Europe, is bounded by latitudes 45.6°N and 46.0°N and longitudes 26.5°E and 27.5°E being crossed by principal and secondary faults. All data information available on the study area have been integrated in a unique database of geologic maps, thematic maps, land use maps. For lineament maps generation both for Landsat TM and SAR ERS-1 images it was applied filtering technique and histogram equalization methods for more enhancement and better interpretability of discrete linear features. Satellite data are very suited for recognizing the continuity and regional relationships of faults. Landsat TM images show shorter and denser distributed linear features, while ERS1-SAR images are dominated by the principal structures which in certain cases they complete lineament patterns from Landsat data. The combination of filtering techniques and image fusion is essential for neotectonic applications and lineament analysis.

In offered paper experience of application of multispectral space images ASTER (Advanced Spaceborne Thermal Emission and Reflector Radiometer) for revealing landscape and geological features of Uronai ore unit (East Transbaikalia) is considered. This territory is perspective on tungsten - molybdenum mineralization. For the decision of geological and hydrogeological problems day time and night ASTER images have been used. By results of research are created geoinformation models of hydrogeochemical conditions and ore controlling factors are created.

Because of some spectral similarities between clay minerals and carbonates most of the processing methods on currently used satellite data are failed to discriminate these two mineral groups. Various principal component analysis (PCA) techniques were applied for an area located in the northern Abadeh, SW Iran on reflective multispectral bands of ETM⁺. PC images owing the highest loadings with opposite signs for bands 3, 5, and 7 in 3-bands method were used for generating a color composite image. The color composite image of "R" for PC2, "B" for PC3, and "G" for PC2+PC3 in the case of three-bands enhanced the clay minerals in white color without ambiguity with carbonates. The same results were obtained while applying this method in Mashayekh-Nowdan area, Fars province, SW Iran.

This paper focuses the models and function of in the sign of urban land grading information system. After analyzing current system the paper discusses the structure of the system, functions of the system, data organization and data processing models etc. the system template, the relevant data structure and the empirical formulas of the affecting factor are proposed. The calculation methods of the service radius, general score and land grade automatically are presented. The urban land grading information system is designed. The system is implemented by Visual C++. The system illustrated by giving an example of the commercial land grading of the urban area of Wuhan City.

This article discusses the importance of forewarning system of dynamic balance between cultivated land supply and demand at multi-measures, and the application of remote sensing technique to it. It presents the structure of forewarning system of dynamic balance between cultivated land supply and demand based on the land use change monitoring and predicting, balance state analysis, finding the imbalance, potential analysis for regulating the balance and warning eliminating. And it simply illuminates its implementation as software system.

Laser interferometry is one of the most sensitive and widely used techniques for small displacement measurement, whose success is mainly due its very good performances and the large availability, high quality and relatively low cost of optical components and laser sources. This technique is flexible enough to be effectively used in very different applications, like the one we present in this paper, that is a laser interferometric system prototype used to read the position of a free mirror to measure the local seismic noise. After a description of the system, its performances were compared to the ones of a standard accelerometer. The results are encouraging although some problems, mainly connected with the sensitivity and the stability of the interferometric system need a better understanding.

Laser interferometry is one of the most sensitive methods to measure small displacement. This technique was successfully used in several fields of physics, giving very good performances also due to the large availability of optical components and laser source of high quality and relatively low cost. At the same time this technique is enough flexible to be effectively used in very different applications. In particular, in this work, we present a laser interferometric system used to read the position of the sensible element of a standard seismic accelerometer. Seismic accelerometers working mechanism is based on a control system that acts on the sensible element to stabilize its position; by looking at the force needed to perform this feedback it is possible to deduce the acceleration. Usually the feedback system of these instruments is based on magnet and coil, or on piezoelectric actuators, while the signal transducer are usually capacitive device. The performances of the interferometric system were analyzed in comparison with standard one. The result are encouraging also if some problem, mainly connected with the dynamic of the read-out system should be better studied.

A seismic section is intermediate step between the material response of the Earth surface to seismic activity. As derived from materials properties (density, elastic modules and attenuation) the seismic response is numerical in nature. Data sets are transformed into plots or are exhibited as images. Geological interpreting of cross section is based on regions of lithological affinity. An interpreter considers the section as a transitional step from a wavefield to a map and views it as an image. Understanding the section is a complex process of features extraction and image analysis. Seismic sections contain 10⁵ times more data that is needed to fully describe a completely interpreted section. This paper suggests three ways to reduce the data set size for seismic section analysis by a global factor of 100 or more. The interpretation becomes a problem of a pattern classification using clustering algorithms that highlight the important statigraphic attributes by operating on various transformed domains. The evaluation of seismic sections means processing the data and the new image interpreting. Could be identified: location and dip of faults, sense of motion along them, boundaries, reflection geometry and the degree of continuity. Such model can be applied for seismic sections in Vrancea seismic area, Romania.

Land cover classification is one of the main applications of remotely sensed data and the capability of airborne hyperspectral data for such a purpose is known. The recent availability of high spatial resolution multispectral data, such as IKONOS and QuickBird, puts the question about advantages and disadvantages of these data in comparison with the hyperspectral ones. We evaluated the cost and accuracy of using IKONOS imagery to perform a land cover classification at high spatial resolution and compared them with results obtained from MIVIS airborne hyper-spectral scanner data (102 bands from VIS to TIR). The study was performed in a rural area (25 km²) of Basilicata region (Southern Italy) characterized by complex topography (altitude ranges from 600 to 1400m) and different land cover patterns (forests, lakes, cultivated areas, and small urban areas). Evaluations were made taking into account time-processing, feature extraction, accuracy for different classification levels, and costs as a function of the extension of the area to be classified. Quite high accuracies were obtained for the first classification level, whereas increasing the class number IKONOS was less accurate than MIVIS. Multispectral classification well identified the different forest species, but had some problems in distinguishing between gravel road and some plowed lands. The obtained results showed that IKONOS data are cost-effective for updating thematic maps to support planning and decision-making processes at local government scale.

Atmospheric correction is an essential part of the pre-processing of satellite remote sensing data. Several atmospheric correction approaches can be found in the literature ranging from simple to sophisticated methods. The sophisticated methods require auxiliary data, however the simple methods are based only on the image itself and are served to be suitable for operational use. One of the most widely used and well-known simple atmospheric correction methods is the darkest pixel (DP). Despite of its simplicity, the user must be aware of several key points in order to avoid any erroneous results. Indeed, this paper addresses a new strategy for selecting the suitable dark object based on the proposed analysis of digital number histograms and image examination. Several case studies, in which satellite remotely sensed image data intended for environmental applications have been atmospherically corrected using the DP method, are presented in this article.

Object-oriented analysis of RS images for landcover mapping is based upon the same hierarchical patch model used in modern Landscape Ecology. In such model, each patch is a loosely integrated whole -- an object that can be viewed simultaneously as part of a superobject and as made of subobjects. The focal level, i.e. the level of the nested hierarchy on which the analysis is focused, is indicated by the minimum size that the objects of this level are supposed to have. Based on this framework, we have developed a segmentation method that defines a partition on a multispectral image such that each segment exceeds the minimum size required for patches of the focal level. The segmented image is subsequently used as the baseline for an object-oriented classification in which segments become the basic units. In our contribution we briefly describe the method, focusing on its region merging stage. The most distinctive feature of the latter is that while the merging sequence is ordered by increasing dissimilarity as in conventional methods, there is no need to define a threshold on the dissimilarity measure between adjacent regions. The initial segments are image blobs (defined here as tiny homogeneous regions, darker, brighter or of different hue than their surroundings), contoured by a morphological method (gradient watersheds). The merging process is conducted iteratively, allowing only one mergence per segment and iteration, and not allowing mergence when (1) one of the two segments to be merged has a neighbor that has been merged in current iteration; (2) both segments exceed the minimum size; and (3) one of both segments is smaller than this size but it has a more similar neighbor than the one under consideration. The method is illustrated with an example on a forested region in Spain.

This work intends to test the use of remotely sensed data, as a mean to identify degraded lands with a high environmental hazard. The approach uses data from the sensor Thematic Mapper on Landsat 5 in synergy with digital ortho-photos (1:10000) and land cover map Corine 1990 to create a methodology useful to identify areas with dumps. The analyzed scene is relative to an area located in the Apulia Region in Southern Italy, where it is known the presence of a dump near the Margherita di Savoia "saline" (salt evaporation pool). As this dump is in its early phase, it is impossible to use thermal anomaly as a characteristic sign of its presence. So its identification proceeds through the extraction of the spectral signatures of the dump area and of the neighborhood zones. The analysis is developed in three steps: (1) Monitoring the change in the zone nearby the pools, especially if abandoned; (2) Pointing out the dump presence by the spectral signature specificity; (3) Individuating areas characterized by the same spectral properties. A pre-processing analysis is carried out by the Principal Component Transformation in order to minimize spectral noise and redundancy. Subsequently, the images are classified by the unsupervised algorithm ISODATA aiming at automatically individuating radiometric classes. The regions of interest are identified by help of the land cover map and then characterized by their spectral signatures. The identification of the dump is a feasible objective because of the temporal stability of its spectral signature, with respect to those of the other areas.

In this paper, an advanced technique for the generation of deformation maps using SAR (Synthetic Aperture Radar) data is presented. The algorithm estimates the linear and non-linear components of the displacement, the error of the Digital Elevation Model (DEM) used to cancel the topographic terms, and the atmospheric artifacts from a reduced set of low spatial resolution interferograms. The pixel candidates are selected from those presenting a good coherence level in the whole set of interferograms and the resulting non-uniform mesh tessellated with the Delaunay triangulation to establish connections among them. The linear component of movement and DEM error are estimated adjusting a linear model to the data only on the connections. Later on, this information, once unwrapped to retrieve the absolute values, is used to calculate the non-linear component of movement and atmospheric artifacts with alternate filtering techniques in both temporal and spatial domains. The method presents high flexibility with respect the required number of images and the baselines length. However, better results are obtained with large datasets of short baseline interferograms. The technique has been tested with ERS SAR data from an area of Catalonia (Spain) and validated with on-field precise leveling measurements.

In 1998 it has been organized the service which uses remote sensing data (RSD) processing techniques for monitoring and prediction of the fire situation on the basis of the West-Siberian Regional Centre of Reception and Data processing (WS RCRDP), at the participation of the Novosibirsk Regional Center of Geoinformation Technologies (NRCGIT SB RAS). Accuracy of referencing for the Novosibirsk region is up to 1 pixel (satellite NOAA-14, sensor AVHRR, space resolution is 1 km). Thin plate spline is used for received NOAA images georeference correction. Currently the data of the MODIS sensor from the Terra satellite is accessible. A hot spot coordinates determination accuracy on images received after the geometrical correction is less than one pixel (up to 1 km) and in the case of higher resolution channels utilization is less than one quarter of the pixel (up to 250m). A whole system of the forest fires monitoring consists of two independent parts: “component of RSD processing” and “GIS-component.” The “component of RSD processing” performs: data reprocessing (calibration, image georeferencing) and hotspots detection. The “GIS-component” is intended for secondary data processing. The input data is analyzed by the overlay analysis of the fire seats geometry. Formation of reports and processing results archiving are also performed. End users derive data not in physical coordinates “latitude-longitude,” but in their standard forest management system nomenclature -- "Forestry - forest area - wood block.” This lightens utilization of the derived result of the processing by forest-guards. It is also planned to develop the module which performs an estimation of the damage, inflicted by forest fires.

Synthetic aperture radar interferometry has been proposed as a potential technique for digital elevation model (DEM) generation, topographic mapping, and surface motion detection especially in the inaccessible areas. Grove Mountains Area locates to the southwest of Princess Elizabeth Land, inland areas of east Antarctica. The topographical map of the core area (11 x 10 KM²) was printed after the field surveying with GPS and total station was finished under the atrocious weather conditions during the 16^th CHINARE (Chinese National Antarctic Research Expedition) 1999/2000. This paper will present an experimental investigation of the ERS-1/2 SAR tandem data in 1996 on DEM generation of the Grove Mountains Core Area, analyze the data processing, and compare the DEM with the actual topographic form. It is confirmed that InSAR is a very useful technique to be utilized in Antarctica, and can be used to produce more products instead of dagnerous field surveying.

Mapping and characterization of forest and vegetation are particularly challenging in urban areas. High resolution imagery is needed for mapping and characterization purposes, due to the areal extent of urban forests, parks and recreational areas. Fusion techniques of panchromatic (1m resolution) and multiband (4m resolution) IKONOS data were used for mapping and characterization of land covering characteristics of urban green areas, allowing the identification of parks, tree areas and fields with a minimal mapping unit of 160 m². Techniques, that integrate the fine details of the input data into the fused image, are used. Experimental results for different image fusion methods (Laplacian, Gradient pyramids, Principal Component Analysis and Wavelet transform) are also demonstrated in order to improve spatial resolution. Classification of urban areas, mapped with fused data, results in higher accuracies than when using a multiband approach with 4 m data alone. Furthermore, high spatial resolution data permitted to obtain new areal extents of green areas of the city, giving a better estimate of international indicators for a suitable green areas policy. Vegetation indexes derived from red and near infrared data IKONOS are used to evaluate vegetation conditions, which, along with their distribution, location and urban context, resulted in better indicators of green areas.

Digital Elevation Models (DEMs) and land cover products are primary inputs for hydrologic models of surface runoff that affects infiltration, erosion, and evapotranspiration. DEM and land cover play important role in determining the runoff characteristics of specific catchment areas. Recently, at local level, a number of data sources have been used to derive land cover products for high resolution studies. These studies have been carried out for a number of different applications, including estimation of biomass and vegetation mapping. A hydrologic land cover classification includes information not only about vegetation species, but also about the land surface and what classes are important hydrologically. This kind of classification must therefore incorporate information on elevation, slope, aspect, surface roughness, as well as vegetation species derived from satellite added-value products. The main problems when generating hydrologic land cover maps is the lack of accurate DEMs and the confusion of spectral responses from different features. In this study, a Terra/ASTER image acquired over the region of Heraklion, Crete, Greece was used. ASTER stereo imagery is used for DEM production because it gives a strong advantage in terms of radiometric variations versus the multi-date stereo-data acquisition with across-track stereo, which can then compensate for the weaker stereo geometry. GCPs (Ground Control Points) derived from differential GPS measurements were also used for absolute DEM production. A hydrologic land cover classification scheme was developed by combining ASTER multispectral imagery, ASTER DEM products and the spectral signatures derived from field observations at predefined training sites.

Polar orbiting satellites with low spatial resolution sensors, such as the AVHRR, provide repeated global coverage of the Earth. The data is directly transmitted to ground stations, and in some cases distributed immediately after the data acquisition. Near real time applications can be implemented if the adequate processing tools are available. This paper presents a near real time processing system, developed for NOAA/AVHRR data acquired from the Dundee satellite station. The system performs image calibration, geometric corrections and atmospheric corrections with minimum operator intervention. The geometric corrections consist of an orbital-based correction refined by the automatic identification of Ground Control Points (GCPs) by image matching. The atmospheric correction is based on simulations performed on the 6S radiative transfer code using a set of typical and expected values for the most significant parameters. An attempt to evaluate the error associated with the simplified atmospheric correction method was carried out. As an illustration, 3 AVHRR images from NOAA 16 were processed. The ranges of values encountered for the most relevant parameters were analyzed. The range and average values for the reflectance channels 1 and 2 with and without the atmospheric correction are compared. These were used to produce standard Normalized Difference Vegetation Index (NDVI) images and atmospheric corrected NDVI images.

In this paper, a new automatic control point selection and matching technique for satellite image registration is proposed. The characteristic of this approach is that it uses features based on image moments and invariant to symmetric blur, scaling, translation, and rotation to establish correspondences between matched regions from two multitemporal images. The automatic extraction of control points is based on an edge detection approach and on local similarity detection by means of template matching according to a combined invariants-based similarity measure. The final transformation of the sensed image according to the selected control points is performed by using the thin-plate spline (TPS) interpolation. The proposed technique has been successfully applied to register multitemporal SPOT images from urban and agricultural areas. The experimental results demonstrate the efficiency and accuracy of the algorithm which have outperformed manual registration in terms of root mean square error at the control points.

In this paper, by use of multitemporal satellite remote sensing, the dynamic change of land use about Xuzhou coal mining area in the east of China was monitored and analyzed. Firstly, with the use of TM images of Xuzhou in 1987, 1994 and 2000, the change maps of land use structure of their relevant periods of its West coal mining area were compiled. Secondly, the conversion matrix of land use structure about the different temporal classified TM images was interpreted. Finally, the status of the dynamic change of land use was analyzed comprehensively. The results indicate: (1) The area of subsided land due to coal mining was increasing, its extent was all bigger in 1987-1994 and 1994-2000, the increase rate of subsided land change every year was 6.33%, and which was the biggest rate of all land use change. (2) The cultivated land was the main land subsided due to coal mining, but the main use direction after subsidence land reclaimed was not cultivated land, and the cultivated land subsided due to coal mining was far exceed to that reclaimed. (3) Although the rapid speed of subsided land reclamation, and the reclamation rate all over 50% in 1987-1994 and 1994-2000, yet the speed of land subsided was also quick, the speed of land reclamation did not catch up with that of land subsidence. Therefore, it is necessary to accelerate the land reclamation, and adopt the effective measures to raise the ratio of cultivated land reclaimed from subsided land.