The availability of remote sensing data that are needed for global, regional and local environmental monitoring has greatly increased over the recent years. New technologies such as global positioning system (GPS), digital photogrammetry and multi-source satellite remote sensing are creating data at higher spatial, spectral and temporal resolution than have been collected at any other time on earth. Geographic Information Systems (GIS) technologies allow - for the first time- the efficient storage and management of spatial datasets in digital formats. In combination with the appropriate data transfer and interoperability standards that are currently being developed the technology is being put in place that will eventually allow standardized data exchange, processing and dissemination. Today, a wide variety of remote sensing systems are used to provide information about the earth, its atmosphere, oceans, and land surfaces. Multispectral satellite scanners in the visible and near infrared domains of the electromagnetic spectrum record solar radiation reflected from the earth's surface. Data derived from multispectral scanners provide information on (among other things): vegetation type, distribution and condition; geomorphology; soils; surface waters; and river networks. In addition, active microwave (radar) systems are commonly used in geological, hydrological and oceanographic applications. The advent of very high resolution satellite and space programs offers new possibilities for satellite remote sensing. In addition, digital airborne cameras offer ultra high resolution for very accurate mapping of the environment.

Physical parameters related to Earth surface and atmosphere show different behaviors when observed at different space-time scales by using both remote sensing or traditional ground based techniques. The main aim of this project was to investigate the information content degradation which results moving from the use of observations obtained by direct-punctual (ground-based), higher spectral/spatial resolution (airborne sensors), higher time-resolution, low cost and low spatial resolution (satellites), in the context of the activities related to natural and environmental risks monitoring in protected natural areas. Several observational techniques have been contemporary used during two fields campaigns in the Pollino National Park (Southern Italy): a) from ground by direct measurements of near surface parameters (from - 70cm of depth up to 200cm of height) as well as by radiosonde and radiometric measurements of surface and atmospheric parameters; b) using hyperspectral (MIVIS) and photographic aerial observations; c) from LANDSAT-TM, NOAAA/AVHRR and ADEOS/AVIRIS satellite sounders. Campaign data have been integrated on a GIS (including high resolution cartographic layers) and long term evolutionary trends (up to 20 years) also considered after the analysis of available historical, LANDSAT and NOAA, satellite records. This paper will present the main achievements of the project with special emphasis on the trade-off between expected performances and economical sustainability of different environmental monitoring strategies in an operational context.

The Seventh Army training Command and the 100th Area Support Group provide the only maneuver and live-fire training areas for the U.S. Army's combat forces in Europe. The U.S. Army's mission includes the protection of forests, range lands, fish, wildlife, and other natural resources entrusted to its care. Therefore, training areas are maintained by professional land and environmental managers to reduce the impact of the training on the environment. In order to keep a functional ecosystem and endangered species within the training area intensive environmental monitoring and data acquisition are necessary. Water and erosion control programs as well as Land Condition Monitoring Programs and other evaluations are necessary tools to provide a basis for the decisions concerning training intensity and land rehabilitation requirements. These tools need an input of data about topography, vegetation, soil and other parameters. To collect many of the data for these tasks the environmental managers use innovative remote sensing systems. Besides aerial photography and satellite imagery airborne sensors such as the High Resolution Stereo Camera (HRSC-A), Laserscanner (LIDAR), interferometric synthetic radar, multispectral scanners (Daedalus, HRSC-A) and hyperspectral scanners (HyMap) are being used. A recent project is presented where hyperspectral reflectance data were used to determine mineral content in soils and differentiate surface cover types using a new unmixing approach. The project showed the high potential of hyperspectral imagery for examination of soils and surface cover types. The results will enable more precise investigations and modeling tasks in the environmental management context.

The main concept behind this paper is that pattern and processes are linked in a mutual way. In the last decades landscape ecology was dominated by quantitative descriptions (landscape metrics) of the landscape under concern and its components. Now there is a growing interest in the cause-effect-relationships between these environmental characteristics. High-resolution aerial photography hold an important amount of valuable information, but until recently only a little proportion of the entire information was usually used in scientific analyses due to conceptual and technical limitations. In this paper we present results derived with a multi-scale image segmentation approach and it is demonstrated how this approach allows for an identification of pattern at several scales simultaneously. First results testify that this is a suitable method for the delineation of meaningful landscape elements and subsequently for landscape monitoring, particularly if dealing with complex or small-scaled pattern. It is shown that hierarchically linked objects are more suitable for monitoring than pixels although the necessity for a comprehensive methodology for object-based change detection arises.

In this study the potential of C-band interferometric SAR data in land-cover classification was investigated at a 2500km2 study area around the Helsinki metropolitan area in southern Finland. The area consists of a variety of land-use classes from dense urban areas to lakes, agricultural fields and boreal forests. The INSAR data consists of a time-series of 14 ERS-1/2 Tandem Image pairs with a 24 hour temporal baseline acquired during the ERS Tandem mission in 1995-1996. The data was interferometrically processed into 28 5-look intensity images, 14 Tandem coherence images and two coherence images with a longer temporal baseline (35 and 245 days). All image data was co-registered and orthorectified into map coordinates using an INSAR DEM. The dimension of the input dataset was reduced using Temporal Averaging and Principal Components Transformation (PCT) prior to classification. ISODATA unsupervised classification was performed on dataset consisting of the intensity and Tandem coherence temporal average images, the first intensity PC, two first Tandem coherence PCs and the average of the longtime coherence images. Classification accuracy was assessed by comparing the classification results with aerial orthophotos and digital base maps. Due to gaps in ground truth information overall accuracy and user's accuracy were not assessed. The overall producer's accuracy for six classes (agricultural fields, forest, vegetation, mixed urban, dense urban, water) was 80.9%.

Managing, controlling and monitoring the adoption, the implementation and the achievements of the Agri-Environmental Programs and their compliance with EC Rural Development Regulations is a very geomatics oriented exercise with a substantial spatial/geographic GIS and image component. Two pilot projects were undertaken, one in Italy and one in England to evaluate the control of Agri-Environmental Measures (AEMs) using Remote Sensing and GIS methodology. This paper concentrates primarily on the Italian project but will include comparative issues between the two. In Italy, a test site was defined on the western side of Lago di Garda, in Northern Italy. A strategy was applied for the selection of measures based on the Regional implementation of the EC Regulation 2078/92 (e.g. input reduction, arable reversion to grassland, farmland conservation). A GIS was set up requiring particular efforts regarding the acquisition, quality checks, georeferencing and harmonization of the several information layers. Different Computer Assisted Photo Interpretation (CAPI) methods (e.g. small-scale landscape features: hedgerows, tree rows; arable farming practices: crop rotation.) were applied in relation to the specific AEMs to be controlled. Hints to the feasibility of controlling selected AEMs, limiting factors of CAPI methods and their impact on the identification/control of the measure are discussed.

Mining activities lead to ground movements at the surface, resulting in changes of the topographic and hydrologic situation. As surface feature changes can lead to long-lasting environmental influences, an up-to-date surveillance is of key importance. Spatial and temporal changes of the hydrological situation result from the amount of subsidence, local ground-water situation, land-cover and land-use. An important factor is the intensity of impairment regulated by properties of the local soil and vegetation data. To estimate changes of the dynamic system subsidence-ground-water-vegetation, the ground-water recharge rate and isobars of the ground-water table are calculated in detail. This was done repeatedly, starting 1993 and for 2004 as a prediction. Because of the large extent of the area which has to be monitored, airborne surveys were done in August 1998 and 2000, using the HyMap imaging spectrometer. Interim monitoring results of terrestrial areas and measured LAI data were used as reference. With use of spectral unmixing routines, specific information on selected land-use classes were obtained and areas affected by water logging could be detected. Results show that spectral features of the observed land-use and land-cover can be related to specific vegetation phenomena caused by changes of the hydrologic situation.

The main purpose of the research presented in this paper is the development and validation, through the application to a case study, of an efficient form of satellite image classification that integrates ancillary information (Census data; the Municipal Mater Plan; the Road Network) and remote sensing data in a Geographic Information System. The developed procedure follows a layered classification approach, being composed by three main stages: 1) Pre- classification stratification; 2) Application of Bayesian and Maximum-likelihood classifiers; 3) Post-classification sorting. Common approaches incorporate the ancillary data before, during or after classification. In the proposes method, all the steps take the auxiliary information into account. The proposed method achieves, globally, much better classification results than the classical, one layer, Minimum Distance and Maximum-likelihood classifiers. Also, it greatly improves the accuracy of those classes where the classification process uses the ancillary data.

Spatial evolutions of anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in researches about the environment. This evolution constitutes one of the major concerns in the domain of environmental space management. The landscape evolution of a region area and the perspectives for a future state rises an issue particularly important. What will be the state of the region in 15, 30 or 50 years? Time can produce transformations over a region area like emergence, disappearance or union of spatial entities... These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area, which reveals these spatial transformations. The proposed method is based on the analysis of terrain landscape given a sequence of n satellite images, which represent the state of a region area in different years. For these purposes, we have developed a specific spatio-temporal prediction approach, linking results of forestry evolution analysis and fuzzy logic. The method is supported by the analysis of the landscape dynamics of a test-site located in a tropical rain country: the oriental piedmont of Andes Mountain in Venezuela. This large area - at the scale of a spot satellite image - is typical of tropical deforestation in a pioneer front. The presented approach allows the geographer interested in environmental prospective problems to get type cartographical documents showing future conditions of a landscape. The experimental tests have showed promising results.

The emphasis of this paper will be on an advanced knowledge-based methodology for the training phase within the change detection process. 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 (Erdas Imagine Expert Classifier). 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. Combining these 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 (reflectance) but a large amount of parameters is checked. For each parameter the inference mechanism automatically checks the separability for different object classes and thus evaluates the suitability of each signature. As an output of the classification stage - again applying knowledge-based rules - we obtain probability vectors which decide in favor of a confirmation, modification or an elimination of the given outlines for the specific class. The new outlines 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. 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.

Commercial satellite images have long been used for environmental monitoring. The improvements in spatial and spectral resolution bring with them new applications in different fields. We have already investigated the use of medium-resolution LANDSAT TM5 images for the routine nuclear verification, based on recently published visualization and change detection algorithms: canonical correlation analysis to enhance the change information in the difference images and Bayesian techniques for the automatic determination of significant thresholds. Now, the high spatial ground resolution of IKONOS and other future satellites provides a good basis for recognizing and monitoring of small-scale structural changes and for planning of routine and/or challenge inspections of nuclear sites. Aside from the advantages of the improved spatial resolution some problems due to sensor and solar conditions exist: Shadow formation and off-nadir images make it more difficult to interpret the complex changes. In order to solve these problems, we supplement the pixel-based change detection analysis with a supervised, object-oriented post-classification of change images carried out with the image analysis system eCognition. Defining of different object classes of the change pixels helps to distinguish between the different man-made, vegetation and other changes. By means of semantic relations between the object classes of changes and other classes it is possible to exclude shadow affected regions and to concentrate on specific areas of interest.

The climate of urban areas is a very sensitive topic in the content of human health a sustainable urban development. There is often a difference between the high demands of urban planning and the reality. Two aspects regarding urban climate are discussed in this paper. First we present a method for the automated analysis of vegetated areas and especially vegetated roofs in the inner city using multispectral and extremely high resolution imagery. The second part of this paper focuses on a potential cooling area near the city and especially the change of this area after its construction. A qualitative estimation and visualization of the impact regarding climatic effects is performed using stereoscopic heights data acquired by a digital scanner.

MOLAND (Monitoring Land Cover/Use Dynamics) is a research project aiming to assess and monitor the evolution of urban and regional land use and traffic networks. The main aims of MOLAND are to offer planners and decision-makers at various levels reliable, up-to-date and harmonized information on land use changes and a tool for monitoring, analyzing and forecasting such changes from the point of view of sustainable development. The method has been tested in more than 30 urban areas situated mainly in Europe. The study period covers 50 years from the 1950s to the late 1990s. The method is based on the usage of high-resolution satellite images such as IRS. Land use data are analyzed and combined with statistical datasets in a GIS application to produce indicators for sustainable development. This paper looks at the principles and technical solutions behind the MOLAND method, a range of urban indicators derived from the airborne imagery and the potential uses of the method in urban monitoring and planning. Special emphasis is on the analysis of sustainability of urban development. The comparative examples are related to various land use indicators such as urban sprawl, re-use of abandoned land, accessibility to green urban areas and exposure to the nuisance of transport network. The paper also sheds light on the potential of combining information gathered by remote sensing with statistical data and how such a method can be used in urban and regional monitoring, planning and integrated impact analysis.

Since RGB images derived from multispectral (TM) images will lose some information, in this paper we present the method to solve such a problem by using principal component analysis (PCA) which transforms TM images into the principal component images (PCs), while the high resolution PAN data is decomposed by wavelet transform. Thus, RGB images are assigned by the first three principal component images which normally have approximately 95% of the information in the original images. The intensity image from RGB to HIS transformation is replaced by the lower frequency coefficient of wavelet transform of PAN data corresponding to multispectral images. HIS to RGB transformation is then applied. The fused RGB image using our method can obtain more details.

The increase in maritime traffic has caused an increment on the risk of accidents that can damage the environment. In recent times, the efficiency on oil spill detection and monitoring with spaceborne systems has been demonstrated. It is however important to remark that satellite data must be complemented with other monitoring platforms and sensors, for better temporal coverage and for an improvement on detection and analysis of the spill. RAPSODI is an European project for the development of a new anti-pollution remote sensing system that results from the integration of airborne sensors: SAR, SLAR, IR, UV and a microwave radiometer and spaceborne data. One of the main operational goals of this project was a real size experiment in completely controlled situations and environment. In this campaign, heavy fuel oil was released and treated. This experiment has also allowed monitoring the efficiency of the dispersing products used by the oil spill response community. In this context, this paper describes the development of the airborne SAR mode optimized for oil spill detection, and the planning and first results of the experimentation campaign.

In the present paper a mosaic of 130 Landsat TM satellite images is used to identify navigable rives in the Western Amazon region and to analyse the accessibility pattern of the area via the river network. The environmental heterogeneity of the Western Amazonia is enormous, and thus the potential for human use varies significantly. In sustainable land use planning accessibility of potential resources is of paramount importance. Most studies and zoning proposals neglect the issue of accessibility or ignore the river network. Our study concentrated on the accessibility in the Western Amazonian region shared by several countries. A Landsat TM mosaic was created over this area and waters were separated by rationing of TM bands 5 and TM, classification and manual corrections. With the 100-meter resolution used, navigable rivers could still be delineated. Grid-based model on the on the accessibility of peripheral areas around regional, provincial and local centres was created and the accessibility pattern of the entire region and within each country was analysed. Nearly entire Peruvian Amazon as well as the vicinities of the main course of the Amazon River were found the most accessible regions, whereas large parts of Colombian and Brazilian Amazon were poorly accessible.

The Dead Sea is very harsh environment even for microorganisms adapted to hypersaline environment. Not only does the Dead Sea contain the highest salt concentration of all natural lakes inhabited by living organisms, but the peculiar ionic composition of its water, with its high concentration of divalent cations magnesium and calcium, is highly inhibitory even to those microorganisms that are the most adapted to life in the sea. In this research imaging spectroscopy and microbiological studied used to investigate the spatial distribution of various Archaea populations according to the salty saturation of Mor swamp, Dead Sea Basin. Data from the DLR airborne sensor DAIS-7915 in the spectral range between 0.4 to 2.4 micrometers were acquired along with field and laboratory spectral measurements. The spatial and spectral data were completed by microbiological analysis. The spectral information helped to detect a concentric distribution of the Archaea population, which seems linked to the state of the salty substrate. In the wet muddy central zone lives an Archaea with the relatively lowest salt tolerance. From this centre to the peripheries, the tolerance to salt of the Archaea population was found to be increasing, as the substation changes from salty pools to salty muds and finally to massive salt layers.

Forest fires in Southern Europe are a major source of concern for environmental security. Every year several hundred thousand hectares of forest are burned. These fires put at risk, not only human life and property but also the sustainability of forests. Hence, it is important to have an accurate and timely knowledge of the total area burned during the fire season as well as the type of forest that is burned. Until now, IRS-WiFS 180 meters spatial resolution images were used to map the burnt areas after the fire season in Southern Europe. However, the lack of a short-wave infrared band is in certain cases a limitation for the detectability of the burnt areas. Fusion of IRS-WiFS with MODIS 500 meters spatial resolution images, that has short-wave infrared bands, could improve the mapping of burned areas. We present results on the data fusion of both images over the Iberian Peninsula on September 25th of 2000. The fused images were obtained through local correlation modeling resulting in MODIS bands of 180 meters pixels. The preliminary results show a good potential to improve the burned area mapping in Southern Europe by using the IRS-WiFS higher spatial resolution images in conjunction with the MODIS short-wave infrared bands.

The Cerro Grande/Los Alamos wildfire devastated approximately 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos. The need to monitor the continuing impact of the fire on the local environment has led to the application of a number of advanced remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multispectral imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before and after the wildfire. Using an existing land cover classification based on a Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, along with clouds and cloud shadows. The details of our evolved classification are compared to the manually produced land-cover classification.

Noisy spectra were simulated from laboratory 2000-2500 nm reflectance spectra of polymineralic sand samples and synthetic composite spectra, using MIVIS calibration parameters and measured solar irradiance both with and without diffuse atmospheric radiance at the sensor. Noise content variation in output data with varying sensor parameters and atmospheric conditions was analysed, as well as noise effects in spectral feature identification. Scaled random noise with a normal distribution was added to the convolved spectra. The rms of the ratio of noisy to original spectra is assumed to represent noise content in output data. Noise content variation with SNR is expressed by a set of curves with a significant dispersion of noisy spectra for low SNRs. For individual SNRs, noise content has an inverse linear relationship with sample albedo. Data dispersion occurs when noise content is compared with reflectance contrast and band depth. No clear relationship resulted for noisy to original spectra match values or noisy spectra classification probability with either band depth or band depth referred noise, due to spectra aliases.

In this work, a simple and robust technique, that allows for automatic identifying of surface materials by using Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) radiances was applied in order to recognize rocky outcrops in the area of the Pollino National Park (southern Apennines, Italy). Tests were made over an area which is topographically complex and geologically characterized by ophiolite-bearing units (Ligurian units), Meso-Cenozoic shallow-water carbonates (Pollino Unit) and Plio-Quaternary sediments. Among ophiolitic rocks, serpentinite was chosen as a test-rock as it is present in isolated outcrops in the test-area, besides subtending important environmental problems because it contains asbestos. Geological information, coming from field observations or geological maps, was used as input and in order to verify the results of the MIVIS data processing.

Tertiary porphyry deposits in Iran are an important source of Cu, Mo and locally Au. Most of the known porphyry Cu deposits are located in the Central Iranian Volcanic Belt(CIVB). Pariz area is located within this belt and is chosen as a test area to evaluate remote sensing data at one such area, and use of these data in exploration of the other parts of CIVB. Eight known mineralization sites were chosen in the area, which are mainly porphyry type. SPOT images in XS mode are used to study geology as well as hydrothermal alteration in the Pariz area, where soil and vegetation cover is substantially poor. Different approaches such as band ratioing, principal component analysis, I-S-H decorrelation processing, digital filtering and hybrid composite were used to enhance the diagnostic features associated with the lithologies as well as the hydrothermal alteration. Color combinations of the principal components, I-S-H transformation and 2/1 ratio have proved to be the best image enhancement techniques for geological studies in such areas. Lineament analysis has shown that the areas with known ore occurrence and the hydrothermally altered areas are closely associated with the higher photolineament factor values. Comparison of geophysical and remote sensing data has shown that there is a good correlation between the airborne geophysical and remote sensing data in the area.

The slopes of the Cotswolds Escarpment in the United Kingdom are mantled by extensive landslide deposits, including both relict and active features. These landslides pose a significant threat to engineering projects and have been the focus of research into the use of airborne remote sensing data sets for landslide mapping. Due to the availability of extensive ground investigation data, a test site was chosen on the slopes of the Cotswolds Escarpment above the village of Broadway, Worcestershire, United Kingdom. Daedalus Airborne Thematic Mapper (ATM) imagery was subsequently acquired by the UK Natural Environment Research Council (NERC) to provide high-resolution multispectral imagery of the Broadway site. This paper assesses the textural enhancement of ATM imagery as an image processing technique for landslide mapping at the Broadway site. Results of three kernel based textural measures, variance, mean euclidean distance (MEUC) and grey level co-occurrence matrix (GLCM) entropy are presented. Problems encountered during textural analysis, associated with the presence of dense woodland within the project area, are discussed and a solution using Principal Component Analysis (PCA) is described. Landslide features in clay dominated terrains can be identified through textural enhancement of airborne multispectral imagery. The kernel based textural measures tested in the current study were all able to enhance areas of slope instability within ATM imagery. Additionally, results from supervised classification of the combined texture-principal component dataset show that texture based image classification can accurately classify landslide regions and that by including a Principal Component image, woodland and landslide classes can be differentiated successfully during the classification process.

Airborne imaging spectroscopy has undergone a rapid development over the last decade. The number of research groups making use of this technology has increased by an order of a magnitude. Starting from the late 1980s at the DLR research center 'Oberpfaffenhofen' spectroscopic earth observation facilities have been continuously improved in order to be able to provide reliable imaging spectrometer data to the scientific community. At the current stage the integrated hyperspectral facilities at DLR Cluster for Applied Remote Sensing consists of the two imaging spectrometers DAIS 7915 and ROSIS, a laboratory calibration facility and the respective processing and archiving facilities. As an additional important factor in airborne remote sensing access to a DLR-own fleet of research aircraft (Dornier Do228, Cessna 208B Grand-Caravan, FALCON 20 E5 jet) is granted. Numerous imaging spectrometer campaigns have been carried out during the last years with flight activities all over Europe. Currently the two airborne imaging sensors are identified by the European Commission as a mayor research infrastructure and supported in a 3 year project. In the frame of this project hyperspectral data sets will be acquired over different test areas proposed by international research teams. In this paper the installation of the facility in an European research environment, the technical components as well as the currently ongoing research activities will be described. A list of already acquired data sets and the corresponding thematic applications is shown. An outlook to future improvements including new sensor initiatives is given.

An area in the Negev desert in Israel was chosen to demonstrate the capability of the SIR-C sensor system to differentiate lithological units. In addition DAIS hyperspectral data were investigated. The area around Timna mountain is characterized by Cambrian sandstones, carbonates and alluvial fans of Pleistocene and Holocene age. The mountain itself consists of diverse magmatic rock assemblage. The whole area is mapped using conventional methods. The remote sensing data are sensitive for the mapping parameters and allow the extraction of their spectral and radar signatures. Specific areas have been classified on their lithology, mineralogy, stratigraphy, grain size distribution, surface roughness and weathering state. In addition, field measurements of the roughness of the alluvial fan's terraces and of the Cambrian rocks surrounding Mount Timna were investigated. The stereoscopic images taken were used as a basis for the height models of the different targets and the standard deviation (RMS) of the height was calculated. This was used as the parameter for the surface roughness and can be correlated with the radar backscatter. The SIR-C system offers the unique possibility of multi-frequency and multi-polarized data. The sensitivity for the surface roughness for the different wavelengths of X-, C- and L-band in accordance to their incidence angle ranges between 0,13 and 27 cm absolute RMS. The RMS measured for example in the alluvial fan ranges between 0,25 and 7, this corresponds to a smooth surface with pebble size of a few mm and a coarse terrace up to 50 cm block diameter. It can be shown that the age of the terraces corresponds to the backscatter. The more ancient the terrace the lower is the backscatter, the smoother the surface, the younger and rougher the terrace, the higher is the backscatter.

The estimation of fluvial sediment transport rate from measurements of morphological change has received growing recent interest in the last five years. The revival of the 'morphological method' reflects continuing concern over traditional methods of rate determination but also the availability of new survey methods capable of high-precision, high-resolution topographic monitoring. In particular, remote sensing of river channels through aerial digital photogrammetry is a potentially attractive alternative to labor intensive ground surveys. However, while photogrammetry presents the opportunity to acquire survey data over large areas, data precision and accuracy, particularly in the vertical dimension is lower than traditional ground survey methods. This paper presents results of recent research in which DEMs have been developed for a reach of a large braided gravel bed river in Scotland using both digital photogrammetry and high resolution RTK GPS ground surveys. For both approaches, a statistical level of detection of change is assessed by intercomparing surfaces with independent check points. The sensitivity of the annual channel sediment budget to this level of detection is presented. Preliminary results suggest that as much as 60% of channel deposition and 30% of erosion may be obscured by the lower level of precision associated with photogrammetric monitoring.

On January 13th 2001, a very strong earthquake struck El-Salvador, causing almost 1000 deaths and huge destruction, leaving more than one million people homeless. As support to the rescue teams, a project was initiated to provide up-to date maps and to identify damages to housing and infrastructures, covering the whole country. Based on the analysis of SPOT Panchromatic satellite imagery, updated maps were delivered to the rescue teams within 72 hours after the earthquake. In addition, during the 10 days following the earthquake, high resolution mapping of the damages was carried out in cooperation and coordination with rescue teams and relief organizations. Some areas of particular interest were even processed and damage maps delivered through the Internet, three hours after the request. For the first time in the history of spaceborne Earth observation, identification and evaluation of the damages were delivered on-site, in real-time (during the interventions), to local authorities, rescue teams and humanitarian organizations. In this operation, operating 24 hours a day and technical ability were the keys for success and contributed to saving lives.

A combined geometric and atmospheric correction processing chain for hyperspectral imagery has been developed. The paper first describes the ortho-rectification solution for the geometric part employing a parametric geocoding approach (PARGE model). The model includes all navigation parameters in a forward transformation methodology. Its basic method and implementation principles are depicted. The output of the processor are the geocoded imagery and interface layers to the atmospheric/topographic correction. The radiometric correction of atmospheric and topographic effects is the second part of the preprocessing (model ATCOR 4). The method accounts for the angular and elevation dependence of the path radiance and atmospheric transmittance functions to remove atmospheric and topographic effects. It uses a database of look-up tables of the atmospheric correction function (path radiance, atmospheric transmittance, direct and diffuse solar flux) calculated with the MODTRAN 4 radiative transfer code. The influence of the adjacency effect is included during the calculation of the surface reflectance cube. Additionally, the terrain shape obtained from a digital elevation model is taken into account for the reflectance computation in a rugged area. For sensors with one or more thermal bands the surface temperature can also be calculated. As an option, value adding channels can be derived, such as LAI, FPAR, albedo, absorbed solar radiation flux, and for coregistered reflective and thermal band sensors the net radiation and heat fluxes. Examples of processing of hyperspectral imagery in flat and rugged terrain will be presented.

The industrial and inhabited complexes of Novosibirsk Academgorodok are built in a natural environment in view of a relief and wind rose and form geochemical steady natural- anthropogenic ecosystem. In this territory, despite of long (more than 40 years) anthropogenic influence, is kept rather high quality of an environment. These conclusions are based on results of ecogeochemical researches of microelemental composition of soil cover (87 samples) on territory of Novosibirsk Academgorodok is closer to natural landscapes, instead of urban. The exception is the local anomalies connected to the increased contents of zinc and lead, dated to highways and parking places.

Within the Pollino National Park environmental monitoring promoted by the Basilicata Region authorities (southern Italy), hyperspectral airborne data surveys were deployed to collect spectra images with higher spectral/spatial resolution. In order to define a standard atmospheric procedure to be applied to MIVIS data set, different radiative transfers code has been tested. Unfortunately generic correction procedures, like ATREM, widely applied to airborne hyperspectral data sets, could not be used for MIVIS data set because of the absence of the specific water absorption bands (0.94 and 1.14micrometers ) in its spectral region coverage. In this paper a technique to recover the right slope of the water absorption bands centered at 0.82micrometers , not measured by the MIVIS spectral resolution, is presented. The synthetic 0.84micrometers channel is spectrally recovered in place, by using the spectral behavior of each pixel. Channel simulations have been conducted on AVIRIS data sets resampled on the MIVIS NIR spectral channel curves and compared with the AVIRIS ones recorded at 0.84 micrometers . Data simulation has proved the efficiency of the method and the spectral coherence of the synthetic bands centred at 0.84(mu) +m. As a consequence the atmospheric correction code ATREM, with the help of appropriate contrivance to rescale the water vapour atmospheric amount, could be applied to the MIVIS data set by selecting, in the ATREM input file, only the water absorption band centred at 0.82micrometers .

In southern Italy the Basilicata Region authorities are strongly active in monitoring the regional environment for planning actions to undertake. To this aim the Basilicata Region has been promoting dedicated remote sensing activities. The criteria adopted in starting the initiatives are based on the use of the best available technologies and their integration with standard methodologies (satellite and airborne data integrated with ground measurements) devoted to the best planning and management of the territory. Airborne surveys were deployed mainly to derive the main land cover and vegetative units to allows the outset of detailed studies of physical-chemical characteristics of the natural (geologic outcrop and vegetative species discrimination) materials in the park zones highly protected. The resulting thematic map at 1:10.000 scale is, in comparison with the available ones, has a better detail whether in term of number of classes depicted or in term of classes distribution. The spectral resolution of the data has also evidenced the aptitude of the land cover maps to describe local ecosystem and to derive local biophysical important variables useful for management implications and to allow a correct parameterization of the surface variables useful to the modeling community.

Ultra-thin silicon-on-sapphire (UTSi) CMOS technology is a commercial, high yield silicon-on-sapphire technology that yields circuitry well suited for optical communication functions on a transparent substrate. This characteristic, unique to the silicon-on-sapphire configuration, allows flip-chip bonding of optoelectronic (OE) devices onto CMOS circuitry to build Flipped Optoelectronic Chip on UTSi (FOCUTS) optical transmit and receive modules. Flip-chip bonding eliminates the wire-bond inductance between driving/receiving circuits and the OE devices which becomes problematic at data rates greater than about 2.5 Gbps. Such flip-chip integration also reduces the number of discrete components that must be handled, packaged, and aligned in a module, thereby improving reliability and reducing costs. Additional functions, such as Electrically Erasable Programmable Read Only Memory (EEPROM) and self aligned Automatic Power Control (APC) photodetectors and control circuits will be discussed. We describe measured results of flip-chip bonding of arrayed OE devices (VCSELs and photodetectors) and test results at 3 Gbps as well as recent integrating and testing of phototransistors in UTSi circuits. We also describe the radiation sensitivity of all components and applicability of this technique to remote sensing applications. These devices, operating at 850 nm, are aimed at multimode, short reach optical fiber networks.

A new method to correct hyperspectral line scanner image data in airborne remote sensing for bidirectional reflectance effects is presented. Those effects prevent a precise intra- and intercomparison of image scenes and affect spectral ratios. The method bases on the linear semiempirical Ambrals model (Algorithm for MODIS Bidirectional Reflectance Anisotropy of the Land Surface, Lucht 2000). The samples for the inversion of the model are retrieved from the column averages which are calculated either over all pixels or separately over the pixels of each class of a spectral classification. The preclassification is supposed to lower the standard deviation within each column means in order to account for the different angular dependence for each surface. The data from a single scene is sufficient to perform an inversion. The application of this method to different sensor types is straightforward. As an example, images from the DAISEX'99 campaign in Barrax, taken with the wide-FOV hyperspectral sensor HyMap from different flight directions and times of the day, are modeled and corrected.

In recent years, we have developed an advanced environmental monitoring system (AEMS) containing the eco-sensor, which means a sensor for the measurement of environmental pollutants, based on lipid membranes for continuous monitoring of underground water in industry areas such as semiconductor factories. The AEMS project is composed of three work packages followed by 1) Eco-sensor, 2) Prediction of plume propagation using a computer simulation technique, and 3) Environmental protection method. In this presentation, we would like to focus on the study of the eco-sensor. The reason why lipid membranes were selected as a sensing element for environmental pollutants is that the pollutants should be interacted with cell membranes because cells are surrounded by cell membranes containing lipid components. Improving the applicability and the responsibility of bilayer lipid membranes (BLMs) in the eco-sensor, we have investigated the automatic BLMs preparation device. An automatic BLMs preparation device was made by use of an inkjet mechanism. The reproducibility of the BLMs preparation was remarkably improved. The sensitivity to volatile organic chloride compounds such as cis-1,2-dichloroethylene was in the order of 10 ppb using the monoolein BLMs even in real underground water. We also have been developing a smaller sized eco-sensor for the practical use.

A semi-automatic road extraction method from high-resolution (1-m) satellite images is presented. As IKONOS, a high-resolution (1-m) satellite has been launched and several companies have plans to launch high-resolution satellites, extraction of man-made objects from high-resolution satellite images has been main concern of many scientists. The method consists of three phases; 1) NUBS (Non Uniform B-Spline) curve is formed by given seed points. 2) A road candidate area is made by straightening image along the NUBS curve. 3) Finally, road is extracted by a tracking algorithm which uses adaptive least squares correlation match method and linearity. Due to straightening image, the tracking algorithm extracts roads accurately even though there are road gaps, and the size of a matrix for least squares correlation match can be reduced. We test our method on high-resolution (1-m) satellite (IKONOS) image. The test result reveals our method is robust and can be one of the feasible solutions of mapping from high-resolution (1-m) satellite images.