This PDF file contains the front matter associated with SPIE Proceedings Volume 8538, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

The demand for the rapid provision of EO products with well-defined characteristics in terms of temporal, spatial, image-specific and thematic criteria is increasing. Examples are products to support near real-time damage assessment after a natural disaster event, e.g. an earthquake. However, beyond the organizational and economic questions, there are technological and systemic barriers to enable a comfortable search, order, delivery or even combination of EO products. Most portals of space agencies and EO product providers require sophisticated satellite and product knowledge and, even worse, are all different and not interoperable. This paper gives an overview about the use cases and the architectural solutions that aim at an open and flexible EO mission infrastructure with application-oriented user interfaces and well-defined service interfaces based upon open standards. It presents corresponding international initiatives such as INSPIRE (Infrastructure for Spatial Information in the European Community), GMES (Global Monitoring for Environment and Security), GEOSS (Global Earth Observation System of Systems) and HMA (Heterogeneous Missions Accessibility) and their associated infrastructure approaches. The paper presents a corresponding analysis and design methodology and two examples how such architectures are already successfully used in early warning systems for geo-hazards and toolsets for environmentallyinduced health risks. Finally, the paper concludes with an outlook how these ideas relate to the vision of the Future Internet.

Rapid change detection is used in cases of natural hazards and disasters. This analysis leads to rapid information on areas of damage. In certain cases the lack of information after catastrophe events is obstructing supporting measures within disaster management. Earthquakes, tsunamis, civil war, volcanic eruption, droughts and floods have much in common: people are directly affected, landscapes and buildings are destroyed. In every case geospatial data is necessary to gain knowledge as basement for decision support. Where to go first? Which infrastructure is usable? How much area is affected? These are essential question which need to be answered before appropriate, eligible help can be established. This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of lack of remote sensing knowledge. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

Since Spanish colonial times, the Canary Islands and especially Tenerife have always been used for intensive agriculture. Today almost 1/4 of the total area of Tenerife are agriculturally affected, whereas especially mountainous areas with suitable climate conditions are drastically transformed for agricultural use by building of large terraces. In recent years, political and economical developments lead to a further transformation process, especially inducted by an expansive tourism, which caused concentration- and intensification-tendencies of agricultural land use in lower altitudes as well as agricultural set-aside and rural exodus in the hinterland. The overall aim of the research at hand is to address the agricultural land use dynamics of the past decades, to statistically assess the causal reasons for those changes and to model the future agricultural land use dynamics on Tenerife. Therefore, an object-based classification procedure for recent RapidEye data (2010), Spot 4 (1998) as well as SPOT 1 (1986-88) imagery was developed, followed by a post classification comparison (PCC). Older agricultural fallow land or agricultural set-aside with a higher level of natural succession can hardly be acquired in the used medium satellite imagery. Hence, a second detection technique was generated, which allows an exact identification of the total agriculturally affected area on Tenerife, also containing older agricultural fallow land or agricultural set-aside. The method consists of an automatic texture-oriented detection and area-wide extraction of linear agricultural structures (plough furrows and field boundaries of arable land, utilised and non-utilised agricultural terraces) in current orthophotos of Tenerife. Once the change detection analysis is realised, it is necessary to identify the different driving forces which are responsible for the agricultural land use dynamics. The statistical connections between agricultural land use changes and these driving forces are identified by the use of correlation and regression analyses.

Pan-sharpening of moderate resolution multispectral remote sensing data with those of a higher spatial resolution is a standard practice in remote sensing image processing. This paper suggests a method by which the spatial properties of resolution merge products can be assessed. Whereas there are several accepted metrics, such as correlation and root mean square error, for quantifying the spectral integrity of fused images, relative to the original multispectral data, there is less agreement on a means by which to assess the spatial properties, relative to the original higher-resolution, pansharpening data. In addition to qualitative, visual, and somewhat subjective evaluation, quantitative measures used have included correlations between high-pass filtered panchromatic and fused images, gradient analysis, wavelet analysis, among others. None of these methods, however, fully exploits the spatial and structural information contained in the original high resolution and fused images. This paper proposes the use of the Fourier transform as a means to quantify the degree to which a fused image preserves the spatial properties of the pan-sharpening high resolution data. A highresolution 8-bit panchromatic image was altered to produce a set of nine different test images, as well as a random image. The Fourier Magnitude (FM) image was calculated for each of the datasets and compared via FM to FM image correlation. Furthermore, the following edge detection algorithms were applied to the original and altered images: (a) Canny; (b) Sobel; and (c) Laplacian. These edge-filtered images were compared, again by way of correlation, with the original edge-filtered panchromatic image. Results indicate that the proposed method of using FTMI as a means of assessing the spatial fidelity of high-resolution imagery used in the data fusion process outperforms the correlations produced by way of comparing edge-enhanced images.

In the last few years, change detection based on remote sensing data has become a highly frequented field of research with multiple applications for practical use. To detect changes between temporarily different satellite images is of interest for example in terms of urban monitoring and disaster management. The approach presented in this paper allows the fully automatic detection of small-scaled changes (e.g. vehicles or construction sites) in time series of SAR amplitude image data. To create a robust method, only one single parameter encoding the size of the detected changes has to be set by the operator. Furthermore, first steps concerning the categorization of the detected changes are presented. As dataset, a time series of high resolution SAR images acquired by the German satellite TerraSAR-X was used. The time span of this time series, acquired in ascending and descending orbit, is about half a year.

This study investigated how different fusion algorithms performed when applied to very high spatial resolution (VHSR) satellite images that encompass ongoing- and post-crisis scenes. The evaluation entailed twelve fusion algorithms. The selected algorithms were applied to GeoEye-1 satellite images taken over three different geographical settings representing natural and anthropogenic crises that had occurred in the recent past: earthquake-damaged sites in Haiti, flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka. Spectral quality metrics included correlation coefficient, peak signal-to-noise ratio index, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence and high-pass correlation coefficient. Under each metric, fusion methods were ranked and best competitors were identified. In this study, the Ehlers fusion, wavelet-PCA fusion (WVPCA), and the high-pass filter fusion algorithms reported the best values for the majority of spectral quality indices. Under spatial metrics, the University of New Brunswick and Gram-Schmidt fusion algorithms reported the optimum values. The color normalization sharpening and subtractive resolution merge algorithms exhibited the highest spectral distortions where as the WV-PCA algorithm showed the weakest spatial improvement. In conclusion, we recommend the University of New Brunswick algorithm if visual image interpretation is involved, whereas the high-pass filter fusion is recommended if semi- or fully-automated feature extraction is involved, for pansharpening VHSR satellite images of ongoing and post crisis sites

A relatively new field of interest, which continuously gains grounds nowadays, is digital 3D modeling. However, the methodologies, the accuracy and the time and effort required to produce a high quality 3D model have been changing drastically the last few years. Whereas in the early days of digital 3D modeling, 3D models were only accessible to computer experts in animation, working many hours in expensive sophisticated software, today 3D modeling has become reasonably fast and convenient. On top of that, with online 3D modeling software, such as 123D Catch, nearly everyone can produce 3D models with minimum effort and at no cost. The only requirement is panoramic overlapping images, of the (still) objects the user wishes to model. This approach however, has limitations in the accuracy of the model. An objective of the study is to examine these limitations by assessing the accuracy of this 3D modeling methodology, with a Terrestrial Laser Scanner (TLS). Therefore, the scope of this study is to present and compare 3D models, produced with two different methods: 1) Traditional TLS method with the instrument ScanStation 2 by Leica and 2) Panoramic overlapping images obtained with DSLR camera and processed with 123D Catch free software. The main objective of the study is to evaluate advantages and disadvantages of the two 3D model producing methodologies. The area represented with the 3D models, features multi-scale folding in a cipollino marble formation. The most interesting part and most challenging to capture accurately, is an outcrop which includes vertically orientated micro folds. These micro folds have dimensions of a few centimeters while a relatively strong relief is evident between them (perhaps due to different material composition). The area of interest is located in Mt. Hymittos, Greece.

Rock microfracturing in the Earth's crust preceding a seismic rupture may cause local surface deformation fields, rock dislocations, charged particle generation and motion, electrical conductivity changes, gas emission, fluid diffusion, electrokinetic, piezomagnetic and piezoelectric effects. Space-time anomalies of Earth’s emitted radiation (radon in underground water and soil , thermal infrared in spectral range measured from satellite months to weeks before the occurrence of earthquakes etc.), ionospheric and electromagnetic anomalies are considered as pre-seismic signals. Satellite remote sensing data provides a systematic, synoptic framework for advancing scientific knowledge of the Earth complex system of geophysical phenomena which often lead to seismic hazards. The GPS data provides exciting prospects in seismology including detecting, imaging and analyzing signals in regions of seismo-active areas. This paper aims at investigating thermal seismic precursors for some major earthquakes in Romania in Vrancea area, occurred in 1977, 1986, 1990 and 2004, based on time series satellite data provided by NOAA and MODIS. Quantitative analysis of land surface temperature (LST) and ongoing long wave radiation (OLR) data extracted from satellite and in-situ monitoring available data recorded before and during the occurrence of earthquake events shows the consistent increasing in the air and land surface in the epicentral locations several days before earthquake, and at different distances of hypocenters function of registered earthquake moment magnitude.

The aim of this paper is to present a freely available data service platform (http://ivfl-info.boku.ac.at/) for executing preprocessing operations (such as data smoothing, spatial and temporal sub-setting, mosaicking and reprojection) of time series of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (NDVI and EVI) on request. The web-application is based on the integration of various software and hardware components: a web-interface and a MySQL database are used to collect and store user’s requests. A server-side application schedules the user’s requests and delivers the results. The core of the processing system is based on the “MODIS” package developed in R, which provides MODIS data collection and pre-processing capabilities. Smoothed and gap-filled data sets are derived using the state-ofthe- art Whittaker filter implemented in Matlab. After the processing, data are delivered directly via ftp access. An analysis of the performance of the web-application, along with processing capacity is presented. Results are discussed, in particular in view of an operative platform for real time filtering, phenology and land cover mapping.

Earth observation techniques intended for archaeological research, such as satellite images and ground geophysical surveys are well established in the literature. In contrast, low altitude airborne systems for supporting archaeological research are still very limited. The “ICAROS” project, funded by the Cyprus Research Promotion Foundation, aims to develop an airborne system for archaeological investigations. The system will incorporate both a GER 1500 field spectroradiometer and NIR camera in a balloon system operated from the ground. The GER 1500 field spectroradiometer has the capability to record reflectance values from 400 nm up to 1050 nm (blue/green/red and NIR band). The Field of View (FOV) of the instrument is 4o while a calibrated spectralon panel will be used in order to minimize illumination errors during the data collection. Existing atmospheric conditions will be monitored using sun-photometer and meteorological station. The overall methodology of the project and the preliminary results from different cases studies in Cyprus are presented and discussed in this paper. Some practical problems are also discussed and the overall results are compared with satellite and ground measurements. Spectroradiometric measurements and NIR images will be taken from different heights from the balloon system. The results will be compared with different satellite images.

In order to reduce the negative influence caused by the temperature and voltage variations of the FPGA (Field Programmable Gate Array), we propose a new FPGA-based time-to-digital converter. The proposed converter adopts a high-stability TCXO (Temperature Compensated Crystal Oscillator), a FPGA and a new algorithm, which can significantly decrease the negative influence due to the FPGA temperature and voltage variations. This paper introduces the principle of measurement, main framework, delayer chain structure and delay variation compensation method of the proposed converter, and analyzes its measurement precision and the maximum measurement frequency. The proposed converter is successfully implemented with a Cyclone I FPGA chip and a TCXO. And the implementation method is discussed in detail. The measurement precision of the converter is also validated by experiments. The results show that the mean measurement error is less than 260 ps, the standard deviation is less than 300 ps, and the maximum measurement frequency is above 10 million times per second. The precision and frequency of measurement for the proposed converter are adequate for the 3D imaging lidar (light detection and ranging). As well as the 3D imaging lidar, the converter can be applied to the pulsed laser range finder and other time interval measuring areas.

Change detection in urban areas by investigating image data of remote sensing satellites is an important topic. Of special interest is, for example, the detection of changes in terms of monitoring and disaster management, where accurate information about dimension and category of changes are frequently requested. Hence, in this paper, a workflow for object-oriented multispectral classification is presented to differentiate between traffic infrastructure, water, vegetation and non-vegetation areas. Changes are detected by analyzing multi-temporal classification results. For this, multitemporal QuickBird images covering the city Karlsruhe and LiDAR data are investigated to detect urban change areas.

In recent years natural disasters have had an increasing impact leading to tremendous economic and human losses. Remote sensing technologies are being used more often for rapid detection and visualization of changes in the affected areas, providing essential information for damage assessment, planning and coordination of recovery activities. This study presents a GIS-based approach for the detection of damaged buildings. The methodology is based on the integrated analysis of vector data containing information about the original urban layout and remotely sensed images obtained after a catastrophic event. For the classification of building integrity a new ‘Detected Part of Contour’ (DPC) feature was developed. The DPC feature defines a part of the building contour that can be detected in the related remotely sensed image. It reaches maximum value (100%) if the investigated building contour is intact. Next, several features based on the analysis of textural information of the remotely sensed image are considered. Finally, a binary classification of building conditions concludes the change detection analysis. The proposed method was applied to the 2010 earthquake in Qinghai (China). The results indicate that a GIS-based analysis can markedly improve the accuracy of change detection analysis. The proposed methodology has been developed solely within the Open Source Software environment (GRASS GIS, Python, Orange). The employment of Open Source Software provides the way for an innovative, flexible and costeffective implementation of change detection operations.

Situation awareness in complex urban environments is an important component for a successful task fulfillment both in military and civil area of applications. In the first area, the fields of deployment of the members of the North Atlantic Alliance have been changed, in the past two decades, from the originally assigned task of acting as national and allied defense forces within the partners’ own borders to out-of-area missions under conditions of an asymmetric conflict. Because of its complicated structure, urban terrain represents a particular difficulty of military missions such as patrolling. In the civil field of applications, police and rescue forces are also often strongly dependent on a local visibility and accessibility analysis. However, the process of decision-taking within a short time and under enormous pressure can be extensively trained in an environment that is tailored to the concrete situation. The contribution of this work consists of context-based modeling of urban terrain that can be then integrated into simulation software, for example, Virtual Battlespace 2 (VBS2). The input of our procedure is made up by the airborne sensor data, collected either by an active or a passive sensor. The latter is particularly important if the application is time-critical or the area to be explored is small. After description of our procedure for urban terrain modeling with a detailed focus on the recent innovations, the main steps of model integration into simulation software will be presented and two examples of missions for military and civil applications that can be easily created with VBS2 will be given.

Urban sprawl driven by shifts in tourism development produces new suburban landscapes of water consumption on Mediterranean coasts. Golf courses, ornamental, 'Atlantic' gardens and swimming pools are the most striking artefacts of this transformation, threatening the local water supply systems and exacerbating water scarcity. In the face of climate change, urban landscape irrigation is becoming increasingly important from a resource management point of view. This paper adopts urban remote sensing towards a targeted mapping approach using machine learning techniques and highresolution satellite imagery (WorldView-2) to generate GIS-ready information for urban water consumption studies. Swimming pools, vegetation and – as a subgroup of vegetation – turf grass are extracted as important determinants of water consumption. For image analysis, the complex nature of urban environments suggests spatial-spectral classification, i.e. the complementary use of the spectral signature and spatial descriptors. Multiscale image segmentation provides means to extract the spatial descriptors – namely object feature layers – which can be concatenated at pixel level to the spectral signature. This study assesses the value of object features using different machine learning techniques and amounts of labeled information for learning. The results indicate the benefit of the spatial-spectral approach if combined with appropriate classifiers like tree-based ensembles or support vector machines, which can handle high dimensionality. Finally, a Random Forest classifier was chosen to deliver the classified input data for the estimation of evaporative water loss and net landscape irrigation requirements.

The importance of having a 3D urban city model is recognized in many applications, such as management offices of risk and disaster, the offices for city planning and developing and others. As an example of urban model, we reconstructed 3D KIT campus manually in this study, by utilizing airborne LiDAR point cloud data. The automatic extraction of building shapes was left in future work.

The anthropogenic impact of the mining industry on the environment is seen all over the world. In the last decades several mining areas and corresponding waste disposal sites in Bulgaria are being monitored for ongoing reclamation processes. In this research we were focused on one environmental status of one of the most important copper producing fields for our country - Medet deposit. The objectives of the study were: (1) to analyze multispectral satellite images for 1980 - 2000 in order to assess the environmental pollution from the mining activity in the Medet open pit mine in temporal perspective; (2) to prove that by means of remote sensing an integrated environmental impact assessment can be made. After ceasing its exploitation in 1994 a rehabilitation program for soil cover and hydrographic network was established and launched. A continuous task is the monitoring of these activities from the beginning for at least 15 years period. We consider that revealing the potential of satellite multispectral and multitemporal imagery will provide valuable information on the impact of this long-term mining activity on the environment. One of the first tasks was to prepare thematic maps for several, non-successive years of the affected areas at regional scale. On the next step change detection methods were used to assess the short-term reclamation activities by examination of vegetation cover status in the areas surrounding the mine. To complete this tasks data from Landsat TM/ETM+ instruments combined with in-situ measured data was used. For data processing several techniques, both standard, such as basic and advanced statistics, image enhancement and data fusion, and novel methods for supervised classification were used. The results obtained show that used data and the implemented approach are useful in environmental monitoring and economically attractive for the company responsible for the ecological state of the region.

This paper establishes a statistical framework of forest coverage models for spatio-temporal data. The forest coverage ratio of grid-cell data is modeled by taking human population density and relief energy as explanatory variables. The likelihood of the forest ratios is decomposed by the product of two likelihoods. The first likelihood discussed by Nishii and Tanaka (2010) is due to trinomial logistic distributions on three categories: the ratios take zero, one, or values between zero and one. We consider a precise modeling to the second likelihood for partlydeforested ratios by considering a) spline functions to the additive mean structure, b) wide spatial dependency of normal error terms, and c) an extended logistic type transform to the forest ratio. For spatio-temporal data, we implement auto-regressive terms based on the ratios observed in past. The proposed model was applied to real grid-cell data and resulted significant improvement compared to our previous model.

The increasing volume of industrial solid wastes presents a critical problem for the global environment. In the detection and monitoring of these industrial solid wastes, the traditional field methods are generally expensive and time consuming. With the advantages of quick observations taken at a large area, remote sensing provides an effective means for detecting and monitoring the industrial solid wastes in a large scale. In this paper, we employ an object-oriented method for detecting the industrial solid waste from HJ satellite imagery. We select phosphogypsum which is a typical industrial solid waste as our target. Our study area is located in Fuquan in Guizhou province of China. The object oriented method we adopted consists of the following steps: 1) Multiresolution segmentation method is adopted to segment the remote sensing images for obtaining the object-based images. 2) Build the feature knowledge set of the object types. 3) Detect the industrial solid wastes based on the object-oriented decision tree rule set. We analyze the heterogeneity in features of different objects. According to the feature heterogeneity, an object-oriented decision tree rule set is then built for aiding the identification of industrial solid waste. Then, based on this decision tree rule set, the industrial solid waste can be identified automatically from remote sensing images. Finally, the identified results are validated using ground survey data. Experiments and results indicate that the object-oriented method provides an effective method for detecting industrial solid wastes.

This study explored the utility of an object-based image classification approach for mapping land cover in a heterogeneous coastal zone using WorldView-2 imagery. Two relatively modern and robust supervised machine learning algorithms i.e. random forest (RF) and support vector machines (SVM) were also compared. Image segmentation was performed, and ten broad land cover classes were identified. Subsequently, we assessed the performance of an object based image classification and the selected machine learning algorithms in mapping the land cover classes. The validation of the thematic land cover maps derived from RF and SVM were assessed using an independent test dataset generated from field work data and aerial photography interpretation. Results showed that both the machine learning classifiers in combination with the object-based approach are useful in mapping land cover in heterogeneous coastal areas. However, SVM achieved the best overall accuracy (93.79%) and kappa statistic (0.93) while RF produced an overall accuracy of 86.94% and kappa value of 0.85. Overall, the study underlined the utility of combining an objectbased image classification with machine learning classifiers for mapping land-cover in heterogeneous coastal areas – a previously challenging task with broad band satellite sensors and traditional pixel-based image classification approaches.

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as user´s and producer´s accuracy.

Vegetation cover is a key parameter in analyzing the state and dynamics of ecosystems. Africa's semi-arid savanna's are particularly prone to degradation, due to increasing population pressure as well as ongoing climatic changes. In most global land cover classifications inhomogeneous areas are aggregated into few discrete classes, delivering unsatisfying results in highly variable biomes, especially savanna's with their small scale patches of woody and herbaceous vegetation and bare soil. Fractional cover(FC) classifications, which provide an estimate of sub-pixel continuous cover percentages of underlying land cover classes, and are therefore an improved thematic representation, can deliver additional information for monitoring and decision making. Prior research demonstrated that multi-scale approaches are suitable for transferring en-detail information from a small subset to a larger study area via statistical up-scaling (e.g. Random Forest). In this case study the robustness of this up-scaling approach and the limits of the spatial and temporal transferability at the very high and intermediate resolution were analysed in the Caprivi Strip in Namibia and the adjacent Western Province of Zambia. The key research questions were to quantify i) the robustness of the upscaling, ii) the loss of accuracy depending on the lag in image acquisitions, iii) the loss of accuracy dependent on the time of image acquisition in the phenological cycle. To this end 12 Worldview(WV) and all usable Landsat TM and ETM+ images, covering all phases of the vegetation cycle were obtained. The analysis showed that continuous FC mapping is a highly suitable concept for semi-arid ecosystems with gradual transitions. The optimal time for WV acquisition was at the beginning of the dry season. The RMSE was unusable for LS images recorded in the rainy season between November and March, but otherwise it was usable even for larger lags up to a month, with deviations below 15%. As long as the spatial training subset(s) cover the whole occurring range of vegetation densities, comparably small WV scenes are sufficient to reliably scale to regional results.

Soil salinization is a form of topsoil degradation due to the formation of soluble salts at deleterious levels. This phenomenon can seriously compromise vegetation health and agricultural productivity, and represents a worldwide environmental problem. Remote sensing is a very useful tool for soil salinization monitoring and assessment. In this work we show some results of a study aimed to define a methodology for soil salinity assessment in Iraq based on SPOT 5 imagery. This methodology allows the identification of salinized soils primarily on bare soils. Subsequently some soil salinity assessment can be done on vegetated soils. On bare soil the identification of salt is based on spectral analysis, using the Minimum Noise Fraction transformation and several indexes found in literature. In case of densely vegetated soils the methodology for the discrimination of salinized soils has been integrated with the results obtained from the classification of vegetation coverage.

Sustainability of irrigated agriculture-based economies, such as in Central Asia, is threatened by cropland degradation. The field-based identification of the degraded agricultural areas can aid in developing appropriate land rehabilitation and monitoring programs. This paper combined the object-based change detection and spectral mixture analysis to develop an approach for identifying parcels of irrigated degraded cropland in Northern Uzbekistan, Central Asia. A linear spectral unmixing, followed by the object-based change vector analysis, was applied to the multiple Landsat TM images, acquired in 1987 and 2009. Considering a spectral dimensionality of Landsat TM, a multiple 4-endmember model (green vegetation, water, dark soil, and bright soil) was set up for the analysis. The spectral unmixing results were valid, as indicated by the overall root mean square errors of <2.5% reflectance for all images. The results of change detection revealed that about 33% (84,540 ha) of cropland in the study area were affected by the degradation processes to varying degrees. Spatial distribution of degraded fields was mainly associated with the abandoned fields and lands with inherently low fertile soils. The proposed approach could be elaborated for a field-based monitoring of cropland degradation in similar landscapes of Central Asia and elsewhere.

Considering the dramatic change occurred in the Blue Nile region of Sudan, this study is of great value for developing a method for identification of forestland cover extents, integrating rate of changes and causes. The study utilizes three consecutive optical multispectral images, two LANDSAT TM images of 1990 and 1999 as well as TERRA ASTER image of 2009 to evaluate forest cover dynamics during the period 1990 to 2009. The method adopted in this research consists in cross operation of classified images of different points in time, which utilizes the overlaying images to be compared for change detection. New layer of segments was created representing the change areas as well as the overlapped areas of each pair of classified images. Consequently, a series of optimized algorithms have been developed to estimate the change in Land Use Land Cover (LULC). At the fundamental stage, smooth and accurate classified images are very essential for any post-classification change detection technique, which were typically achieved by object-based approach (OB) with overall accuracy 91 %, 93 % and 95 % for the years 1990, 1999 and 2009 respectively. Nine LULC classes were generated from each, i.e. agriculture (Ag.), bare-land (Br.), crop-land (Cr.), dense-forest (DF), grassland (Gr.), orchard (Or.), scattered-forest (SF), settlements (St.) and water (W). Therefore, and considering the dramatic change observed in the area, the fusion operation of multi-temporal data results initially in quite numerous change "from-to" information classes, which allows for aggregation of these classes at any hierarchical level of details. Moreover, the developed approach allows the operator to effectively know the spatial pattern of change, trend and magnitude of the dynamics occurred in each of the classified LULC classes. While many change-detection techniques have been developed, a little has been done to assess the quality of these techniques. Hence, the change maps resulting from cross operation were assessed, which reveals that, the accuracies of the change maps for the two time intervals were consistently high.

Increasing of atmospheric ozone concentrations have received great attention around the whole because of its characteristic, in order to degrade air quality and brings hazard to human health and ecosystems. Ozone, one of the most pollutants source and brings a variety of adverse effects on plant life and human being. Continuous monitoring on ozone concentrations at atmosphere provide information and precautions for the high ozone level, which we need to be established. Satellite observation of ozone has been identified that it can provide the precise and accurate data globally, which sensitive to the small regional biases. We present measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) included on the European environmental satellite ENVISAT, launched on 1st of March 2002. Main objective of this study is to examine the ozone distribution over Peninsular Malaysia using SCIAMACHY level-2 of total ozone column WFMD version 1.0 with spatial resolution 1° x 1.25°. Maps of time averaged (yearly, tri-monthly) ozone was generated and analyzed over Peninsular Malaysia for the year 2003 using PCI Geomatica 10.3 image processing software. It was retrieved using the interpolation technique. The concentration changes within boundary layer at all altitude levels are equally sensitive through the SCIAMACHY nearinfrared nadir observations. Hence, we can make observation of ozone at surface source region. The results successfully identify the area with highest and lowest concentration of ozone at Peninsular Malaysia using SCIAMACHY data. Therefore, the study is suitable to examine the distribution of ozone at tropical region.

High spatial and temporal resolution Normalized Difference Vegetation Index (NDVI) data can be used to describe vegetation dynamics and provide the variation of surface for monitoring phenology and land cover change quantitatively. This paper presents a method using MODIS Land Cover data with 30m LULC map calculates the percentage of every class in the MODIS pixel. And the mean MODIS NDVI can be got through the average value of pure pixels using MODIS NBAR product from 2004 to 2010. Then the logistic model is fitted to the average MODIS NDVI to simulate the variation in NDVI time series. At last, the simulated NDVI time series of all vegetation types are extracted as background values and the HJ-1 CCD NDVI is used to adjust the curve of time-series NDVI to estimate the NDVI at high spatial and temporal resolution. The method is applied to the Heihe River basin and the region growing two crops a year. The results are compared with some filed measured data, which shows the high feasibility of the method to generate accurate and reliable data. It is proved that the method can be used in small scales to lager regions and the results can be a kind of fundamental data in other studies.

Spectral reflectance for soils and vegetation of the Yarmouk basin were correlated with surficial soil properties and vegetation biomass and cover. The overall aim of the study was to identify bands suitable for assessing soil and vegetation as indices for land degradation and desertification. Results showed that vegetation was well separated from soils in the shortwave infrared wavelength at 1480 nm. For most sites, the differences in the bandwidths (in the range of 8.5 nm to 90 nm) did not improve the differentiation of vegetation types. For all wavelengths, stronger correlation values (maximum R² = 0.85) were obtained for vegetation cover when compared with biomass (maximum R² = 0.54). Soil spectral reflectance tended to increase with salinity, with maximum correlations obtained in the blue wavelengths (470±10 nm, 485±90 nm), followed by green and the NIR bands, where R² values were around 0.60. Comparing results from radiometer measurements with results obtained from ASTER image bands showed that correlations tended to decrease with decreased spatial resolution for the investigated soil properties. For all wavelengths, spectral reflectance of degraded soils was higher than that for natural vegetation and irrigated crops with partial surface cover. Results of the study showed that the use of remote sensing indices related to vegetation cover and soil salinity would be recommended to map the extent of land degradation in the study area and similar environments. However, spectral unmixing should be applied to improve the correlations between satellite remote sensing data and surficial soil properties.

Heavy rain from Tropical Storm Lee resulted in a major flood event for the southern tier of New York State in early September 2011 causing evacuation of approximately 20,000 people in and around the city of Binghamton. In support of the New York State Office of Emergency Management, a high resolution multispectral airborne sensor (WASP) developed by RIT was deployed over the flooded area to collect aerial images. One of the key benefits of these images is their provision for flood inundation area mapping. However, these images require a significant amount of storage space and the inundation mapping process is conventionally carried out using manual digitization. In this paper, we design an automated approach for flood inundation mapping from the WASP airborne images. This method employs Spectral Angle Mapper (SAM) for color RGB or multispectral aerial images to extract the flood binary map; then it uses a set of morphological processing and a boundary vectorization technique to convert the binary map into a shapefile. This technique is relatively fast and only requires the operator to select one pixel on the image. The generated shapefile is much smaller than the original image and can be imported to most GIS software packages. This enables critical flood information to be shared with and by disaster response managers very rapidly, even over cellular phone networks.

The monitoring of flood events with synthetic aperture radar (SAR) sensors has attracted a considerable amount of attention during the last decade, owing to the growing interest in using spaceborne data in near-real time flood management. Most existing methods for classifying flood extent from SAR data rely on pure image processing techniques. In this paper, we propose a method involving a priori knowledge about an area taken from a multitemporal time series and a digital elevation model. A time series consisting of ENVISAT ASAR acquisitions was geocoded and coregistered. Then, a harmonic model was fitted to each pixel time series. The standardised residuals of the model were classified as flooded when exceeding a certain threshold value. Additionally, the classified flood extent was limited to flood-prone areas which were derived from a freely available DEM using the height above nearest drainage (HAND) index. Comparison with two different reference datasets for two different flood events showed that the approach yielded realistic results but underestimated the inundation extent. Among the possible reasons for this are the rather coarse resolution of 150 m and the sparse data coverage for a substantial part of the time series. Nevertheless, the study shows the potential for production of rapid overviews in near-real time in support of early response to flood crises.

Winter weather conditions in the Eastern Black Sea are characterized by fast changes affecting deeply on the territory of the Krasnodar Region of Russia and the coastal water area of the Black Sea. Offshore blowing local winds in the east of the Black Sea are often strongly variable in space and time due to the mountainous coastline. In particular, near the Russian city of Novorossiysk the winds can be quite strong (above 15 m/s), in which case they are boras. In the beginning of 2012 the most extreme weather conditions were observed on January 25-29 and February 6-9 in Novorossiysk and its neighborhoods during anomalously strong bora events. Acquired remote sensing (SAR and optical) imagery allowed to study these events. Useful information on the near-surface conditions were derived from the SAR and optical images by using analysis of the surface manifestations and cloud signatures associated with bora.

This paper introduces an automatic flood mapping application that is hosted on the Grid Processing on Demand (GPOD) Fast Access to Imagery (Faire) environment of the European Space Agency. The main objective of the online application is to deliver operationally flooded areas using both recent and historical acquisitions of SAR data. Having as a short-term target the flooding-related exploitation of data generated by the upcoming ESA SENTINEL-1 SAR mission, the flood mapping application consists of two building blocks: i) a set of query tools for selecting the “crisis image” and the optimal corresponding “reference image” from the G-POD archive and ii) an algorithm for extracting flooded areas via change detection using the previously selected “crisis image” and “reference image”. Stakeholders in flood management and service providers are able to log onto the flood mapping application to get support for the retrieval, from the rolling archive, of the most appropriate reference image. Potential users will also be able to apply the implemented flood delineation algorithm. The latter combines histogram thresholding, region growing and change detection as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. Both algorithms are computationally efficient and operate with minimum data requirements. The case study of the high magnitude flooding event that occurred in July 2007 on the Severn River, UK, and that was observed with a moderateresolution SAR sensor as well as airborne photography highlights the performance of the proposed online application. The flood mapping application on G-POD can be used sporadically, i.e. whenever a major flood event occurs and there is a demand for SAR-based flood extent maps. In the long term, a potential extension of the application could consist in systematically extracting flooded areas from all SAR images acquired on a daily, weekly or monthly basis.

Satellite remote sensing is providing a systematic, synoptic framework for advancing scientific knowledge of the Earth as a complex system of geophysical phenomena that, directly and through interacting processes, often lead to natural hazards. The recent eruption of a submarine volcano at the El Hierro Island has provided a unique and outstanding source of tracer that may allow us to study a variety of structures. The island off the Atlantic coast of North Africa—built mostly from a shield volcano—has been rocked by thousands of tremors and earthquakes since July 2011, and an underwater volcanic eruption 300 meters below sea level started on October 10, 2011. Thanks to this natural tracer release, low and high-resolution satellite images obtained from MODIS, MERIS and WorldView sensors have been processed to provide information on the concentration of a number of marine parameters: chlorophyll, phytoplankton, suspended matter, yellow substance, CDOM, particulate organic and inorganic, etc. This oceanographic remote sensing data has played, as well, a fundamental role during field campaigns guiding the Spanish government oceanographic vessel to the appropriate sampling areas. This paper illustrates the capabilities of satellite remote sensing systems to improve the understanding of submarine volcanic processes and hazards by providing more frequent observations and scientific information at a wide variety of wavelengths.

One of the main objectives of image processing is to optimize visualization of particular thematic dataset. The processing methodology and strategy are very different from broadband image processing in many aspects. This strategy highly depends on the application and its objectives. For natural disasters such as earthquakes and tsunamis which affect a large area, the data obtained from satellite image processing can be utilized. The data can be used for disaster management for rescue and relief plan during disaster and disaster preparedness for future disasters. In order to meet objectives of disaster management, it is normally required to have a complete information system. The type of disaster may also dictate the type of processing and interpretation technique of images. This paper reviews the methods of satellite image processing and also the disaster management requirements. Based in these two issues the advantages and limitations of image processing methods have been discussed considering important natural disasters.

This paper presents algorithm for flood inundation mapping to understand seasonal and annual changes in the flood extent and in the context of emergency response. Time-series profiles of Land Surface Water Index (LSWI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Snow Index (NDSI) are obtained from MOD09 8-day composite time-series data (resolution 500m; time period: 2000-2011). The proposed algorithm was applied for MODIS data to produce time-series inundation maps for the ten annual flood season over the period from 2000 to 2011. The flood product has three classes as flood, mixed and long-term water bodies. The MODIS flood products were validated via comparison with ALOS AVINIR / PALSAR and Landsat TM using the flood fraction comparison method. Compared with the ALOS satellite data sets at a grid size of 10km the obtained RMSE range from 5.5 to 15 km² and the determination coefficients range from 0.72 to 0.97. The spatial characteristics of the estimated early, peak and late and duration of inundation cycle were also determined for the period from 2000 to 2011. There are clear contracts in the distribution of the estimated flood duration of inundation cycles between large-scale floods (2008-2010) and medium and small-scale floods (2002 and 2004). Examples on the analysis of spatial extent and temporal pattern of flood-inundated areas are of prime importance for the mitigation of floods. The generic approach can be used to quantify the damage caused by floods, since floods have been increasing each year resulting in the loss of lives, property and agricultural production.

The objective of this study is to investigate the effects of orography on the rainfall, wind, and cloud systems of the TCs in Malaysia and Indochina. To determine the relationship of the typhoon with the orographic effect, remote sensing techniques such as the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite, rainfall data from the Fengyun 2D (FY-2D), and radiosonde data were applied in this study. From this study, the following conclusions can be drawn: 1) rainfall tends to be distributed over high mountain regions; 2) wind flow will change its direction upon encountering any restrictions, especially those of high terrain regions; and 3) cloud patterns are deformed by high mountains and tend to flow with the mountains' structure because of the orographic effects. The regions most affected by Typhoon Ketsana in the study area were Vietnam in Indochina, Sabah in East Malaysia (EM), Kelantan and Terengganu in Peninsular Malaysia (PM). From the comparison among the study areas, it was found that Indochina had the most significant results for the orographic effects on typhoon activity, followed by the tail effects in EM. This phenomenon was found in PM, although it was not as significant as the other study areas. This remote sensing technique allows tropical cyclones to be forecasted and their impacts to be defined, and it allows disaster zones to be determined.

As a kind of huge environmental risk source, tailings pond could cause a huge environmental disaster to the downstream area once an accident happened on it. Therefore it has become one key target of the environmental regulation in china. Especially, recently environmental emergencies caused by tailings pond are growing rapidly in China, the environmental emergency management of the tailings pond has been confronting with a severe situation. However, the regulatory agency is badly weak in the environmental regulation of tailings pond, due to the using of ground surveys and statistics which is costly, laborious and time consuming, and the lacking of strong technical and information support. Therefore, in this paper, according to the actual needs of the environmental emergency management of tailings pond, we firstly make a brief analysis of the characteristics of the tailings pond and the advantages and capability of remote sensing technology, and then proposed a comprehensive and systematic indexes system and the method of environmental risk monitoring of tailings pond based on remote sensing and GIS. The indexes system not only considers factors from the upstream area, the pond area and the downstream area in a perspective of the risk space theory, but also considers factors from risk source, risk receptor and risk control mechanism in a perspective of risk systems theory. Given that Zhangjiakou city has up to 580 tailings pond and is nearly located upstream of the water source of Beijing, so finally we apply the proposed indexes system and method in Zhangjiakou area in China to help collect environmental risk data of tailings pond in that area and find out it works well. Through the use case in Zhajiakou, the technique of using remote sensing to monitor environmental risk of tailings pond is feasible and effective, and would contribute to the establishment of ‘Space-Ground’ monitoring network of tailings pond in future.

Remote sensing is a key application in global-change science and urban climatology. Urbanization, the conversion of other types of land to uses associated with growth of populations and economy has a great impact on both micro-climate as well as macro-climate. By integrating high-resolution and medium-resolution satellite imagery with other geospatial information, have been investigated several land surface parameters including impervious surfaces and land surface temperatures for Bucharest metropolitan area in Romania. The aim of this study is to examine the changes in land use/cover pattern in a rapidly changing area of Bucharest in relation to urbanization since the 1990s till 2011 and then to investigate the impact of such changes on the intensity and spatial pattern of the UHI (Urban Heat Island) effect in the region in relation with heat waves assessment. Investigation of radiative properties, energy balance, heat fluxes and NDVI, EVI is based on satellite data provided by various sensors Landsat TM/ETM, ASTER, MODIS and IKONOS. A detailed analysis was done for summer 2003, 2007 and 2010 years heat wave events in and related impacts on human health. So called effect of “urban heat island” must be considered mostly for summer periods conditions and large European scale heat waves. As future climate trends have been predicted to increase the magnitude and negative impacts of urban heat waves in Bucharest metropolitan area, there is an urgent need to be developed adequate strategies for societal vulnerability reducing.

In order to enhance the time discrimination precision in the 3D imaging lidar, we propose a new time discrimination circuit, which improves both the delayer and the attenuator in the previous CFD (Constant Fraction Discriminator) circuit. The proposed circuit mainly includes a delayer, a low-pass filter, and a comparator. The delayer is implemented with a series of inductors and capacitors, which has some advantages: low signal distortion, small volume, easy adjustment, etc. The low-pass filter attenuates the signal amplitude and broadens the signal width, as well as reduces the noise by decreasing the equivalent noise bandwidth, and increases the signal slope at the discrimination time. Therefore, the time discrimination error is reduced significantly. This paper introduces the proposed circuit in detail, carries out a theoretical analysis for the noise and time discrimination error in the proposed circuit and compares them with the previous CFD circuit. The comparison results show that the proposed circuit can reduce the time discrimination error by about 50% under the same noise level. In addition, some experiments have been carried out to test the performances of the circuit. The experiments show that the time delay of the circuit is about 14ns, the time discrimination error is less than 150 ps when the voltage SNR ranges from 18.2 to 81.8, and the time discrimination error is less than 100 ps when the signal amplitude ranges from 0.2 V to 1.86 V. The tested time discrimination error is well in accordance with the theoretical calculation.

In this paper we present the scientific data recorded by tunable mechanical monolithic horizontal seismometers located in the Gran Sasso National Laboratory of the INFN, within thermally insulating enclosures onto concrete slabs connected to the bedrock. The main goals of this long term test are a preliminary seismic characterization of the site in the frequency band 10⁻⁷÷1Hz and the acquisition of all the relevant information for the optimization of the sensors.

This paper describes a new mechanical implementation of a folded pendulum based inertial sensor, configurable as seismometer and as accelerometer.1 The sensor is compact, light, scalable, tunable (< 100mHz), with large band (10−6 Hz÷10Hz), high quality factor (Q < 1500 in air) instrument and good immunity to environmental noises, guaranteed by an integrated laser optical readout. The measured sensitivity curve is in very good agreement with the theoretical one (10−12 m/ √ Hz in the band (0.1 ÷ 10Hz). Typical applications are in the field of earthquake engineering, geophysics, and in all applications requiring large band-low frequency performances coupled with high sensitivities.

We aim to compare the potentialities of four remote sensing/image processing software: PCI Geomatica V8.2, ENVI 4.7, SPRING 5.1.8, and ORFEO toolbox integrated in Monteverdi 1.11. We listed and assessed the performance of several classification algorithms. PCI Geomatica and ENVI are commercial/proprietary software and SPRING and ORFEO are open source software. We listed the main classification algorithms available in these four software, and divided them by the different types/approaches of classification (e.g., pixel-based, object-oriented, and data mining algorithms). We classified using these algorithms two images covering the same area (Porto-Vila Nova de Gaia, Northern Portugal): one Landsat TM image from October 2011 and one IKONOS image from September 2005. We compared time of performance and classification results using the confusion matrix (overall accuracy) and Kappa statistics. The algorithms tested presented different classification results according to the software used. In Landsat image, differences are greater than IKONOS image. This work could be very important for other researchers as it provides a qualitative and quantitative analysis of different image processing algorithms available in commercial and open source software.

The paper describes a tilt meter sensor for geophysical applications, based on Folded Pendulum (FP) mechanical sensor. Both the theoretical model and the experimental results of a tunable mechanical monolithic FP tilt meter prototype are presented and discussed. Some of the most important characteristics, like the measured resolution of ≈ 0.1 nrad at 100mHz, are detailed. Among the scientific results, earth tilt tides have been already observed with this monolithic FP tilt meter prototype.

In the eve of the Beijing Olympics Games, Qingdao in China, as the host city of OSC of Beijing 2008 Olympic Games, was surrounded by Enteromorpha prolifera, which was followed with interest by whole China and the world. The Enteromorpha often comes from other ocean, monitoring the drifting path of the Enteromorpha will become very important.The Study area is mainly the Yellow Sea. And the data sources are Terra MODIS 1B images from 2000-2010 years. The data preprocessing include BOW-TIE processing, image registration, clip, merge, and masking. And the NDVI was selected as the index of derived Enteromorpha prolifera information, to get the range of Enteromorpha prolifera, and get that of dynamic change with time, and monitor the drifting path of the Enteromorpha.

Multi-scale analysis of landscape statistics is very important to research ecological processes. However, it was lack of high-resolution data to extract the landscape pattern features of suspected ecological degradation region a few decades ago. CORONA panchromatic film photos acquired between 1960 and 1972 provide the historical high-resolution image resource in small-scale ecological pattern research. The purpose of this research was to assess the feasibility of landscape pattern analysis within the alpine steppe area of the Yellow River source through historical CORONA panchromatic imagery. Histogram analysis result shows that the swamp meadow is difficult to be distinguished from water, but there is a potential of mapping four patch types in alpine steppe in gray imagery. Through segmentation image of alpine steppe area, different landscape pattern metrics were calculated in several selected areas. The result also shows that landscape pattern metrics are closely related to NDVI level of alpine grassland. It can be concluded that historical CORONA panchromatic imagery is a potentially valuable high-resolution remote sensing source for long-time ecological monitoring.

Wind flow estimation over complex terrain is a crucial procedure for several applications such as prediction of wind energy resources, pollution dispersal and bridge design. Several computational models have been developed to simulate wind flow. The accuracy of the estimations is highly affected by the complexity of the terrain domain and consequently by the resolution and accuracy of the topographical input data, that describe terrain characteristics. Three wind flow computational models (MS-3DJH/3R, WaSP and 3D-RANS) are evaluated using topographical data from a complex terrain domain in Southern Greece using three remote sensing datasets of different spatial resolution. The influence of the topographical data on the accuracy of wind flow estimation over complex terrain is assessed and some interesting conclusions are derived for the three models.

The Portuguese coastline, like many other worldwide coastlines, is often submitted to several types of extreme events resulting in erosion, thus, acquisition of high quality field measurements has become a common concern. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite or aircraft mounted remote sensing systems. As an alternative, video-monitoring systems proved to be an economic and efficient way to collect useful and continuous data, and to document extreme events. In this context, is under development the project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to develop and implement monitoring techniques for the coastal zone based on a low cost video monitoring system. The pilot study area is Ofir beach (north of Portugal), a critical coastal area. In the beginning of this project (2010) a monitoring video station was developed, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented in Matlab®, allowing achieve the main video-monitoring system products, such as, the shoreline detection. An algorithm based on image processing techniques was developed, using the HSV color space, the idea is to select a study and a sample area, containing pixels associated with dry and wet regions, over which a thresholding and some morphological operators are applied. After comparing the results with manual digitalization, promising results were achieved despite the method’s simplicity, which is in continuous development in order to optimize the results.

The analysis of land cover dynamics provides insight into many environmental problems. However, there are few data sources which can be used to derive consistent time series, remote sensing being one of the most valuable ones. Due to their multi-temporal and spatial coverage needs, such analysis is usually based on large land cover datasets, which requires automated, objective and repeatable procedures. The USGS Landsat archives provide free access to multispectral, high-resolution remotely sensed data starting from the mid-eighties; in many cases, however, only single date images are available. This paper suggests an objective approach for generating land cover information from 30m resolution and single date Landsat archive satellite imagery. A procedure was developed integrating pixel-based and object-oriented classifiers, which consists of the following basic steps: i) pre-processing of the satellite image, including radiance and reflectance calibration, texture analysis and derivation of vegetation indices, ii) segmentation of the pre-processed image, iii) its classification integrating both radiometric and textural properties. The integrated procedure was tested for an area in Sardinia Region, Italy, and compared with a purely pixel-based one. Results demonstrated that a better overall accuracy, evaluated against the available land cover cartography, was obtained with the integrated (86%) compared to the pixel-based classification (68%) at the first CORINE Land Cover level. The proposed methodology needs to be further tested for evaluating its trasferability in time (constructing comparable land cover time series) and space (for covering larger areas).

One of the newest satellite sensors with stereo collection capability is ALOS. ALOS has a panchromatic radiometer with 2.5m spatial resolution at nadir and a multispectal radiometer with 10m spatial resolution. Two ALOS Prism data sets and two ALOS AVNIR collected over the same area within a year were used. The same ground control points were used for the creation of two DSMs and the orthorectification of the multispectral data. The elevation difference between the DSMs and the difference of the NDVI images created from the multispectral data were used in order to detect landslides.

From a didactic point of view the procurement and the application of modern geographical methods and functions become more and more important. Although the integration of GIS in the classroom is repeatedly demanded, inter alia in Baden-Württemberg, Germany, the number of GIS users is small in comparison to other European countries or the USA. Possible reasons for this could, for instance, lie in the lack of GIS and computer knowledge of the teachers themselves and the subsequent extensive training effort in Desktop-GIS (KERSKI 2000, SCHLEICHER 2004). Today you have the technological possibilities to provide the broad public with geoinformation and geotechnology: Web technologies offer access to web-based, mobile and local applications through simple gateways. The objective of the project “GIS4schools” is to generate a service-based infrastructure, which can be operated via mobile clients as well as via Desktop-GIS or a Browser. Due to the easy availability of the services the focus is in particular on students. This circumstance is a novelty through which a differentiated approach to the implementation of GIS in schools is established. Accordingly, the pilot nature of this project becomes apparent as well as its greater importance beyond its actual content especially for the sector of media development at colleges of education. The continuity from Web-GIS to Desktop-GIS is innovative: The goal is to create an adapted multi-level solution which allows both, an easy introduction if desired or a detailed analysis – either to be achieved with a focus especially on students and their cooperation among one another.

The Aral Sea ecological crisis resulted from the USSR government decision in 1960s to deploy agricultural project for cotton production in Central Asia. Consequently water flow in the Aral Sea decreased drastically due to the regulation of Amydarya and Syrdarya Rivers for irrigation purposes from 55-60 km3 in 1950s to 43 km3 in 1970s, 4 km3 in 1980s and 9-10 km3 in 2000s. Expert land cover classification approach gives the opportunity to use the unlimited variable for classification purposes. The band algebra (band5/band4 and Band4/Band3) and remote sensing indices (Normalized differential Salinity Index (NDSI), Salt Pan Index (SPI), Salt Index (SI), Normalized difference Vegetation Index (NDVI), Albedo, Crust Index) utilized for the land cover classification has shown satisfactory result with classification overall accuracy 86.9 % and kappa coefficient 0.85. Developed research algorithm and obtained results can support monitoring system, contingency planning development, and improvement of natural resources rational management.

The issue of island ecological environment deterioration has been a major concern worldwide, and is considered one of the critical problems crying out for solutions in the ocean ecosystem research. Based on principle of social equity, Payments for Environmental Services(PES) has, by far, become an effective means of internalization to settle the external problems of public goods in recent years. Taking islands as the object, this paper is to expound the cocept of island PES , and to reveal the mechanism of island PES with the purpose of constructing the PES mechanism of island with multidisciplinary methods, such as the marine ecology, ecological economics combined with remote-sensing data. This study will lay the theoretical foundation for ameliorating island environmental protection policy, and provide guidance to the government in making island development strategy and PES programs.