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 main aim of the research is to address the agricultural land use dynamics of the past decades and to statistically assess the causal reasons for those changes in the study area 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. Therefore, 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, pan-sharpening 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 high-resolution 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 high frequented field of research with multiple applications for practical use. To detect changes between temporarily different satellite image data is of interest for example in terms of urban monitoring and disaster management. The approach presented in this paper aims at the fully automatic detection of small scaled changes (e.g. vehicles or building 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.

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. Fused images were assessed subjectively and objectively. Spectral quality metrics included correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak signal-to-noise ratio index, entropy, 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, high-pass correlation coefficient, and RMSE of Sobel-filtered edge images. Under each metric, fusion methods were ranked and best competitors were identified. In this study, The Ehlers fusion, wavelet-PCA fusion (WV-PCA), 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 on-going 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.

According to classical earthquake theory, small earthquakes should continue to grow into large earthquakes until they spread all along the fault line. The mechanical processes of earthquake preparation are always accompanied by deformations, afterwards complex short- or long term precursory phenomena can appear. Earthquake prediction has two potentially compatible but distinctly different objectives: (a) phenomena that provide information about the future earthquake hazard useful to those who live in earthquake-prone regions and (b) phenomena causally related to the physical processes governing failure on a fault that will improve our understanding of those processes. 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 air (T) and land surface temperature (LST) and sensible surface latent heat net flux (SLHF) 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.

Continuous observations of the land surface from medium to coarse spatial resolution satellite sensors provide input to a variety of ecosystem models and environmental applications. They also provide an opportunity to investigate the potential of existing and new methodologies for land surface characterization. Most of the analysis and interpretations at global and regional scale have been based on time series of the Normalised Difference Vegetation Index (NDVI). The integration of NDVI time series in various modelling frameworks is nowadays facilitated by readily available and standardized (in time and space) products. Despite the continuous effort for making these products globally available in near real time (for example from the MODIS team), various processing steps are still required before complete and efficient integration in any modelling framework is possible. The aim of this paper is to present a freely available data service platform for managing and executing pre-processing operations of archived MODIS NDVI time series on request. An analysis of the performance of the system, along with processing and storing capacity is presented. Results are discussed, in particular in view of an operative platform for real time filtering, phenology and land cover mapping applications with a focus on Europe.

Remote sensing has been successfully used in the last years, for the detection of shallow buried relics such as archaeological remains. The detection is based mainly in photointerpretation of crop marks from high resolution satellite or aerial data. It has been shown by several researchers that remote sensing data may have a significant potential for archaeological investigations. Indeed, many studies have used different multispectral and hyperspectral satellite sensors. However, in most of these studies there is lack of ground verification for all these satellite investigations. Satellite images need to be atmospherically and geometrically corrected, before being subjected to any post-processing techniques, while some other corrections taken into account are sun elevation, elevation of the area of interest e.t.c. Therefore, in situ spectroradiometric measurements can provide more accurate results (ground "truth"data) regarding the reflectance of each target. Furthermore the use of low altitude airborne system for archaeological research is very limited. "ICAROS" project aims to develop an airborne systematic tool for archaeological investigations. The system will incorporate both ground spectroradiometer and NIR camera, in a balloon. The GER 1500 spectroradiometer has the capability to record the reflectance from 400 nm up to 1050 nm. 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. The overall methodology of the project, the calibration procedure of the system and preliminary results from different cases studies in Cyprus, are also presented and discussed in this paper. Calibration includes both geometric and radiometric calibration. Some practical problems are also discussed and the overall results are compared either with satellite or ground measurements. Spectroradiometric measurements and NIR images will be taken from different heights from the balloon system. The results will be compared with standard satellite images.

The time-to-digital converter is a key part in the 3D imaging lidar (light detection and ranging), which determines the precision of measurement and ranging of the 3D imaging lidar. Recently, the recent 3D imaging lidar begins to use the interpolating converter based on FPGA. In the interpolating converter, FPGA logical cells are used as time delayers. The delay time of the logical cell changes with FPGA temperature and voltage, which decreases the measuring precision of time interval. 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. For the 3D imaging lidar, the proposed converter has some advantages: easy application, adequate measurement frequency and precision. As well as the 3D imaging lidar, the proposed converter can be applied to the pulsed laser range finder and other time interval measuring areas.

Nowadays, change detection in urban areas by investigating image data of remote sensing satellites has become an extreme important topic. To detect changes is of interest, for example, in terms of monitoring and disaster management, where accurate information about dimension and category of changes are frequently requested. Hence, this paper treats the development of rule sets for object-oriented multispectral classification to differentiate between traffic infrastructure, water, vegetation and non-vegetation areas. Changes are detected by analyzing multi-temporal classification results. For this study, multi-temporal QuickBird images covering the city Karlsruhe and LiDAR data are used to detect areas of high construction activity.

In the last years natural disasters have had an increasing impact involving tremendous economical 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 elimination, planning and coordination of recovery activities. This study presents a GIS-based approach for detection of destroyed 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 catastrophe. For identification of building conditions a 'Detected Part of Contour' (DPC) feature was developed. The basic idea behind the proposed feature is the assessment of building contour integrity. The feature defines a part of the building contour that can be detected in the remotely sensed image. It reaches maximum value (100%) if the investigated building is intact. Furthermore, several features based on the analysis of textural information are analyzed. 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 essentially improve the accuracy of remote sensing change detection. The proposed methodology has been developed solely within the Open Source software environment (GRASS GIS, Python, Orange). The use of open source software implies an innovative, flexible and cost-effective solution of change detection operations.

The approach presented in this paper goes the way from the UAV-borne data acquisition and 3D model urban terrain modeling towards integration of the reconstruction results into the VBS2 software for mission planning and combat simulation. Small, inexpensive UAVs have become a good alternative to classical high-resolution aerial imagery, especially in cases of a time-critical demand of scene exploration or if the area of interest is too small to perform an aerial imagery mission cost-effectively. After this ad-hoc data acquisition, we create a dense 3D point cloud of the surface on which all state-of-the-art surface reconstruction approaches can be applied. We chose the context-based approach that presupposes computation of the (not-necessarily geo-referenced) DSM and DTM, separation of vegetation from buildings, reconstruction and texturing building roofs. These boundary representations are exported in the last step into the VBS2-proper format in order to create a mission for training soldiers in different levels of command in virtual 3D environments.

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 high-resolution imagery (WorldView-2) to generate GIS-ready information for urban water consumption studies. Moving beyond measuring and monitoring of urban physical properties, we demonstrate the relevance of new remote sensing imagery and analysis techniques for urban environmental 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 spatio-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 features - which can be concatenated at pixel level to the spectral signature. This study assesses the predictive power of object features and investigates the interplay of different machine learning techniques (kernel methods and tree-based ensembles), data dimensionality and the distribution and size of training and test data. The results from the image analysis are combined with estimates of evaporative water loss and net landscape irrigation requirements. Visualized in maps, the results are support for powerful communication means in decision-making, planning and water conservation campaigns. Another application field are urban microclimate studies looking into the spatial patterns of urban vegetated cooling as ecosystem service and the potential water requirements to supply these services.

The needs of having a 3D model of urban city are increasing in a wide range of practical applications, such as risk and disaster managements, various city mapping and planning and others. The main objective of this study is to find appropriate and effective procedures which can make a 3D modeling of urban area efficiently, using the airborne LiDAR point cloud data. In this study we selected our KIT campus as such a target, consisting of many complicated buildings like the urban city in the residential environment. The 3D modeling of the campus was made, using the point cloud data as basic information on the shape and height of the buildings. Since the point cloud data provides little information on the side of the buildings, we had to use many building pictures from different directions, in the practical realization of the 3D campus. Nevertheless, the use of point cloud data was very attractive to make an accurate modeling of 3D campus, because of many advantages of the point cloud data, such as a wide coverage of the area and high accuracy of target registration. In the completion of the 3D model campus we had to manipulate several software applications, including GRASS GIS, FUSION, and AutoCAD Civil 3D. We found a few capabilities of AutoCAD Civil 3D 2011 were particularly useful in dealing with the point cloud data. Making the use of importing and exporting capabilities from and into Google Earth, we successfully exported a 3D KIT campus into Google Earth and a walkthrough simulation of 3D campus was demonstrated. Finally, we clarified many merits and faults of these applications in the procedures of 3D modeling. The discussion on the point clouds from MVS(Multi-View Stereo Software) using a set of target pictures was also given in comparison of LiDAR point clouds.

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.

Worldwide decrease of forest area is chiefly due to negative impact of human activities, but that is restricted to topographical circumstances. In this research, we consider statistical distributions on forest area ratio F taking values from zero to one observed on a grid-cell system. The F will be predicted by covariates: population density (N) and relief energy (R), where R is the difference between the maximum and minimum altitudes in each site. Tanaka and Nishii (2009, IEEE Transaction on Geoscience and Remote Sensing) explored a regression model of logit-transformed forest area ratios, where the mean function is given by the sum of two non-linear parametric functions of the covariates. We will make detailed discussion for the modeling: 1) Classification: We derive a procedure to classify each observed site into completely-deforested (F =0), fully-forest-covered (F =1), or remaining (0< F <1) site by a function of the population density and the relief energy at the site and its neighbors. 2) Regression: For the ratio with 0< F <1, regression model is discussed after the logit transformation of the ratio. The model has the mean represented by the sum of two natural spline functions of the covariates, and the error has spatial dependency from the first- and second-order neighborhoods. The proposed model was applied to Japanese test field with grid-cell sites each measuring about 1 square km. The field has 159 sites with F = 0 and 1713 sites with F =1. The remaining 6825 sites have the ratios 0 < F < 1. It was seen that the classification using the neighbors' covariates is effective. Also, the spline-based functions were superior in detecting the small changes of regression functions to the former parametric model in the sense of the model evaluation criterion AIC.

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 ASTER images. 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, which is a typical clustering area of different industrial solid wastes mainly including phosphogypsum. The object-oriented method we adopted consists of the following steps. 1) Multi-scale 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. From the object-based ASTER images, we explore the features of industrial solid waste and other typical objects based on sample training. The features we used include spectral features as well as shape features, texture features, spatial features, etc. 3) Detect the industrial solid wastes based on 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 identification 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.

Various image classification methods have been developed for land cover mapping. Among them classification and regression trees (CART) and their new modifications such as random forests (RF) have proven effective. However, these CART-based methods perform per-pixel classification, which often produces suboptimal results with scattered misclassifications. This paper introduces an anisotropic diffusion technique to address the problem and combines this technique with RF for land cover classification using remote sensing imagery and ancillary data. RF is used to produce probability maps for each type of land cover, the anisotropic diffusion technique is employed to smooth the probability maps and then independent maximum probability rules are applied on the smoothed probability maps to generate a land cover map. This method was applied to land cover classification for the Jiuzhaigou Nature Reserve in China using Landsat TM Images and topographic data and its classification accuracy was assessed. The results demonstrated that the combined use of RF and anisotropic diffusion achieved improved accuracy compared to land cover classification without the smoothing process.

Currently, most of the deforested area in the Brazilian Legal Amazon (BLA) is under agricultural use, but over 35% has been abandoned and often supports regenerating forest. Regrowth and the growth stage maps are required to better understand the dynamics of these forests in regional carbon budgets and in the recovery of biodiversity. A combination of high frequency time series of Landsat 5 Thematic Mapper (TM) data were used to assess regrowth and deforestation processes in the Western BLA (Machadinho d’Oeste, Rondônia). In total, 21 Landsat 5 TM scenes covering the period 1984-2011 were acquired, pre-processed and classified into mature forest (MF), secondary succession forest (SSF), non-forest (NF). Metrics associated with the areas undergoing regrowth were calculated including the period of active land use (PALU) prior to abandonment, age of regenerating forest (ARF) and frequency of clearance (FC). Also, deforestation/regrowth rates were calculated between consecutive dates. In 1984, 99 % of the study area was occupied by MF. In 2011, 38 % of the area was occupied by NF, followed by MF (35%) and SSF (27%). Deforestation rates in MF ranged from 1.21-9.95 %yr-1, while in SSF were between 2.48-88.05 %yr-1, indicating reclearance. Regrowth rates ranged from 3.68-84.18 %yr-1. Approximately 90% of the area with SSF in 2011 had a PALU not exceeding four years. Around 45 % of the area with SSF in 2011 was less or equal than 5 years of age and 40 % between 6 and 15 years. About 95 % of the area with SSF in 2011 had been cleared a maximum of three times. In 2011, SSF have established on land with a short PALU and have been cleared several times prior to abandonment. Based on previous studies, these observations suggest that the regenerating forests are likely to support a reasonably high level of tree species but if future reclearance operations and extended periods in active use prevail, the capacity of the forests to recover tree species diversity may be reduced.

The advent of new generation satellites with moderate resolution is seen as a trade-off between the advantages of multispectral resolution satellite data and hyperspectral data. WorldView-2 is one such sensor which contains a reasonable number of spectral bands that are configured in unique portions of the electromagnetic spectrum, including the red edge. This study explored the possibility of object-based image analysis approach for mapping land- cover/use in a heterogeneous coastal zone using WorldView-2 imagery. WorldView-2 image comprised eight multispectral bands with spatial resolution of 2 m. The spectral ranges of the eight bands are 400-450 nm (B1-coastal), 450-510 nm (B2-blue), 510-581 nm (B3-green), 585-625 nm (B4-yellow), 630-690 nm (B5-red), 705-745 nm (B6-red edge), 770-895 nm (B7-near infrared-1), and 860-1040 nm (B8-near infrared-2) (Fig 1). Two relatively modern and robust supervised machine learning algorithms (random forest (RF) and support vector machines (SVM) ) were also compared. Image segmentation was performed (Fig 1A), and eleven broad land cover classes were identified in a heterogeneous coastal area in KwaZulu-Natal-South Africa. Subsequently, we assessed the performance of object-based image analysis method and each of the selected machine learning algorithms in mapping these land cover classes. The validation of the developed land-cover/use maps form RF and SVM were performed through error matrix statistics using independent test dataset generated from a field work data and an aerial photography. Results showed that both the machine learning classifiers in combination with object-based approach are useful in mapping land-cover/use in heterogeneous coastal areas. However, SVM achieved the best overall accuracy (86%) and kappa statistic (0.84) than RF which produced an overall accuracy of 80% and kappa value of 0.77 (Fig 1). Overall, the study underline the utility of WorldView-2 imagery and the combination of object-based image analysis and machine learning classifiers in mapping land-cover/use in high heterogeneous coastal areas - a previously challenging task with broad band satellite sensors and traditional pixel-based image classification

Irrigated agricultural regions in Middle Asia are characterized by different environmental and crop management settings. Crop yield and irrigation water demand modeling is a critical task and requires that both, the total surface that is cultivated and the distribution of crops is known. This in turn requires accurate crop identification at the field-scale. A hybrid classifier approach using Random Forest (RF) and Support Vector Machine (SVM) is tested with RapidEye time series to classifying crops in the four study sites. The time series consist of eight RapidEye images and were acquired over the growing period in 2011. Depending on the diversity of different crops in the test sites five up to eight classes were distinguished. The potential synergistic use of the two classifiers is explored and compared with the results from the single classifiers. Three major outcomes were derived among the study sites: a hybrid classification map, classification confidence maps, and assessment of feature usefulness in the classification. The latter was derived by the RF internal computation of the variable importance for each candidate predictor based on the out-of-bag (OOB) observations as mean decrease of accuracy. Overall accuracies of RF and SVM in the study sites range between 84%-87% and 85%-94%, respectively, indicating a better performance of the SVM. The ensemble classifier results in increased overall accuracies over three of the test sites (89%-94%). Predictive errors of the two single classifiers tend to be different among the classes and the study sites: agreement of the two single classifiers in classifying crops ranges between 72% and 85%. The hybrid approach seems suitable to improve classification accuracy in most of the study sites and provides field based information on classification confidence which might aid stakeholders in the region in making more informed decisions and incorporate uncertainty information in decision support systems.

Vegetation cover is a key parameter in analysing the state and dynamics of ecosystems. Africa's semi-arid savannah'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 savannah'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 to transferring en-detail information from a small subset to a larger study area via statistical up-scaling (eg 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 18 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.

Climate change and its potential effects are of global interest. Lake ice and snow coverage act as effective indicators of climate change due to their sensitivity to climate elements, and can be observed on a large-scale with the help of remote sensing. The lakes on the Tibetan Plateau are important indicators for the development of the high mountain ecosystems facing the impacts of future climatic warming on runoff from snow and ice. Multisensoral remote sensing is a valuable tool to generate hydrological relevant information as modeling input (land cover, soil moisture, trends in mountain lake ice cover, etc.) or validation base (lake level changes). For the monitoring of the lake ice, the first and the last day of the partial and the period of total ice cover are defined on the basis of temporal high resolution MODIS data. The larger lakes were compared in order to delineate and define different local trends. The study is enhanced by the use of passive microwave data (SSM/I, AMSR-E) which provides very high temporal resolution information as validation input. Simultaneously TerraSAR-X ScanSAR data is analysed in order to derive the spatial distribution of different ice types. The snow cover is an important intermediate storage of water and plays a crucial role in the water budget of the Tibetan endorheic lakes. It is observed by multitemporal MODIS 8-day snow cover composites, which were split into three different seasons and displayed as RGB synthesis of snow distribution (RSD, Kropacek et al. 2010). The different colors indicate the spatial distribution of the dominant precipitation mechanism within the large Tibetan Plateau. Further multisensoral remote sensing analysis include the observation of lake level alterations by satellite altimetry as well as the investigation of the ice thickness or the glaciated area inside the basins with time series of IceSat data.

Miombo woodlands are the largest savanna in the world and dominate southern Africa. They are strongly influenced by anthropogenic fires and support the livelihoods of over 100 million people. However, it is threatened by desertification processes, deforestation, degradation of land and water resources, and loss of biodiversity. The main goal of this study was to test a combination of field data and Japan's Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) backscatter intensity data to reduce the uncertainty in the estimation of forest aboveground biomass in the Miombo woodlands of Mozambique (East Africa). A detailed field campaign took place in July 2011 in a Miombo woodland region of the Zambézia province, Mozambique (East Africa). A set of 51 plots were selected based on a stratified sampling approach on the basis of three tree crown cover classes: 10-30%, 30-40%, 40-50%. ALOS PALSAR fine beam dual (FBD; HH and HV polarizations) data were acquired in 21 June 2010 to cover the study area. The data were acquired with an off-nadir angle of 34.3o (ascending orbit) and were provided by the European Space Agency in level 1.1 (single look complex, SLC). A machine learning algorithm, based on bagging stochastic gradient boosting (BagSGB), was used to model forest aboveground biomass as a function of ALOS PALSAR FBD backscatter intensity metrics. The application of this method resulted in a coefficient of correlation (R) between observed and cross-validation predicted forest aboveground biomass values of 0.95 and in a root mean square error (RMSE) of 5.03 Mg ha-1. Furthermore, the BagSGB model produced also a measure of forest aboveground biomass prediction variability (coefficient of variation) on a pixel-by-pixel basis, with values ranging from 7 to 148 % (mean = 25 %) across the study area.

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 is threatened by cropland degradation, particularly in irrigated agriculture-based economies, such as in Central Asia. The field-based identification of the degraded agricultural areas is important for developing land rehabilitation and monitoring programs. In heterogeneous irrigated agro-ecosystems, traditional pixel-based analysis of satellite data could lead to inaccurate mapping of degraded cropland due to mixed pixels and pixel heterogeneity. In contrast, object-based image analysis can handle these issues, at the same time providing parcel-specific information on the land's state. We combined the object-based image classification and spectral mixture analysis (SMA) to develop an approach for identifying parcels of irrigated degraded cropland in Northern Uzbekistan, Central Asia, where ongoing land degradation is acute due to soil salinization. A linear spectral unmixing, followed by the object-based image classification, was applied to the multiple Landsat TM images, acquired in 1987 and 2010. A spatial pattern of degraded cropland was further interpreted, using supplementary datasets. Considering a spectral dimensionality of Landsat TM, a multiple 4-endmember (green vegetation, water, dark soil, and bright soil) model 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 classification revealed an increase in area of degraded croplands by 2% during last 14 years, while 5821 fields (241 km2) were affected by degradation processes, to varying degrees. During the observation period, spatial distribution of degraded fields was mainly associated with the abandoned croplands (R2=0.82) and lands with inherently low fertile soils (R2=0.98). The proposed approach could be further elaborated for a field-based monitoring of cropland degradation, thus revealing areas, which should be primarily targeted by rehabilitation measures 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 near-infrared 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. Keywords: Ozone, SCIAMACHY, and tri-monthly.

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 NBAR NDVI to simulate vegetation dynamics and combining curves with few HJ-1 CCD NDVI data to get the High spatial and temporal resolution data. The data we used include the 8-day MODIS NBAR product (MOD43B4, 1 km) from 2004 to 2010, the MODIS Land Cover data (MOD12Q1, 1 km), the high spatial land use and land Cover(LULC) map(30m) and the HJ-1 CCD NDVI (30m). First, using MODIS NBAR product to calculate the mean MODIS NDVI .Then the logistic model is fitted to the average MODIS NDVI to simulate the variation in NDVI time series and the intra-annual vegetation dynamics for every types are achieved. This seasonal cycle is consistent with the known processes of vegetation and is continuous in time series. So 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 results are compared with some filed measured data, which shows the high feasibility of the method to generate accurate and reliable data. The method is also used in other typical area to estimate its practical applicability. It is proved that the method can be used in small scales to lager regions. The high spatial and temporal resolution NDVI time series data can be used to analysis the characteristics of NDVI spatial and temporal changes in large scales or compare the differences of NDVI time series in various regions.

Spectral reflectance measurements with 16-bands multispectral radiometer, with an instantaneous field of view (IFOV) of 3-4 m, were collected from degraded soils and vegetation of the semiarid basin of Yarmouk river in Jordan. Spectral reflectance measurements were correlated with surficial soil properties and vegetation biomass and cover using linear regression analysis. The same procedure was followed to correlate soil and vegetation parameters with spectral reflectance obtained from ASTER images. 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 correlations (maximum R2 = 0.85) values were obtained for vegetation cover when compared with biomass (maximum R2 = 0.54). For soil, significant correlations were observed between salinity and spectral reflectance. In all visible to NIR bands, soil spectral reflectance tended to increase with salinity. The maximum correlations were 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. All SWIR bands, except the 1480±14.5 nm, were significantly correlated with soil salinity, with slightly higher correlation in the narrow wavelength than the broad wavelength. The relationship between soil salinity and ASTER image bands was significant but with relatively low correlation (maximum R2 = 0.31) when compared with the results obtained for the radiometer at the same wavelengths. The correlations between ASTER bands and vegetation cover were slightly lower (maximum R2 = 0.62) when compared with results of radiometer. Results of the study showed that the use of remote sensing indicators related to vegetation and soil salinity would be recommended to map the extent of land degradation in the study area and similar environments.

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 focus 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 comprehensive 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. 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 metropolitan area 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+, MODIS and IKONOS. A detailed analysis was done for summer 2003, 2007 and 2010 years heat wave events in Bucharest and periurban areas and related impacts on human health. This paper demonstrates the potential of moderate-and high resolution, multispectral imagery to map and monitor the evolution of the physical urban environment in relation with micro and macroclimate conditions. So called effect of "urban heat island" must be considered mostly for summer periods conditions and large European scale heat waves. The rates of urbanization are characterized by creating a typology of urbanization trends based on quantities that are indicative to the potential environmental impacts. As future climate trends have been predicted to increase the magnitude and negative impacts of urban heat waves in metropolitan areas, there is an urgent need to be developed adequate strategies for societal vulnerability reducing.

At present, the CFD (constant fraction discriminator) is the main time discrimination method used in the 3D imaging lidar. The CFD circuit is usually composed of a time delayer, an attenuator and a comparator. The time delayer in the previous CFD circuit is generally implemented with a delay line. The signal distortion of the delay line is very small. However, the delay line has some shortcomings: big volume, difficult adjustment, and etc. Recently, the delay line is replaced by a low-pass or high-pass filter which is usually implemented with resistors and capacitors. This method reduces the volume and adjustment difficulties of the delay line, but increases the signal distortion, resulting in the time discrimination precision reduction. In order to reduce the signal distortion and enhance the time discrimination precision, we propose a new time discrimination circuit, which improves both the time delayer and the attenuator in the previous CFD circuit. The proposed circuit mainly includes a time delayer, a low-pass filter, and etc. The time delayer is implemented with a series of inductors and capacitors. The time delayer has some advantages: low signal distortion, small volume, easy adjustment, and etc. The low-pass filter attenuates the signal amplitude and broadens the signal width. The low-pass filter reduces the noise by decreasing the equivalent noise bandwidth, and increases the signal slope at the discrimination time. Therefore, the time discrimination error is decreased significantly. This paper carried out a theoretical analysis for the noise and time discrimination error in the proposed circuit and compares them to the previous CFD circuit. The comparison shows that the proposed circuit reduces the time discrimination error about 50 percent under the same noise level. The proposed circuit is discussed in detail. To test the performance of the proposed circuit, some experiments have been carried out. The experiments show that the time delay of the circuit is about 14ns, and the time discrimination error due to signal amplitude variation is less than 112 ps when the signal amplitude ranges from 0.2 V to 1.86 V. The time discrimination circuit is used and tested in a high-speed 3D imaging lidar. Besides the 3D imaging lidar, the circuit can be applied to the pulsed laser range finder and other time discrimination system.

In this paper we present the scientific data recorded by tunable mechanical monolithic horizontal seismometerslocated 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&#8722;7 Hz ÷ 1 Hz 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&#8722;6 Hz ÷ 10 Hz), 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&#8722;12 m/&#8730;Hz in the band (0.1 ÷ 10 Hz). Typical applications are in the field of earthquake engineering, geophysics, and in all applications requiring large band-low frequency performances coupled with high sensitivities.

The main objective of this work was to compare the potentialities and list the available tools of four remote sensing/image processing softwares. For this study, we assessed the performance of several algorithms available in: PCI Geomatic V8.2, Envi 4.7, Spring 5.1.8, and Orfeo toolbox integrated in Monteverdi 1.10.0. The PCI Geomatic and Envi are commercial softwares and Spring and Orfeo are open source softwares. In this work, two images were used: one image Landsat 5 Thematic Mapper from October 2011 (path 204; row 32) and one IKONOS image from September 2005. Both images cover the same area. A list with all the classification algorithms available in PCI Geomatic, Envi, Orfeo and Spring software were listed, and divided into the different approaches of classification (pixel-based, object-oriented, learning algorithms, and principal components analysis). The two images were classified with each algorithm in the four softwares. Then the time of performance and classification results were assessed and compared between the four software's. The classes of image classification are based in CORINE Land Cover, and are the same for the Landsat 5 TM and IKONOS image. The accuracy of the classification algorithms available in the different softwares were evaluated trough the confusion matrix (overall accuracy) and Kappa statistics. The performance of classification algorithms were identically in all softwares. This work could be very important for the researchers because it provides a qualitative and quantitative analysis of different image processing algorithms available in different software (commercial and open source).

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 &#8776; 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 located at 31-41°N, 117-127°, 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. And the results show that: 1) Appear year of Enteromorpha prolifera: they were founded firstly in 2001, and there are little appearance in the following years of 2003 and 2005. However, we did not find any Enteromorpha prolifera in study area in the subsequent three years of 2002,2004 and 2006. The first outbreak is in 2007 and subsequent four years remained the tread of outbreak. 2) Place and time: The sea area of Yancheng is usually the first place that we find Enteromorpha prolifera, and sometimes in esat of Yangtze estuary. 3) Drift path: Generally, they appeared in south area of Yellow sea, and moved northward in the process of gradually breeding. The area covered by Enteromorpha prolifera increased year by year, and Shandong is the mainly affected waters. 4) Growth cycle: From the data analysis, the distribution of Enteromorpha prolifera not only has changed, its density also has the change; the smaller density is in front of the range in drift process. And its life cycle is about 15 days, but which still need further work to validate.

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 estimation and bridge design. In this work we evaluate three wind flow computational models (MS-3DJH/3R, WaSP and 3D-RANS) in a complex terrain domain in Southern Greece using three remote sensing datasets of different spatial resolution. The evaluation is based on a pair-wise intercomparison of results derived from two meteorological stations. The first anemometer mast is used as a predictor and the second is used for evaluation of wind speed predictions. For the simulation of the wind field, terrain characteristics are described by a terrain roughness map and by a digital elevation model. Object-oriented segmentation techniques have been applied to Landsat, ASTER and IKONOS images in order to derive terrain roughness maps with spatial resolution of 30 meter, 15 meter and 1 meter respectively. Furthermore, two DEMs with global coverage (SRTM and ASTER GDEM) and a high-resolution DEM, derived from digital photogrammetric techniques, paired with corresponding terrain roughness maps produce three different resolution input data to wind flow computational model. The influence of the topographical input data on the accuracy of wind flow estimation over complex terrain has been tested with three computational models: MS-3DJH/3R, WaSP and 3D-RANS. From the results, it has been found that the spatial resolution of topographic input data is a crucial factor in achieving accurate wind flow estimation over complex terrain. Moreover non-linear 3D-RANS computational model with topographical input data derived from high resolution remote sensing images results to higher accuracies in wind flow estimation. It is remarkable that the wind speed predictions from the simplest linear model MS-3DJH/3R with high resolution input data are more accurate than results from the sophisticated 3D-RANS solver with low resolution input data.

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 for scientists and coastal managers. The nearshore survey systems have been traditionally based on in situ measurements or in the use of satellite and aircraft mounted remote sensing systems. Other technique is the video-monitoring systems, which proved to be an economic, efficient and successful way to collect useful and continuous data and also to document extreme events. In this context, was developed project MoZCo (Advanced Methodologies and Techniques Development for Coastal Zone Monitoring), which intends to implement and develop 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, with low-cost cameras, and installed on the top of a building with about 40 meters height, collecting snapshots and 10 minutes videos every hour. In order to process the data, several video image processing algorithms were implemented and a Graphical User Interface (GUI) was developed in Matlab®, allowing the achievement of the main products of a video-monitoring system, such as image rectification, timex images, variance images, timestack images and shoreline detection. For the shoreline detection was developed an algorithm based on image processing techniques. 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 simplicity of the method, which is in continuous development in order to optimize the results.

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. 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 this project 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 rapid urbanization increases impervious surfaces has altered the energy balance of the environment. This research attempts to study on the relationship between the discomfort level and temperature in order to mitigate the heat effect to the environment. The satellite imagery of Landsat was retrieved on 2nd February 2006 and according to its date and time, five variables were retrieved from air quality and weather stations for identification of discomfort level. Atmospheric and geometric corrections are applied to the image to remove the effects of the atmosphere on the reflectance values of image and to avoid geometric distortion of the image used. Land surface temperature was extracted by using mono window algorithm which was developed by Qin et al. (2001). The discomfort index was measured the whole year of 2006. The calculated discomfort level was evaluated by using Thom's index based on the ground station and compared to the retrieved land surface temperature.

Rationale: 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. The analysis of the recent satellite images evidences that in August 2011 the area of the dried bottom of the Aral Sea was 57,529 km2. The water bodies' area was 2,317 km2, 4,411 km2 and 3,243 km2 for Eastern, Western and Small Arals respectively. Methods: According to high changeability character of the current perimeter timely reliable information on Land Cover and Land Use in different formats and scales is significant assistance for the solution of wide range of ecological problems and implementing contingency planning. Expert land cover classification approach gives the opportunity to use the unlimited variable for classification purposes. Thus the band algebra (band5/band4 and Band4/Band3) and remote sensing indices utilized for the land cover classification has shown satisfactory result with classification overall accuracy 86.9 % and kappa coefficient 0.85. Results: After the sea desiccation, the exposed bottom turned mostly to the solonchaks (salty pans). On the other hand areas of former deltaic rivers' branches are covered by sands with sparse vegetative cover, which are transported/spread by wind from eastern part of the former Aral Sea contour to the south-western and western directions. Abandoned agricultural fields are transforming into solonchaks in Amudarya delta (56%) and sandy massifs in Syrdarya delta (8%). The pathway of exposed bottom transformation is: marsh solonchaks, coastal solonchaks, dry solonchaks (puffy, crusty, white salt on top surface). The other prevailing process is expanding of "new" (young) sands prone to wind erosion and their mixing with solonchak soils. Consequently, vast bared areas prone to the wind erosion processes turn to the powerful emission sites of salty dust storms.

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.