Satellite hyperspectral imagery and especially missions like CHRIS Proba provide new capabilities for environmental and geological studies since they offer high spectral and spatial resolution. This work exploits the potential of CHRIS Proba data to be used for classification purposed of areas with high geological interest. For this purpose different classification methods are employed while the matched filtering (PCT-BSS) approach seems to be the most promising. The approach is tested in the area of Araxos peninsula in Greece, which is an area of high environmental and geological interest.

GeoEye-1 is the first commercial satellite that collects images at nadir with 0.41m panchromatic and 1.65m multispectral resolution (panchromatic imagery sold to commercial customers is resampled to 0.5m resolution). In this study nine fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, PCA and Wavelet were used for the fusion of Geoeye panchromatic and multispectral data. The panchromatic data have a spatial resolution of 0.5m while the multispectral data have a spatial resolution of 2.0m. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q and entropy were examined and the results are presented. The broader area of Agrinio city in Western Greece was selected for this comparison. It has a complex geomorphology. At the west the area is flat and the elevation ranges between 5 and 20 meters. At the east there are many hills and the elevation rises to more than 450 meters. The area combines at the same time the characteristics of an urban and a rural area thus it is suitable for a comparison of different fusion algorithms.

Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a hyperspectral scene. Most of the spectral-based endmember extraction methods relay on the ability to discriminate between pixels based on their spectral characteristics and the assumption that pure pixels exist in the image. In some cases, though pure pixels are available inside image, spectral complexity of the image (e.g. low spectral contrast) makes it difficult to extract the best endmember candidates from hyperspectral imagery. This paper investigates the use of statistical convex partitioning (SCP) as a preprocessing tool for endmember extraction. The SCP method comprises three main steps: 1) partitioning input hyperspectral data set into partitions or so called convex regions using K-mean clustering algorithm; 2) finding the best candidate endmembers for each convex region; and, 3) comparing and listing of candidate endmembers extracted from each partition in order of spectral similarity. In order to demonstrate the performance of the proposed method, the sequential maximum angle convex cone (SMACC) algorithm was used to extract endmembers of each partition and the results were compared to pixel purity index (PPI). Optimum number of convex regions as well as the impact of different dimensionality reduction transforms, principal component analysis (PCA), minimum noise fraction (MNF), and independent component analysis (ICA) were also investigated. Experimental results on both simulated and real AVIRIS hyperspectral image indicate that SCP is an effective method to preprocess hyperspectral data spectrally and extract low contrast and similar endmembers effectively.

Mapping of alterations in a geological terrain can be considered as a classification task in the remote sensing data processing. Training dataset is an important part of a classification process. Collecting of precise training data is generally expensive and time consuming. In this study, the alteration map resulted by Hyperion is used as training data for classification of the ASTER scene in Erongo, Namibia. This extends results to a much broader in comparison to Hyperion scene. Ten alterations detected by the matched filtering unmixing method on the Hyperion dataset are therefore training classes of the classification. The separability of the classes was computed to evaluate the ability of ASTER data to spectrally discriminate between these classes. The outcome of this computation is satisfactory for the high-probability training dataset. In order to improve the accuracy of upcoming processes, classes with high similarity (low separability) were combined. The classification of ASTER scene is then performed with the use of both individual and combined classifiers. An accuracy analysis was performed to compare the accuracy of each classifier. The Mahalanobis distance method has the best performance among all classifiers regarding to its highest overall accuracy.

In this paper, a new integrated approach between airborne laser scanner and photogrammetric aerial images is proposed. This procedure is focused on the possibility of overcoming the problems of each technique separately through their integration during the data processing. The LIDAR and multi-image matching techniques combine data in order to extract building boundaries in the space and define other map details visible from the images in an automatic way. This process could allow the extracted edges to be exploited as building boundaries in the segmentation when an ambiguity occurs in this process. The detailed description of this approach and its first promising results on an urban area will be presented and discussed.

Information of disaster damage assessment is very significant to disaster mitigation, aid and post disaster redevelopment planning. Remotely sensed data, especially very high resolution image data from aircraft and satellite have been long recognized very essential and objective source for disaster mapping. However feature extraction from these data remains a very challenge task currently. In this paper, we present a method to extract building damage caused by earthquake from two pairs of Worldview-2 high resolution satellite image. Targeting at implementing a practically operational system, we develop a novel framework integrating semi-automatic building extraction with machine learning mechanism to maximize the automation level of system. We also present a rectilinear building model to deal with a wide variety of rooftops. Through the study case of Haiti earthquake, we demonstrate our method is highly effective for detecting building damage from high resolution satellite image.

This paper aims at establishing the spectral reflectance signature for a number of geological mapping features and specific rocks over the area of Cyprus. This will enable the investigation for specific geological features through classification using satellite images. The purpose is to provide a useful tool for geologists in observation of surface strata. Methodology followed includes extraction of the spectral reflectance signature of the geological features by using satellite imagery, such as those of Landsat TM/ETM+, ASTER etc. In addition in-situ spectro-radiometric measurements were collected for the same feature locations. The selected sites included mines and quarries, with no vegetation cover and therefore no influence on results. Spectral reflectance for each feature refers to average value of retreated satellite image value and measurement result. An algorithm is finally established, aiming to be used for classification purposes of geological mapping and other applications. This innovated approach will, also, prove by validation the accuracy of each method for the spectral reflectance signature estimation. This additional benefit would conclude recommendation for future satellite sensors navigation and work processes. NIR band was found to be suitable for discriminating betonite, limestone and diabase geological features (as found at quarries and mines).

This work is aimed at differentiation of ophiolitic mélange rocks which were outcropped 60 km far from Sivas city center using image processing and spectral measurement methods. These rocks are known as oceanic crust remnants which were made up of different rocks. Turkey hosts several paleo-oceans and their realms in Alpine-Himalayan orogenic belt. The Neotethyan ophiolites in Turkey are characterized by supra subduction zone (SSZ-type) ophiolites. Ophiolitic rocks are generally coloured with greenish tones and human eye could not separate these tone differences. But satellite images such as ASTER can realize these separation utilizing spectral enhancement methods such as classification and decorrelation stretching. Chromite is a valuable mineral and is formed in only ophiolitic rocks. Dunites and harzburgites named as also ultramafic tectonits of ophiolitic serie mainly contain these deposits in study area. In this study, an approach was also realized to find target regions of chromite deposits with the aid of spectral methods. Spectral measurements were realized to determine boundaries between different mélange rocks using spectroradiometer. Reflectance curves collected from field and laboratory analysis were evaluated together and compared with ASTER image of the study area respectively. A detailed differentiation generally was accompanied with petrographic and geochemical analyses.

This paper describes a new mechanical architecture of the monolithic tunable folded pendulum, developed at the University of Salerno, configurable both as seismometer and, in a force-feedback configuration, as accelerometer. Typical applications are the remote monitoring of seismic and newtonian noises for geophysical applications. This sensor, shaped with precision machining and electric-discharge-machining, like the previous versions, is a very compact instrument, very sensitive in the low-frequency seismic noise band, with a very good immunity to environmental noises. Important characteristics are the large band (10^-6 - 10mHz), the tunability of the resonance frequency and the laser optical readout, that integrates an optical lever and a laser interferometer. The theoretical sensitivity curves, largely improved due to a new design of the pendulum arms and of the electronics, are in a very good agreement with the measurements. In particular, a very good sensitivity (10^-12 m/Hz^1/2) has been obtained in the band 0.1 - 10 Hz). Prototypes of monolithic seismometers are already operational in selected sites around the world both to remotely acquire data for scientific analysis of seismic noise and to collect all the useful information to understand their performances in the very low frequency band. The results of the monolithic sensor as accelerometer (force feed-back configuration) are also presented and discussed.

Very High Resolution Satellite (VHRS) images have already demonstrated their great potentialities both for the generation of satellite orthoimages and for map production and updating at the middle scale (1:10000 - 1:5000). Nevertheless a big research effort has still to be done in order to investigate how different data with similar features can be integrated to improve the final result and especially to overcome the objective difficulty, for a common customer, of getting stereopairs from a single sensor. In this work a Geo GeoEye image and an Orthoready QuickBird one covering about 120 Km² in the region of Tera (Niger), are considered to determine how successfully they can be integrated to exploit the maximum of resident information required to describe as better as possible the test area. A comparative process was employed to determine the planimetric positional difference affecting the original acquired images, the orthoimages obtained through a Rational Function Model (RFM) approach based on the released RPC (Rational Polynomial Coefficients) and a "rigorous" multi-sensor bundle adjustment performing the simultaneous orientation of both the images in a single block.

Beginning in 2000, Lianyungang's urbanization entered a period of rapid growth, spatially as well as economically. Rapid and intensive expansion of "construction land" imposed increasing pressures on regional environment. With the support of remote sensing data and GIS tools, this paper reports a "present-capacity-potential" integrated suitability analysis framework, in order to characterize and evaluate the suitability of urban expansion in Lianyungang. We found that during the rapid coastal urbanization process from 2000 to 2008, the characteristics of physical expansion in the study area were characterized by a combination of high-density expansion and sprawling development. The land use conversion driven by urbanization and industrialization has not occurred only in city districts, but also the surrounding areas that were spatially absorbed by urban growth, while closely associated and greatly influenced by the explosive growth of industrial establishment. The over-consumption of land resources in the areas with low environmental carrying capacity, particularly in the eastern coastal area, should be strictly controlled. Compared to conventional land suitability analysis methods, the proposed integrated approach could better review the potential environmental impacts of urban expansion and provide guidance for decision makers.

Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

Today's mega cities could serve as good predictors of future urbanization processes in incipient mega cities. Measuring and analysing the past effects of urban growth in the largest category of urban agglomerations aims at understanding spatial dynamics. In this study we use remote sensing, landscape metrics and gradient analysis to measure, quantify, and analyze spatiotemporal effects of massive urbanization in 10 sample mega cities throughout the world. By using timeseries of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban patterns, sprawl and densification processes and various types of urban development. A multi-scale analysis starts at city level using landscape metrics to quantify spatial urban patterns. We relate the metrics, like e.g. landscape shape index, edge density or class area to each other in spider charts. Furthermore, we use gradient analysis to provide insight into spatial pattern development from the urban core to the periphery. The results paint a characteristic picture of spatiotemporal urbanization for the individual mega cites and enable comparison of all cities across the board. Spatial characteristics of urbanization dynamics allow indirectly conclusions on causes or future consequences.

The knowledge of ground elevation is essential in most remote sensing applications especially for very high resolution images. This ground elevation information can be retrieved from a pair of stereoscopic images, by correlation methods. The improving resolution of Earth observation systems and their increasing stereoscopic capabilities open up new horizons for automatic Digital Elevation Model (DEM) generation and allow buildings reconstruction to be considered. To reach this goal, the correlation methods used for computing disparities has to be improved and adapted to urban scenes. This paper aims to describe a new method of DEM generation from a stereoscopic pair of high resolution images, whether aerial or satellite, fitted to urban scenes, focusing on correlation improvement. The proposed framework relies on a multiscale dense correlation method with a step of regularisation/interpolation and a step of filtering. It efficiently improves the quality of disparity maps, by reducing the level of noise, and allows us to generate high quality DEM from high resolution images.

One of the newest satellite sensors with stereo collection capability is ALOS. ALOS has a panchromatic radiometer with 2.5m spatial resolution at nadir. According to the specifications its extracted data will provide a highly accurate digital surface model (DSM). Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) has three independent optical systems for viewing nadir, forward and backward producing a stereoscopic image along the satellite's track. Each telescope consists of three mirrors and several CCD detectors for push-broom scanning. The nadir-viewing telescope covers a width of 70km; forward and backward telescopes cover 35km each. Two ALOS data sets collected over the same area within a year were used. The same ground control points were used for the creation of the two DSMs. The two DSMs were compared to elevation data from different sources: 1/50.000 topographic maps and airphotos stereo-pair. The area of study is the broader area of Andritsena, Western Peloponnese, Greece. After a first control for random or systematic errors a statistical analysis was done. Points of known elevation have been used to estimate the accuracy of the DSMs. The elevation difference between the different DSMs was calculated. 2D RMSE, correlation and the percentile value were also computed and the results are presented.

Vegetation biomass is an important ecological variable for understanding responses to the climate system and currently observed global change. It is also an important factor influencing biodiversity and environmental processes, especially in semi-arid areas. These areas cover large parts of the land surface and are especially susceptible to degradation and desertification. Therefore, a great need exists for the development of accurate and transferable methods for biomass estimation in semi-arid areas. This paper presents an overview of previously applied remote sensing based approaches for above-ground biomass estimation in semi-arid regions. Based on the literature analysis a summary and discussion of commonly observed difficulties and challenges will be presented. Further research is especially required on the transferability of remote sensing based methods for biomass estimation in semi-arid areas. Additional analyses should be directed towards efficient field sampling schemes, and the synergetic use of optical and radar data.

In the last ten years, with the help of satellite remote sensing, we build up a huge database of fire points in China. The remote sensing data that we used to do the fire monitoring include NOAA, FY-1, FY-3 and MODIS. In this paper, we present a new model for fire forecast base on the former database and NCEP reanalysis data of last ten years. As we know, the reason of land surface fire can be divided to two groups: subsurface property and meteorological factors. Both of them are very complicated. For subsurface property, there are many factors that relational to wild fire, such as land surface type and combustible material. For meteorological factors, they also strongly impact to the fire occur. There are four factors of meteorological should be pay attention in the fire forecast, they are wind speed, precipitation, temperature and humidity. For the former two groups of reasons of fire's taken place, we build a two-part model to do the fire forecast. For the first part, corresponding to the subsurface factors, we used the ten years fire points monitoring database to describe it. We do the statistics on the database by five days (overlapping, 366 periods totally) and 0.5625 degree grid (according to NCEP). In each grid and each period of days, the average number of fire points describes the fire status corresponding to the average meteorological conditions and subsurface condition at that grid and at that time period. For the second part, firstly, we average the four meteorological factors into five days periods and 0.5625 degrees grids; secondly we evaluate the different of the four factors from the average value in the target day (forecast day).

Remote sensing technology continues to play a significant role in the understanding of our environment and the investigation of the Earth. Ocean color is the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments, and colored dissolved organic material and so can provide valuable information on coastal ecosystems. We propose to make the browsing of Ocean Color data more efficient for users by using image processing techniques to extract useful information which can be accessible through browser searching. Image processing is applied to chlorophyll and sea surface temperature images. The automatic image processing of the visual level 1 and level 2 data allow us to investigate the occurrence of algal blooms. Images with colors in a certain range (red, orange etc.) are used to address possible algal blooms and allow us to examine the seasonal variation of algal blooms in Europe (around Ireland and in the Baltic Sea). Yearly seasonal variation of algal blooms in Europe based on image processing for smarter browsing of Ocean Color are presented.

The use of satellite remote sensing for water quality monitoring in inland waters has substantial advantages over the insitu sampling method since it provides the ability for overall area coverage and also for study and supervision of isolated locations. The development of algorithms for water quality monitoring using satellite data and surface measurements can be widely found in literature. Such algorithms require validation and one of the major problems faced during these attempts was the need for continuous surface measurements requiring numerous in-situ samplings that imply also very high costs due to the need of increased human labour. The development of an automatic and autonomous sensor system able to be remotely controlled, will cover this gap and will allow the real time combined analysis of satellite and surface data for the continuous monitoring of water quality in dams as well as the overall water resources management. Wireless Sensor Networks (WSN) can provide continuous measurements of parameters taken from the field by deploying a lot of wireless sensors to cover a specific geographical area. An innovative, energy-autonomous floating sensor platform (buoy) transferring data via wireless network to a remote central database has been developed for this study which can be applied on all dams in Cyprus. Indeed this project describes the results obtained by an existing running campaign in which in-situ spectroradiometric (GER1500 field spectroradiometer) measurements, water sampling measurements (turbidity), sensor measurements (turbidity) and Landsat TM/ETM+ data have been acquired at the Asprokremmos Dam in Paphos (Cyprus). By applying several regression analyses between reflectance against turbidity for all the spectral bands that correspond to Landsat TM/ETM+ 1-2-3-4, the highest correlation was found for TM band 3 (R²=0.83).

Active quarries near to urban centers are at the same time a necessity but also a source of pollution. Necessity as they supply to the construction companies the necessary aggregates and source of pollution as they affect biodiversity, vegetation cover and threaten water resources. The objective of this work is to indicate a monitoring methodology in order to survey the present state of the quarry sites and their evolution in time, which are the basic data needed to implement an adequate land reclamation project. The land monitoring has been realised both by using remote sensing techniques, supported by a Geographic Information System of the studied area, and by in situ surveying. The in situ surveying was able to assess the capability of the remote sensing model to describe the state of each site. High resolution satellite data from different sensors were used for the monitoring of an active quarry. More especially, Ikonos Quickbird, and Worldiew data were orthorectified and inserted in a GIS database in order to quantify the changes.

The SW-coast of Pakistan is vulnerable to natural disasters, such as cyclones and tsunamis. Lack of spatially referenced information is a major hinder for proper disaster risk management programs in Pakistan, but satellite remote sensing being reliable, fast and spatially referenced information can be used as an important component in various natural disaster risk reduction activities. This study aimed to investigate vulnerability of coastal communities to cyclone and tsunamis based on satellite derived information. It is observed that SPOT-5 is relevant source on threatened features with respect to certain vulnerabilities like road, settlements, infrastructure and used in preparation of hazard zonation and vulnerability maps. Landsat ETM found very useful in demarcation of flood inundated areas. The GIS integrated evaluation of LANDSAT and ASTER GDEM helps identify low lying areas most susceptible to flooding and inundation by cyclone surges and tsunamis. The GIS integrated evaluation of SPOT, LANDSAT and ASTER GDEM data helps identify areas and infrastructure most vulnerable to cyclone surges and tsunami. Additionally, analysis of the vulnerability of critical infrastructures (schools, hospitals) within hazard zones provides indicators for the degree of spatial exposure to disaster. Satellite derived information in conjunction with detailed surveys of hazard prone areas can provide comprehensive vulnerability and risk analysis.

The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

Within the project "Expedition Bog: Young researchers are experimenting, exploring and discovering" a bog-information- system is developed by the Department of Geography (University of Education Heidelberg, Germany), the Institute for Geoinformatics and Remote Sensing (University of Osnabrueck, Germany) and the NABU Umweltpyramide gGmbH. This information system will be available for schools and to the public. It is supplemented by teaching units on various topics around the bog via an online platform. The focus of the project, however, is the original encounter with the bog habitat. This is realized by a GPS scavenger hunt with small research tasks and observations, mapping and experiments. The project areas are the Huvenhoops bog and the Lauenbruecker bog in Rotenburg in Lower Saxony, Germany. Equipped with a researcher backpack, GPS device and a mobile bog book by means of a pocket PC, students can discover different learning stations in the project bogs. In our areas the students can learn more about different topics such as "the historical memory of the bog", "water", "peat moss and other plants" and "animals of the bog". Moreover small inquiry research projects can be executed. Experimenting on site helps students to develop important scientific findings and increases their curiosity and enthusiasm for nature. It also promotes a number of other basic skills such as literacy, language skills, social skills or fine motor skills. Moreover it also fosters the development of a positive attitude to science in general. The main objective of the project is to promote sustainable environmental education, as well as the development of environmental awareness. This will be accomplished through the imparting of knowledge but also through experiencing nature with all senses in the context of original encounters.

Archaeological remains can be detected using crop marks, during different periods of crop cycle. Vegetation indices and spectral signatures can be used in order to examine and evaluate such crop marks. This paper presents the methodology applied for detecting crop marks over an archaeological site of Cyprus using Landsat TM/ETM+ satellite images. Moreover the GER1500 spectro-radiometer was used to retrieve in-situ spectral signatures over the area of interest (Kouklia Village in Paphos Cyprus). The results found are characterizing very promising since crop marks were identified as spectral anomalies. This paper aims to record the phenological cycle of barley crops, over agricultural fields in which archaeological areas existed and areas where only healthy agricultural fields are presented. NDVI values from the available satellite images (Landsat TM and Landsat ETM+) are used to plot the life cycle of barley crops. For the area in which archeological crop marks were found, the NDVI plot is significantly differs from one non-stressed crop. Such area covered by barley crop has been recently excavated (summer 2010) and the excavations have verified some linear buried archaeological remains -probably houses- just 30cm below ground surface.

This paper apply an statistical technique to correct radiometric data measured by Advanced Very High Resolution Radiometers(AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites(POES). This paper study Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data for the period 1982-2003. AVHRR weekly data for the five NOAA afternoon satellites NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 are used for the China dataset, for it includes a wide variety or different ecosystems represented globally. GVI has found wide use for studying and monitoring land surface, atmosphere, and recently for analyzing climate and environmental changes. Unfortunately the POES AVHRR data, though informative, can not be directly used in climate change studies because of the orbital drift in the NOAA satellites over these satellites' life time. This orbital drift introduces errors in AVHRR data sets for some satellites. To correct this error of satellite data, this paper implements Empirical Distribution Function (EDF) which is a statistical technique to generate error free long-term time-series for GVI data sets. It allows one to represent any global ecosystem from desert to tropical forest and to correct deviations in satellite data due to orbit degradation. The corrected datasets can be used as proxy to study climate change, epidemic analysis, and drought prediction etc.

Satellite remote sensing technology and the science associated with evaluation of land use and land cover (LULC) in urban region makes use of the wide range images and algorithms. Yet previous processing with LULC methods is often time-consuming, laborious, and tedious making the outputs unavailable within the required time window. This paper presents a new image classification approach based on a novel neural computing technique that is applied to identify the LULC patterns in a fast growing urban region with the aid of 2.5-meter resolution SPOT-5 image products. Since some different classes of LULC may be linked with similar spectral characteristics, texture features and vegetation indexes are extracted and included during the classification process to enhance the discernability. The classifier is constructed based on the partial lanczos extreme learning machine (PL-ELM), which is a novel machine learning algorithm with fast learning speed and outstanding generalization performance. A validation procedure based on ground truth data and comparisons with some classic classifiers prove the credibility of the proposed PL-ELM classification approach in terms of the classification accuracy as well as the processing speed. It may be applied for "rapid change detection" in urban region for regular emergency response, regular planning, and land management in the future.

The island Tenerife is a popular destination for tourists, especially from European countries. From the middle of the 1970s, the mass tourism increased from about 1.3 million to 6 million tourists nowadays (2008).¹ This development lead not only to an increasing expansion of infrastructure but also to a spatial concentration of settlements.² Moreover, the Canary Islands and especially Tenerife are a hotspot of climate change with possible reorientation of atmospheric circulation. The presented research project follows the question how sensitive ecosystems (e.g. laurel forest or pinewood) on Tenerife will be affected by, on the one hand, global impacts of climate change and on the other hand by local socioeconomic effects in future. For this purpose existing time series of land cover and land use change, derived from medium spatial scaled remotely sensed data, will be upgraded with regard to the spatial and temporal resolution. Therefore an object-based classification of high spatial scaled satellite scenes has to be done followed by a change detection analysis. Taking into account the different local and global driving forces for these changes the spatial future development of the most important land use processes like e.g. increase of agricultural land (monocultures) and fallow land will then be simulated and visualised. Based on these results the impacts for different sensitive ecosystems can finally be analysed and valuated.

Landscape ecology as a modern interdisciplinary science offers new concepts, theories, and methods for land evaluation and management. One main part of landscape ecology is describing patterns in the landscape and interpreting the ecological effects of these patterns on flora, fauna, flow of energy and materials. Landscape studies require methods to identify and quantify spatial patterns of landscape. Quantification of spatial patterns is essential to understand landscape functions and processes. Landscape indices as diversity and naturalness can provide quantitative information about landscape pattern. Remote sensing and GIS techniques have high ability for landscape researchers to specify map and analyze landscape patterns. In this study the changes in a selected set of indices were investigated, in order to strengthen the management efforts of Mouteh wildlife refuge in Iran. Using different satellite analysis, Land use/ land cover map were produced from satellite data and then the number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated by GIS approaches and compared for a 35 years. The results showed an increasing concern with regards to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. To sum up, attention to conservation of natural landscape in this area is important in order to repair the natural conditions of habitats.

Ecotourism may be defined as voluntary travels to intact natural areas in order to enjoy the natural attractions as well as to get familiar with the culture of local communities. The main factor contributing to inappropriate land uses and natural resource destruction is overaggregation of ecotourists in some specific natural areas such as forests and rangelands; while other parts remain unvisited due to the lack of a proper propagation about those areas. Evaluating the ecotourism potentials of each area would lead to a wider participation of local people in natural resource conservation activities. In order to properly introduce the ecotourism potential areas, at first, we carried out land preparation practices using Geographic Information System (GIS) and Remote Sensing (RS) techniques; then, the maps of height, slope and orientation were produced using the digital elevation model (DEM) of the study area. Afterwards, we overlaid these maps and the ecotourism potential areas were identified on the map. These specified areas were classified into two land uses of mass and alternative ecotourism, with three subclasses (including class1, class2 and an inappropriate class) considered for each land use. To classify the image, the training areas determined on the ground using a GPS device (Ground Positioning System) were transferred on the RS image. Subsequently, the ecotourism potential areas were determined using a hybrid method. At the final phase, these areas were compared with the areas determined on the ecotourism potential map; as a result of this comparison, the overlaid ecotourism potential areas were distinguished on the Geographic information System.

With the availability of large digital frame cameras like the UltraCamX (UCX) additional benefits through a combination of high-resolution multispectral aerial images with highly accurate digital surface models emerge. This ongoing study examines the level of detail of urban information that can be extracted. High resolution and the unprecedented geometric accuracy of the multispectral and the 2.5D object information enable the derivation of detailed and characteristic object features. The method of object-based classification is not only used to extract meaningful objects, even more important is a detailed assessment of semantic relationships. Our study shows the explicit advantage of high geometric resolution to increase the stability of classification and the number of classes in a representative area of Berlin.

Urban greenbelt plays a positive role in improving the ecology and harmonious interacting between human being and nature, especially in tourism city. Dynamic monitoring of urban greenbelt is very important to effective manage the city and construct the city. This study provides the scientific basis to analysis the change of city. This paper uses the ETM and SPOT image in 2003, 2007 and TM image in 1993 of the whole city of Hangzhou to calculate the urban greenbelt change. The result shows that, in the past ten years, because of the ceaseless development of Hangzhou and its intensive exploitation, the city has reduce a lot of greenbelt, and the spatial distribution do not meet the need of development of tourism city. So the greenbelt can not produce their ecological function effectively in ecological tourism city. Monitoring the Greenbelt Dynamic based on remote sensing is useful way to solve the problem in managing the city.

The ocean color remote sensing metadata describes the content, quality, condition, and other characteristics of ocean color remote sensing data. Paper presents a metadata standard draft based on XML, and gives the details of main ocean color remote sensing metadata XML elements. The ocean color remote sensing data platform-sharing is in developments as a part of the digital ocean system, on this basis, the ocean color remote sensing metadata directory service system based on web service is put forward, which aims to store and manage the ocean color remote sensing metadata effectively. The metadata of the ocean color remote sensing data become the most important event for the ocean color remote sensing information more retrieved and used.

In the precondition of the different land coverage classes response the different LSE values, an improved mono-window algorithm retrieval the LST from Landsat-5 thermal infrared (TIR) data is presented in this paper. Four classes (built-up area, vegetation area, bare land and water) have been selected in the experiment in Tianjin Binhai New Area. Based on supervised classification image, the experiment result shows that precision of the retrieved LSTs from the improved algorithm is higher than that from the single-channel algorithm.

This paper focuses on the application of multi-resolution remote sensing images. Remote sensing data from WorldView-2 is used to access the coastal zone land-use information derived from SPOT5 satellite remote sensing data. Uniform sampling, random sampling are used as two different sampling methods to obtain the evaluation samples. Point samples are used to carry out precision evaluation. In this article, land use information from 5 meter spatial resolution image acquired by SPOT5 are being evaluated, inspection data are 0.5 meter spatial resolution fusion imagery of WorldView-2 panchromatic and multi-spectral images. From the point of view of spatial resolution, significant differences exist between the two. The information from 0.5 meter spatial resolution remote sensing imagery can be used as true ground information to evaluate the information from low-resolution remote sensing images.

In this paper we present and discuss the scientific data recorded along one month of data taking of two mechanical monolithic horizontal sensor prototypes located in a blind-ended (side) tunnel 2000 ft deep in the Homestake mine (South Dakota, USA), chosen to host the Deep Underground Science and Engineering Laboratory (DUSEL). The main goal of this test is to provide preliminary data to characterize the Homestake site in the frequency band 10^-4 ÷ 30Hz and to estimate the level of Newtonian noise, information necessary to understand the feasibility of underground gravitational-wave interferometers sensitive at 1Hz and below. The recorded scientific data and all the technical information obtained with this test are very useful also to understand the performances of the monolithic sensors in the very low frequency band (10^-6 ÷ 10^-3 Hz) and to organize an experiment for the low frequency seismic characterization of the Homestake site with monolithic sensors positioned at different levels and orientations.

The paper focuses on Hangzhou Bay beach reclamation of the past three decades, and reclamation land use patterns evolution using remote sensing technology. 7 years remote sensing data acquired from Landsat series satellites in 1979,1986, 1990, 1995, 2000, 2004 and 2008 are used in this survey. Six period beach reclamation (1979-1986,1986-1990, 1990-995, 1995-2000, 2000-2004, 2004-2008) are obtained. According to various image characteristics of different land-use types, reclamation land use information is interoperated. Based on the above Hangzhou Bay reclamation land use status and statistical data, some discussions about reclamation land use pattern are presented.

Saemangeum, located on the southwest coast of the Korean peninsula, is a 40 100 ha ongoing "reclamation" project in South Korea, concomitance damming the estuaries of the Mangyong and Dongjin rivers, replacing vast tidal land and sea-shallows with land and a huge freshwater reservoir. In 1991, the South Korean government announced that a seawall (dyke) would be constructed to link two headlands just south of the South Korean industrial port city of Gunsan and Buan, 270 kilometers southwest of Seoul, to create 400 km² of farmland and a freshwater reservoir. Started in 1991, the 33km long seawall was finally completed on April 2006. Chlorophyll-a concentration, Suspended solids (SS), Sea surface temperature (SST), and turbidity are four important water quality variables, among other environmental factors such as salinity and pH, for tidal land production in Saemangeum. Change detection of the SST and SS during Saemangeum seawall construction was carried out by using LANDSAT TM and ETM imagery data. The spatial and temporal distribution of SST and SS are estimated and mapped with various degrees of success in Saemangeum area. Here we assessed the potential of these data to derive water quality parameters in a reclaimed estuary environment. We found that the evolution of the estuary, coastline, delta, and change detection results derived from LANDSAT TM and ETM images recorded in 1989, 2001 and 2008, respectively. Due to the limitations of image acquisition and noise, many researchers have employed the image processing technique to improve satellite data in order to assess water quality. The interpolation approach is a useful tool for the analyses and assessment on SST and SS on the basis of available satellite imagery data. Ordinary kriging (OK) were used to improve the SST and SS images in the study area. Results indicate that sedimentary transport, SS, and SST in Saemangeum has significantly changed during the past 20 years, with a dramatic increase in the amount of sediment moved by the river, and deposited in the estuary and in river mouth. The analysis of the spatial structure showed that SST and SS in the study area were spatially correlated and therefore spatial interpolation was valid. Also, we recognized that LANDSAT TM and ETM data have sufficient sensitivity for estuary environmental monitoring.

The interpretation of satellite imagery benefits from merging the spatial structure of the high-resolution panchromatic image with the spectral information. Such "pan-sharpening" has been the topic of extensive research. One objective of our investigations is to process satellite images within seconds. In this work, we build upon the "Fast IHS" technique, using a weighted linear combination of the up-sampled multispectral bands to derive a composite image closer to what the panchromatic sensor had seen. The difference to the actual panchromatic image approximates the high-frequency detail signal and is added to the multispectral bands. However, fixed band weights (exemplified by the "Modified IHS" algorithm) cannot account for differing radiometry and atmospheric conditions. To further reduce color distortion, we compute the optimal band weights for a given data set in the sense of minimizing the mean-square difference between the composite and panchromatic images. Since the noise in the panchromatic image (sometimes non-linear) impacts a subsequent graph-based segmentation algorithm, an additional denoising step is applied before fusion. We use an improved approximation of the Bilateral Filter, which preserves edges and requires only one fast iteration. The quality of the fused image is evaluated in a comparative study of pan-sharpening algorithms available in ERDAS IMAGINE 9.3. Objective metrics such as Q4 show an improvement in terms of color fidelity. The image segmentation results also demonstrate the applicability of this method towards automated image analysis.

Hyperspectral data offers a powerful tool for predicting soil heavy metal contamination due to its high spectral resolution and many continuous bands. However, band selection is the prerequisite to accurately invert and predict soil heavy metal concentration by hyperspectral data. In this paper, 181 soil samples were collected from the suburb of Nanjing City, and their reflectance spectra and soil lead concentrations were measured in the laboratory. Based on these dataset, we compare Least Angle Regression, which is a modest forward choose method, and least squares regression and partial least squares regression based on genetic algorithm. As a result, regression with band selection has better accuracy than those without band selection. Although both Least Angle Regression and partial least squares regression with genetic algorithm can reach 70% training accuracy, the latter based on genetic algorithm is better, because it can reach a larger solution space. At last, we conclude that partial least squares regression is a good choice for the soil lead content retrieval by hyperspectral remote sensing data, and genetic algorithm can improve the retrieval by band selection promisingly. Bands centered around 838nm,1930nm and 2148nm are sensitive for soil lead content.

In this paper the linear unmixing method has been applied in classification of manmade objects, namely urbanized zones, roads etc. The idea is to exploit to larger extent the possibilities offered by multispectral imagers having mid spatial resolution in this case TM/ETM+ instruments. In this research unmixing is used to find consistent regression dependencies between multispectral data and those gathered in-situ and airborne-based sensors. The correct identification of the mixed pixels is key element for the subsequent segmentation forming the shape of the artificial feature is determined much more reliable. This especially holds true for objects with relatively narrow structure for example two-lane roads for which the spatial resolution is larger that the object itself. We have combined ground spectrometry of asphalt, Landsat images of RoI, and in-situ measured asphalt in order to determine the narrow roads. The reflectance of paving stones made from granite is highest compared to another ones which is true for open and stone pits. The potential for mapping is not limited to the mid-spatial Landsat data, but also may be used if the data has higher spatial resolution (as fine as 0.5 m). In this research the spectral and directional reflection properties of asphalt and concrete surfaces compared to those of paving stone made from different rocks have been measured. The in-situ measurements, which plays key role have been obtained using the Thematically Oriented Multichannel Spectrometer (TOMS) - designed in STIL-BAS.

Under the scope of the "Blue Flag" project, a field campaign in order to collect water samples and a photogrammetric survey were performed at the urban seashore beaches of Porto, in August of 2008. Several water quality parameters were measured in different stations, following the European Directive 2006/7/CE. However, only 14 stations appear in the area covered by the aerial photographs. Multiple linear regressions were established in order to estimate the relationship between the DNs and three different water quality parameters (WQP). All the established models were found to be statistically significant and can be used to explain a considerable part of the data variability (R2>66%). A qualitative analysis was also performed in order to identify hydromorphologic features/patterns and correlate them with several WQP. The aerial photographs were classified in 6 classes (beach, beachface, breaking zone, rocks, sediments and sea). The maximum likelihood classifier presented the best performance. Analyzing the results in a GIS environment, it is clear that: for coliforms parameter the highest values appear near the mouth of urban small rivers (beach and beachface); for turbidity the highest values are located in the sediments class; and for the dissolve oxygen the highest values are located in areas with higher dynamics (breaking zone and beachface).

Rational feature selection from the varieties of spectral channels in the optical wavelengths of electromagnetic spectrum (VIS and NIR) is very important for effective analysis and information extraction of remote sensing data. Feature selection is one of the most important steps in recognition and classification of remote sensing images. Therefore, it is necessary to select features before classification. Three factors-the information quantity of bands, the correlation between bands and the spectral characteristic (e.g. absorption specialty) of classified objects in test area Romanian North Western Black Sea coastal area have been considered in our study being suggested a method of multi-level feature selection. Spectral signatures of different terrain features have been used to extract structural patterns aiming to separate surface units and to classify the general land cover categories. The synergetic analysis and interpretation of the different satellite images (LANDSAT: TM, ETM; IKONOS) acquired over a period of 20 years reveals significant aspects regarding impacts of climate and anthropogenic changes on coastal area as well as in Constanta town urban/periurban environment. Information on the spatial pattern and temporal dynamics of marine coastal areas land cover is critical to address regarding sustainability and rational planning policy.

Environmental urban changes assessment is providing information on environmental quality for identifying the major issues, priority areas of the policy making, planning and management. Effective planning is based on the completely and precisely understanding of the environmental parameters in urban area. Remote sensing is a key application in globalchange science, being very useful for urban climatology and land use/land cover dynamics and morphology analysis. Multi-spectral and multi-temporal satellite imagery (LANDSAT TM and ETM , and IKONOS) for Bucharest urban area over 1989 - 2009 period provides the most reliable technique of monitoring of different urban structures regarding the net radiation and heat fluxes associated with urbanization at the regional scale. The main objectives of this investigation aimed to develop and validate new techniques for mapping and monitoring land cover and land use within and around Bucharest urban area using satellite sensor images and new digital framework data and to analyze the spatial pattern of land cover and the detailed morphology of urban land cover across the study area as well as to develop an improved information base on urban land cover and land cover change for transportation models, urban development planning, urban ecology and local plans.

Arid ecosystems are very sensitive to a variety of physical, chemical and biological degradation processes. Tarim Basin, the biggest endorheic basin in the Central Asia continent, is considered as one of the least water-endowed regions in the world and arid and semi-arid environmental conditions are dominant. For the purposes of the convention, arid, semi-arid and dry sub-humid areas were defined as "areas, other than polar and sub-polar regions, in which the ratio of annual precipitation to potential evapotranspiration falls within the range from 0.05 to 0.65." In this study, the Aridity Index (AI), the ratio of precipitation and land surface temperature, was also adopted as the base method for determining dry land types and thereby delineating boundaries and showing changes of aridity conditions in Tarim Basin. Here, precipitation is from TRMM/PR, and land surface temperature is from Modis LST. To analyze the spatial and temporal variations of arid environmental conditions in Tarim basin, we calculated the yearly aridity index (the ratio of total yearly rainfall to yearly mean Land Surface Temperature) based on the accumulated monthly precipitation and the monthly Land Surface Temperature in growing season for the period 2000-2009. The results indicated it is possible to work out an aridity index map with more detailed spatial patterns, which is valuable for identifying human impacts by associated with vegetation and soil moisture characters.

The petroleum pollution is one of the main pollutants of Chinese ocean, and developing algorithms and systems for realtime oil spill monitoring is an urgent matter of the moment. Because the satellite remote sensing is efficient, fargoing and inexpensive, this paper is focused on making use of satellite data of EOS/MODIS, and attempting to monitor oil spill of maritime space in China. The method is built based on the analysis of oil spectral characteristic, and then the remote sensing spectral characters of several petroleum types under the maritime circumstance and the difference of thermal infrared spectrum of oil are observed to provide oil information for oil film monitoring. The research will be used water quality monitoring and early warning for larger ocean disaster, and shows a good application example of satellite data of the EOS/MODIS.

This paper describes the application of a monolithic folded pendulum (FP) as a tiltmeter for geophysical applications, developed at the University of Salerno. Both the theoretical model and the experimental results of a tunable mechanical monolithic FP tiltmeter prototype are presented and discussed. Some of the most important characteristics, like the possibility of tuning its resonance frequency to values as low as 70mHz and its measured resolution of ≈ 0.1 nrad at 100mHz, are detailed. Among the scientific results, earth tilt tides have been already observed with a monolithic FP tiltmeter prototype.

Vegetation indices from MODIS data are subject to residual atmospheric noise, affecting processes requiring data continuity and analyses. This work reconstructed a time series of MODIS EVI mosaics for the Amazon using a novel combination of curve-fitting and spatiotemporal gap-filling. TIMESAT was used for initial curve fitting and gap filling, using a Double Logistic method and MODIS Usefulness values as weights. Pixels with large temporal gaps were handled by a spatiotemporal gap filling approach. The method scans Julian Days before and after the image being gap filled, searching for a good quality pixel (Pg) at the location of the pixel to be replaced. If Pg is found, a window is defined around it and a search for good quality pixels (Px) with spectral characteristics similar to Pg is performed. Window size increases during processing and pixel similarity uses Euclidean distance based on MOD13A2 reflectances. A good quality EVI value for the image being gap filled and at the location analogous to the minimum distance Px replaces the low quality pixel. Results from the spatiotemporal gap filling were then used in TIMESAT for smoothing. An evaluation strategy of the spatiotemporal approach involved flagging 5,000 randomly selected good-quality pixels as low-quality, running the algorithm and regressing the results with the original EVI values (R²= 0.62). The combined strategy was able to find replacement pixels and reduce spikes for images with high cloud cover and was used to rebuild a time series of EVI over the Amazon region for the period 2000-2010.

We propose a flexible probabilistic method for the extraction of earthquake-damaged areas from aerial images. We segment an aerial image into regions and classify each region on the basis of the features appearing in damaged areas. We consider the similarity of neighboring regions in the classification. As a result of segmentation, the classification is independent of the color of each region. Our results show the likelihood of a region being damaged and enable the flexible estimation of damage based on human decisions. The result is displayed on a digital map that can be used for various rescue and humanitarian activities.

There have been many reports on the analysis of the Earth's surface by remote sensing. The purpose of this study is to analyze traffic information, and we have been studying methods of collecting traffic information by remote sensing. To collect traffic information, sensors installed on the roadside are frequently used. However, methods using sensors only collect information around the positions of the sensors. In this study, we attempt to solve this problem by using satellite images, which have recently become increasingly available. We propose a method of collecting traffic information over a large area using satellite images as well as three-dimensional digital maps. We assess traffic conditions by computing the number of edges of vehicles per road section as follows. First, the edges of vehicles are detected in satellite images. During this processing, three-dimensional digital maps are used to increase the accuracy of vehicle edge detection. The number of vehicles per road section, which is computed from the number of edges of vehicles, is computed and referred to as the vehicle density. Traffic conditions can be assessed from the vehicle density and are considered useful for collecting information on traffic congestion. In this study, we experimentally confirm that congested roads can be extracted from satellite images by our method.

Water quality assessment and monitoring from remote sensing data is strongly affected by the accuracy of the atmospheric effect correction. Two algorithms, based respectively on Modtran 4 and on 6SV radiative transfer codes, and an empirical image-based method have been compared, also examining the sensitivity to different parameterizations of water vapour content and aerosols. The experimentation has been carried out on a specific case study, lake Qarun, a conservation area located in the Fayyum Oasis (Egypt). Simple water quality indicators have been computed by multispectral and hyperspectral data and compared to literature data.

One of the influential tools in the study field of pasture and vegetation cover science is technology of remote sensing and satellite data. Satellite data have essential role in preparing needed information for different vegetation aspects studying. One of the applications of satellite data is to prepare the vegetation cover percentage map. In this studying order to prepare the vegetation cover crown percentage maps of Mouteh wildlife refuge between 1972 and 2007, the satellite data were used. vegetation indices were produced using MSS sensors for 1972, TM for 1987, TM for 1998 and image of LISS III sensor for 2007. In this study cover crown percentage Map was provided by using indices which could decrease the soil reflectance. At first corrections was performed on each images. To make correlation between cover crown percentage and satellite data, 290 plot data with appropriate distribution across the region were collected. By using data and several image processing cover crown percentage was estimated for previous years. For each image cover crown percentage models were produced by simple linear regression between produced vegetation indices from each image and field data calculated. Regarding to data analysis SAVI plant index had the highest correlation with cover crown percentage and selected for producing vegetation crown cover percentage. using produced model from SAVI index vegetation crown cover percentage maps were produced in four classes percentage for each year. Results showed that cover crown percentage had decreasing trend in this period.