This PDF file contains the front matter associated with SPIE Proceedings Volume 8181, including the Title Page, Copyright Information, Table of Contents, Introduction, and the Conference Committee listing.

This study is to make an accuracy assessment of the DEM extracted from a stereo pair of ALOS PRISM images of Kanazawa area, Japan. First of all, we computed 14 linear coefficient parameters in 3D perspective transformation (3DPT), using 7 and 9 triangular points whose three dimensional coordinate values were given by Geospatial Information Authority of Japan. The DLT model characterizes the relationship between two dimensional image coordinate system and three dimensional object coordinate system. As for the tie point on the stereo pair image, we selected the GCP set L, the GCP set M, and the GCP set H, consist of 15 triangular points with low elevations (0m<Z<50m), middle elevations (50m<Z<200m) and high elevations (Z>200m), respectively. The three DEMs were generated with an aid of the OrthoEngine module (PCI Geomatica Ver. 10.3), by assigning these three GCP sets as the tie point sets. It is very important to input the correct set of pixel and line coordinate values for the DEM extraction. For this purpose, the pixel and line coordinate values for the Nadir view image and the Forward View image were calculated by the DLT with newly computed linear coefficients. The accuracy analysis of the extracted DEMs was examined at independent 10 Check Points (CPs). We found the most accurate DEM was generated using the GCP set M. The overall accuracy of the DEM with 2.5m spatial resolution was computed to be RMSE = 5.8m in the vertical direction by comparing the extracted elevation values with measured values at 10 CPs.

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. Four areas with different physiographic and geomorphologic features were selected for the ALOS DSM validation. The ALOS DSMs were compared to elevation data from different sources: 1/50.000 topographic maps and airphoto stereo-pairs. Points of known elevation have been used to estimate the accuracy of the DSMs. 2D RMSE, correlation and the percentile value were computed and the results are presented. A new optimization method is proposed in order to ameliorate the accuracy of the DSMs. The first results are quite satisfactory.

This study focused on testing the feasibility of up-scaling ground-spectra-derived parameters to HyMap spectral and spatial resolution and whether they could be further used for a quantitative determination of the following geochemical parameters: As, pH and C_lignite content. The study was carried on the Sokolov lignite mine as it represents a site with extreme material heterogeneity and high heavy-metal gradients. A new segmentation method based on the unique spectral properties of acid materials was developed and applied to the multi-line HyMap image data corrected for BRDF and atmospheric effects. The quantitative parameters were calculated for multiple absorption features identified within the VIS/VNIR/SWIR regions (simple band ratios, absorption band depth and quantitative spectral feature parameters calculated dynamically for each spectral measurement (centre of the absorption band (λ), depth of the absorption band (D), width of the absorption band (Width), and asymmetry of the absorption band (S)). The degree of spectral similarity between the ground and image spectra was assessed. The linear models for pH, As and the C_lignite content of the whole and segmented images were cross-validated on the selected homogenous areas defined in the HS images using ground truth. For the segmented images, reliable results were achieved as follows: As: R²=0.84, C_lignite: R²=0.88 and R² pH: R²= 0.57.

HISUI, a Japanese future spaceborne hyperspectral and multispectral imaging system, is currently being developed by Japanese Ministry of Economy, Trade, and Industry. Because of the narrow swath of the imager as well as the limits on the operation time and data downlink resource allocation, the operation strategy of HISUI should be examined thoroughly to fully utilize HISUI's earth observation capability. A software which simulates HISUI's operation is being developed for the detailed analysis of HISUI's long term operation plans. The simulation results indicate that 1) one-time priority area mapping will be completed within eight months with moderate data downlink allocation, 2) one-time global observation in a year will be possible if the allocated downlink capability is more than 250 GByte per day, 3) the nighttime volcano monitoring will not significantly affect the daytime observation if the cross track pointing only for nighttime observation is not allowed.

The application of co-occurrence matrices for the calculation of contrast in satellite imagery is a common approach. The textural as well as contextual information from these grey level co-occurrence matrix (GLCM) calculations encounter restrictions due to compromises in their practical implementation. As an alternative, a contrast calculation inside an object-based (OBIA) environment (eCognition) using single-pixel objects is considered. This requires fewer compromises in the implementation, with the flexibility of experimenting on the influence of much larger contextual information for single pixels by expanding the search radius. The contextual information based on contrast can be applied in the classification of the agricultural domain as well as a variety of classes in the 1:25.000 land use/cover classification. The OBIA environment enables a rapid evaluation on various spatial and spectral feature attributes. This allows an evaluation of context on an ever increasing search radius without using larger disk space for synthetic imagery. After the initial evaluation, a small selection of essential contrast maps can be exported as GeoTiff files to allow an input for automated methods. If proven useful, GeoTiff export becomes redundant and the integration of classification methods such as self-organizing maps into the OBIA environment allows effective use of contrast characteristics on small and large neighborhoods.

Image processing is a very helpful tool in many fields of modern sciences that involve digital imaging examination and interpretation. Processed images however, often need to be correlated with the original image, in order to ensure that the resulting image fulfills its purpose. Aside from the visual examination, which is mandatory, image quality indices (such as correlation coefficient, entropy and others) are very useful, when deciding which processed image is the most satisfactory. For this reason, a single program (script) was written in Matlab language, which automatically calculates eight indices by utilizing eight respective functions (independent function scripts). The program was tested in both fused hyperspectral (Hyperion-ALI) and multispectral (ALI, Landsat) imagery and proved to be efficient. Indices were found to be in agreement with visual examination and statistical observations.

The paper describes an object-based method to detect destroyed buildings as a consequence of an earthquake. The investigation is based on the analysis of remotely sensed raster and vector-based data. The methodology includes three main steps: generation of features defining the states of buildings, classification of building state and data import in GIS. This paper concentrates on the first step of the three, the generation of features. The appropriately selected features are indispensable for the following successful classification. The described methodology is applied to remotely sensed images of areas that had been subject to an earthquake. Our preliminary results confirm the potential of the proposed approach for detection of the building state. The change detection methodology has been developed solely with Open Source Software. GRASS GIS is involved for vector and raster data processing and presentation. Programming languages Python and Bash are used to develop new GRASS-modules.

This study aims at an area-wide detection of the building structure of settlements from individual, single-polarized TerraSAR-X (TSX) intensity datasets recorded in stripmap mode. Due to SAR side-looking acquisition, the building-related information is located in areas which do spatially not exactly correspond with the true location of the buildings. To perform a supervised classification approach we at first create a mask of areas which are affected by scattering from the buildings based on reference datasets of the building footprints with their respective height by considering the viewing geometry of the TSX data. The generated mask is used in the following to randomly extract training samples in order to determine the relationships between the SAR data and the class membership. For the classification of the areas carrying the building-related information we utilize a random forest algorithm. As input features for classification we compare the suitability of the Grey Level Co-occurrence Matrix based textures measures according to Haralick, Mathematical Morphology and Spatial Autocorrelation texture measures. These features are calculated from TSX data using a pixel-based multiple-scale moving window approach. For each texture feature set and each moving window width the relationship to the class membership is modeled on the basis of the extracted training samples. The different models are used in the following to perform different classification runs of the entire TSX dataset. With the described approach we achieve overall classification accuracies of up to 78 %. The influence of the simultaneous usage of input texture features calculated with different window widths on the classification accuracy is of the same magnitude as the influence of the usage of the different texture feature sets.

Spatial modeling of air pollutants in the mega cities such as Tehran is a useful method for the estimation of pollutants in the non-observed positions in Tehran. In addition, spatial modeling can determine the level of pollutants in different regions of Tehran. There are some typical interpolation techniques (e.g., Inverse Distance Weighting (IDW), Thin Plate Splines (TPS), Kriging and Cokriging) for spatial modeling of air pollutants. In this study, different interpolation methods are compared for spatial modeling of carbon monoxide in Tehran. The three-hourly data of wind speed and direction was received from 5 meteorological stations in Tehran. The hourly data of carbon monoxide in 2008 have been extracted of 16 air pollution monitoring stations in Tehran. The hourly data of 3 selected days in 2008 (72 hours) and similarly, the daily data of 36 days in 2008 (3 days in each month) were utilized for spatial modeling in this study. Different typical interpolation techniques were implemented on different hourly and daily data using ArcGIS. The percent of absolute error of each interpolation techniques for each hourly and daily interpolated data was calculated using cross validation techniques. Results demonstrated that Cokriging has better performance than other typical interpolation techniques in the hourly and daily modeling of carbon monoxide. Because it utilizes three input variables (Latitude, Longitude and altitude) data for spatial modeling but the other methods use only two input variables (Latitude and Longitude). In addition, the wind speed and direction maps were compatible with the results of spatial modeling of carbon monoxide. Kriging was the appropriate method after Cokriging.

The modern Earth Observation System (EOS) technology takes important role in the uranium geological exploration, and high resolution remote sensing as one of key parts of EOS is vital to characterize spectral and spatial information of uranium mineralization factors. Utilizing satellite high spatial resolution and hyperspectral remote sensing data (QuickBird, Radarsat2, ASTER), field spectral measurement (ASD data) and geological survey, this paper established the spectral identification characteristics of uranium mineralization factors including six different types of alaskite, lower and upper marble of Rössing formation, dolerite, alkali metasomatism, hematization and chloritization in the central zone of Damara Orogen, Namibia. Moreover, adopted the texture information identification technology, the geographical distribution zones of ore-controlling faults and boundaries between the different strata were delineated. Based on above approaches, the remote sensing geological anomaly information and image interpretation signs of uranium mineralization factors were extracted, the metallogenic conditions were evaluated, and the prospective areas have been predicted.

This study aimed at studying the whole of geological environment associated with a semi-arid, hard-rock terrain utilizing hyperspectral satellite data, topographic analysis and 3D visualization techniques to infer hydrological regime in form of GIS outputs. The study area selected has a rapidly changing land use pattern and subjected to mining activity. Several natural lakes have dried up and few abandoned mining pits have turned up into lakes. Efforts are being made to restore the landscape and protect any further degradation. Availability of water is prime decisive factor in all such efforts. In this study, Hyperion data has been utilized in a unique and elaborated methodology designed to effectively isolate the intruding urban cover and extract maximum information in an unmixing approach. This has been coupled with ground surveys as well as analysis of soil / sediment, rock and water samples. ASTER DEM has been used for topographic analysis using TauDEM to infer watershed and drainage pattern. Apart from geological endmembers, available pockets of vegetation have been identified as these are themselves an indicator of groundwater availability. This also resulted in identification of sites suitable for future geophysical surveys to determine the nature of sub-surface fractures. This whole exercise in detail has been helpful in obtaining a synoptic view of water resource in the region and then subsequent planning for integrated water resource management.

Rapid change detection is used in cases of natural hazards and disasters. This analysis lead to quick information about 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 questions which need to be answered before appropriate, eligible help can be established. This study presents an innovative strategy to retrieve post event information by use of an object-based change detection approach. Within a transferable framework, the developed algorithms 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 normalized temporal change index (NTCI) panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas which are developing new for cases where rebuilding has already started. The results of the study are also feasible for monitoring urban growth.

Due to complexity and spectral similarity in semi-arid areas, land cover mapping with remotely sensed data encounters serious problems when applying methods based on spectral information and ignore spatial information. A research was conducted in the Blue Nile area of Sudan to evaluate the effectiveness of Object-Based analysis (OB) approaches versus pixel based approach to generate Land Use Land Cover (LULC) thematic maps based on multi-spectral imagery data. Maximum Likelihood Classifier (MLC) was applied to examine if the spectral properties of the selected classes alone can be discriminated effectively. Nine land cover classes were generated with only about 82% overall accuracy. Different segmentation strategies were applied with the OB paradigm that might be effective to separate similar spectral values into a basic unclassified image objects in groups of relatively homogeneous pixels based on shape and compactness criterion at different scales. The segmented objects assigned to different classes with methods of membership functions and Nearest Neighbor Classifiers (NNC). The membership functions (RB) provided highly overall classification accuracy (95%), while NNC achieved about 89% accuracy. This study emphasized that the OB methods applied in this study provides more accurate results than the classical per-pixel approach especially when user's expert knowledge is presented.

The method to extract phenological information for different land cover types is presented. Phenological features are two different start dates of growing season, date of maximum growth, end of growing season and two growing season lengths. Also, quality indicators are estimated for some phenological features. The method is based on NDVI-time series extracted from MODIS-images. The errors between extracted dates and in-situ measurements are reasonably small. For example, the residuals of the estimation of the start of Flux Growing Season are on only 2 days for broadleaf forest in one Southern Finland hydrological drainage basin. The method has been tested on Northern Boreal forest zone, where there are freezing temperatures and snow during winter.

This paper describes the ideas, data and methods to produce Finnish Corine Land Cover 2006 (CLC2006) classification. This version is based on use of existing national GIS data and satellite images and their automated processing, instead of visual interpretation of satellite images. The main idea is that land use information is based on GIS datasets and land cover information interpretation of satellite images. Because Finland participated to CLC2000-project, also changes between years 2000 and 2006 are determined. Finnish approach is good example how national GIS data is used to produce data fulfilling European needs in bottom-up fashion.

Due to the particular geographical location and geological condition, Taiwan suffers from many natural hazards which often cause series property damages and life losses. To reduce the damages and casualty, an effective real-time system for hazard prediction and mitigation is necessary. In this study, a case study for Landslide Hazard Zonation (LHZ) is tested in accordance with Spatial Data Mining and Knowledge Discovery (SDMKD) from database. Many different kinds of geospatial data, such as the terrain elevation, land cover types, the distance to roads and rivers, geology maps, NDVI, and monitoring rainfall data etc., are collected into the database for SDMKD. In order to guarantee the data quality, the spatial data cleaning is essential to remove the noises, errors, outliers, and inconsistency hiding in the input spatial data sets. In this paper, the Kriging interpolation is used to calibrate the QPESUMS rainfall data to the rainfall observations from rain gauge stations to remove the data inconsistency. After the data cleaning, the artificial neural networks (ANNs) is applied to generate the LHZ map throughout the test area. The experiment results show that the accuracy of LHZ is about 92.3% with the ANNs analysis, and the landslides induced by heavy-rainfall can be mapped efficiently from remotely sensed images and geospatial data using SDMKD technologies.

Nowadays, climatic and socio-economic conditions require a change in thinking in the field of state forest management. A high demand for up to date and precise forest information is given - especially in regard to increasing forest damages by natural hazards. The increasing availability of high-resolution and shortly-revisiting satellite systems (e.g., TerraSAR-X, Cosmo-SkyMed, RapidEye) allows to support such monitoring tasks. A TerraSAR-X image pair was analyzed focusing on the image analysis of forest areas. There, the advantage of the higher geometric resolution and the independance to sun-illumination of the SAR imagery compared to electro-optical image data was taken. The study in this paper deals with the extraction of tree and forest heights as structural parameters.

Remote sensing can become a very useful tool in order to monitor coastal water quality. Economically benefits of using remote sensing techniques are obviously comparatively to the field-based monitoring because water quality can be checked daily or weekly depended on satellite overpass frequency rather than monthly as done by traditional methods which involve expensive sampling campaigns. Moreover remote sensing allows the spatial and temporal assessment of various physical, biological and ecological parameters of water bodies giving the opportunity to examine a large area by applying the suitable algorithm. This paper describes the overall methodology in order to retrieve a coastal water monitoring tool for a high risk area in Cyprus. This project is funded by the Research Promotion Foundation of Cyprus and is been developed by the Department of Civil Engineering & Geomatics, Remote Sensing Laboratory, Cyprus University of Technology in corporation with the Department of Fisheries and Marine Research in Cyprus. Firstly a time series of pigments will be done in order to determine the concentrations of the expedient parameters such as Chlorophyll, turbidity, suspended solids (SS), temperature etc at the same time of satellite overpass. At the same time in situ spectroradiometric measurements will be taken in order to retrieve the best fitted algorithm. Statistical analysis of the data will be done for the correlation of each parameter to the in situ spectroradiometric measures. Several algorithms retrieved from the in situ data are then applied to the satellite images e.g. Landsat TM/ETM+, MODIS in order to verify the suitable algorithm for each parameter. In conclusion, the overall approach is to develop regression models in which each water quality parameter will be retrieved using image, field spectroscopy, and water quality data.

Mining is an important activity contributing to the economic development with long lasting environmental impacts. A major disaster took place in 2001 in Ocnele Mari salt mining area located in the central-south part of Romania when the artificial lake brine was poured in rural areas, devastating homes and polluting the Olt River. Towards a sustainable and harmonious development of the Ocnele Mari area, the Romanian Authorities decided to ecology and rehabilitate it. This ongoing project is focused on land degradation monitoring from 2001 disasters until now. High Resolution Spotlight TerraSar-X synthetic aperture radar data acquired within TerraSAR-X proposal LAN0778 are used to analyze terrain deformation by interferometric techniques knowing the mine subsidence is not constant; periods of relative stability are followed by quick deformation. The rocks forming in the region situated above the salt cushion have very low mechanical resistance and high intergranular fissured permeability so the hill's slopes are affected by landslides which are reactivated periodically. Additionally, analysis of the vegetation coverage (leaf area index and normalized difference vegetation index) from the optical data gathered by different sensors such as LANDSAT and MODIS combined with the meteorological data (temperature, wind speed, humidity and solar radiation) provide indicators for the land degradation. The results will be validating on ancillary data. Satellite derived information in conjunction with in-situ measurements can provide valuable information for existing conservation development models for defining the essential elements of a planning process designed to maximize the values provided by salt ponds from Ocnele Mari.

This paper presents a systematic approach to utilize multi-temporal remote sensing images and spatial analysis for the detection, investigation, and long-term monitoring of landslide hazards in Taiwan. Rigorous orthorectification of satellite images are achieved by correction of sensor orbits and backward projections with ground control points of digital elevation models. Individual images are also radiometrically corrected according to sensor calibration factors. In addition, multi-temporal images are further normalized based on pseudo-invariant features identified from the images. Probable landslides are automatically detected with a change-detection procedure that combines NDVI filtering and Change-Vector Analysis. A spatial analysis system is also developed to further edit and analyze detected landslides and to produce landslide maps and other helpful outputs such as field-investigation forms and statistical reports. The developed landslide detection and monitoring system was applied to a study of large-scale landslide mapping and analysis in southern Taiwan and to the long-term monitoring of landslides in the watershed of Shimen Reservoir in northern Taiwan. Both application examples indicate that the proposed approach is viable. It can detect landslides effectively and with high accuracy. The data produced with the developed spatial analysis system are also helpful for hazard investigation, reconstruction, and mitigation.

Heterogeneous Earth Observation missions pose the problem that each of them offers its own way and technology of how to search for and access to mission results such as Earth observation datasets. Typically, these tasks are provided by ground segment software services which may be called through corresponding interfaces by client geospatial software applications. This paper presents the design and the architecture of the Heterogeneous Mission Accessibility (HMA) which is an interoperability initiative of the European/Canadian Ground Segment Coordination Body. The final objective of HMA is to leverage the idea of a service-oriented architectural style. This means, that the individual ground segment systems shall be loosely-coupled by means of an HMA Service Network. The paper is an excerpt of the comprehensive "HMA cookbook" to be published soon by the European Space Agency (ESA). It describes the HMA approach for user authentication and authorization based upon standard Web services and the discovery of, the access to and the presentation of datasets by means of Open Geospatial Consortium (OGC) standards. It is outlined how the feasibility analysis of sensor observation tasks and the ordering of products may be expressed by the service and information models of the OGC Sensor Web Enablement initiative. The paper concludes with a discussion about the follow-on research topic of service-oriented design of Earth observation applications.

This paper focuses on the wetland of Ximen Island special marine protected areas in Yueqing Bay, Zhejiang, China. In this paper, four remote sensing images from Landsat-7, SPOT-4, SPOT-5 and WorldView-2 satellites are collected. These images are used for wetland investigation and analysis. Wetland information of island and tidal flat is derived from the remote sensing images. Wetland in island includes aquaculture water, pond water, paddy fields and reservoirs. Tide wetland includes vegetation areas, breeding areas, mud tide flat and water. The results mainly showed that the area of island wetland is 1,281,973.04 square meters, accounting for 18.09% of the whole island area, and that the mangroves communities distribute along the coast of Ximen Island.

The wetland is one kind of very important ecosystem on the Earth. Lianyungang has a large amount of wetland which are decreased in area and ecosystem function in recent decades. The purpose of this paper is to extract wetland information and assess ecosystem health of coastal wetlands in Lianyungang. The TM images of 1987 and 2009 were used to extract wetland information through visual interpretation and supervised classification methods. And nine indexes were used to establish the evaluation system for the methods of single-factor and PSR (Pressure-State-Response) model used to evaluate wetland ecosystem health of Lianyungang. The results showed that: Coastal wetland of Lianyungang is decreasing in area, and has general level of wetland ecosystem health; Natural sate of wetlands has some change; External pressure is large. The ecological function has a certain degree of degradation, and the ecosystem can still maintain.

We propose a method to estimate lightning hazard by integrating various physical surface properties and an electromagnetic parameter in order to present a lighting hazard map of northern Alberta, Canada. Physical surface properties include the land class, roughness, and temperature; whereas the electromagnetic parameter implies the estimated dielectric constant in this study. Geographic information system (GIS) data mining and spectral correlation methods are mainly carried out to estimate the potential lightning strike and consequent lightning hazard over the study area. The GIS data mining technique is implemented to find out the rule between the physical surface properties at each pixel and the lighting records. We compute the relative frequencies of the rules containing three different physical surface properties and sort them to identify which rule retains the highest possibility of lightning strikes. The potential lightning strike map is generated by normalizing the derived frequencies ranging from 0 to 1 and used with the non-hierarchical dielectric constant map in order to extract the pixels satisfying the condition of high dielectric constant and high frequency of a lightning strike by the wavenumner correlation filtering (WCF) method. The two maps filtered by the WCF are then combined by the local favorability index (LFI) to enhance the result. By correlating the potential lightning strike map with the non-hierarchical dielectric constant values in the spectral domain using the WCF and integrating them by the LFI, a lightning hazard of the study area is presented.

Alqueva dam was projected for the gorge of Guadiana River (Portugal) and resulted in the creation of the Europe's largest artificial lake with a flooding surface of 25,000 ha. Landsat imagery can be used for detecting terrestrial land cover conditions. In this work, 18 years of Landsat data, covering the period between 1992-2009 were used. The land use/land cover rates and flooding surface estimation were based on image classification algorithms (pixel-based and object-oriented approaches). The Landsat images are all from April and were already geometrically corrected. The selection of land-use classes is based on the Corine land cover nomenclature. In the pixel-based classification three supervised classification algorithms were applied to the dataset. The pixel-based classification algorithms presents a very good performance, demonstrated by the results of the overall accuracy (>91.55%) and Kappa statistics (>0.90). In the object-oriented approach, the region growing segmentation method was applied followed by the unsupervised Mahalanobis classification algorithm. Lastly, an estimation of the flooding surface was performed and land cover/land use maps were produced. GIS techniques were also used to quantify the land/use change rates and to compute the flooding surface increase (14,000 ha in 1992 and 23,000 ha in 2009).

The assessment of surface water salinity is a long standing feature request for water quality assessment by remote sensing. The aim of the present work is to test an empirical method for surface water salinity retrieval by means of multispectral satellite images at medium resolution (30 m). For this purpose, two case studies were selected: the first is Lake Qarun (Egypt), the second is an on-shore tract of central Adriatic Sea, located between the mouths of Tronto and Salinello Rivers (Italy). For the experimentation ALI (Advanced Land Imager) and Landsat ETM imagery was collected. Field data were acquired at both sites by means of in situ conductivity measurements, for calibration purpose. The model applied to convert atmospherically corrected reflectance value in practical salinity units (PSU) has been developed analysing the correlation between field data and an expressly defined salinity index. First results show a promising overall correlation (R² = 0.85), even if further work is required to provide a better validation.

Satellite remote sensing is playing an increasingly important role in the detection and documentation of archaeological sites. Surveying an area from the ground using traditional methods often presents challenges due to the time and costs involved. In contrast, the multispectral synoptic approach afforded by the satellite sensor makes it possible to cover much larger areas in greater spectral detail and more cost effectively. This is especially the case for larger scale regional surveys, which are helping to contribute to a better understanding of ancient Egyptian settlement patterns. This study presents an overview of satellite remote sensing data products, methodologies, and image processing techniques for detecting lost or undiscovered archaeological sites with reference to Egypt and the Near East. Key regions of the electromagnetic spectrum useful for site detection are discussed, including the visible near-infrared (VNIR), shortwave infrared (SWIR), thermal infrared (TIR), and microwave (radar). The potential of using Google Earth as both a data provider and a visualization tool is also examined. Finally, a case study is presented for detecting tell sites in Egypt using Landsat ETM+, ASTER, and Google Earth imagery. The results indicated that principal components analysis (PCA) was successfully able to detect and differentiate tell sites from modern settlements in Egypt's northwestern Nile Delta region.

Mining plants are one of the factors having major negative impact on the area where they are situated. In our study this is the case of the mine production plant consisting of Elacite mine and Mirkovo floatation plant both located in central part of Stara Planina Mountain. In this study an attempt is made to delineate the overburden dumps and open pit mines by means of remotely sensed multispectral data with moderate spatial resolution (e.g. Landsat TM/ETM+ 30m) is a challenging task. The major difficulties arise from: 1) large period using the dump (introducing the need for multitemporal data); 2) the unknown proportions of vegetation, soil and embedding rock samples in the boundary areas and their seasonal variations; 3) relatively restricted access to places of interest. A variety of methods have been proposed to overcome the problems with pixels corresponding to two or more end-members, but a promising one is the soft classification which assign single pixel to several land cover classes in proportion to the area of the pixel that each class covers. In this scenario for every pixel of the data the correct proportion of the end-members should be found and then co-registered with the corresponding original pixel. As a result this sub-pixel classification procedure generates a number of fraction images equal to the number of land cover classes (end-members). The sub-pixel mapping algorithms we have exploited so far have one property in common: accuracy assessment of sub-pixel mapping algorithms is not easy because of missing high resolution ground truth data. One possible solution is to incorporate in the method adopted additional ex-situ and in-situ measured data from field and laboratory spectrometers with bandwidth about 1 nm. This study presents a successful implementation of soft classification method with additional, precise spectrometric data for determination of dump areas of the copper plant and open ore mine. The results achieved are proving that the in-situ gathered data provide coincidence of 93.5%. The main advantage of the presented technique is that mixed pixels are used during the training phase. Compared to these other techniques, the present one is simple, cheap and objective oriented. The results of this sub-pixel mapping implementation indicate that the technique can be useful to increase the resolution while keeping the classification accuracy high.

Phenology is the important indicator of reflecting climate and environment change. Development of remote sensing provides a new method for mapping phenology. Normalized difference Vegetation Index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is a key indicator to vegetation monitoring and phenology analysis. This paper uses time-series of MODIS NDVI 16 days vegetation indices of 250 meters, making use of double Logistic model, extracting deciduous forest phenology of Beijing area in the year 2001-2009. The results show that in most of Beijing area, deciduous forest growing season start date begins between 110th and 160th day; Growing season end date begins between 280th and 330th; Length of growing season in most parts of deciduous forest is mainly between 120th and 200th day. Among them, 2001 and 2006 growing season start date, growing season end date have a large difference from previous years, and have relations with precipitation and length of day. Compared the results with phenology field observation data, the results have a certain reliability.

The quality of the environment has a great impact on public health while air quality is a major factor that is especially relevant for respiratory diseases. PM10 (particulate matter below 10 μ) particles are among the most dangerous pollutants, which enter the lower respiratory tract and cause serious health problems. Obtaining reliable air pollution data is limited to a number of ground measuring stations and their spatial location. We used an alternative approach and created statistical models that employed remotely sensed imageries. To establish empirical relationships, we used multi-temporal (2006-2009) MODIS aerosol optical thickness data (product MOD04, Level 2) and the PM10 ground mass concentrations. The north-western part of the Czech Republic (namely the Karlovarský and the Ustecký regions) was chosen as a test site, as all the different types of cultural landscape (forest-economical, agricultural, mining, and urban) can be found within one MODIS scene. This study was focused on the various aspects as follows (i) analysis of MODIS AOT / stationary PM10 time-series trend between 2006-2009, (ii) establishing a linear relationship between PM10 and AOT values for each station and (iii) evaluation of a spatial relationship of the annual mean AE (Ångstrom Exponent) and PM10 values.

The present work is part of the Enviland-2 research project, which investigates the synergism between radar- and optical satellite data for ENVIronment and LAND use applications. The urban work package of Enviland aims at the combined analysis of RapidEye and TerraSAR-X data for the parameterization of different urban land use structures. This study focuses on the development of a transferable, object-based rule set for the derivation of urban land use structures at block level. The data base consists of RapidEye and TerraSAR-X imagery, as well as height information of a LiDAR nDSM (normalized Digital Surface Model) and object boundaries of ATKIS (Official Topographic Cartographic Information System) vector data for a study area in the city of Rostock, Germany. The classification of various land cover units forms the basis of the analysis. Therefore, an object-based land cover classification is implemented that uses feature level fusion to combine the information of all available input data. Besides spectral values also shape and context features are employed to characterize and extract specific land cover objects as indicators for the prevalent land use. The different land use structures are then determined by typical combinations and constellations of the extracted land use indicators and land cover proportions. Accuracy assessment is done by utilizing the available ATKIS information. From this analysis the land use structure classes residential, industrial/commercial, other built-up, allotments, sports facility, forest, grassland, other green spaces, squares/parking areas and water are distinguished with an overall accuracy of 63.2 %.

The Huangshui River basin is located in a transitional zone between the Loess Plateau and Qinghai-Tibetan Plateau in northwest China. Rapid urbanization has resulted in loss of a large amount of cultivated land in valley region; at the same time, a lot of steep slope cultivated land which located in hilly-gully region and mountain region was returned back into forest land and grassland for ecological land use conservation. The objective of this study is to monitor and analyze the spatial and temporal patterns change of cultivated land in the Huangshui river watershed by the combination method of Landsat TM image and Geographical information System technologies. Our study results indicated that in 2007, the total cultivated land was 540792.1ha whereas in 2007 it dropped to 484159.15 ha, with a net loss of 56633.01ha. Specially, during 1996-2007, the irrigated land in valley region decreased from 110446.25 ha to 104141.07 ha with a net loss of 6305.18ha, mainly being converted to built-up land. In hilly and gully region and mountain region, dry farmland rapidly decreased from 430345.88ha in 1996 to 380018.08ha in 2007, with a net decease of 50327.8ha, respectively. The decrease in cultivated land in hilly-gully region and mountain region was mainly converted to forest land and grassland.

The island Tenerife has always been used for intensive agriculture, whereby the natural landscape was continuously altered. Especially mountainous areas with suitable climate conditions have been drastically transformed for agricultural use by building of large terraces to get flat surfaces. In recent decades political and economic developments lead to a transformation process (especially inducted by an expansive tourism), which caused concentration- and intensificationtendencies of agricultural land use as well as agricultural set-aside and rural exodus. In order to get information about the land use and land cover (LULC) patterns and especially the agricultural dynamics on Tenerife, a multi-scale, knowledge-based classification procedure for recent RapidEye data was developed. Furthermore, a second detection technique was generated, which allows an exact identification of the total ever utilised agricultural area on Tenerife, also containing older agricultural fallow land or agricultural set-aside with a higher level of natural succession (under the assumption that long-term fallow areas can be detected mainly together with old agricultural terraces and its specific linear texture). These areas can hardly be acquired in the used satellite imagery. 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. Through the detection of recent agricultural land use in the satellite imagery and total ever utilised agricultural area in the orthophotos, it is possible to define the total non-active agricultural land as well as hot spots of agricultural decrease.

Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

Satellite remote sensing is an invaluable tool to assess the status and changes of irrigated agricultural systems. Agricultural sites are among the most heterogeneous sites at the landscape level: spatial pattern of agricultural fields, within-field heterogeneity, crop phenology and crop management practices vary significantly. Highly dynamic objects (crops and crop rotations) result in large temporal variability of surface spatial heterogeneity. Technological advances have opened the possibility to monitor agricultural sites combining satellite images with both high spatial resolution and high revisit frequency, which could overcome these constraints. Yet depending on the field sizes and crop phenology of the agricultural system observed, requisites in terms of the instrument´s spatial resolution and optimal timing of crop observation will be different. The overall goal is to quantitatively define region specific satellite observation support requirements in order to perform land use classification at the field basis. The main aspect studied here is the influence of spatial resolution on the accuracy of land use classification over a variety of different irrigated agricultural landscapes. This will guide in identifying an appropriate spatial resolution and input parameters for classification. The study will be performed over distinct locations in irrigated agro-ecosystems in Central Asia, where reliable information on agricultural crops and crop rotations is needed for sustainable land and water management.

Thermal mapping is an highly relevant tool for the assessment of the quality of coastal waters. Remote sensing is an useful technique for monitoring large surfaces in near real time, nevertheless, spatial resolution represents an important limiting factor. In this work it the spatial improvement, from 1km to 250m, of MODIS thermal imagery on coastal water obtained with the SWTI (SharpeningWater Thermal Imagery) is shown. This algorithm is applied, for the first time, to MODIS images acquired on the lagoon of Venice and on the delta of the Po River. The performances of SWTI are evaluated taking as a reference a couple of ASTER images acquired simultaneously to the MODIS images and on the same areas. Moreover, the water temperatures obtained with a simple bilinear interpolation of the MODIS images is also considered. Several statistical parameters, as bias and root mean square difference, are used to quantify the the difference between ASTER and MODIS/SWTI water temperatures along coastlines. In all the the cases these differences are lower than 1K.

The ocean satellite observation is more and more important to study the global change, protect ocean resource and implement ocean engineering for their large area cover and high frequency observation, which have already given us a global view of ocean environment parameters, including the sea surface temperature, ocean color, wind, wave, sea level and sea ice, etc... China has made great progress in ocean environment remote sensing over the last couple of years. These data are widely used for a variety of applications in ocean environment studies, coastal water quality monitoring environmental, fishery resources protection, development and utilization of fishery resources, coastal engineering and oceanography. But the data are no online information access and dissemination, no online visualization & browsing, no online query and analyze capability. To facilitate the application of the data and to help disseminating the data, a web-service system has developed. The system provides capabilities of online oceanic satellite information access, query, visualize and analyze. It disseminates oceanic satellite data to the users via real time retrieval, processing and publishing through standards-based geospatial web services. A region of interest can also be exported directly to Google Earth for displaying or downloaded. This web service system greatly improves accessibility, interoperability, usability, and visualization of oceanic satellite data without any client-side software installation.

The subject intersection becomes one of the hot research topics recently. It is a new direction to integrate the GIS technologies with Bibliometrics. The literatures concerned with geosciences normally involve some spatial related information. In this paper, the spatial information of the study area and sampling or observing points was extracted. Then these data were analyzed and presented by using the GIS technologies. The results indicate that there are big variations of the spatial distribution. For the whole Qinghai-Tibet plateau, the degree of interest increase as follow: southwest, northwest, southeast, and northeast. For the regions, Qilian Mountains, Qiangtang plateau, Qinghai-Tibet Road and Qinghai-Tibet Railway, Qinghai Lake, and Sichuan-Tibet Road are the hotspot regions. There are differences of the distribution characteristics in the different segments along the latitudinal direction and longitudinal direction. There is transfer tendency from middle Qinghai-Tibet Plateau to northern Qinghai-Tibet Plateau. Most of sampling and observing points are close to the traffic lines. The point numbers decrease quickly along with the increasing distance to the traffic lines.

Soil salinity principally affects soil properties, environment and productivity of agricultural areas for developing countries. Currently, no inversion algorithms exist for directly determining soil salinity from microwave remote sensing data, but we hope to draw on the soil moisture retrieval algorithms to obtain soil salinity amount. So the effect of moisture and salinity on dielectric constant and the backscattering coefficient (VV and HH polarization mode) are simulated using the advanced integral equation model (AIEM) combined with the modified Dobson dielectric mixing model. The results indicate that real part of dielectric constant decreases with soil salinity content, however, the imaginary part increases with it especially for the high moisture regions. Both soil moisture and salinity affect the VV and HH polarization backscattering coefficient, with moisture the backscattering coefficient increases evidently, but with soil salinity backscattering coefficient increase at the small moisture region and it remains unchanged for the HH polarization or expresses the weakly downward tendency for VV polarization respectively at the high moisture region. Moreover, the simulation results also suggest that VV or HH polarization can be used to retrieve soil salinity for the soil with low moisture (<0.3 cm³•cm^-3).

The geometric correction of images under the scope of remote sensing applications is still mostly a manual work. This is a time and effort consuming task associated with an intra- and inter-operator subjectivity. One of the main reasons may be the lack of a proper evaluation of the different available automatic image registration (AIR) methods, since some of them are only adequate for certain types of applications/data. In order to fulfill a gap in this context, a first reference dataset of pairs of images comprising some types of geometric distortions was created, different spatial and spectral resolutions, and divided according to the Level 1 of CORINE Land Cover nomenclature (European Environment Agency). This dataset will allow for gaining perception of the abilities and limitations of some AIR methods. Some AIR methods were evaluated in this work, including the traditional correlation-based method and the SIFT approach, for which a set of measures for an objective evaluation of the geometric correction process quality was computed for every combination of pair of images/AIR method. The reference dataset is available from an internet address, being expected that it becomes a channel of interaction among the remote sensing community interested in this field.

The MODIS land surface temperature data were used to analysis the temporal and spatial characteristics of heat island of Lianyungang. Based on preprocessing data, this paper mainly discuss the relationship between urban heat island and its ground, vegetation, illumination and man-made features, specifically analyze the influences of these factors to urban heat island in Lianyungang. The results showed that: the city heat island has a close relationship with the degree of the urbanization (city buildings, population number, population density, industry development, transportation, and so on), geographic conditions, human activity manner, etc.; and the urban heat island intensity is strongest at autumn and winter, weakest at summer. Finally, we give some suggestions about heat island and building a technical system for Lianyungang's future developing.

Simulation of LADAR systems is particularly important for the verification of the system design through the performance assessment. Although many researchers attempted to develop various kinds of LADAR simulators, most of them have some limitations in being practically used for the general design of diverse types of LADAR system. We thus attempt to develop high-speed simulation software that is applicable to different types of LADAR system. In summary, we analyzed the previous studies related to LADAR simulation and, based on those existing works, performed the sensor modeling in various aspects. For the high-speed operation, we incorporate time-efficient incremental coherent ray-tracing algorithms, 3D spatial database systems for efficient spatial query, and CUDA based parallel computing. The simulator is mainly composed of three modules: geometry, radiometry, and visualization modules. Regarding the experimental results, our simulation software could successfully generate the simulated data based on the pre-defined system parameters. The validation of simulation results is performed by the comparison with the real LADAR data, and the intermediate results are promising. We believe that the developed simulator can be widely useful for various fields.

LC-based spectral imaging is a novel spectral imaging technology using the liquid crystal tunable filter(LCTF), which is a miniaturized device based on the electrically controlled birefringence of nematic liquid crystal. Continuously tuning electrically controlled through a spectral coverage is realized using LCTF under low voltages. Spectral imaging system based on LCTF is a miniaturized, multi-functional and real-time system with high spatial resolution and spectral resolution, which means that more and further information about the Earth and its resources can be acquired for new applications in large-scale mapping and environmental monitoring. LC-based tunable filter with large aperture has been developed utilizing the effect of electric controlled birefringence. Spectral test indicates that this filter can operate on the visible band with average 20 nm FWHM. A small scale spectral imaging system is established based on this tunable filter. Spectral imaging experiments on certain number of samples show that this system can provide continuously, and random-access selection of any wavelength, and has a higher level of resolving power in respect of both imaging and spectral tuning in the visible band, which indicates a brilliant application potentiality in environmental protection, resource detection.