In most forestry remote sensing applications in steep terrain, simple photometric and empirical corrections are confounded as a result of variable stand and species structure with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we test two new topographic correction approaches based on Sun-Canopy-Sensor (SCS) geometry. SCS is more appropriate than strictly terrain-based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. The first SCS approach accounts for diffuse atmospheric irradiance based on the C-correction (SCS+C). Secondly, a new multiple forward mode (MFM) canopy reflectance model based correction (MFM-TOPO-COR) is introduced which normalizes topographically induced signal variance as a function of forest stand structure and sub-pixel scale components, while also maintaining proper SCS geometry. These two new techniques are compared to existing correction methods (cosine, c correction, Minnaert, statistical-empirical, SCS, and b correction) in a Rocky Mountain forest setting in western Canada. The ability of these eight correction methods are tested and compared for removing topographically induced variance and for improving the classification accuracy of a SPOT image over this sub-alpine and alpine forest area. The new MFM-TOPO-COR canopy reflectance model correction method is shown to provide the greatest improvement in classification accuracy within a species and stand density based class structure. The potential and limitations of this new approach are critically discussed.

An adaptive multi-spectral and panchromatic remote sensing images fusion method is discussed. High spatial resolution panchromatic image can provide detail geometric features, while multi-spectral image can provide very good spectral information. A high spatial resolution multi-spectral image can be obtained by combining these two images. Multi-wavelet transform based fusion method is better than ordinary wavelet transform based fusion method, IHS fusion method and PCA fusion method. Different decomposition levels of multi-wavelet transform may lead to different images fusion performance. An adaptive multi-wavelet transform based fusion scheme is presented in this paper. Experiment results show that the adaptive multi-wavelet transform based fusion method can determine the best multi-wavelet decomposition level and provide the best fusion results.

Sub-pixel classification is a tough issue in remote sensing field. Although many kinds of software or its Module can be used to address this problem, their rationale, algorithms and methodologies are different, resulting in different use of different method for different purpose. This makes many users feel confused when they want to detect mixed feature content within a pixel and to use sub-pixel approach for practical application. It is necessary to make an in-depth comparison study for different sub-pixel methods in order for RS&GIS users to choose proper sub-pixel methods for their specific applications. After reviewing the basic theories and methods in dealing with sub-pixels, this paper made an introductory analysis to their principles, algorithms, parameters and computing process of three sub-pixel calculation methods, or Linear Unmixing in platform ILWIS3.0, Erdas8.5's Sub-pixel Classifier, eCognition3.0's Nearest Neighbor. A case study of three sub-pixel methods was then made of flood monitoring in Poyang Lake region of P.R.China with image data of band-1 and band-2 of NOAA AVHRR image. Finally, a theoretic, technological and practical comparison study was made of these three sub-pixel methods in aspects of the basic principles, the parameters to be set, the suitable application fields and their respective use limitation. Opinions and comments were presented in the end on the use of the sub-pixel calculation results of these three methods in a hope to provide some reference to future sub-pixel application study for the researchers in interest.

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the LIDAR data compression. A newly developed data compression approach to approximate the LIDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become a case in point for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original LIDAR data. The results show that this method can be used for significant reduction of data set.

The spectral classifiers allow a good estimate for the mapping of the materials from the similarity between the reference curve and the image. Initially the spectral classifiers had been developed for hyperspectral images analysis. However, some works demonstrate good results for the application of these techniques in multispectral images. The present work aims to evaluate the spectral classifier Spectral Identification Method (SIM) in ASTER image. The Spectral Identification Method (SIM) is proposed to establish a new similarity index and three estimates according to the significance of regression (5%, 10% and 15%) of the materials. This method is based on two statistical procedures: ANOVA and Spectral Correlation Mapper (SCM) coefficient. This information can be used to evaluate the degree of correlation among the materials in analysis. The advantage of this method is to validate according to significance of regression most probable areas of the sought material. The method was applied to ASTER image at the Parque Nacional (DF - Brazil). The images were acquired with atmosphere correction. The pixels size from the SWIR image was duplicated in order to join the VNIR and SWIR images. Endmembers were detected in three steps: a) spectral reduction by the Minimum Noise Fraction (MNF), b) spatial reduction by the Pixel Purity Index (PPI) and c) manual identification of the endmembers using the N-dimensional visualizer. The classification was made from the endmembers of nonphotosynthetic vegetation (NPV), photosynthetic vegetation (PV) and soil. These procedures allowed identifying the main scenarios in the study area.

This paper is based on the research project "State-wide acquisition of settlement area in North Rhine-Westphalia, Germany focusing on residential and industrial areas". The integrated GIS/Remote Sensing environment facilitated the combined processing of ground truth measurement, scanned topographic maps and multisensor imagery from SPOT 5, Landsat 7 and Aster satellites. In selected urban and suburban areas, methods for multisensor data fusion were developed and tested. The goal of this project is an accurate and current information layer about the urban and suburban state for North Rhine-Westphalia. The methodology is based mainly on an adapted texture and object oriented hierarchical classification approach: based on SPOT 5 imagery segments in different scales (levels) were created. These segments are the basis for a hierarchical based classification procedure. For each segment we calculated not only a texture but also the shape parameter. In addition we used the normalized vegetation index (NDVI) calculated from the multispectral satellite images to distinguish between vegetation and non vegetation areas. The GIS environment is of prime importance because it offers adapted tools for data modelling and data combination. For the verification of our results we developed a GPS based mobile application running on a windows mobile pocket pc. It allows a direct communication with our GIS environment which is necessary to import or export our datasets.

During the last thirty-five years the capital of Greece has suffered from an enormous internal immigration. Its population has overpassed the five millions and today almost the half population of Greece is squeezed in Athens metropolitan area. Because of the significant increase of population, the urban expansion in the basin of Athens was also excessive and in some cases catastrophic. Buildings have covered all the free places, new roads have been constructed, the drainage networks have been covered or disappeared and a lot of changes have been occurred to the landforms. The construction of the new airport (Elefterios Venizelos) at the beginning of this decade created a new commercial and urban pole at the eastern part of Athens and the constructive activity has been moved to new areas around the airport. Our aim was to detect and map all the changes that occurred in the urban area, estimate the urban expansion rate and the human interferences in the natural landscape, using GIS and remote sensing techniques. We have used satellite images from three different periods (1973, 1992, 2002) and topographic maps of 1:25.000 scale. The spatial resolution of all the satellite images ranges from 5 to 10 meters and is it acceptable for the monitoring and mapping of the urban growth. Supervised classification and on screen digitizing methods have been used in order to map the changes. Finally the qualitative and quantitative results of this study are presented in this paper.

In this paper we examine the possibilities of using VHR images for locating major changes (i.e. buildings, roads and vegetation) in order to focus ground topographical measurements and to keep a topographic 1:1000 database (UrbIS) of urban areas up-to-date in between two aerial photographic flights. The techniques implemented are multi-temporal classification and post-classification change detection. These techniques are used as such and in GIS-based change detection. This latest is implemented with masks exported from information contained in the UrbIS database. Main changes are well detected, though a number of "false changes" is detected and remain significant. Solutions to reduce the number of false alarms will be investigated through the application of post-classification rules.

AL Sammalyah Island is considered an important protected area in Abu Dhabi Emirate. The island has witnessed high rates of change in land use in the past few years starting from the early 1990s. Change detection analysis is conducted to monitor rate and spatial distribution of change occurring on the island. A three-phase research project has been implemented, an integrated Geographic Information System (GIS) database for the Island is the focus; the current phase main objective was to assess rate and spatial distribution of the change on the island using multi-date large scale aerial photos. Results of the current study demonstrated that total vegetation cover extent has increased from 3.742 km² in 1994 to 5.101 km² in 2005, an increase of 36.3% between 1994 and 2005. The study also showed that this increase in vegetation extent is mostly attributed to the increase in mangrove planted areas with an increase from 2.256 km² in 1994 to 3.568 km² in 2005, an increase of 58.2% in ten years. Remote sensing and GIS have been successfully used to quantify change extent, distribution and trajectories of change. The next step will be to complete the GIS database for AL Sammalyah Island.

Many applications of remote sensing - like, for example, urban monitoring - require high resolution image data for a correct determination of object geometry. The desired geometry of an object's surface is created in dieffernet studies by use of well known segmentation techniques. In this study, we evaluate the influence on image quality of analog and digital image data on the results of a image segmentation in eCognition. We compare the suitability of analog middle format camera data with image data produced by a commercial "of the shelf" digital camera taken during two campaigns in 2003 and 2004. Furthermore, the results of a multiresolution classification of an urban test site by use of both datasets will be presented. An outlook for future work on a multiresolution data fusion with hyperspectral data will be given at the end of this paper.

This paper demonstrates the characterization of the water properties, bathymetry, and bottom type of the Indian River Lagoon (IRL) on the eastern coast of Florida using hyperspectral imagery. Images of this region were collected from an aircraft in July 2004 using the Portable Hyperspectral Imager for Low Light Spectroscopy (PHILLS). PHILLS is a Visible Near InfraRed (VNIR) spectrometer that was operated at an altitude of 3000 m providing 4 m resolution with 128 bands from 400 to 1000 nm. The IRL is a well studied water body that receives fresh water drainage from the Florida Everglades and also tidal driven flushing of ocean water through several outlets in the barrier islands. Ground truth measurements of the bathymetry of IRL were acquired from recent sonar and LIDAR bathymetry maps as well as water quality studies concurrent to the hyperspectral data collections. From these measurements, bottom types are known to include sea grass, various algae, and a gray mud with water depths less than 6 m over most of the lagoon. Suspended sediments are significant (~35 mg/m³) with chlorophyll levels less than 10 mg/m³ while the absorption due to Colored Dissolved Organic Matter (CDOM) is less than 1 m^-1 at 440 nm. Hyperspectral data were atmospherically corrected using an NRL software package called Tafkaa and then subjected to a Look-Up Table (LUT) approach which matches hyperspectral data to calculated spectra with known values for bathymetry, suspended sediments, chlorophyll, CDOM, and bottom type.

The synergistic use of multi-temporal and multi-spectral remote sensing data offers the possibility of monitoring of environment quality in the vicinity of nuclear power plants (NPP). Advanced digital processing techniques applied to several LANDSAT, MODIS and ASTER data are used to assess the extent and magnitude of radiation and non-radiation effects on the water, near field soil, vegetation and air for NPP Cernavoda , Romania . Cernavoda Unit 1 power plant, using CANDU technology, having 706.5 MW power, is successfully in operation since 1996. Cernavoda Unit 2 which is currently under construction will be operational in 2007. Thermal discharge from nuclear reactor cooling is dissipated as waste heat in Danube-Black -Sea Canal and Danube river. Water temperature distributions captured in thermal IR imagery are correlated with meteorological parameters. Additional information regarding flooding events and earthquake risks is considered . During the winter, the thermal plume is localized to an area within a few km of the power plant, and the temperature difference between the plume and non-plume areas is about 1.5 ^oC. During the summer and fall, there is a larger thermal plume extending 5-6 km far along Danube Black Sea Canal, and the temperature change is about 1.0 ^oC. Variation of surface water temperature in the thermal plume is analyzed. The strong seasonal difference in the thermal plume is related to vertical mixing of the water column in winter and to stratification in summer. Hydrodynamic simulation leads to better understanding of the mechanisms by which waste heat from NPP Cernavoda is dissipated in the environment.

This paper analyzed land use change in Bashang area of Hebei province, with 1992 and 2002 TM (ETM). Based on GIS and statistical methods, the intensity, state index of land use change and transfer matrix were used to study spatio-temporal land use change in the region. The results showed that the area of arable land decreased greatly, also the area of wetland decreased. The other way round, the area of grassland, forest land and building land increased. As a whole, the intensity of forest land change was higher, but others were lower. From the transfer matrix, most of the arable land changed into grassland and forest land, some to building land. The grassland and forest land was mainly transferred from unused land, except for arable land. The building land mostly came from arable land. The wetland was used for grass and forest area. It was showed that the eco-environment degraded, and the land use change was an important driving force of eco-environment change in the study area. Unfeasible land use pattern and land reclamation by human beings resulted in soil loss and sandy land increase.

The bioclimatic index most commonly used in urban climate studies to describe the level of thermal sensation that a person experiences due to the modified climatic conditions of an urban area, is the discomfort index (DI) of Thom. DI reflects the proportionate contribution of air temperature (T_a) and relative humidity (RH) on the human thermal comfort. In this study, the discomfort index is estimated using thermal infrared data as acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the National Oceanic and Atmospheric Administration (NOAA) satellite. For this purpose, a dataset of AVHRR-14 daytime images collected during the warm season from June to August 2000 covering the Greater Athens Area, in Greece, was used. Air temperature was related to a split-window estimate of land surface temperature (T_s), whereas relative humidity was assessed in terms of dew point temperature (T_d) and of a split-window estimate of atmospheric precipitable water (PW). AVHRR-estimated DI values were compared with coincident DI values obtained from air temperature and relative humidity observations recorded at standard meteorological stations. Statistical analysis showed a good agreement (r² = 0.79) between the AVHRR-estimated and the station-observed DI values, with a root mean square error (RMSE) of 1.2^oC and a bias of 0.9^oC. Results demonstrate the potential of using AVHRR data for defining the spatial variation of the DI index at a higher resolution (1.1 km) than is feasible from meteorological stations.

Mid-wavelength infrared (MWIR) and long-wavelength infrared (LWIR) 1024x1024 pixel quantum well infrared photodetector (QWIP) focal planes have been demonstrated with excellent imaging performance. The MWIR QWIP detector array has demonstrated a noise equivalent differential temperature (NEΔT) of 17 mK at a 95K operating temperature with f/2.5 optics at 300K background and the LWIR detector array has demonstrated a NEΔT of 13 mK at a 70K operating temperature with the same optical and background conditions as the MWIR detector array after the subtraction of system noise. Both MWIR and LWIR focal planes have shown background limited performance (BLIP) at 90K and 70K operating temperatures respectively, with similar optical and background conditions. In addition, we are in the process of developing MWIR and LWIR pixel collocated simultaneously readable dualband QWIP focal plane arrays. In this paper, we will discuss the performance in terms of quantum efficiency, NEΔT, uniformity, operability, and modulation transfer functions of the 1024x1024 pixel arrays and the progress of dualband QWIP focal plane array development work.

Gabal Gerf area in southeastern Egypt is covered by a sequence of Pan-African basement rocks comprising allochthonous ophiolitic rock assemblages thrusted over calc-alkaline metavolcanics and high grade metasediments. These rocks are intruded by syn-to late-tectonic intrusions including gabbro-diorite and tonalite-granodiorite and late tectonic intrusions of layered gabbro and monzogranite as well as dykes and veins. Sheared metamorphosed ultramafic rocks with the locally pillowed basic metavolcanics are the main components of the Gerf huge ophiolitic nappe complex extending between the N-S Hamisana zone and the Heinai Allaqi ophiolitic belt. The processed digital data of Landsat MSS and ETM ratio images covering the study area have been used. The digital number values (pixel values) and the spectral signature curves have been delineated for the different encountered basement rocks on both raw ETM and false colour composite (FCC) ratio images. This study revealed that the TM image bands 7, 4, 2 improves the lithological discrimination of the schistose rocks and massive intrusions and distinguish the linear features (dyke swarms and faults). On the other hand, the different basement rocks exposed in the investigated area particularly the different varieties of the same lithologic unit can be accurately discriminated using the FCC ratio image (5/7, 5/1, 4) in red, green and blue (RGB).

This paper quantified regional concentration of various metals(e. g. Pb, Cu, As, Hg, Mo) in the leaves by developed regression equations based on Kokaly and Clark(1998) using Pushbroom Hyperspectral Imager (PHI) data which acquired at Daxin'anling area, Helongjiang Province, China. The regression equations were developed and established between metal concentration and spectral absorption band-depth of vegetation branches which both were measured in the field in study area. An iterative algorithm was used to select suitable wavebands from 80 bands corresponding to PHI band center wavelengths during the regression processing, which maximize R² and minimize Std. Except Pb, the correlation coefficient(R²) of all the other metals are up to 0.8. These regression equations were applied to PHI data in order to estimate the regional metal concentration in close vegetation cover of study area after spectral reconstruction and absorption band-depth transformation of PHI data. The distribution tendency of concentration of various metals quantified from PHI data were in good agreement with the ground geochemical distribution.

The SWIR spectral bands of the AST_07 and IAR calibrated datasets of ASTER instrument were evaluated and compared for mapping the alteration zones around porphyry copper deposits and occurrences at Meiduk area, SE Iran. The porphyry copper deposits are hosted by the Eocene andesitic and basaltic rocks and the zonal alteration patterns are concentric and almost symmetrically arranged. The field sample spectra as well as spectra from USGS library were applied for determining the absorption bands of each mineral at the spectral range of ASTER. The Spectral signatures of index minerals in phyllic, propylitic and argillic alteration zones were considered in directed principal component analysis (DPCA) and Spectral Angle Mapping (SAM) algorithms. Carrying out selective or directed PCA method on four and three spectral bands enhanced the alteration haloes in the last PC images. Generating R-G-B color composite image using the end member PC images differentiated three alteration zones from the host rocks. Spectral Angel Mapping algorithm was implemented on SWIR AST_07 and IAR calibrated datasets using both the field and USGS spectra for index minerals of alteration zones. The SAM results of IAR calibrated dataset revealed that it is possible to delineate the propylitic, argillic and phyllic alteration zones validated by the field evidences, while the AST_07 dataset does not map the similar alteration zones. It is concluded that although the higher spectral resolution of ASTER instrument is effective for mineral mapping, the degree of data calibration is critical for validity of the outputs.

Vast areas of the world consist of hard rocks (basement complexes), where water is restricted to secondary permeability, and thus to the fractures and the weathered zones. As the success ratio of drilling in hard rock terrain may be low, and the use of geophysics is often judged as too expensive, the study of lineaments from remote sensed imagery offers an attractive alternative analysis technique. High production areas in hard-rock aquifers are generally associated with conductive fracture zones. An effective approach for delineation of fracture zones is based on lineament indices extracted from satellite imagery. Together with a detailed structural analysis and understanding of the tectonic evolution of a given area it provides useful information for geological mapping and understanding of groundwater flow and occurrence in fractured rocks. The accuracy of extracted lineaments depends strongly on the spatial resolution of the imagery, higher resolution imagery result in a higher quality of lineament map. The ASTER sensor provides imagery with a higher resolution (15m) than the LANDSAT sensor (30m). It is tested and shown here that extracted lineaments from the VNIR ASTER imagery are considerably less noisy and show a higher accuracy than lineaments extracted from other imagery.

The study area is about 2650 sq. kms that is located in the southern part of Central Iranian volcanic-sedimentary belt. This belt runs parallel with the Zagros mountain ranges. The main aim of this study is to differentiate between the altered rocks with the carbonates and calcareous shales. Both ASTER and ETM+ data are used here for hydrothermal alteration mapping. This study compares these two data for hydrothermal alteration mapping. Different image processing techniques such as band ratio, principal component analysis and band combinations are used here aiming at enhancing the altered areas. The band ratio has shown that band7/band9 and b4/b5 ratios of ASTER data are better than band5/band7 ratio of ETM+ data for mapping the clay minerals. Principal component analysis has shown that PC4 of ASTER data is better than PC5 of ETM+ data for enhancing phyllic and argillic alterations. Color combination of PC4 (Red), PC5 (green), and PC6 (Blue) for the ASTER data is enhancing the altered areas and, at the same time, suppressing the effects of carbonates, flysch and calcareous sediments. ASTER data was used for image classification using spectral angle mapper algorithm. Through this analysis it was found that clay minerals such as kaolinite and montmorilonite can be differentiated. Comparison of the above image processing techniques have shown that except the enhancement of the iron oxide bearing rocks, the ASTER data is more useful for alteration mapping than ETM+ data.

Quantitative analysis of unknown PAH contaminated soil samples have been performed using laser-induced fluorescence (LIF) spectroscopy and strategies for a safe application of calibration routines under field conditions have been developed. Influences of varying matrix properties have been investigated using LIF and diffuse reflectance (DR) spectroscopy. A petroleum hydrocarbon contaminated former military site has been investigated and the results from the on-site LIF measurements are compared to the results of additional laboratory analysis. A three dimensional dataset of analyte concentrations has been prepared and extent, distribution and origin of the contamination are discussed. A fiber optical probe for in situ LIF-investigations in the subsurface has been developed. The probe has been tested in investigations of soil columns and within in situ measurements at a former gas work site.

In the last 20 years air photograph and remote sensing, both from airplane and satellite, allowed to gain, from the analysis of the superficial land unit characteristics, useful information for the location of buried archaeological structures. For this kind of investigation, hyperspectral MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data revealed to be very useful, for example, since 1994, for the purpose CNR-LARA research project, many archaeological studies have been supported by MIVIS data on several italian archaeological sites: Selinunte, Arpi (Foggia), Villa Adriana (Tivoli) and Marsala. Marsala town, the ancient Lilybaeum, lies on the western coastline of Sicily, at about 30 km south of Trapani. Founded by the Phoenicians, it intensely lived during the Punic, Roman, Arab and Norman periods, whose dominations left many important remains. This archaeological area was investigated by means of several techniques, such as excavations, topographic studies based on airborne campaigns, etc. On this site the main archaeological information were provided by the analysis of the VIS-NIR spectral bands and by Thermal Capacity image.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite has five spectral bands (bands 10 to 14) in the thermal infrared (TIR) spectral region. In February-March 2005, we conducted a field campaign for ASTER/TIR on frozen Lake Kussharo in Japan using a multi-band radiometer (CIMEL CE312) which has five spectral bands compatible with the ASTER/TIR bands. The first purpose is to make vicarious calibration (VC) of the ASTER/TIR bands using a low-temperature target below 270 K, and the second purpose is to investigate the spectral behavior of snow/ice emissivity in the ASTER/TIR bands. The VC experiment was successfully conducted on 4 March, using the coldest target (about 262 K at the sensor) among the past VCs conducted for ASTER/TIR. The results show that the at-sensor radiances predicted by VC match the ASTER image radiances within the designed calibration accuracy (2 K for 240 to 270 K), indicating that the ASTER/TIR bands are well calibrated for the temperature range around 262 K. On the other hand, band emissivity measurements of snow and ice surfaces show that spectral emissivity changes with snow/ice conditions and with a viewing angle, particularly in bands 13 and 14 (10.2 to 11.7 μm). Finally, the surface emissivity ratio (SER) between bands 13 and 14 is shown to be useful for snow/ice monitoring, using ASTER imagery obtained in February-March 2005.

Authors have ever developed the spherical thermograph system and clarified the realities of the thermal environment of the urban built space. In this paper, we propose the method that enables us to obtain surface temperature with this system from the thermal image including reflections and shows the result of applying the method to the actual space.

The paper presents the GIS technology application allowing mapping the precipitation from the microwave satellite data. The analysis results are prepared in the form of maps of precipitation intensity and range from an Advanced Microwave Sounding Unit (AMSU) on board of NOAA (15-17) satellites. The products such as Rain Rate (RR), Scattering Index (SI), Total Precipitation Water (TPW), Precipitation Probability (PP) and Liquid Water Path (LWP) were prepared basing on the regression algorithms. Surface data are converted into thematic coverages, too. The developed system allows displaying the precipitation observed with the satellite data and other ancillary information. Satellite and lightning data layers were also introduced to the system. Such approach allows presentation and analysis of the data coming from the various sources and enables validating the methods for the precipitation algorithms from microwave data. The problems related to the data specific spatial, temporal resolution and variability are presented and discussed. The maps of precipitation with additional geographical data and administrative boundaries are available for the weather forecasting units via Intranet. It is planned to make images available on the web for internal and external customers using web map server.

Desertification is one of the most serious ecological and environmental problems in China, especially in the arid and semi-arid area of China. Based on investigation of current research and previous efforts on desertification, in this paper we propose a desertification index system suitable for large-scale desertification monitoring using remote sensing techniques. According to the desertification index design principle, we selected five desertification indexes (MSAVI, FVC, Albedo, LST and TVDI) suitable for large-scale desertification monitoring using remote sensing technique. After applying different index and index combinations on desertification monitoring and its precision evaluation in test area, the result shows that the precision of index combination of MSAVI, FVC, Albedo, LST and TVDI is superior than others. Based on analysis and comparison of current retrieval algorithms, we utilized a suitable algorithm on large scale to retrieve five desertification indexes with ten-day NOAA AVHRR data set in 1995 and 16-day MODIS data set in 2001. In term of the desertification climate types, the potential extent of desertification in China was respectively divided into four categories: dry sub-humid area, semi-arid area, arid area, high and cold area. Different desertification index system was built for each area. By assessing the classification accuracies of three types of classifiers (unsupervised classifier, maximum likelihood classifier and decision tree classifier), we select decision tree classifier for desertification monitoring. Supported by desertification index system and the database of desertification indexes, the desertification status in 1995 and 2001 was classified by decision tree classifier, and analysis of desertification changes from 1995 to 2001 was also completed in study area. Statistical result according to individual country shows that the speed of desertification developing is faster than that of rehabilitating, there is a trend of development as a whole and improving locally in desertificated areas in China.

Land degradation processes, which imply a reduction of the potential productivity of the land (e.g., soil degradation and accelerated erosion, reduction of the quantity and diversity of natural vegetation), result from a long history of human pressure upon land resources as well as from interactions between varying climatic characteristics and ecologically unbalanced human intervention. The north-west region outside of Beijing, is one of the most important regions where many departments invest most and pay most attention. The land degradation and other environmental problems in this region affect not only Beijing but also the surrounding area. This paper analyzed characteristics of land degradation actuality situation in the NW region of Beijing, based on TM (ETM) in 2002. The wind-eroded land was mainly distributed in north of Yin Shan Mountain. Due to degradation of grassland, the sandy land increased from 1991-2002, mostly distributed in the monitoring zone of Hunshandake sandy land. The water-eroded land was mainly distributed in monitoring zone of the south of Yin Shan Mountain and south of monitoring zone of Horqin sandy land. The salination-land was mainly distributed in lake surrounded area and the drainage basin of Sanggan River. And To better understand the drive forces of land degradation processes in study area, a multivariate spatial model associated with land degradation is found by the explanatory variables of Logistic multivariate regression model(LMR). The explanatory variables include wind speed, soil humidity, soil organic matter, NDVI, average precipitation, soil slope, et al. The value of the parameter estimated by model with their corresponding standard error, chi-square statistics, and significance probability are analyzed to find the driver of land degradation in studied area. And the high or low probability of land degradation is predicted. Finally, suggestions to the eco-environment construction of the studied region have been put forward.

The eastern Qinghai province is located in the transitional belt between the Loess Plateau and the Qinghai-Tibetan Plateau. In this paper, the spatial and temporal change patterns of land use were quantified by visually interpreting remote sensing (RS) data and use geographical information system (GIS). The objectives of this paper were to investigate the land use situation and the change trend of land use during 14 years from 1986-2000 and to understand causes of land use change. Firstly, land use maps were derived from visually interpreting the Landsat TM images with the help of MGE and ARC/INFO 7.11. In the analyzing process, digital maps were overlaid in order to generate land use dynamic map, transition matrix and to calculate rates of conversion. Secondly, the relative change rate model was introduced and calculated. The results showed that there has been a net decrease of 3736.84ha in grassland which was mainly converted to cultivated land. Urban and /or built up land increased by 4.39%, originated from the conversion of cultivated land. At the same time Relative change rates revealed that there were significant differences in quantitative change of land use in every county/city. The trend of land use change was that urban and built-up was expanded, farmland encroached and grassland continuously reclaimed as farmland. In the end, the driving factors of land use change were analyzed. The increasing population pressure and urban expansion and the development of industry were the major driving factors for land use change in the northeastern Qinghai province.

As one of the most seismically active area in Europe, Vrancea region in Romania presents a relatively high potential of seismic hazard mainly due to the subcrustal earthquakes located at the sharp bend of the Southeast Carpathians. Is assumed to be placed at conjunction of four tectonic blocks which lie on the edge of the Eurasian plate. Seismic hazard maps have been produced on the basis of the regional geomorphologic maps which combines information on rock types, soil types and slopes for seismically active areas. Specific stages of disaster management programs are pre-disaster planning and preparedness, forecast and warning, emergency response and rehabilitation. Spatial technologies offer a source of complementary information for ground-based sources. Fusing satellite Landsat :MSS,TM and ETM;MODIS; SAR; ASTER) data, GPS and ground measurements, greatly improved the interpretability of the images and subsequent more accurate terrain features and lineament analysis of geologic structures in Vrancea region. Based on Global Position System (GPS) monitoring data were detected relative ground motions of the tectonic blocks both in horizontal direction ( relative motion of 5- 6 millimeters/year), as well as in vertical direction(of a few millimeters/ year). Digital Elevation Models (DEM) generation from ASTER data are highly correlated with ground topography from GPS measurements in the test area. Satellite remote sensing data used provided useful information for lithological and geological lineaments mapping as well as for land geodynamic measurements of strain locating stricken and monitoring of seismic hazard.

Using remote sensing data of TM and ETM+ in 1992 and 2002, land degradation based on land use changes, especially sand changes were analyzed and land degradation status in 2002 was evaluated in the Huan Beijing Area. The area of sand in 2002 is 6669.6 km², increased 716.2 km² compared to that in 1991, and most of the newly-produced sand came from grassland. Land degradation status in 2002 was evaluated by the combination of vegetation, soil and topography information and the region was divided by 1km ×1km cell as the evaluation unit by the application of the GIS. The indicators of land degradation evaluation included soil organic, soil depth, vegetation cover (NDVI) and slope. Land degradation index (DI) was computed, considering the contribution of different indicators to land degradation. The land degradation status was divided into four types according to DI, no-degradation (DI > = 55), slight degradation (50 = < DI < 55), moderate degradation (40 = < DI < 50) and severe degradation (DI < 40). The results showed that the area of degraded land is 132900 km², which occupied the percent 58.2 of the whole Huan Beijing Area and the proportion of slightly-degraded land to degraded land is about 0.47. The political county taken as an evaluation unit, the partition of land degradation in this area was also analyzed based on land degradation area proportion and degree. Six types of land degradation partition were got.

Lefkas Island is situated in the Ionian Sea, Greece. The Ionian Sea presents a very big seismicity and the landslides phenomena are very often. As a result many landslides have been recorded on Lefkas Island during the last twenty-five years. Also the island suffers from summer forest fires and large rural areas have been burnt. In this paper a combined use of multitemporal and multisensor Remote Sensing data and GIS techniques for the environmental monitoring of Lefkas Island is presented. Multisensor and multitemporal satellite data were used for landslide detection and burnt area detection. The satellite data used cover the period from 1977 to 2003. More especially we have used: A Landsat MSS scene of 1977, A Lndsat TM scene of 1986, A Landsat TM scene of 1989, A Landsat TM scene of 2000, A Landsat ETM scene of 2000, An Aster Vnir scene of 2000, An Aster Vnir scene of 2003. All the images have been orthorectified and resampled to 30m pixel size. Then using different band ratios we have managed to locate the burnt areas and the areas damaged by landslides. All the results have been verified by in situ measurements using a GPS receiver. The classification results from the satellite data, the in situ measurements and all the necessary maps (topographic geological etc.) have been integrated in a GIS database.

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.

A sustainable development of urban areas demands adequate information both spatially and punctually. The study focuses on the assessment of the potentialities of satellite remote sensing data to study environmental impact classification of urban land cover by fuzzy logic. The evaluation of urban landscapes is based upon different sub-functions which refer to landscape features such as soil, land-use, buildings, groundwater, biotope types. Mixed pixels result when the sensor's instantaneous field-of-view includes more than one land cover class on the ground. For mixed pixels, fuzzy classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. These fraction values can be assigned to sub-pixels, based on the assumption of spatial dependence and the application of linear optimization techniques. A newly proposed sub-pixel mapping algorithm was first applied to a set of multispectral and multitemporal satellite data for Bucharest and Constantza urban areas in Romania. This paper describes how fuzzy logic can be applied to analysis of environmental impacts for urban land cover. Based on classified Landsat MSS, TM, SPOT, ASTER, SAR and MODIS data was performed a land cover classification and subsequent environmental quality analysis. Spectral signatures of different terrain features were used to separate and classify surface units of urban and sub-urban area. A complete set of criteria to evaluate and examine the urban environmental quality, including the air pollution condition indicators, water pollution indicators, solid waste treated indicators, noise pollution indicators, urban green space have been widely used to assess the urban environmental quality.

Karst is a characteristic geological feature of areas comprised of limestone. Due to the solubility of these rocks in water, exhibit an extreme heterogeneity of hydraulic conductivities. The characterizing features of karst aquifers are the open conduits, which provide low resistance pathways for ground water flow. Overall cave orientation is largely controlled by hydraulic gradient, joint patterns and other tectonic features, such as faulting and folding. The karst depressions may form on the surface by subsurface actions (dissolution and collapse). Thus, the depressions often show regularity of pattern or alignments, frequently in association with structurally guided cave systems below. The present work aims at to detect depressions zone, as dolines and uvalas in the limestone of the Bambui Group (Central Brazil) using ASTER and ASTERDEM images. A photogeological study, carried out on aster image allowed us to elaborate geomorphological map of dolines. Some guidance to detect dolines can be associated with fracture permeability dominated by nearly vertical joints and joint swarm is provided by fracture trace mapping from remote sensing. Commonly, dolines can be identified on the image and DEM as topographic depressions, which very often contain water or moist vegetation. The methodology allowed determining a doline distribution pattern what is important to environmental planning.

The Crisscross Region of Wind-drift Sand Regions along the Great Wall and Loess Plateau locates in southern Ordos Plateau and northern Chinese Loess Plateau, where wind erosion and water erosion coexist and specified environmental and socio-economic factors, especially human activities induce serious land degradation. However, there are only a few studies provide an overall assessment consequences. Integrated land quality assessment considering impacts of soil, topography, vegetation, environmental hazards, social-economic factors and land managements are imperative to the regional sustainable land managements. A pilot study was made in Hengshan County (Shanxi Province) with the objective of developing comprehensive land quality evaluation model integrating data from farmers' survey and Remote Sensing. Surveys were carried out in 107 households of study area in 2003 and 2004 to get farmers' perceptions of land quality and to collect correlative information. It was found out that farmers evaluated land quality by slope, water availability, soil texture, yields, amount of fertilizer, crop performance, sandy erosion degree and water erosion degree. Scientists' indicators which emphasize on getting information by RS technology were introduced to reflecting above indicators information for the sake of developing a rapid, efficient and local-fitted land quality assessment model including social-economic, environmental and anthropogenic factors. Data from satellite and surveys were integrated with socio-economic statistic data using geographical information system (GIS) and three indexes, namely Production Press Index (PPI), Land State Index (LSI) and Farmer Behavior Index (FBI) were proposed to measure different aspects of land quality. A model was further derived from the three indexes to explore the overall land quality of the study area. Results suggest that local land prevalently had a poor quality. This paper shows that whilst the model was competent for its work in the study area and evaluation results would supply beneficial information for management decisions.

The hydrology of the Sahel is characterised by the degradation of the drainage network that induces a lack of large watersheds. In the Niamey degree, different studies have shown the importance of pools in the hydrology of the region. It was shown that different processes such as evaporation or deep infiltration depend on the level of filling of the pools. During the last years, several observations have shown different evolutions of these pools in the Niamey degree. Our objectives in this paper are to identify the pools and their evolution. Our approach is based on high resolution optical remote sensing data, SPOT/HRV (20m) and SPOT5 (10m) images. This study uses a large data base of optical images (5 images in 1992, 1 image in 1994, 1 image in 1996 and 2 images in 2003). The identification approach is based on the NDVI coefficient calculated from Near Infrared and Red channels for each SPOT image. It is observed that the pools present the lowest values of NDVI in the studied optical images. The distribution of NDVI for pools is estimated for the different images, then a threshold is chosen to separate pools from the other types of land use. First, we observe the evolution of pool surface and their number in the monsoon period from June to November in 1992. It is clearly shown that the maximum of pool surface corresponds to August 1992. This result is well correlated with rainfall statistics. Second, the estimation of pool surface and number from 1992 to 2003 shows an increase of the pools, particularly in the tiger bush. This behaviour could be explained particularly by the increase of the surface runoff in the region.

The China-Brazil Earth Resources Satellite (CBERS) was developed by China in cooperation with Brazil. As one of the most important payload, CCD camera is expected to play an important role in the application of CBERS. Vicarious calibrations have been conducted every one year and cross-calibration is one of the methods to the calibration. Our effort is to probe the methodology of radiometric cross-calibration the CCD with MODIS and detect the degradation of the CCD camera since it was launched. The radiometric and reflectance coefficients and offsets for four CCD spectral bands were obtained based on the cross-calibration with four calibration targets. Results were validated by the synchro surface targets spectral measurement at Dunhuang site. The TOA radiances from calculation and simulation were consistent within 1%. Comparing our results with the coefficients based on vicarious calibration show that the average variation of the two independent methods was with 6%. Based on many times of radiometric-cross calibration of the CCD with the MODIS, the time series of radiometric coefficients for the CCD were obtained. Results illuminated that the response of the CCD have degraded, which could reach up to 3%- 7% per month

To meet the demand of monitoring water pollution in China, Information Center of State Environmental Protection of China (ICSEP) and Institute of Remote Sensing Applications, Chinese Academy of Sciences (IRSA,CAS) have carried out a project to utilize the data extracted from Environment and Hazard Monitoring Constellation. This project is to build the first Remote-sensing and Environmental Monitoring System (REMS) in China. The most important component of REMS is the Hyperspectral-Environmental Database (HED). This paper is to describe the architecture and mechanism of HED. HED consists of five parts: Environmental backgrounds, Spectrums, Hyperspectral images, Basic geographic information and Environmental quality data. The interactions and relationships among the five parts are depicted. The workflow of HED assisting REMS is delineated. A preliminary research in Taihu Lake based on HED is also described in this paper.

Spatial conception exists in remote sensing imagery as well as spectral information. It acts as more importance role in dominant landscape objects detection in high-resolution remote sensing imagery. Multiscale analysis is a new approach to meet the requirement of how to use spatial information in classification. Compared with traditional pixel based classification methods, multiscale analysis is composed of two fundamental components: the generation of a multiscale representation and information extraction. The paper focuses on one segmentation techniques- Fractal Net Evolution Approach (FNEA) and its usage in improvement in coastal remotely sensed image classification. FNEA is considered as one of effectual region-based segmentation and its threshold is a combination of size and homogeneity. We discuss two different segmental strategies which are speed-first and scale-first, and their impacts on image-objects. We can get the optimal segmental scale by analyzing the relationship between average size of each image-object and the different scale.

Red soil is susceptible for soil erosion because of its intrinsic material components, and together with the eroded soil material. The plant nutrients are washed away, and the concentrations of nutrients are even higher in the sediment than in the topsoil of the former soil. From 1978 onwards, at the beginning of the period of opening and reforming, the farmers were assigned a piece of land and were encouraged to cut down the forest as a measure to increase local economy. All of these human activities further result in significant red soil erosion. An eight-band Landsat ETM data acquired in 2000, soil map, digital topographical maps with scale of 1:10 000 and field data were used to identify and map the eroded areas in the red soil region in Jiangxi province. The satellite image was calibrated, registered and georeferenced, and a supervised training technique based on areal extraction of spectral values, with spatial (topographical information) and spectral constraints was used to generate the spectral signatures of the informational classes. Ancillary data such as land use map from local government were used to assist the satellite image classification and interpretation. The results showed that low and very low erosion area accounted for 41.3% of the total study site, medium for 9.4%, high and very high for 37.7%, and no erosion area (water surface, etc.) for 11.6%. It also indicated that Landsat ETM supported by thematic GIS data can discriminate the soil erosion features in the red soil region in China.

The Bam earthquake of 26/12/2003 (Mw=6.5) demolished the city of Bam and provoked serious damages in Baravat city, which are located in a tectonic intersection zone in the SE of Iran. The present study focus on Bam earthquake seismotectonic investigations and damages assessment based on Envisat interferometric coherence images. Field observations, SAR magnitude and multitemporal SAR images were also used to support and verify the coherence image interpretation. Concerning the damages assessment the results were very poor in terms of recognition and operational capabilities. On the contrary the used of interferometric coherence image came to be very useful for seismic fault and rupture zones detection. Through this method a hidden fault, a parallel segment of the already known Bam fault, was identified.

Policy and decision making in the context of sustainable development requires rapid, effective and efficient access to and integration of appropriate current information from a wide range of sources, including land cover changes information derived from remotely sensed data. Geomorphic factors, such as altitude, slope, aspect and lithology presented in the area comprise the main parameters, including the climate, influencing the distribution of land cover. The use of a Geographic Information System (GIS) allows further spatial analysis of the data derived from remotely sensed images and digital terrain spatial models, and analysis of the impact of land cover change on regional sustainable development. The remotely sensing data used in this study was Landsat 5 TM and Landsat 7 ETM+ images. Normalized Difference Vegetation Index (NDVI) and Selective Principal Component Analysis (SPCA) techniques were applied to detect land cover change and especially vegetation changes from multitemporal satellite data. The area under study is the basin of River Sperchios, which covers an area of some 1.780 km², is approximately 60-80 km long, 20-30 km wide with its southern and western flanks characterized by high elevations and steep slopes, whilst its northern flank presents lower elevations and more gently slopes. The conclusions obtained show that extensive land cover changes has occurred in the last decades as a result of both natural forces and human activities, which has in turn impacted on the regional sustainable development. The results thus provide very useful information to local government for decision making and policy planning.

Leaf area index (LAI) is an important characteristic of vegetation and a critical vegetation parameter for the global and regional scale studies of the climatic and environmental change. There are many methods that can be used to get LAI, generally, they belong to the three types: filed measurement; empirical and modeling methods. In this paper, we try to get one method that can be used in Arid and Semi-arid Northwestern China to derived LAI in the case of lack of LAI measurements. The empirical method was selected to derive LAI for different type vegetation from SPOT-VGT and landuse data. The study area was the Heihe River basin that has a large-scale area and diverse vegetation types. There were 7 types of vegetation to be mapping LAI using the methodology. They were irrigated, dry, forest, shrub, dense grass, moderate-dense grass and alkaline lands. The parameters of vegetations were modified based on the study area and vegetation types. The results were compared with the whole China LAI map and filed measured LAI. The results suggested that the method was feasible in arid and semi-arid northwestern China. And the results could be greatly improved if using big scale vegetation class map or plant function type data, and the parameters were derived based on the vegetation types in their own region.

Community land model or common land model (CLM) describes the exchange of the fluxes of energy, mass and momentum between the earth's surface and the planetary boundary layer. This model is used to simulate the environmental changes in China. Hence, it requires a complete parameters field of the land surface. The present paper focuses on making the surface datasets of CLM in China. In the present paper, vegetation was divided into 39 Plant Function Types (PFTs) of China from its classification map. The land surface datasets were created using vegetation type, five land cover types (lake, wetland, glacier, urban and vegetated), monthly maximum Normalized Difference Vegetation Index (NDVI) derived from SPOT_VGT data and soil properties data. The percentages of glacier, lake and wetland were derived from their own vector maps of China. The fractional coverage of PFTs was derived from China vegetation map. Time-independent vegetation biophysical parameters, such as canopy top and bottom heights and other vegetation parameters related to photosynthesis, were based on the values documented in literatures. The soil color dataset was derived from landuse and vegetation data based on their correspondent relationship. The soil texture (clay%, sand% and silt%) came from global dataset. Time-dependent vegetation biophysical parameters, such as leaf area index(LAI) and fractional absorbed photosynthetically active radiation(FPAR), were calculated from one year of NDVI monthly maximum value composites for the China region based on equations given in Sellers et al. (1996a,b) and Los et al. (2000). The resolution of these datasets for CLM is 1km.

In Romania there are many areas flooded every year. The estimation of the surfaces covered by water in the post-crisis periods is of real use for the decision makers at all levels. Due to the constraint that high spatial resolution satellite images are low temporal resolution, there exists a need for a reliable method to obtain accurate information from medium resolution data, for example, MODIS satellite images. The overall goal of this paper is to classify MODIS data to get an estimate of water surface area. To develop the classification technique, the strategy was to obtain MODIS and ASTER data acquired at the same time over the same location, and use the ASTER data as "ground truth". For this study, two lakes in the Bihor County of Romania were chosen and MODIS and ASTER data from October 31, 2002 were utilized. The ASTER data were used to create a detailed water mask to be used as ground truth for the MODIS water classification. The percent water image derived from ASTER was superimposed on the MODIS image. A supervised classification for water was performed on the 3-band MODIS image using the feature space algorithm. The water surface area as measured from the MODIS classification was about 16% more than the ASTER ground truth-value. This approach provided useful information concerning the water classification from different resolution data that could help in the estimation of water surface area from MODIS imagery.