Soil moisture is one of the most important indicators for agricultural drought monitoring. In this paper we present a comprehensive review to the progress in remote sensing of soil moisture, with focus on discussion of the method details and problems existing in soil moisture estimation from remote sensing data. Thermal inertia and crop water stress index (CWSI) can be used for soil moisture estimation under bare soil and vegetable environments respectively. Anomaly vegetation index (AVI) and vegetation condition index (VCI) are another alternative methods for soil moisture estimation with Normalized difference vegetation index (NDVI). Both NDVI and land surface temperature (LST) are considered in temperature vegetation index (TVI), vegetation supply water index (VSWI) and vegetation temperature condition index (VTCI). Microwave remote sensing is the most effective technique for soil moisture estimation. Active microwave can provide high spatial resolution but is sensitive to soil rough and vegetation. Passive microwave has a low resolution and revisit frequency but it has more potential for large scale agricultural drought monitoring. Integration of optical/ IR and microwave remote sensing may be the practical method for drought monitoring in both accuracy and in efficiency.

Sangong waddi basin in the north piedmont of Tianshan Mountains is a typical inland arid ecosystem in China. Desert environment especially land cover and land use in the basin changes dramatically in recent decades under the anthropogenic impacts. In order to develop an approach to highlight the environmental changes, we present a case study in the paper to examine the effects of different vegetation indices to the spatial changes of desert environment in the basin using Moderate-resolution Imaging Spectroradiometer (MODIS) data. First we compute the different vegetation indices including Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) for the basin from MODIS data and then compare their applicability to indicate the seasonal changes and spatial variation of vegetation in the basin. The results show that when the two vegetation index EVI and NDVI were used at the same time in monitoring the desert vegetation situation, Smaller the difference value between their values were, less the human activities interfere. The vegetation gradient variation of the desert vegetation is distinct in the basin. Therefore EVI can be used to highlight vegetation growth over the alluvial fans while NDVI is suitable to monitor vegetation growth in the hilly regions. With this finding, we further develop an approach to examine the desert environment changes in the basin. Based on the examination, several policy recommendations have been proposed in the study for better administration and utilization of arid land resources in the basin.

Nomadic and semi-nomadic livestock breeding is a significant income for Turkmenistan's economy. Thus, natural vegetation is an important resource for the area. The natural desert pastures of Turkmenistan have limited carrying capacity, and any changes of the fragile balance can lead to the destruction of this valuable resource. Since the 1980's, no research has been carried out concerning the ongoing changes of vegetation cover despite dramatic political and economical changes that took place throughout central Asia. The primary results of this research show the potential of remote sensing in general and specifically Spectral Mixture Analysis (SMA) for vegetation mapping. The information on vegetation change is important to quantify man-nature relationship. From this information vegetation cover is a useful indicator of the magnitude of change. Landsat TM and ETM+ images were processed, and maps of land use/land cover changes in northern Turkmenistan were produced. From the 1970's about 4000 km² of natural pastures were transformed into irrigated agricultural land, theoretically increasing the grazing pressure in the remaining areas. By applying SMA based on field end-members signatures, a sub-pixel fraction was obtained for each end-member. Our results indicate that most of the desert experiences vegetation rehabilitation.

The update of land use databases, if done on the basis of very high-resolution orthophotos, is usually carried out by means of visual interpretation, a both time-consuming and expensive task. In this paper an object-oriented classifier is used to support the update process by highlighting where changes have taken place. Basic land cover types are first identified in the orthophotos and then compared to the land use data to create a change map. The procedure was developed using true-color orthophotos and highly detailed land use data for a study area in Austria. Special emphasis was put on the identification of urban features. The resulting change map was compared to an independently updated version of the land use data base, showing the reliability of the results. The methodology was then tested on data sets provided by mapping agencies of Germany and Switzerland during the project "Change Detection" carried out within the framework of EuroSDR. The results are very consistent and even though they may not always meet the specific demands of the different mapping agencies, its potential for routine updating could be shown.

The 1:50.000 topographic maps present a nominal horizontal accuracy of 20 meters and a nominal vertical accuracy of 10 meters with 90% confidence. The data were in most cases extracted with photogrammetric techniques from aerial stereo-photographs during the 80's. The usual update rate for these maps ranges from ten to twenty years. The Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER) offers along-track stereoscopic viewing capability. Its viewing geometry is suitable for DEM generation even without the use of ground control points. Recent studies have proved that in this case the vertical accuracy of DEM is about 20m with 95% confidence. The horizontal geolocation accuracy appears to be limited by the spacecraft position accuracy which is considered to be better than 50 m. Other studies have shown that the use of GCP's resulted in a plannimetric accuracy of 15 m and in a near pixel size vertical accuracy. The Shuttle Radar Topography Mission (SRTM), used an Interferometric Synthetic Aperture Radar (IFSAR) instrument to produce a near-global digital elevation map of the Earth's land surface with 16 m absolute vertical height accuracy at 30 meter postings. An SRTM 3-arc-second product (90m resolution) is available for the entire world. In this paper we examine the possibility of updating the 1:50.000 topographic maps using ASTER and SRTM DEMs. The area of study is the broader area of Athens, Greece. Presupposing, that the horizontal and vertical accuracy of the ASTER and SRTM DEM is similar to the relative accuracies of the DEM from digitized contours, optical comparison of the DEMs and statistical analysis (difference, correlation) can immediately prove if there is any need for update to the topographic maps. A DEM from digitized contours from the 1:50.000 topographic maps was created and compared with ASTER and SRTM derived DEMs. Almost three hundreds points of known elevation have been used to estimate the accuracy of these three DEMs. The resulted accuracy of the SRTM and ASTER derived DEMs was satisfactory, therefore they are considered as suitable for updating 1:50.000 topographic maps.

A method is proposed to derive spatially enhanced imagery for all seven Moderate Imaging Spectroradiometer (MODIS) land spectral bands at 250 m spatial resolution. Originally, only bands B1 and B2 [visible (VIS) at 0.65 μm, and near-infrared (NIR) at 0.85 μm] are available from MODIS at 250 m spatial resolution. The remaining five land channels (bands B3 to B7) are observed at 500 m resolution. The adaptive regression is constructed for each individual MODIS L1B granule of 500 m spatial resolution by splitting the area into smaller blocks and generating nonlinear regression between bands B3 to B7 and B1, B2 and NDVI. Once a set of regression coefficients is generated based on 500 m image, it is then applied to 250 m data containing only channels B1 and B2 to produce five intermediate synthetic channels (B3 to B7) at 250 m spatial resolution. The final step involves normalizing the generated 250 m images to original 500 m images to preserve radiometric consistency. It is achieved in two stages and ensures that downscaled results are unbiased relative to original observations. The developed method was applied to generate Canada-wide clear-sky composites containing all seven MODIS land spectral channels at 250 m spatial resolution over the area of North America 5700 km by 4800 km.

This study focuses on forest monitoring at landscape level on the basis of a methodology combining satellite data mapping and image morphological processing. It aims to contribute to reporting on trends of forest fragmentation and connectivity, by using forest spatial pattern metrics. The Carpathians were selected as a study area. For five case study areas single-date forest - non-forest maps derived from Landsat images acquired in the 1980s and 2000s were an input for the post-classification change detection. Morphological image processing was applied then to map forest spatial pattern into six classes (core, patches, edges, perforation, connectors and branches). Further, connectivity and fragmentation processes were assessed on the basis of the proportion of forest pattern classes. We found a general trend of forest increase over the last decade. An increase of forest fragmentation and connectivity was noticed for four case study areas and a decrease for one case study area. The increase of forest cover may indicate the decline of importance of mountain agriculture, while changes of forest fragmentation and connectivity are probably related to the transformation of forest management practices in the 1990s in the region. We conclude, that the proposed methodology allows assessing trends in forest fragmentation and connectivity at approximately 1 ha minimum mapping unit.

The identification of buried archaeological structures, using remote sensing technologies is based on the principle that any buried ruins, either of human or natural origin, affects over time, soil surface characteristics creating anomalies. These anomalies are due to different factors, such as soil physical and chemical features, and vegetation cover status. The above factors are strictly connected and are responsible of surface spectral responses. Several images processing are applied and their results compared in order to define the one that fits better the various archaeological research goals. Among them, Vegetation Indices revealed to be very useful archaeological study. Spectral Vegetation Indices are important products in observing spatial and temporal variations of vegetation biophysical properties and photosynthetic activities, by which is possible to analyse the effects of buried ruins presence on vegetation cover status. The aim of this work is to assess the usefulness of vegetation indices in order to identify archaeological traces and verify the quantitative estimates of presence of buried archaeological structures in every type of elaboration (RVI, VIN, NDVI and SAVI). Statistical analysis were conducted on several Italian archaeological test sites processing by hyperspectral MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) various typologies of vegetation cover. The study of these anomalies on MIVIS hyperspectral data is the main goal of a research project that the CNR-IIA has carried ahead since 1994 over some archaeological sites: Selinunte, Lilybaeum, Sipontum and Arpi. The Arpi area could be considered as the wider Pre-roman settlement in Italy.

In this paper is presented a land use/cover classification methodology of the rural/urban fringe, by means of the application of a neuronal network, with resource to the multiresolution image segmentation, construction of complex elements through object oriented analysis and integration of not spectral (ancillary) information. The study area is the municipality of Almada, located in the south bank of Tagus River and corresponding to one of the core regions of the Lisbon Metropolitan Area (Portugal). The data used was 2004 HRVIR SPOT images fused with supermode panchromatic image and the Portuguese urban quarter statistical data. The developed procedure is based in five steps: 1) Legend creation; 2) deriving statistical ancillary data; 3) deriving object (texture) data; 4) deriving spectral data and 5) neural network classification.

The present study is an illustration of the application of Nonnegative Matrix Factorization (NMF) to the problem of linear unmixing of mineral endmembers in hyperspectral images. NMF can be seen as for nonnegative linear coding of the data points. We will show how a novel implementation of the NMF is able to perform both endmember extraction and abundance calculation. A synthetic example, used to illustrate the issue shows that NMF correctly identifies endmembers in a random mixing of real library spectra.

The Environmental Mapping and Analysis Program (EnMAP^1,2) is a joint response of German Earth observation research institutions, value-adding (VA) resellers and space industry to the increasing demand on accurate, quantitative information about the evolution of terrestrial ecosystems. With its hyperspectral capabilities covering the visible, near- and short-wave infrared wavelengths, EnMAP will provide high quality, standardized, and consistent data on a timely and frequent basis. Its primary focus will be on the considerable improvement of already standardized products and the development of new quantitative and highly informative data and its derivatives. Only an imaging spectrometer, such as EnMAP, can resolve and detect biophysical, biochemical, and geochemical variables in distinct detail. This will tremendously increase our understanding of coupled biospheric and geospheric processes and thus, enable the management to ensure the sustainability of our vital resources. After a successfully accomplished phase A, EnMAP has been approved by the German Aerospace Agency in the beginning of 2006. The instrument performance allows for a detailed monitoring, characterisation and parameter extraction of vegetation targets, rock/soils, and inland and coastal waters on a global scale. By the scientific lead of the GeoForschungsZentrum Potsdam (GFZ) and the industrial prime ship of Kayser-Threde, the ongoing planning aims towards an internationalisation of the mission approach. The EnMAP instrument provides information based on 218 contiguous spectral bands in the wavelength range from 420 nm to 2450 nm. It is characterized by a SNR of > 500:1 in the VNIR and an SNR of >150:1 in the SWIR range at a ground resolution of 30 m x 30 m.

Satellite remote sensing technique has been used to monitor the oceanic environment. This paper presents the technique development and applications of satellite remote sensing to the oceanic environment in China. The technique development includes the development of algorithms and of methodology for extracting oceanographic parameters from satellite data. Applications of satellite remote sensing range from environmental monitoring to oceanographic research.

Wadi Defeit area in the extreme South Eastern Desert of Egypt is occupied mainly by Precambrian ophiolitic and island-arc assemblages. The ophiolitic rocks are principally represented by serpentinite, talc-carbonate and listwaenite thrusted over the metasediments and island-arc schistose metavolcanics and intruded by syn to late tectonics intrusions of gabbro-diorite and monzogranite (G2). For discrimination of the basement rock units covering the study area, tracing the major structural features and identification of the mineralized alteration zones, the ETM+ with 7 bands composition and resolution about 15 m was processed where the geometric corrections and radiometric balancing to the image were carried out. The image enhancement techniques including spatial and spectral enhancement, ratioing and stretching have been applied and different band ratios were testes, the band selection for different ratio images used is based on the spectral signatures of these rocks. The study revealed that, based on the spectral signatures of the different basement rocks forming Gabal Al Adraq area, the RGB band ratio image (5/7, 5/1, 4) is proved to be very effective in the discrimination of the different basement rocks and the gold bearing alteration zones. The gold mineralization is introduced into the listwaenites, alteration zones and quartz veins during the sulfidization processes and the remobilization of gold took place along the thrust zones during listwaenization. This study indicates that the alteration zones in both the metavolcanics and the listwaenites are promising and need more detailed exploration for Au and Ag mineralizations to evaluate their potentiality.

This papers describes a new low-frequency seismic sensor for geophysical applications. The instrument is basically a monolithic tunable folded pendulum with an interferometric readout system, that can be configured as seismometer or as accelerometer. The monolithic mechanical design and the introduction of a laser interferometric technique for the readout implementation make it a very sensitive and compact instrument with a very good immunity to environmental noises. Preliminary tests on the mechanical performances of the monolithic structure and on the optical reaodut have been performed. Interesting result is the measured resonant frequency of the instrument of ≈ 150mHz obtained with a rough tuning, demonstrating the feasibility of a resonant frequency of the order of 5mHz with a more refined tuning. The mechanics of the seismic sensor, the optical scheme of the readout system, the theoretical predictions and the preliminary experimental performances as seismometer are discussed in detail, together with the foreseen further improvements.

A series of analyses has been carried out in a landscape where for three decades a dump was located by using three orthophotos and a multispectral image from ASTER sensor. The orthophotos acquisition time spans from 1953 to 2002. The aim of the analyses is twofold. On one side the analysis detects some paleao structures that were visible in 1953 and that under the pressure of human activities have disappeared. The change detection analysis indicates two main phases of changes. The first main change from 1953 to 1973 interests mainly woodland and wild vegetation while the second one is driven by agricultural practices. On the other side, the analysis allows finding some useful indices that are able to reveal degraded areas. The combination of some spatial indices, such as Shannon's diversity index (SHDI) ad contiguity index, has been found suitable to distinguish the characteristic texture of a dump from that of other fields. Furthermore, by using thermal infrared bands from the ASTER image, a thermal anomaly is found in correspondence of the dump location.

The importance of Remote Sensing and GIS into malaria studies has been widely demonstrated. Most of the studies that integrate remote sensing are analysing the link between environmental factors and potential mosquito's nidus presence, but focusing only on some few meteorological variables. Nevertheless, the complexity and biological dynamism of malaria transmission require the integration of multiple ecological variables. In this study remotely sensed based environmental variables have been used to build a comparative model. All these data have been integrated into a GIS, layered with other ground data sources of climate and epidemiological origin. Since the final survey is concerned with only four villages, the general purpose was to use the environmental variables derived from remote sensing as malaria transmission risks predictors for the whole study area. Another challenge was to avoid predictors' thematic redundancy. The last challenge was to avoid spatial autocorrelation since we were dealing with high spatial resolution data. For these purposes the intelligence between Remote Sensing and GIS tools is of a focal utility.

This study aims the development of a practical method for the extraction of vegetation cover rate (VCR) in urban areas using Landsat TM/ETM+ data. The linear mixture model in which two main categories, vegetation and non-vegetation, have two sub-categories respectively is employed combined with the least square estimation using six bands data of TM/ETM+ data. The experimental results show fairly good coincidence between the VCRs from Landsat and the ground surface conditions from ground survey, and high correlation between the VCRs from Landsat and from Quickbird. The experiments also show the possibility to generate VCR distribution maps in urban areas, and to extract their yearly changes. In addition the relationships between the VCRs estimated in this study and some vegetation indices are investigated. Finally the algorithm is modified to extract VCR conditions in wider urban areas, Tokyo-metropolis and Kinki-district, the biggest and the secondary biggest urban areas in Japan respectively.

This paper begins with a brief literature review concerning hedonic land value modeling, following which is a section illustrating research objective and study area. In the next section basic data collection and preparation are introduced and research methods and steps, including variable measurement and modeling process are stated thoroughly in the fourth section. Then analyze and discuss the results of modeling in the fifth section. The paper ends with conclusion and discussion about the limitation of the research and future research issues.

A 3D representation of a city embedded in an Internet environment is not a new technology. It is already used in many projects which present tourist information. But 3D modelling assumes a lot of manual operations and these are still not fully operational to handle, even if there exist special software (e.g. CyberCity) nowadays, that support the generation of 3D scenes. The emphasis of this paper is to present a method for improving the automation of generating 3D VRML scenes by using simple and adapted procedures which are implemented in a GIS environment. Our goal was to create a backdrop which is easily modifiable and replaceable. Especially within a city we find areas (e.g. shop windows), whose appearance changes continuously. Their representations can be replaced by simple work steps in the 3D workflow. A 3D environment is based on a pool of different data e.g. cadastral maps, floor plans, and aerial images, which should have a minimum resolution of 50 cm, to guarantee the recognition of all important items. The cadastral maps should be controlled by using current aerial images to eliminate errors. More complex buildings are assembled from several simplified building sections. Subsequently, an automated modelling takes place after adding a few parameters (building height, roof form, etc.). For a realistic natural representation, the texturizing was assisted by using digital photographs of the building facade. The whole VRML scene is modular. Each wall was generated as an individual VRML file, turned and shifted into the suitable position. Every single wall can be addressed individually and this facilitates the adjustment of the textures enormously. In this way small changes of the buildings could be easily maintained without generating the whole VRML scene again. Finally, the modular city file was loaded into a main file with optional additives such as trees, viewpoints etc. This additional information could also be generated likewise automatically. For orientation purposes a general map was integrated, in which the position and line of sight of the user is indicated by an arrow. In conclusion, it can be stated that the proposed method and its implementation have been proved to be very valuable and reliable for automated 3D urban modelling.

The aim of this study was to apply a spectral reflectance approach to account for small amounts of sediment dust in occupied homes. We examined the method's ability to predict the gravimetric weight of sediment dust particles solely from the reflectance data (1250-2400 nm). Multivariate data analysis based on Partial Least Squares (PLS) regression was run to predict the dust loads solely from reflectance data. Use of difference spectral index in the PLS analyses was found to demonstrate the best pre-treatment in PLS modeling. In this study, 92 measurements of dust settled on glass traps in living and bed room environments were performed, in 90 buildings within Tel-Aviv city. A map of dust distribution, based both, on the reference gravimetric and spectrally predicted weight values was generated. Reasonable explanation was provided to the found distribution that can be easily used by decision makers to improve the indoor life quality. We conclude that this methodology (simple and rapid in-situ spectral measurements with appropriate analyses), can be employed to assess dust in both indoor and outdoor environments (in small and high dust environment). This information can be used for initial decision making, improving indoor conditions, and tracking dust contamination following environmental change. This method can be further used to assess on-line very small amounts of dust and accordingly to identify shade on the environmental air quality on regular non dusty-days.

This paper concerns the study of visual impact assessment of routes on landscape by using remote sensing imagery. Basic indicators are created which can be useful for the analysis of route characteristics from the Landsat TM and Spot P fusion. The area which is the object of study is the Ancares Wildlife Reserve (Lugo, Spain). This paper focuses on the digital processing of rural roads to create indicators to determine if the road fits the landscape. Traditional methods are substituted by the application of satellite image processing through the application of linear enhancing, directional filtering, masking, supervised and unsupervised classifications and operations between images. A procedure has been developed which permits to map qualitative classes of visual integration of routes on landscape from the association of three variables: route linearity, vegetation in the roadside and visibility of the route. Fieldwork has allowed verification of the ground-truth between the physical reality of the area and the obtained thematic mapping. The information provided by the map will determine which routes require to take measures to reduce its visual impact on landscape.

Using image and LiDAR data the identification of image objects can be improved over using image data alone but the classification will still be mainly on the level of land cover. In order to move from land cover to land use it is suggested to include data stored in databases which can supply some information on land use. Although some of these will be stored in a GIS and can thus be linked more easily, others will typically only be indexed by street addresses with very little other geographical references. With the availability of georeferenced postal addresses these data can now be linked to image objects and used for classification purposes. An example is presented where a land cover classification is linked to the information taken from the yellow pages. This allows the identification of buildings used for commercial as well as public services down the specific branches of business. Inversely this also allows the identification of all residential buildings or those that have some kind of mixed use.

Visual perception based different scale remote sensing images fusion with multi-wavelet transform is discussed. 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. A visual perception based remote sensing images fusion algorithm is presented in this paper. Experiment results show that visual perception based remote sensing images fusion algorithm with multi-wavelet transform is better than the ordinary wavelet transform based fusion method. The fused image can provides more spatial information. Keywords Visual perception, image fusion, multi-wavelet transform, multi-spectral image, Panchromatic image

While the increase in spatial resolution for digital images has been hailed as a significant progress, methods for their automated analyses (i.e. land cover mapping, change analysis, GIS integration) are still in the process of being developed. Object (or segment) based preprocessing techniques seem to be an adequate methodology because inter-class variances can be minimized and the image interpretation techniques of the human eye be mimicked. A number of papers has proven the validity of an segment based image analysis for automated processing, however, the question of appropriate data fusion techniques within this context has hardly been addressed. In this paper, we will investigate techniques for the combination of image fusion and segment based image analysis. The examples include (i) color preserving iconic fusion with subsequent segmentation and classification; (ii) 'cookie cutter' approach for the integration of high resolution RGB and low resolution hyperspectral image data for urban class material detection; and (iii) decision based integration of panchromatic high resolution data with multispectral images for the identification of settlement areas. We will show that the combination of segment based image analysis and fusion techniques at iconic, feature and decision level does indeed improve the final analysis and can be seen as a first step to a an automated result driven processing line. It has to be noted that there is no general theory for segment based image fusion although the feature level fusion seems to be the most promising path for a combination of the two processing paradigms.

LIDAR has revolutionized the acquisition of digital elevation data for large scale mapping applications. Integrated with airborne GPS/IMU, it is possible to compile DTM from an aircraft platform through laser distance measurements. The precision of laserscanner slant distance measurement is primarily determined by the precision of time-of-flight measurement. But the distance measurement accuracy is not equivalent to the final 3D coordinate measurement accuracy. The final accuracy also depends on the precision of airborne GPS and IMU. This accuracy varies with flying height. The height precision of a single ground point is often in the order of 10-15 cm, with a typical planimetric precision in the order of 0.5-1.0 meter at a flying height of 1000 meter. Generally, Airborne LIDAR system integrates a digital camera hard hounded to the LIDAR sensor. Images captured by the integrated digital camera are mainly used to provide the necessary visual coverage of the area and generate the orthimages. We want to find a way to integrate LIDAR and imagery, so that the final 3D measurement errors can be suppressed. A simple simulation system is developed, and the preliminary result shows that the proposed method improves the 3D coordinate measurement accuracy.

Can the satellite QuickBird data detect buried archaeological remains and provide an effective spatial characterization of them? To answer this question, the capability of satellite QuickBird imagery for the identification of archaeological marks is herein tested for two test sites located in the South of Italy. The investigations were performed using a semi-automatic algorithm specifically developed for the detection of linear alignments. Results from our analyses showed that QuickBird images can represent a valuable data-source for archaeological investigations, ranging from small details to synoptic view. In particular, landscape archaeology can especially benefit from satellite images because such data can place local field studies within a regional context, can be promptly updated at low cost for large area, and can be directly imported into a GIS environment.

Remote sensing is a key application in global-change science for urban climatology and landuse/landcover dynamics analysis. Multi-spectral and multi-temporal satellite imagery (LANDSAT TM, ETM ;SAR ) over 1984 - 2004 period for Bucharest metropolitan area provide the most reliable monitoring technique of different urban structures regarding the net radiation and heat fluxes associated with urbanization at the regional scale. Bucharest City, the biggest industrial, commercial center in Romania experienced a rapid urban expansion during the last decades. A large amount of forest and agricultural land have been converted into housing, infrastructure and industrial estates. The resultant impervious urban surface alters the surface energy balance and surface runoff, which in turn could pose serious environmental problems for its inhabitants (e.g., urban waterlogged and thermal pollution).Investigation of radiation properties, energy balance and heat fluxes is based on satellite data from various satellite sensors and in-situ monitoring data , linked to numerical models and quantitative biophysical information extracted from spatially distributed data and net radiation. The changes over the years of surface biophysical parameters are examined in association with landuse changes to illustrate how these parameters respond to rapid urban expansion in Bucharest and surrounding region. For detailed landuse classifications in a digital form these properties were analyzed in a statistical way. This study attempts to provide environmental awareness to urban planners in future urban development. The land cover information, properly classified, can provide a spatially and temporally explicit view of societal and environmental attributes and can be an important complement to in-situ measurements.

This paper applied satellite data fusion technique for changes assessment and environmental impact over Romanian North-Western Black Sea and Danube deltaic coastal area. Since single sensor solutions for automatic object recognition provide only partial answers was proposed a multi-sensor, multi-resolution approach to be effective tool of exploiting the complimentary nature of different data types for change-detection studies. In order to extract environmental features for determining surface patches and surface boundaries, grouping surface patches based on spatial proximity, geometric and radiometric properties was performed object recognition for North-Western Black Sea coastal zone based on different satellite data (Landsat MSS, TM, ETM, ERS, SPOT XS, ASTER and MODIS). Preliminary results show significant coastline position changes of North-Western Black Sea during the period 1975-2004. The direct impacts are clearly shown, but it is less straightforward to link the changes in coastline to indirect impacts of the changing land use/cover. As an indication of land use/cover change, the extension of the road network and the urban areas were compared. The growth of coastal urban areas generates a range of threats to the nearby shoreline habitats. Direct physical damage is caused by construction works on harbors, airports and tourist resorts. Also the coastline change is examined and linked to the urban expansion in order to determine if the changes are mainly human induced or natural. A distinction is made between landfill/sedimentation processes on the one hand and dredging/erosion processes on the other.

This paper systematically introduces the practice of adding remotely sensed image processing functions to ArcGIS8.3 using Matlab COM Builder and AO. We also compare the performance of our system with that of the proprietary RS software ERDAS IMAGE8.5. The performance of our system is a bit lower than ERDAS IMAGINE, but the availability of power functions, the high efficient development methods and the integrated application environment provide us with large probabilities of the integrated applications of RS and GIS. Furthermore, it is possible for not only the field of remotely sensed image processing but also any other fields, which need to integrate the functions from GIS and Matlab to improve efficiency, low cost, mine and couple the functions from different professional software packages.

The intent of this study is to remotely monitor the status of revegetation growth in a reclaimed, previously mined region on the island of Milos, Greece. Quickbird multispectral (spatial resolution 2.4m×2.4m) and panchromatic (spatial resolution 0.6m×0.6m) images have been fused to obtain an optimal combination of the initial spatial and spectral resolution. The Blue (450nm-520nm), Green (520nm-600nm) and Near Infrared (760nm-900nm) bands of the multispectral image have been used for vegetation monitoring. Different fusion methods, like the Principal Component Analysis, the Intensity-Hue-Saturation transform and the Wavelet The goal of this study is to remotely monitor the status of re-vegetation growth in a reclaimed, previously mined region on the island of Milos, Greece. Quickbird multispectral (spatial resolution 2.4m×2.4m) and panchromatic (spatial resolution 0.6m×0.6m) images have been fused to obtain an optimal combination of the initial spatial and spectral resolution. The Blue (450nm-520nm), Green (520nm-600nm) and Near Infrared (760nm-900nm) bands of the multispectral image have been used for vegetation monitoring. Different fusion methods, like the Principal Component Analysis, the Intensity-Hue-Saturation transform and the Wavelet Analysis have been applied to Quickbird images. Both statistical (correlation coefficient, accuracy measures, etc.) and subjective (i.e., visual) measures have been used to evaluate the produced fused images. The degree to which each of the fused images retains the spectral and spatial features of the initial images has been thus estimated. Based on statistical measures, it has been found that the Additive Wavelet Principal Component "A Trous" and the Additive Wavelet Intensity Mallat methods effectively preserve most of the spectral information of the original multispectral image. On the other hand, the "A Trous" and Intensity-Hue-Saturation fusion techniques retain most of the spatial information of the panchromatic image. Additionally, the IHS transform offers a compromise between the spectral and spatial content of the fused image. Since the spectral content in the NIR band is of primary importance for monitoring re-vegetation growth, the Additive Wavelet Mallat and the IHS transforms are the most suitable choices.

In this paper we describe the architecture and the performances of a hybrid acquisition system prototype we implemented in Napoli for remote sensing applications, which allow the fusion of multi-source data produced by environmental noise sources. In particular, we discuss how the system is able to integrate geographically distributed sensors for seismic, electromagnetic, acoustic, etc. noises, sampled at different frequencies, too. This system is an improvement of the environmental monitoring system developed by our group for interferometers for gravitational wave detection. In this paper we discuss the system, together with its characteristics and performances in connection with its application for the implementation of a geographically distributed monitoring system.

Landsat TM has a thermal band (TM6) operating in 10.45-12.6mm, which can be used for land surface temperature (LST) retrieval. Land surface emissivity (LSE) is an essential parameter for LST retrieval. However, LSE information is generally no available for many applications. In this paper we intend to develop an applicable approach for LSE estimation so that LST can be retrieved from Landsat TM6 data. Spatial resolution of TM6 is 120m under nadir. Pixels under this scale can be viewed as composed of three land cover patterns for most natural surfaces: vegetation, bare soil/rock and water. Emissivities of these land cover patterns are relatively stable and well known, which enables us to propose a method for LSE estimation using the visible and near infrared (NIR) bands. The composition ratio of vegetation and bare soil or building under pixel scale can be estimated from bands 3 and 4 (TM3 and TM4). LSE for TM6 can then be estimated through thermal radiance equation with the composition ratio and the emissivities of the patterns known. The proposed methodology for LSE estimation is simple and easy to use, hence provides opportunity to promote the application of TM6 data to agriculture and environments. Finally we apply this methodology to Lingxian region of Shangdong Province in North China Plain, the most important agricultural region in China, for LSE estimation and LST retrieval, which has produced a reasonable estimation of thermal variation of the region.

Located in the east and the middle section of Qilian Mountain respectively, the upstream basins of Shiyang River and Heihe River are the main body of forest landscape ecosystem of Qilian Mountain. The Landscape patterns of these two districts were compared using TM image and DTM, under the help of Geographic Information System. The basic landscape type maps are complied based on the land use maps interpreted from TM images in two research districts in 2000. The correlative landscape indices are calculated to compared fragmentation, connectivity, diversity and heterogeneity of the two districts by using the landscape structure analysis software - FRAGSTATS. The spatial distribution features of all the landscape types in the two districts are analyzed and compared by superposing the maps of DEM, slope gradient and aspect with the maps of landscape types. The results shows that the main difference of the landscape patterns in the two study districts is the proportion of farmland area, this is also the main reason responsible for the difference in the other features of landscape pattern. The upper Shiyang basin have smaller patches but are more uniformly distributed, and the fragmentation degree of the landscape entirety and grassland is higher than that of the upper Heihe basin, which caused by the disturbance of land reclamation. The proportion of farmlands and grasslands are also influence the spatial distribution features of the landscape types at different terrain conditions. The results well demonstrate the relationship of human activities and the arid mountain ecosystem structure.

Two vital shortcomings of existed topological relation models for indeterminate topological relation description are summarized firstly. In order to get them over, a novel topological relation model for indeterminate geographical object is constructed based on fuzzy Close-degree analysis, where the five familiar topological relation predicates of RCC5 are adopted as the basic topological relation set. Then the workflow of computation of fuzzy topological relation is worked out. In the end a case study is implemented, and the results illustrated that the model is reasonable and viable.

General land use planning is the sticking point of the land resource management. As the chief means of conservative use for the land resources and macroeconomic control for the national economy, it plays an important role in Chinese land resource management. In the paper, the author firstly generalizes the deficiency of the current methods of general land use planning in China, and puts forward a way for general land use planning basing on the SD-MOP Integrated Model. Secondly, the author takes Hangpi country of Wuhan city for example and studies carefully the construction model, the parametric estimation and factors' identification and optimization. Through the model simulation run and result analysis, the author also prospect application of SD-MOP Integrated Model in land use planning, which is regarded as a new method for the project study of general land use planning.

Rangeland ecosystems play important roles in both economic development and ecological service in China. The ecosystems are facing challenges of degradation in recent decades due to heavy population pressure and overgrazing. Valuation of the ecosystem degradation is thus urgently required for better administration of the rangeland ecosystem for sustainable development. An approach was developed to use remote sensing technology for valuation of rangeland ecosystem degradation in the paper. A model has been improved from the conventional method for the valuation. Parameterization of the model is retrieved from MODIS data hence featured with spatiality, which is required for the valuation. The model is then applied to MODIS data for the period between 2003 and 2005. Results indicate that the total degradation in Chinese rangeland ecosystem can be valued as 6660.3 million US dollars. The degradation can be evaluated into several categories according to the amount per square kilometer. Slight degradation with a value of 0~1000 US$ • km^-2 accounts for ~1/4 of the total rangeland area in China. Severe degradation with a value of 1000~3000 US$ • km^-2 accounts for ~1/3 of the total rangeland area. This indicates that the degradation is very common in the ecosystems. In spatial distribution, Inner Mongolia, Xinjiang, Tibet, Qinghai, Gansu, Yunnan and Sichuan are the main provinces with rangeland ecosystems, hence become the main contributors to the degradation. Inner Mongolia has the greatest degradation value among the provinces. Its degradation value accounts for 25.89% of the entire China. The seven provinces in the west have a degradation value of 5221.9 million US dollars, accounting for 78.41%.

Leaf area index (LAI) is 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. In this paper, the method, developed by Qi et al. (2000) was selected. The process includes three steps: the first step is model inversion, using BRDF model to produce LAI with pixels chose randomly in one vegetation type region; the second step is quality control, removing the outliers, fitting equations using the LAI from second step and satellite data NDVI; the third step is LAI mapping, selecting the best equation and applying it to the whole region to mapping spatial LAI distribution. The main objective of this paper is to get one method that can be used in Arid and Semi-arid Northwestern China to derive LAI in the case of lack of LAI measurements. The results derived by the above approach were compared with ones derived from the empirical method (Sellers et al. 1996) and the LAI measured in field. The results suggested that the method can get good result and R² was 0.7947, though they were greater than field measurements. The results from empirical method were closer to the measurements than ones from Qi's method, but the higher the values of NDVI were, the greater the values of estimated LAI were than LAI measurements, when the values of NDVI were greater than a certain values (here 0.74). However, the result derived from Qi's method is closer to the LAI measured in field. In general, this method was feasible in arid and semi-arid northwestern China and can get satisfactory results.

In this paper, a mathematic model for POS based bundle adjustment is introduced. The model is made up of four types of linearized observation equations. The intention of the POS based bundle adjustment is to minimizing the error between the four types of observed value and its model value. We use the Levenberg-Marquardt algorithm to achieve this purpose. Our work is supported by China 863 program titled 'airborne multiangular imaging technique in power line inspection' (AMPLI). The purpose of this program is to monitor the relative distance between the power lines and the objects beneath them with accuracy as high as 0.5 meters. A number of high-resolution images must be captured along the power lines to ensure the accuracy. Based on an automatic matching method proposed by other team members in this program, hundreds of homonymous points can be extracted in one image. About 30 to 50 images are used in one block adjustment. As a result, large number of unknowns will contribute to the minimized error, and numerous equations should be solved. So, the minimization algorithm must incur the high computational costs in the problem. Fortunately, the normal equations reconstructed from the observation equations above exhibiting a sparse block structure. Considering the sparse characteristic of the normal equation, we propose a sparse bundle adjustment method based on Levenberg-Marquardt algorithm to save computation cost. A software package is developed based on this algorithm. A comprehension test was performed to investigate the performance of the algorithm. We used a data set provided by a field experiment in Wuhan, China. It is found that our algorithm showed both high accuracy and high efficiency in the test.

In this work, we integrate a popular remote sensing technique with ArcGIS to build a tool bar, named rGIS-ET, for estimating regional evapotranspiration (ET) from Landsat data and MODIS data with improved resolution. The development of rGIS-ET enables quick processing of large amount of remote sensing and other spatial data. It also provides user-friendly interfaces for modelling, output display and result analyses. Both surface temperatures and albedo were key parameters for calculating ET using Surface Energy Balance Algorithm. We adopted algorithms for estimating surface temperatures and albedo of winter wheat and summer maize field from MODIS data at 250 m spatial resolution to improve the resolutions of ET map of MODIS. We apply improved rGIS-ET to eight plain counties of Shijiazhuang city, a typical agricultural region in North China Plain, to demonstrate its utility for calculating regional ET and evaluating agriculture water resource usage. The improved ET map of MODIS could represent the spatial variation of crop ET much better than that without improved.

Land degradation is the most important environmental issue facing arid and semi-arid region, affecting regional land sustainable development. This article analyzed land degradation in Hebei province of China located in arid and semi-arid region with TM images of 1991 and 2000, and 1991 to 2000 land use change surveying data. Based on RS and GIS, comprised with statistic methods, land degradation index was used to study land degradation process in the study area. The results showed that land degradation was serious in the study area, especially land desertification, land salinity, pasture degradation, rocky desertification, deforestation and wetland shrink. On the other hand, the government strengthened reconstruction of degraded land. Some degraded land was restored, but land desertification and wet shrinking was still serious. At last, according to the land resources degradation process, natural resources and social economical condition in the region, some advices were given to construct eco-environment in Hebei province. Firstly, the population should be controlled strictly. Secondly, it is important to keep the total area of arable land, especially the area of main farm. Thirdly, land degradation process was different in the study area, and different measures would be taken according to local conditions to reconstruct degraded land. Fourthly, scientific decision depends on reliable information, so the dynamics of land degradation process should be monitored timely.

The geological survey was used to a common method before, in which the representation form of the survey results also was very old, especially aspects of location distributing, growth dimensions for hazard bodies, etc. It is thought a lack of veracity in work. This paper, by the geological hazards survey in Fengjie-Badong sect (case study) regarded as an example, illuminates to be capable of rightly drawing the size and position of hazard bodies with application of satellite remote sensing technology and assistant aerial images. It will most important for externally showing the status of geological hazard growth and for truly expressing the disserving of the whole Sanxia immigrant project caused by geological hazard. This remote sensing application has established foundation for carrying through geological hazard survey in the whole Sanxia reservoir areas.

The coastal zone is a place where land meets sea. It includes both coastal waters and adjacent shorelands areas that strongly influence each other. This area generally has various characteristics in any aspect of land or water with specific behaviors. Some of these behaviors have physical emergent that can be observed and monitored with remote sensing. The research has been done in northwest of Persian Gulf, Iran. The study area is consisted of four major estuaries and wide wetlands. Water level variation and efforts of great rivers e.g. Arvandrud, Bahmanshir and Hindijan strongly affects hydrodynamics and morphology of that area. Five series of Landsat remotely sensed images from acquisition period 1989 to 2002 have been used in this study. The research is based on an empirical approach. This approach was used to process multi-temporal images data with digital numbers combination with field measurements data. Several thematic maps have been generated from satellite images. The results of the study indicated that combination of field data and remotely sensed data provides a powerful tool for detecting environmental behaviors in coastal zones.

Several sandstorms invading the capital of China in recent years cause many concerns to the issues of grassland ecosystem degradation in arid and semiarid grassland region of north China. Actually the degradation can be viewed as the decrease of primary productivity in the grassland. This provides the possibility to monitoring the degradation using satellite remote sensing technology. In the study we present our experiences in conducting the monitoring of grassland ecosystem degradation in north China. Using the EOS/MODIS data, we develop an applicable method for the monitoring on the basis of net primary productivity (NPP). We assume that there is always a turf without degradation in the area of the same hydrothermal condition and type of grassland. We then use the NPP of the turf to determine the level of degradation in this area. The grassland region in north China can be divided into a number of small sub-regions for the determination and the division of sub-regions can be done according to the types of grassland. As far as every sub-region is concerned, we take the max NPP as the base line to determine the degradation of other pixels in the sub-region. The degradation can be graded into five levels: serious degradation, high degradation, medium degradation, light degradation and non-degradation. Finally we apply the method to analyze the spatial characteristics of grassland degradation in north China in the year 2005. The results show that the situation of grassland degradation in north China is very serious. 95.57% of the grassland in north China has suffered from deterioration to various levels, among which serious degradation, high degradation, medium degradation and light degradation account for 41.06%, 33.52%, 11.72% and 9.28% of the total, respectively.

With the objective of developing accurate global image data and to find the effects of difference of observation time, several reliable global cloud free data sets of Terra MODIS and Aqua MODIS will be developed utilizing personal computers. Out of 36 bands seven bands (Band 1 through 7) with similar spectral features to those of Landsat-7 ETM+ are selected, since these bands cover the most important spectra to derive land cover features. And then, some statistical information of the developed data sets and NDVI computed from the data sets were investigated. From the analysis, it was clarified that the means and standard deviations of band 2, 3, 4 and 6 of Aqua MODIS data are more slightly larger than those of Terra MODIS data although the values of band 5 and 7 of Aqua MODIS are more slightly smaller than those of Terra MODIS data. And it was also found that NDVI of Aqua MODIS data computed from band 2 (NIR) and band 1 (R) are also higher (approximately from 0.03 to 0.05 at the range of NDVI) than those of Terra NDVI because of the above mentioned reasons.

Image fusion is a very useful technique of obtaining high-resolution multi-spectral images from low spatial resolution multi-spectral and high-resolution panchromatic images. Nowadays many fusion techniques are available. However, those conventional fusion techniques existed also some shortcomings, which could not keep balance for preserving spectral information very well in a fused image with high-resolution spatial information. Hence, in this research a recent and efficient technique of fusion based on wavelet transformation was applied. The results presented the wavelet transform method is proved to be the best option for visual appreciation, preserving most 93% of the spectral information content, and as well improving the interpretability of low-resolution multi-spectral image classification. Meanwhile the results also further show the application potentiality of fusion technique based on wavelet transform for improving urban land classes in urban fringe.