This PDF file contains the front matter associated with SPIE Proceedings Volume 8893, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

Natural resources development, spanning exploration, production and transportation activities, alters local land surface at various spatial scales. Quantification of these anthropogenic changes, both permanent and reversible, is needed for compliance assessment and for development of effective sustainable management strategies. Multi-spectral high resolution imagery data from SPOT5 and RapidEye were used for extraction and quantification of the anthropogenic and natural changes for a case study of Alberta bitumen (oil sands) mining located near Fort McMurray, Canada. Two test sites representative of the major Alberta bitumen production extraction processes, open pit and in-situ extraction, were selected. A hybrid change detection approach, combining pixel- and object-based target detection and extraction, is proposed based on Change Vector Analysis (CVA). The extraction results indicate that the changed infrastructure landscapes of these two sites have different footprints linked with their differing oil sands production processes. Pixeland object-based accuracy assessments have been applied for validation of the change detection results. For manmade disturbances, other than fine linear features such as the seismic lines, accuracies of about 80% have been achieved at the pixel level while, at the object level, these rise to 90-95%. Since many disturbance features are transient, the land surface changes by re-growth of vegetation and the capability for natural restoration on the mining sites have been assessed.

The development of automatic extraction methods for urban building and vegetation objects and the realization on a large urban data set have been accomplished within the project ‘Derivation of Building- and Vegetation Heights and Structures in Berlin’. This project was executed on behalf of the Senate Department for Urban Development and Environment of the City of Berlin. As input an UltraCamX dataset consisting of the true ortho mosaic with four spectral channels and the normalized digital surface model (nDSM) with 30 cm resolution were used. The size of the area adds up to about 450 km². For the delineation of roof tiles additional slope and aspect layers were created.

The workflow was developed in eCognition for automatic extraction of elevated urban objects and their height structures. Additionally we used focal statistics in ArcGIS to extend the workflow for the extraction of single tree crowns, since detailed information about the correct position and number of urban trees is relevant and completes the urban geo database. In this way a unique, complete and extensive workflow for an automatic urban objects extraction arises. Within the project methods for robust extraction of buildings with roof tiles as well as vegetation were applied. Greened roofs are automatically extracted and assigned to the class buildings. Furthermore building tiles which are located under trees are extracted by the intersection with the cadastral building data. For the transferability of the complete rule set a multi-layered workflow consisting of automated data import and export, iterating segmentation methods and fuzzy classification as well as object reshaping was developed and applied. The methods are transferable and effectively operate on large data sets.

As results complete layers with building and vegetation shapes including single tree tops and crown outlines with comprehensive attributes like height and area are derived. The accuracies are in the range of 85% (trees) to 95 % (buildings). The shapes primarily serve as input for a complex urban-specific climate model, which also takes the morphology of hinterlands into account. In addition this data can be used as a basis for a variety of urban planning tasks e.g. for modeling of noise pollution, estimation of urban structure types, for town- or green space planning. They are a suitable basis for supplementing and upgrading of the official cadastral database, even though certain restrictions concerning the accuracies exist. Furthermore the object shapes are suitable for visualization tasks.

Urban heat island effects are well known nowadays and observed in cities throughout the World. The main reason behind the effects of urban heat island (UHI) is the transformation of land use/ land cover, and this transformation is associated with UHI through different actions: i) removal of vegetated areas, ii) land reclamation from sea/river, iii) construction of new building as well as other concrete structures, and iv) industrial and domestic activity. In rapidly developing cities, urban heat island effects increases very hastily with the transformation of vegetated/ other types of areas into urban surface because of the increasing population as well as for economical activities. In this research the effect of land use/ land cover on urban heat island was investigated in two growing cities in Asia i.e. Singapore and Johor Bahru, (Malaysia) using 10 years data (from 1997 to 2010) from Landsat TM/ETM+. Multispectral visible band along with indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Build Index (NDBI), and Normalized Difference Bareness Index (NDBaI) were used for the classification of major land use/land cover types using Maximum Likelihood Classifiers. On the other hand, land surface temperature (LST) was estimated from thermal image using Land Surface Temperature algorithm. Emissivity correction was applied to the LST map using the emissivity values from the major land use/ land cover types, and validation of the UHI map was carried out using in situ data. Results of this research indicate that there is a strong relationship between the land use/land cover changes and UHI. Over this 10 years period, significant percentage of non-urban surface was decreased but urban heat surface was increased because of the rapid urbanization. With the increase of UHI effect it is expected that local urban climate has been modified and some heat related health problem has been exposed, so appropriate measure should be taken in order to reduce UHI effects as soon as possible.

Land cover classification of remote sensing imagery is an important topic of research. For example, different applications require precise and fast information about the land cover of the imaged scenery (e.g., disaster management and change detection). Focusing on high resolution (HR) spaceborne remote sensing imagery, the user has the choice between passive and active sensor systems. Passive systems, such as multispectral sensors, have the disadvantage of being dependent from weather influences (fog, dust, clouds, etc.) and time of day, since they work in the visible part of the electromagnetic spectrum. Here, active systems like Synthetic Aperture Radar (SAR) provide improved capabilities. As an interactive method analyzing HR InSAR image pairs, the CovAmCoh^TM method was introduced in former studies. CovAmCoh represents the joint analysis of locality (coefficient of variation – Cov), backscatter (amplitude – Am) and temporal stability (coherence – Coh). It delivers information on physical backscatter characteristics of imaged scene objects or structures and provides the opportunity to detect different classes of land cover (e.g., urban, rural, infrastructure and activity areas). As example, railway tracks are easily distinguishable from other infrastructure due to their characteristic bluish coloring caused by the gravel between the sleepers. In consequence, imaged objects or structures have a characteristic appearance in CovAmCoh images which allows the development of classification rules. In this paper, a generalized interpretation scheme for arbitrary InSAR image pairs using the CovAmCoh method is proposed. This scheme bases on analyzing the information content of typical CovAmCoh imagery using the semisupervised k-means clustering. It is shown that eight classes model the main local information content of CovAmCoh images sufficiently and can be used as basis for a classification scheme.

In rapidly urbanizing old cities underground utility maps or urban utility information systems seldom exist. Close range remote sensing using Ground penetrating radar (GPR) for detection of the buried utilities is increasingly becoming common. GPR can help in developing or supplementing and updating an urban utility information system. The GPR data (radargram) needs to be interpreted by applying knowledge of buried utility responses under the influence of (a) antenna center frequency (b) host medium relative permittivity, conductivity and
(c) object shape, size and material.

In view of impracticality of generating utility response information directly in the field, Finite difference time domain (FDTD) simulation of radar wave propagation is carried out. The present work describes the database generation of GPR responses through simulation using an exclusive software GprMaxV2.0. A buried utility pipe produces a hyperbolic pattern in the radargram. In general, utilities may be made of metal, concrete or PVC; may lie within a shallow depth of about 0.5m-1.0m and their diameters may range upto to 1.0m; the relative permittivity of a dry soil could vary from about 4 to 15. Considering these aspects, problem of a pipe buried in soil is formulated, radargrams are simulated and variations of amplitudes and hyperbolic patterns are studied. To minimize the time-intensive simulations, Response surface method (RSM) is used to model amplitudes and hyperbolic patterns as functions of their influencing parameters. A database of simulated responses along with RSM modeling is seen to be a useful component of or complement to an urban utility information system.

From a didactic point of view the procurement and the application of modern geographical methods and functions become more and more important. Although the integration of GIS in the classroom is repeatedly demanded, inter alia in Baden-Württemberg, Germany, the number of GIS users is small in comparison to other European countries or the USA. Possible reasons for this could, for instance, lie in the lack of GIS and computer knowledge of the teachers themselves and the subsequent extensive training effort in Desktop-GIS [1]. Today you have the technological possibilities to provide the broad public with geoinformation and geotechnology: Web technologies offer access to web-based, mobile and local applications through simple gateways. The objective of the project “GIS4schools” is to generate a service-based infrastructure, which can be operated via mobile clients as well as via Desktop-GIS or a Browser. Due to the easy availability of the services the focus is in particular on students. This circumstance is a novelty through which a differentiated approach to the implementation of GIS in schools is established. Accordingly, the pilot nature of this project becomes apparent as well as its greater importance beyond its actual content especially for the sector of media development at colleges of education. The continuity from Web-GIS to Desktop-GIS is innovative: The goal is to create an adapted multi-level solution which allows both, an easy introduction if desired or a detailed analysis – either to be achieved with a focus especially on students and their cooperation among one another.

The hotspot of biodiversity in the Andes of Southern Ecuador has been severely threatened by climate change and unsustainable land use. The high biodiversity requires strategies for conservation and management of natural resources to be developed at both individual and area-wide levels. In this paper we focus on the development of an automatic treecrown detection and classification approach, which is in line individual-based investigations in the tropical mountain environment. Airborne laser scanning of discrete type was used with a very high granularity (<10 returns per square meter). The individual tree crown detection reached an accuracy of 51% while supervised classification of palm-trees reached an accuracy of 69%. Accuracy measurements are given in the paper. The detection and characterization of individual tree crowns is the first step in the development of a monitoring approach for the tropical mountain forest.

With the availability of high-resolution satellite data, much research has been focused on the automatic detection and classi cation of individual tree crowns. Most of these studies were applied to temperate climates of the northern hemisphere, especially for forests of coniferous. Very few studies have been applied to the detection of trees in the tropical regions, least of all in the urban environment. Urban trees play a major role in maintaining or even improving the quality of life in cities by their contribution to the quality of the air, by absorbing rain water, by refreshing the air through transpiration and providing shadow. In this study we explored the potential of high-resolution WorldView-2 satellite data for the identi cation of urban individual tree crowns in the city of Belo Horizonte, Minas Gerais, Brazil, through an object-oriented approach. Irrelevant areas were masked (e.g. buildings, asphalt, shadows, exposed soil) using a threshold of NDVI. Three di erent approaches were tested to isolate and delineate individual tree crowns: region growing, watershed and template matching. For the rst two approaches several parameters were tested to nd the best result for the isolation of the individual tree crowns. An in-house program has been developed for template matching using a set of seven di erent templates of di erent species. A set of 300 individual tree crowns were visually interpreted in the WorldView-2 image to serve as validation and to compare the performance of the three di erent approaches. Then, the comparison was performed between the visual interpretation and the results of each approach by calculating the di erence between the areas as a ratio of the validated area. Our results show that the region growing approach provided the best results, with an accuracy of over 80%.

Vegetation indices have been widely used for the detection of archaeological traces, based on crop marks, during different phenological stages. Such indices can be used in order to enhance interpretation performance of multispectral and hyperspectral satellite data, for identification of buried archeological remains. Although a variety of indices exists in the literature, research is still limited and only a small group of these indices have been explored. This paper aims to highlight the prospects as well the limitations of several broadband vegetation indices for the detection of Neolithic tells in the Thessalian plain (Greece). Several multispectral Landsat 5 TM and Landsat 7 ETM+ satellite images have been used for evaluating the effectiveness of such indices. In addition, new developed algorithms and hyperspectral narrowband indices, specially designed for archaeological research, have been also used and compared using hyperspectral satellite data (EO-Hyperion). Indeed, the Normalized Archaeological Vegetation Index (NAVI) as well as a linear transformation of the Landsat 5 TM were applied to satellite data. The above were also compared to other processing algorithms such as Tasseled – Cap algorithm and Principal Component Analysis. The results have shown that several indices and new algorithms may be used for the enhancement of crop marks, while some no-widely used indices can be successfully used for archaeological purposes.

Early diagnostics and documentation fulfill an essential role for an effective planning of conservation and restoration of cultural heritage assets. In particular, remote sensing techniques that do not require the use of scaffolds or lifts, such as fluoresence lidar, can provide useful information to obtain an overall assessment of the status of the investigated surfaces and can be exploited to address analytical studies in selected areas. Here we present the results of a joint Italian-Swedish project focused on documenting and recording the status of some sections of the part closed to the public by using fluorescence hyperspectral imaging lidar. The lidar used a tripled-frequency Nd:YAG laser emitting at 355 nm as excitation source and an intensified, gated 512x512-pixel CCD as detector. The lidar had imaging capabilities thanks to a computer-controlled scanning mirror. The fluorescence characteristics of fresco wall paintings were compared to those of fresco fragments found at the same archaeological site and separately examined in the lab using FT-IR and Raman techniques for the identification of pigments. The fluorescence lidar was also used to remotely detect the growth of phototrophic biodeteriogens on the walls. The fluorescence lidar data were compared with results from biological sampling, cultivation and laboratory analysis by molecular techniques.

Mapping the spatial distribution of crops has become a fundamental input for agricultural production monitoring using remote sensing. However, the multi-temporality that is often necessary to accurately identify crops and to monitor crop growth generally comes at the expense of coarser observation supports, and can lead to increasingly erroneous class allocations caused by mixed pixels. For a given application like crop classification, the spatial resolution requirement (e.g. in terms of a maximum tolerable pixel size) differs considerably over different landscapes. To analyse the spatial resolution requirements for accurate crop identification via image classification, this study builds upon and extends a conceptual framework established in a previous work¹. This framework allows defining quantitatively the spatial resolution requirements for crop monitoring based on simulating how agricultural landscapes, and more specifically the fields covered by a crop of interest, are seen by instruments with increasingly coarser resolving power. The concept of crop specific pixel purity, defined as the degree of homogeneity of the signal encoded in a pixel with respect to the target crop type, is used to analyse how mixed the pixels can be (as they become coarser), without undermining their capacity to describe the desired surface properties. In this case, this framework has been steered towards answering the question: “What is the spatial resolution requirement for crop identification via supervised image classification, in particular minimum and coarsest acceptable pixel sizes, and how do these requirements change over different landscapes?” The framework is applied over four contrasting agro-ecological landscapes in Middle Asia. Inputs to the experiment were eight multi-temporal images from the RapidEye sensor, the simulated pixel sizes range from 6.5 m to 396.5 m. Constraining parameters for crop identification were defined by setting thresholds for classification accuracy and uncertainty. Different types of crops display marked individuality regarding the pixel size requirements, depending on the spatial structures and cropping pattern in the sites. The coarsest acceptable pixel sizes and corresponding purities for the same type of crop were found to vary from site to site, and some crops could not be identified using pixels coarser than 200 m.

Global warming induced by greenhouse gases is increasing wildfire frequencies and scale. Since wildfire again releases greenhouse gases(GHGs) into the air, the vicious cycle is repeated. Satellite remote sensing is a useful tool for detecting wildfire. However, estimating the GHGs emission from wildfire has not been challenged yet. Wildfires are estimated to be responsible for, on average, around 30% of global total CO emissions, 10% of methane emissions, 38% of tropospheric ozone, and over 86% of black carbon. So we need to quantify the emitted gases by biomass combustions, which can be measured by the FRP (fire radiative power) derived from the spectral characteristics of satellite sensors. This paper described the algorithm for retrieval of FRP using COMS(Communication, Ocean and Meteorological Satellite), the Korean geostationary meteorological satellite. The FRP of wildfire is retrieved by single waveband methods suitable to COMS channels. The retrieval of FRP is dependent on the emissivity of each bandwidth. So, we used MODIS NDVI through a spatio-temporal calibration for the emissivity calculations. We made sure that the FRP in wildfire pixel is much higher than its spatially and temporally neighboring pixels. For future work, we should quantify the relationships between FRP and the biomass combustion according to fuel types.

The recognition and monitoring of vegetation and habitats for nature conservation is a vital point of research within the remote sensing community. It has been agreed on that there is no general solution on deriving information on habitats due to different data availability and spectral as well as textural behaviour of habitat main types (e.g. woodlands, grasslands, etc.). Therefore the monitoring should be rather multi-scale, versatile, user-friendly, and cost-efficient for predefined indicators. In the presented study, five Central European test sites of natural vegetation communities in an Alpine area (1), a temperate forest (2), a Pannonian grassland (3), a shallow lake (4) and a Carpathian grassland (5) have been investigated by multi-temporal remote sensing. For these studies, different time-series of RapidEye images from the years 2009 to 2011 were acquired. The amount of the images was depending on required acquisitions dates as well as weather conditions. The definition of the indicators was relying on the available ground truth data as well as the demands and judgement of the managing authorities in the nature conservation areas. The selected methods for deriving of the indicators depend on the time-series as well as the available calibration and validation data. The techniques vary between unsupervised classification, object-based approaches and supervised classification methods with algorithms such as support vector machines (e.g. SVM) or classification trees (e.g. See5). Often the named methods are utilized in combined approaches. The resulting indicators for the monitoring are shrub encroachment (for 1), share of naturally occurring tree type (for 2), differentiation of grassland types (for 3 and 5) and the changing extent of a reed belt (for 4). All indicators seem to be valid and useful. However, a transferability of the methods or a general statement on good-practice remote sensing applications can hardly be derived from these specific case studies.

Researchers who use remotely sensed data can spend half of their total effort analysing prior data. If this data preprocessing does not match the application, this time spent on data analysis can increase considerably and can lead to inaccuracies. Despite the existence of a number of methods for pre-processing Landsat time series, each method has shortcomings, particularly for mapping forest changes under varying illumination, data availability and atmospheric conditions. Based on the requirements of mapping forest changes as defined by the United Nations (UN) Reducing Emissions from Forest Degradation and Deforestation (REDD) program, the accurate reporting of the spatio-temporal properties of these changes is necessary. We compared the impact of three fundamentally different radiometric preprocessing techniques Moderate Resolution Atmospheric TRANsmission (MODTRAN), Second Simulation of a Satellite Signal in the Solar Spectrum (6S) and simple Dark Object Subtraction (DOS) on mapping forest changes using Landsat time series data. A modification of Breaks For Additive Season and Trend (BFAST) monitor was used to jointly map the spatial and temporal agreement of forest changes at test sites in Ethiopia and Viet Nam. The suitability of the pre-processing methods for the occurring forest change drivers was assessed using recently captured Ground Truth and high resolution data (1000 points). A method for creating robust generic forest maps used for the sampling design is presented. An assessment of error sources has been performed identifying haze as a major source for time series analysis commission error.

Evaluation of beach hydromorphological behaviour and its classification is highly complex. The available beach morphologic and classification models are mainly based on wave, tidal and sediment parameters. Since these parameters are usually unavailable for some regions – such as in the Portuguese coastal zone - a morphologic analysis using remotely sensed data seems to be a valid alternative. Data mining for spatial pattern recognition is the process of discovering useful information, such as patterns/forms, changes and significant structures from large amounts of data. This study focuses on the application of data mining techniques, particularly Decision Trees (DT), to an IKONOS-2 image in order to classify beach features/patterns, in a stretch of the northwest coast of Portugal. Based on the knowledge of the coastal features, five classes were defined: Sea, Suspended-Sediments, Breaking-Zone, Beachface and Beach. The dataset was randomly divided into training and validation subsets. Based on the analysis of several DT algorithms, the CART algorithm was found to be the most adequate and was thus applied. The performance of the DT algorithm was evaluated by the confusion matrix, overall accuracy, and Kappa coefficient. In the classification of beach features/patterns, the algorithm presented an overall accuracy of 98.2% and a kappa coefficient of 0.97. The DTs were compared with a neural network algorithm, and the results were in agreement. The methodology presented in this paper provides promising results and should be considered in further applications of beach forms/patterns classification.

Nowadays, remote-sensing technologies are becoming increasingly interlinked to the issue of deforestation. They offer a systematized and objective strategy to document, understand and simulate the deforestation process and its associated causes. In this context, the main goal of this study, conducted in the Blue Nile region of Sudan, in which most of the natural habitats were dramatically destroyed, was to develop spatial methodologies to assess the deforestation dynamics and its associated factors. To achieve that, optical multispectral satellite scenes (i.e., ASTER and LANDSAT) integrated with field survey in addition to multiple data sources were used for the analyses. Spatiotemporal Object Based Image Analysis (STOBIA) was applied to assess the change dynamics within the period of study. Broadly, the above mentioned analyses include; Object Based (OB) classifications, post-classification change detection, data fusion, information extraction and spatial analysis. Hierarchical multi-scale segmentation thresholds were applied and each class was delimited with semantic meanings by a set of rules associated with membership functions. Consequently, the fused multi-temporal data were introduced to create detailed objects of change classes from the input LU/LC classes. The dynamic changes were quantified and spatially located as well as the spatial and contextual relations from adjacent areas were analyzed. The main finding of the present study is that, the forest areas were drastically decreased, while the agrarian structure in conversion of forest into agricultural fields and grassland was the main force of deforestation. In contrast, the capability of the area to recover was clearly observed. The study concludes with a brief assessment of an 'oriented' framework, focused on the alarming areas where serious dynamics are located and where urgent plans and interventions are most critical, guided with potential solutions based on the identified driving forces.

This paper presents the structure and contents of a hydro - geological GIS database that stores hydro – geological data and constitutes the basis for processing and extracting spatial and non spatial data. The geodatabase contains information on hydrology, hydrography, hydrochemistry, geology, topography, land use, protected area, administration as well as the maps that were produced using the data from the base. Additional data that related to hydrogeological study of the lakeside area located at the NW part of lake Trichonida and DEMs are also included at this database. Then a part of these files were used to make hyper- links whose objective was to give direct access to tables of precipitation and chemical data. With the SQL, various queries have been set in order to lead to the data retrieval. This hydrogeological database is implemented within a GIS (geographic information system) framework coupled to a Relational Database Management System (RDBMS) as a file geodatabase (ESRI format) GIS technology. At this point must be emphasized the dynamic nature of the database as well as provide support for a multiuser environment. The structure of the hydro – geological database presented at this paper is exemplified for a study site around Aitoloakarnania in Western Greece.

Landslide hazard assessments using computational models, such as artificial neural network (ANN) and frequency ratio (FR), were carried out covering one of the important mountain highways in the Central Himalaya of Indian Himalayan Region (IHR). Landslide influencing factors were either calculated or extracted from spatial databases including recent remote sensing data of LANDSAT TM, CARTOSAT digital elevation model (DEM) and Tropical Rainfall Measuring Mission (TRMM) satellite for rainfall data. ANN was implemented using the multi-layered feed forward architecture with different input, output and hidden layers. This model based on back propagation algorithm derived weights for all possible parameters of landslides and causative factors considered. The training sites for landslide prone and non-prone areas were identified and verified through details gathered from remote sensing and other sources. Frequency Ratio (FR) models are based on observed relationships between the distribution of landslides and each landslide related factor. FR model implementation proved useful for assessing the spatial relationships between landslide locations and factors contributing to its occurrence. Above computational models generated respective susceptibility maps of landslide hazard for the study area. This further allowed the simulation of landslide hazard maps on a medium scale using GIS platform and remote sensing data. Upon validation and accuracy checks, it was observed that both models produced good results with FR having some edge over ANN based mapping. Such statistical and functional models led to better understanding of relationships between the landslides and preparatory factors as well as ensuring lesser levels of subjectivity compared to qualitative approaches.

In this study ten commercial available fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, PCA, HCS (Hyperspherical Color Space) and Wavelet were used for the fusion of Geoeye panchromatic and multispectral data. The panchromatic data have a spatial resolution of 0.5m while the multispectral data have a spatial resolution of 2.0m. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q, entropy were examined and the results are presented. The broader area of Pendeli mountain near to the city of Athens Greece and more especially two sub areas with different characteristics were chosen for the comparison. The first sub area is located at the edge of the urban fabric and combines at the same time the characteristics of an urban and a rural area. The second sub area comprises a large open quarry and it is suitable to examine which fused product is more suitable for mine monitoring.

The GEOBIA (GEOgraphic Object-Based Image Analysis) approach persists to reveal its effectiveness in remote sensing data analysis, which provides paradigms that integrate analyst’s expert knowledge to generate semantically meaningful image-segments. These segments might contribute to the reduction of problems associated with the analysis of the discrete spectral value of a pixel, such as illumination and shaded tree crowns. However, the challenge in this paper is to introduce a GEOBIA as a sophisticated framework toward the automation of forest structural attributes estimate. Optical sensor was examined to develop models for the estimation of forest attributes. Analyses were performed over a forested selected site in the Blue Nile region of Sudan. The framework of the present research involved; segment extraction, field sample selection, forest attributes generalization, model validation, and mapping the predicted attributes. The rationale for incorporating these data is to offer a semi-automatic GEOBIA approach from which forest attribute is acquired through automated segmentation algorithms at the delineated tree crowns or clusters of crowns level. Correlation and regression analyses were applied to identify the relation between a wide range of spectral, textural, contextual metrics, and the field derived forest attributes. Forest structural attribute estimation results acquired from our GEOBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with independent set of field samples, which revealed a high degree of precision.

The impact of mining industry and metal production on the environment is presented all over the world. In our research we set focus on the impact of already non-operating ferrous “Kremikovtsi”open pit mine and related waste dumps and tailings which we consider to be the major factor responsible for pollution of one densely populated region in Bulgaria. The approach adopted is based on correct estimation of the distribution of the iron oxides inside open pit mines and the neighboring regions those considered in this case to be the key issue for the ecological state assessment of soils, vegetation and water. For this study the foremost source of data are those of airborne origin and those combined with ground-based in-situ and laboratory acquired data were used for verification of the environmental variables and thus in process of assessment of the present environmental status influenced by previous mining activities. The percentage of iron content was selected as main indicator for presence of metal pollution since it could be reliably identified by multispectral data used in this study and also because the iron compounds are widely spread in the most of the minerals, rocks and soils. In our research the number of samples from every source (air, field, lab) was taken in the way to be statistically sound and confident. In order to establish relationship between the degree of pollution of the soil and mulspectral data 40 soil samples were collected during a field campaign in the study area together with GPS measurements for two types of laboratory measurements: the first one, chemical and mineralogical analysis and the second one, non-destructive spectroscopy. In this work for environmental variables verification over large areas mulspectral satellite data from Landsat instruments TM/ETM+ and from ALI/OLI (Operational Land Imager) were used. Ground-based (laboratory and in-situ) spectrometric measurements were performed using the designed and constructed in Remote Sensing Systems Department at Space Research and Technology Institute thematically oriented spectrometric system TOMS working in the 0.4-0.9 μm range of the electromagnetic spectrum (EMS). For proper comparison between the data obtained from the different sources mentioned spectral transformations such as normalized difference and rationing data for two wavelengths were applied in order to avoid misinterpretation. Statistically significant dependence between the various spectral transformations and the quantitative content of the iron in the different type of compounds was established. The achieved results provided evidence that methodology used could be extended to other regions of the country polluted by the mining activities and should be also tested in the region of the copper and zinc extraction. In the next step of our research we intend to use the results obtained by the multitemporal analysis of the satellite and ground-based multispectral data for the same and the similar regions of interest.

The forest fires do much damage to our life in ecological and economic aspects. South Korea is probably more liable to suffer from the forest fire because mountain area occupies more than half of land in South Korea. They have recently launched the COMS(Communication Ocean and Meteorological Satellite) which is a geostationary satellite. In this paper, we developed forest fire detection algorithm using COMS data. Generally, forest fire detection algorithm uses characteristics of 4 and 11 micrometer brightness temperature. Our algorithm additionally uses LST(Land Surface Temperature). We confirmed the result of our fire detection algorithm using statistical data of Korea Forest Service and ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer) images. We used the data in South Korea On April 1 and 2, 2011 because there are small and big forest fires at that time. The detection rate was 80% in terms of the frequency of the forest fires and was 99% in terms of the damaged area. Considering the number of COMS’s channels and its low resolution, this result is a remarkable outcome. To provide users with the result of our algorithm, we developed a smartphone application for users JSP(Java Server Page). This application can work regardless of the smartphone’s operating system. This study can be unsuitable for other areas and days because we used just two days data. To improve the accuracy of our algorithm, we need analysis using long-term data as future work.

Many remote sensing products that are useful for time series analysis and seasonal monitoring studies are offered in form of composites. A composite combines a number of observations of a defined period and selects or computes one value. This results in observations sampled at varying time intervals that rules out a high number of time series analysis techniques. This study investigates the impact of either using the actual day of observation to generate a time series from composites or assuming the starting or middle day of the compositing period. For this study 16-day MODIS VI composites of 1km spatial resolution from Terra and Aqua were employed. A 1100x500km region in central Mexico served as study site. Statistical measures including temporal cross-correlation and the root mean square error were used for time series analysis. A temporal shift of approximately seven days with a high variability is introduced when using the starting day of the compositing period. The middle day mitigates the mean error close to zero but still shows a high error variability. Only time series that take into account the day of observation and estimate from that samples at equidistant intervals can be used for a correct estimation of temporal characteristics.

Non-point source pollution (NPSP) is perhaps the leading cause of water quality problems and one of the most challenging environmental issues given the difficulty of modeling and controlling it. In this article, we applied the Manning equation, a hydraulic concept, to improve models of non-point source pollution and determine its influence as a function of slope - land cover roughness for runoff to reach the stream. In our study the equation is somewhat taken out of its usual context to be applies to the flow of an entire watershed. Here a digital elevation model (DEM) from the SRTM satellite was used to compute the slope and data from the RapidEye satellite constellation was used to produce a land cover map later transformed into a roughness surface. The methodology is applied to a 1433 km² watershed in Southeast Brazil mostly covered by forest, pasture, urban and wetlands. The model was used to create slope buffer of varying width in which the proportions of land cover and roughness coefficient were obtained. Next we correlated these data, through regression, with four water quality parameters measured in situ: nitrate, phosphorous, faecal coliform and turbidity. We compare our results with the ones obtained by fixed buffer. It was found that slope buffer outperformed fixed buffer with higher coefficients of determination up to 15%.

Realizing the potential usage, the country has identified National Bamboo mission for addressing the issues relating to the development of bamboo in the country. Therefore knowledge of spatial distribution of bamboo patches becomes necessary for the evaluation and monitoring of this resource. Medium resolution satellite imagery is ineffective for accurate classification when bamboo occurs in mosaic of small and mixed patches. The present study attempts to address this problem using Very High Resolution (VHR) multispectral (MS) WorldView 2 (WV 2) imagery in South 24 Parganas, West Bengal. West Bengal, a part of Bengal Bay ecological region is one of the areas where bamboo grows naturally. The classification was carried out using additional features namely second order texture components of the first 3 principal components (PCs) of pan-sharpened 8 MS bands. Supervised kernel based (Support Vector Machine, SVM) and ensemble based (Random Forest, RF) machine learning algorithms were applied on the dataset. Moreover, RF based variable importance was analyzed to find the most informative input predictors for classification of bamboo. Altogether seven land use land cover classes were mapped which include agricultural land, built-up, bamboo patches, two canopy classes, fallow land and water body. Variable importance analysis indicated that mean texture measure of PC1 and PC3, and spectral information of MS band 8 (NIR 2) were the most important predictor layers for bamboo mapping. Unlike, RF (accuracy 72%) higher overall accuracy of 80% was achieved using SVM classifier for bamboo mapping. Intermixing of bamboo was seen with other canopies.

The changes in the agricultural patterns and the forest land are one important indicators of serious socio economic and environmental management implications. The main challenge for the agriculture sector from the rainfed areas is to sustain present livelihood conditions and to secure future demands and typically they are more fragile to the changing climates and demands. Remote Sensing and Geographical Information System coupled with empirical modeling is recognized as a powerful and effective tool in detecting land use change and predict future changes. In this study, modeling and prediction of agricultural land use change across space and time using Logistic Regression and Geographically Weighted Regression in dry land region is attempted as the land use and land cover change is the function of interacting factors which are known as drivers, agents and determinants. The land use pertaining to 1985,1995 and 2005 for the study area were generated to study and model the land use dynamics. The drivers for land us change based on the factors such as climatological data and socio economics data were identified and a model for changes land use using geographically weighted regression and binary logistic regression methods . The models were validated with the help of land use map generated with remote sensing data.

The dynamics of vegetation cover changes may provide vital information for ecological environmental protection and early warning of ecosystem degradation in arid and semiarid regions. The West Liaohe River Basin is the east fringe of agro-pasture transitional zone in northern China and highly sensitive to global change. With the SPOT VEGETATION (SPOT-VGT) NDVI dataset during 1999–2010, temporal and spatial change trends of vegetation cover was investigated using yearly and seasonal average NDVI, Vegetation Anomaly Index (VAI) and correlation analysis. The relationship between vegetation change, climatic and anthropogenic factors were explored. The results indicated that yearly NDVI slightly increased with an undulating trend. 30.24% of the study area had experienced a significant vegetation increase at the 0.05 level from 1999 to 2010. The VAI negative values exhibited vegetation cover degradation impacted by the drought in 2000-2002 and 2009.The average NDVI values in autumn increased by 5.92%, whereas the spring NDVI decreased by -5.82%. 16.46% and 15.49% of the study area showed a significant vegetation increase in summer and autumn respectively. Changes in vegetation growth in the West Liaohe River Basin may be affected by spring precipitation, summer temperature and precipitation and autumn temperature. The NDVI increase trends in the study area were related to the increased crop yield.

Coverage rates of vegetation and exposed bedrock are two key indicators of karst rocky desertification (KRD) envionmnents. Based on spectral unmixing algorithm, abundance of vegetation and exposed rock were retrieved from the hyperspectral Hyperion image. They were verified by the spectral indices of Karst rocky desertification (SIRD) and vegetation chlorophyll index. It showed that the abundance had significant linear correlation with SIRD. The determinate coefficients (R²) were 0.93,0.66, 0.84 for vegetation, soil and rock respectively, indicating that the abundances of vegetation and bedrock can characterize their coverage rates to a certain extent. Then, the abundances of vegetation and bedrock were graded and integrated together to evaluate the rocky desertification in typical Karst region. This study implies that spectral unmixing algorithm based on hyperspectral remote sensing image will have potential use in monitoring and evaluating KRD.

Climate change has become a cause of concern as well as the challenge of this century. Himalayan mountain ranges with high snow fields and numerous valley glaciers may bear the brunt of such changes already being reported including Intergovernmental Panel on Climate Change (IPCC). Gangotri is one of the most prominent snow-fed catchments of Indian Himalayan Region (IHR) due to origin of river Ganga situated within it. Spatio-temporal changes in snow covered area of this basin were examined for melting seasons of the years 2006 to 2010 and a latest reference year of 2012 as a special test case. Standard snow data products (MOD10A2) of Moderate Resolution Imaging Spectroradiometer (MODIS)-Terra sensor with spatial resolution of 500 m were used. For all the years of reference, snow covered area percentage was derived for respective months representing usual ablation or melting periods. Snow depletion curves (SDCs) were generated for such periods of the respective years. CARTOSAT digital elevation model (DEM) was used for topographic information of terrain. Relationship of SDCs with the land surface temperatures (LST) of the basin was worked upon using MODIS-Terra LST (MOD11A2) product (version 5) with 1 km resolution at 8-day interval for the day time temperature for respective months of above reference years. Thereafter, interpolation and simulation of snow covered areas was carried out on the basis of LST data. The study thus produced snow cover maps for the years of reference as well as their relationship with LST for climate change inferences.

In semi arid land in Algeria the ecosystem of steppe presents a different vegetal formation, generally used for pasture, and the forest are in most time composed by species like Aleppo pine sparse. And seen climatic unfavourable conditions in zone and impact of forest fires; we notes deterioration of physical environment particularly, deterioration of natural forest. This deterioration of forests provokes an unbalance of environment witch provokes a process of deterioration advanced in the ultimate stadium is desertification. The specific regeneration of plants are influenced greatly by the regime of fire (season of fire, intensity, interval), who leads to the recuperation of the vegetation of meadow- fire, but in the most case there are unfavourable climatic conditions. In this survey we used satellite data for detection of zones with risk of forest fire and their influenced parameters witch permit generally a desertification process. A thematic detailed analysis of forests ecosystems well attended, some processing on the satellite data (2003) allowed us to identify and classifying the forests in there opinion components flowers. We identified ampleness of fire on this zone also. The parameters slope, the proximity to the road and the forests formations and fire regime were studied in the goal of determining the zones to risk of fire drill. A crossing of information in a geographic information system according to a very determined logic allowed us to classify the zones in degree of risk of fire. These results compared with image data (2011) permit to conclude that in semi arid land the forest ecosystem after fire becomes steppe courses permitting installation of process of desertification.

Nile River, the longest river in the world, 6,695 km long from its remotest headstream, the Luvironza River in Burundi, central Africa, to its delta on the Mediterranean Sea, NE Egypt. The Nile River islands form an attractive agricultural area characterized with its nightly fertile soils, easy source, and its suitability to a wide range of land use. The present use of these Nile islands does not reach the maximum capability of these resources due to improper land use of these areas.The current study aims at identifying the changes of the Nile course and its islands during the last three decades using remote sensing and GIS techniques in order to provide the scientific bases, which help in planning the most suitable programs of land use, soil management and conservation. Six MSS, eight TM and Eight ETM+ satellite images dated to 1972,1984 and 2002 respectively were used to study the changes occurred during the above-mentioned periods. The study area was divided into five sectors along the Nile River course i.e. Aswan – Qena, Qena - Assiut , Assiut - Qalubia , Qalubia - Damietta and Qalubia – Rosetta . The changes in Nile course from early seventieth to middle eighteenth were decreased by 51.34 Km², from middle eighteenth to the millennium were decreased by 40.30 Km². The overall change in Nile course area decreased by 91.64 Km² in the investigation period. Belonging to the islands number and their areas in the investigation period, the changes in islands number from early seventieth to middle eighteenth were increased by 171 islands, from middle eighteenth to the millennium were decreased by 86 islands. Meanwhile, the islands areas from early seventieth to middle eighteenth were decreased by 4512.39 Feddan., from middle eighteenth to the millennium were decreased by 5446.97 Feddan. The overall change in the investigation period for the total number of the islands was increased by 85 islands, meanwhile the islands areas were decreased by 9959.36 Feddan. Changes of soil characteristics of some islands were recognized. It is found that, there is no regular pattern of the changes that related to soil characteristics due to many reasons i.e. differences in human agricultural practices, differences in Nile deposition, which differ from year to year, and differences in Nile water levels. Conservation and management program was suggested for the optimum use of these islands.

Sustainable use and management of natural resources require strategic responses using non-destructive tools to provide spatial and temporal data for decision making. Experiments conducted at John F. Kennedy Space Center (KSC) demonstrate ground penetrating radar (GPR) can provide high-resolution images showing depth to water tables. GPR data at KSC were acquired using a MALÅ Rough Terrain 100 MHz Antenna. Data indicate strong correlation (R²=0.80) between measured water table depth (shallow monitoring wells and soil auger) and GPR estimated depth. The study demonstrated the use of GPR to detect Holocene and Pleistocene depositional environments such as Anastasia Formation that consists of admixtures of sand, shell and coquinoid limestone at a depth of 20-25 ft. This corresponds well with the relatively strong reflections from 7.5 to 13 m (125-215 ns) in GPR images. Interpretations derived from radar data coupled with other non-GPR data (wells data and soil auger data) will aid in the understanding of climate change impacts due to sea level rise on the scrub vegetation composition at KSC. Climate change is believed to have a potentially significant impact potential on near coastal ground water levels and associated water table depth. Understanding the impacts of ground water levels changes will, in turn, lead to improved conceptual conservation efforts and identifications of climate change adaptation concepts related to the recovery of the Florida scrub jay (Aphelocoma coerulescens) and other endangered or threatened species which are directly dependent on a healthy near coastal scrub habitat. Transfer of this inexpensive and non-destructive technology to other areas at KSC, Florida, and to other countries, may prove useful in the development of future conservation programs.

In June 2011, Oil Spill disasters occurred at platforms of Penglai 19-3 oil field in the Bohai Sea off the China's northeast coast which hugely degraded the water quality and damaged the balance of marine environment and ecosystem in Bohai Sea, as even slight changes in water quality could be catastrophic for marine lives. Simulation, Visualization and GIS analysis of Oil Spill disasters is crucial to rapidly assessment of the vulnerability of coastal areas and to make a full understanding of possible impact of disasters on infrastructure and environment. Globe model become a hotspot in the area of GIS research presently. It is a useful platform for integration and sharing of spatial information with its strong abilities of spatial data management and visualization. In this paper, Multi-resolution remote sensing imagery and DEM of terrain is implemented with LOD based on Globe Model. A numerical simulation result of PL19-3 oil spill based on General NOAA Operational Modeling Environment (GNOME) is visualized using particle trace and analysed on the globe model. It proves that the method of virtual reality and GIS analysis based on globe model for oil spill is vivid and intuitionistic; it may be applied in other ocean research.

This paper describes a new mechanical implementation of a triaxial sensor, configurable as seismometer and/or as accelerometer, consisting of three one-dimensional monolithic FP sensors, suitably geometrically positioned. The triaxial sensor is, therefore, compact, light, scalable, tunable instrument (frequency < 100mHz), with large band (10⁻⁷ Hz − 10Hz), high quality factor (Q < 2500 in air) with good immunity to environmental noises, guaranteed by an integrated laser optical readout. The measured sensitivity curve is in very good agreement with the theoretical ones (10⁻¹²m/pHz) in the band (0.1 ÷ 10Hz). Typical applications are in the field of earthquake engineering, geophysics, civil engineering and in all applications requiring large band-low frequency performances coupled with high sensitivities.

Urban air pollution and especially aerosols have significant negative health effects on urban population, of which children are most exposed for the rapid increase of asthma disease. An allergic reaction to different allergens is a major contributor to asthma in urban children, but new research suggests that the allergies are just one part of a more complex story. Very early exposure to certain components of air pollution can increase the risk of developing of different allergies by age 7. The epidemiological research on the mutagenic effects of airborne particulate matter pointed their capability to reach deep lung regions, being vehicles of toxic substances. The current study presents a spatio-temporal analysis of the aerosol concentrations in relation with meteorological parameters in two size fractions (PM10 and PM2.5) and possible health effects in Bucharest metropolitan area. Both in-situ monitoring data as well as MODIS Terra/Aqua time-series satellite data of particle matter PM2.5 and PM10 concentrations have been used to qualitatively assess distribution of aerosols in the greater metropolitan are of Bucharest comparative with some other little towns in Romania during 2010- 2011 period. It was found that PM2.5 and PM10 aerosols exhibit their highest concentration mostly in the central part of the towns, mainly due to road traffic as well as in the industrialized parts outside of city’s centre. Pediatric asthma can be managed through medications prescribed by a healthcare provider, but the most important aspect is to avoid urban locations with high air pollution concentrations of air particles and allergens.

Satellite remote sensing offers unique perspectives to study the relationship between urban systems and climate change because it provides spatially explicit and synoptic views of the landscape that are available at multiple spatial grains, spatial extents, and over time, able to provide detailed information of urbanization patterns. Due to significant anthropogenic changes that have occurred in the last several decades in Bucharest city’s landscape, urbanization has become an important factor affecting urban surface parameters, hence in the surface–atmosphere interaction processes, with a great potential to alter the local climate. Analysis of surface biophysical parameters changes in urban/periurban areas of Bucharest metropolitan area based on multi-spectral and multi-temporal satellite imagery (MODIS, Landsat TM/ETM and IKONOS) provides the most reliable technique of environmental monitoring regarding the net radiation and heat fluxes associated with urbanization at the regional scale. Investigation of radiation properties, energy balance and heat fluxes is based on information derived from various satellite sensors and in-situ monitoring data, linked to numerical models and quantitative biophysical information extracted from spatially distributed NDVI-data and net radiation. The aim of this paper was to provide information on surface biophysical parameters such as vegetation indices, land surface temperature and albedo over 1990 – 2012 period for Bucharest town and its periurban areas. These changes have been then, examined in association with climate changes and land cover changes feedbacks in order to illustrate how these parameters respond to rapid urban expansion in Bucharest and surrounding region and human health effects.

Despite that relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. However, livestock grazing is widely accepted as a major factor. This study investigated spectral differences of vegetation patterns along gradients of grazing intensities using plot-based hyperspectral measurements. The measurements were used to define spectral indicators for pasture degradation, which were applied to map asserted proxies for degradation from satellite images. For this purpose, hyperspectral measurements were taken at 11 sites on the north-eastern QTP using a transect design from heavy grazing and therefore asserted degradation near the settlement to less degradation with increasing distance. Potential spectral indicators for degradation were derived from the spectra by calculating the size of continuum removed absorption features and narrow-band indices (NBI). They were compared between degraded and less degraded plots. Linear regressions between proxies and each of the potential spectral indicators were calculated to assess its predictive power. The findings were transferred to larger scales by applying the indicators on two WorldView-2 (WV-2) scenes. Spectral differences between degraded and less degraded plots were obvious regarding a wide range of tested indicators. Several NBIs were considered as good indicators for vegetation cover and species numbers. WV-2 images could be successfully classified into vegetation cover whilst the estimation of species numbers was afflicted with uncertainties. The results demonstrate the potential to estimate degradation proxies using spectrometer measurements and satellite data. Applying these techniques will contribute to a better estimation of spatial degradation patterns on the QTP.

Climate change has become one of the hotspots of global attention in recent progress of globalization and industrialization. The mainstream opinion presented by Intergovernmental Panel on Climate Change (IPCC) regards that the global warming was caused mainly by greenhouse gases generated by human activities, such as anthropogenic CO₂, which also resulting in the high-frequent happening of abnormal climate events. Satellite remote sensing is an efficient and economic method for CO₂ flux observation. In this paper, we describe an Information System of Ocean Satellite Monitoring for Ari-sea CO₂ Flux (IssCO2) which developed by the Second Institute of Oceanography, China. The IssCO2can achieve the whole procedure automatically from the satellite remote data receiving to products distribution, including the data acquirement and satellite image process, products generation, etc. The IssCO2 can process various types of in situ data, satellite data and model data, and validate the final satellite-derived CO₂ flux products by in situ data; it can provide a real-time browsing and download of remote sensing products on the web based on the Geo-information System (GIS) technologies. The IssCO2 can meet the concurrent queries of different levels of users, and the query results can be visual displayed and analyzed on the client.

Due to anthropogenic and natural factors, forest land covers changes result is the land surfaces albedo changes. The main aim of this paper is to investigate the albedo patterns dynamics due to the impact of atmospheric pollution and climate variations on a periurban forest Cernica-Branesti placed to the North-Eastern part of Bucharest city, Romania based on satellite remote sensing MODIS Terra/Aqua (Moderate Imaging Spectroradiometer) data over 2003–2012 time period. This study is based on MODIS derived biogeophysical parameters land surface BRDF/albedo products and in-situ monitoring ground data (as air temperature, aerosols distribution, relative humidity, etc.). For forest land cover changes over the same investigated period have been used also Landsat TM/ETM and IKONOS imagery. Have been analyzed also other biogeophysical effects of forest land cover change in addition to surface albedo, particularly changes in the surface moisture budget leading to shifts in the ratio of latent and sensible heat fluxes and changes in rate of land surface temperature and precipitation. Due to deforestation albedo change appears to be the most significant biogeophysical effect in temperate forests. Satellite data and climate station observations show that surface albedo changes of a forested zone placed close to a large urban area highly respond to atmospheric pollution influence and climate variations. As the physical climate system is very sensitive to surface albedo, forest ecosystems could significantly feedback to the projected climate change modeling scenarios through
albedo changes.

Impervious surface is an important part of urban underlying surface, as well as an important monitoring index for city ecological system and environment changes. However, accurate impervious surface extraction is still a challenge. This paper uses the color, shape and overall heterogeneity features from the high spatial resolution remote sensing image to extract the impervious surface. An edge-based image segmentation algorithm is put forward to fuse heterogeneous objects which integrates edge features and multi-scale segmentation algorithm and uses the edge information to guide image objects generation. Results showed that this method can greatly improve the accuracy of image segmentation. Accuracy assessment indicated that the overall impervious surface classification accuracy and a Kappa coefficient yield 87% and 0.84, respectively.

Satellite images are a tool increasingly used in environmental monitoring and in recent years have become also strongly used in the field of archaeology. In this study it was conducted an experimental analysis on the identification of wetlands from satellite images in order to identify sites of interest from the archaeological point of view because probable sites of ancient settlements. The studied area is the Plan de la Limagne which is located in North-East of the French city of Clermont-Ferrand. For wet areas identification were used two ASTER satellite images and pre-existing carthography. Different indexes have been used to identify wet areas. First of all, it was used the NDVI (Normalized Difference Vegetation Index) to discriminate bare soils. Secondly, through the Tasseled Cap transform, other indexes were obtained, such as the Greeness Index, the Brightness Index (SBI – Soil Brightnes Index) and the Wetness Index. Then it has been used the ATI index (Apparent Thermal Inertia) that provides information on the thermal inertia of soils. Through these indexes, visual inspection and the study of spectral signatures, it has been tried not only to identify wetlands within the images, but also to find repeatable processes for the detection of these areas. Some “anomalous” areas, that are probably wet areas, have been identified with this procedure. The identification of wet areas has been carried out in a raw way, this is surely a first approximation analysis. Certainly the in situ analysis would provide the possibility of a better evaluation, in fact field measurements could be used to calibrate the model and then find an effective and repeatable procedure for identifying wetlands.

Information regarding the spatial extent and inundation state in the internationally important Wetlands as designated by Ramsar Convention is important to a series of research questions including wetland ecosystem functioning and services, water management and habitat suitability assessment. This study develops an expedient digital mapping technique using optical remotely sensed imagery of the Landsat Thematic Mapper (TM), ENVISAT ASAR active radar C-band imagery, and topographical indices derived from topographic maps. All data inputs were resampled to a common 30 m resolution grid. An ensemble classifiers based on trees (random forest) procedure was employed to produce a final map of per-grid cell wetland probability map. This study also provides a general approach to delineate the extent of flooding builds upon documented relationships between fields measured inundation state and SAR data response on each vegetation types. The current study indicated that multi-source data (i.e. optical, radar and topography) are useful in the characterization of freshwater marshes and their inundation state. This analysis constitutes a necessary step towards improved herbaceous wetland monitoring and provides ecologists and managers with vital information that is related to ecology and hydrology in a wetland area.

There is a strong need for accurate estimation of radiance from satellite regarding establishing a climate records such as global climate circulation, change and Earth’s atmosphere. It is important that exact radiance measurements from satellite to numerical weather prediction models for climate change detection. Furthermore, accurate measurements from satellite rely on calibration of channel data in terms of the radiometric characteristics. Related to improved calibration and inter-calibration of the sensors, the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellite (CGMS) initiated the Global Space-based Inter-Calibration System (GSICS) in 2005, which provide coefficients to the user community to adjust satellite observations. To assess influence of the GSICS corrections and impacts of input parameters changes on satellite products, the coefficients of the GSICS corrections were applied to infrared (IR) data from Communication Ocean and Meteorological Satellite (COMS), which have Meteorological Imager (MI) sensor for meteorological missions. The IR data centered at wavelengths of 10.8 (IR1) and 12.0^μm (IR2) from the COMS MI were compared with that of the Infrared Atmospheric Sounding Interferometer (IASI) sensor, which is reference sensor of the GSICS corrections. The IR1 and IR2 data that were corrected by GSICS produced Sea Surface Temperature (SST), which has been influenced by input parameters such as IR data and solar zenith angle. As a result of comparison with in situ measurements, the Global Telecommunication System (GTS) buoy data, COMS IR data that were corrected by the GSICS corrections produced high quality products of SST than original COMS IR data.

The possibility to create DEM from stereo pairs is based on the Pythagoras theorem and on the principles of photogrammetry that are applied to aerial photographs stereo pairs for the last seventy years. The application of these principles to digital satellite stereo data was inherent in the first satellite missions. During the last decades the satellite stereo-pairs were acquired across the track in different days (SPOT, ERS etc.). More recently the same-date along the track stereo-data acquisition seems to prevail (Terra ASTER, SPOT5 HRS, Cartosat, ALOS Prism) as it reduces the radiometric image variations (refractive effects, sun illumination, temporal changes) and thus increases the correlation success rate in any image matching.Two of the newest satellite sensors with stereo collection capability is Cartosat and ALOS Prism. Both of them acquire stereopairs along the track with a 2,5m spatial resolution covering areas of 30X30km. In this study we compare two different satellite stereo-pair collected along the track for DSM creation. The first one is created from a Cartosat stereopair and the second one from an ALOS PRISM triplet. The area of study is situated in Chalkidiki Peninsula, Greece. Both DEMs were created using the same ground control points collected with a Differential GPS. After a first control for random or systematic errors a statistical analysis was done. Points of certified elevation have been used to estimate the accuracy of these two DSMs. The elevation difference between the different DEMs was calculated. 2D RMSE, correlation and the percentile value were also computed and the results are presented.

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The objective of the paper is to study the classification, changes and analysis of the land use and land cover in the gum arabic belt in North Kordofan State in Sudan. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season. The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based) and accuracy assessment were applied. Application of multi-temporal remote sensing data demonstrated successfully the identification and mapping of land use and land cover into five main classes. Forest dominated by Acacia senegal class was separated covering an area of 21% in the year 2007. The obvious changes and reciprocal conversions in the land use and land cover structure indicate the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. Also the study revealed that a drastic loss of forest resources occurred in the gum arabic belt in North Kordofan during 1972 to 2007 (25% for Acacia senegal trees). The study concluded that, using of traditional Acacia senegal-based agro-forestry as one of the most successful form in the gum belt.

China, the most populous country on Earth, has experienced rapid urbanization which is one of the main causes of many environmental and ecological problems. Therefore, the monitoring of rapid urbanization regions and the environment is of critical importance for their sustainable development. In this study, the object-based classification is employed to detect the change of land cover in Shenzhen, which is located in South China and has been urbanized rapidly in recent three decades. First, four Landsat TM images, which were acquired on 1990, 2000 and 2010, respectively, are selected from the image database. Atmospheric corrections are conducted on these images with improved dark-object subtraction technique and surface meteorological observations. Geometric correction is processed with ground control points derived from topographic maps. Second, a region growing multi-resolution segmentation and a soft nearest neighbour classifier are used to finish object-based classification.

After analyzing the fraction of difference classes over time series, we conclude that the comparison of derived land cover classes with socio-economic statistics demonstrates the strong positive correlation between built-up classes and urban population as well as gross GDP and GDPs in second and tertiary industries. Two different mechanisms of urbanization, namely new land development and redevelopment, are revealed. Consequently, we found that, the districts of Shenzhen were urbanized through different mechanisms.