This PDF file contains the front matter associated with SPIE Proceedings Volume 9644, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

The present paper describes the challenging diffuser design and verification activities of TNO under contract of a customer for an earth observation instrument with observation conditions that require feasible BRDF under large angles of incidence of up to 70° with respect to the surface normal. Not only the design though but also the verification of the diffuser performance under such angles including "out-of-plane", i.e. angle theta detection of the scattered light, was an essential activity to be executed. In this paper we will summarize the R&D activities with respect to diffuser design and verification that were recently carried out at TNO and present its applicability to current and future earth observation missions with challenging observation conditions and thus challenging diffuser requirements under high illumination angles.

Floods are suddenly and temporary natural events, affecting areas which are not normally covered by water. The influence of floods plays a significant role both in society and the natural environment, therefore flood mapping is crucial. Remote sensing data can be used to develop flood map in an efficient and effective way. This work is focused on expansion of water bodies overtopping natural levees of the river Evros, invading the surroundings areas and converting them in flooded. Different techniques of flood mapping were used using data from active and passive remote sensing sensors like Sentinlel-1 and Landsat-8 respectively. Space borne pairs obtained from Sentinel-1 were processed in this study. Each pair included an image during the flood, which is called “crisis image” and another one before the event, which is called “archived image”. Both images covering the same area were processed producing a map, which shows the spread of the flood. Multispectral data From Landsat-8 were also processed in order to detect and map the flooded areas. Different image processing techniques were applied and the results were compared to the respective results of the radar data processing.

The aim of this study is to assess the performance of single-pass X-band bistatic SAR interferometric forest height estimation of boreal and temperate deciduous forests under variable seasonal conditions. For this, twelve acquisitions of single- and dual-polarized TanDEM-X coherence images over 118 forest stands were analyzed and compared against LiDAR forest height maps. Strong correlations were found between interferometric coherence magnitude and LiDAR derived forest stand height for pine forests (r²=0.94) and spruce forest (r²=0.87) as well as for deciduous trees (r²=0.94) during leaf-off conditions with temperatures below 0°C. It was found that coherence magnitude based forest height estimation is influenced by leaf-on and leaf-off conditions as well as daily temperature fluctuations, height of ambiguity and effective baseline. These factors alter the correlation and should be taken into account for accurate coherence-based height retrieval. Despite the influence of the mentioned factors, generally a strong relationship in regression analysis between X-band SAR coherence and LiDAR derived forest stand height can be found. Moreover, a simple semi empirical model, derived from Random Volume over Ground model, is presented. The model takes into account all imaging geometry dependent parameters and allows to derive tree height estimate without a priori knowledge. Our results show that X-band SAR interferometry can be used to estimate forest canopy height for boreal and deciduous forests in both summer and winter, but the conditions should be stable.

Terrain surfaces conserve human activities in terms of textures and structures. With reference to archaeological questions, the geological archive is investigated by means of models regarding anthropogenic traces. In doing so, the high-resolution digital terrain model is of inestimable value for the decoding of the archive. The evaluation of these terrain models and the reconstruction of historical surfaces is still a challenging issue. Due to the data collection by means of LiDAR systems (light detection and ranging) and despite their subsequent pre-processing and filtering, recently anthropogenic artefacts are still present in the digital terrain model.

Analysis have shown that elements, such as contour lines and channels, can well be extracted from a high-resolution digital terrain model. This way, channels in settlement areas show a clear anthropogenic character. This fact can also be observed for contour lines. Some contour lines representing a possibly natural ground surface and avoid anthropogenic artefacts. Comparable to channels, noticeable patterns of contour lines become visible in areas with anthropogenic artefacts. The presented workflow uses functionalities of ArcGIS and the programming language R.¹ The method starts with the extraction of contour lines from the digital terrain model. Through macroscopic analyses based on geomorphological expert knowledge, contour lines are selected representing the natural geomorphological character of the surface. In a first step, points are determined along each contour line in regular intervals. This points and the corresponding height information which is taken from an original digital terrain model is saved as a point cloud. Using the programme library gstat, a variographic analysis and the use of a Kriging-procedure based on this follow.^2-4

The result is a digital terrain model filtered considering geomorphological expert knowledge showing no human degradation in terms of artefacts, preserving the landscape-genetic character and can be called a prehistoric terrain model.

Fluorescence LIDAR imaging has been already proposed in several studies as a valuable technique for the remote diagnostics and documentation of the monumental surfaces, with main applications referring to the detection and classification of biodeteriogens, the characterization of lithotypes, the detection and characterization protective coatings and also of some types of pigments. However, the conservation and documentation of the cultural heritage is an application field where a highly multi-disciplinary, integrated approach is typically required. In this respect, the fluorescence LIDAR technique can be particularly useful to provide an overall assessment of the whole investigated surface, which can be profitably used to identify those specific areas in which further analytical measurements or sampling for laboratory analysis are needed.

This paper presents some representative examples of the research carried out in the frame of the PRIMARTE project, with particular reference to the LIDAR data and their significance in conjunction with the other applied techniques. One of the major objectives of the project, actually, was the development of an integrated methodology for the combined use of data by using diverse techniques: from fluorescence LIDAR remote sensing to UV fluorescence and IR imaging, from IR thermography, georadar, 3D electric tomography to microwave reflectometry, from analytical techniques (FORS, FT-IR, GC-MS) to high resolution photo-documentation and historical archive studies. This method was applied to a 'pilot site', a chapel dating back to the fourteenth century, situated at 'Le Campora' site in the vicinity of Florence. All data have been integrated in a multi-medial tool for archiving, management, exploitation and dissemination purposes.

An active landslide can be monitored using many different methods: Classical geotechnical measurements like inclinometer, topographical survey measurements with total stations or GPS and photogrammetric techniques using airphotos or high resolution satellite images. As the cost of the aerial photo campaign and the acquisition of very high resolution satellite data is quite expensive the use of cameras on board UAV could be an identical solution. Small UAVs (Unmanned Aerial Vehicles) have started their development as expensive toys but they currently became a very valuable tool in remote sensing monitoring of small areas. The purpose of this work is to demonstrate a cheap but effective solution for an active landslide monitoring. We present the first experimental results of the synergistic use of UAV, GPS measurements and remote sensing data. A six-rotor aircraft with a total weight of 6 kg carrying two small cameras has been used. Very accurate digital airphotos, high accuracy DSM, DGPS measurements and the data captured from the UAV are combined and the results are presented in the current study.

Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.

In this paper, we compare the conventional methods in hydrocarbon seepage anomalies with the signature based detection algorithms. The Crosta technique [1] is selected as a basement in the experimental comparisons for the conventional approach. The Crosta technique utilizes the characteristic bands of the searched target for principal component transformation in order to determine the components characterizing the target in interest. Desired Target Detection and Classification Algorithm (DTDCA), Spectral Matched Filter (SMF), and Normalized Correlation (NC) are employed for signature based target detection. Signature based target detection algorithms are applied to the whole spectrum benefiting from the information stored in all spectral bands. The selected methods are applied to a multispectral Advanced SpaceBorne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction prior to the realization of the algorithms. ASTER provides multispectral bands covering visible, short wave, and thermal infrared region, which serves as a useful tool for the interpretation of the areas with hydrocarbon anomalies. The exploration area is selected as Gemrik Anticline which is located in South East Anatolia, Adıyaman, Bozova Oil Field, where microseeps can be observed with almost no vegetation cover. The spectral signatures collected with Analytical Spectral Devices Inc. (ASD) spectrometer from the reference valley [2] have been utilized as an input to the signature based detection algorithms. The experiments have indicated that DTDCA and MF outperforms the Crosta technique by locating the microseepage patterns along the mitigation pathways with a better contrast. On the other hand, NC has not been able to map the searched target with a visible distinction. It is concluded that the signature based algorithms can be more effective than the conventional methods for the detection of microseepage induced anomalies.

The objective of this work is to indicate a monitoring methodology in order to survey the present state of the quarry sites and their evolution in time, which are the basic data needed to implement an adequate land reclamation project. The land monitoring has been realised by UAV photogrammetry and GPS measurements supported by a Geographic Information System. A six-rotor aircraft with a total weight of 6 kg carrying two small cameras has been used. Very accurate digital airphotos have been used in order to create orthophotos mosaic and DSM from the quarry planes. DGPS measurements and the data captured from the UAV are combined in GIS and the results are presented in the current study.

Debris flows triggered by the rainstorm in Hiroshima, Japan on August 20th, 2014 produced extensive damage to the built-up areas in the northern part of Hiroshima city. In order to consider various emergency response activities and early-stage recovery planning, it is important to evaluate the distribution of the soil volumes in the debris flow areas immediately after the disaster. In this study, automated nonlinear mapping technique is applied to light detection and ranging (LiDAR)-derived digital elevation models (DEMs) observed before and after the disaster to quickly and accurately correct geometric locational errors of the data. The soil volumes generated from the debris flows are estimated by subtracting the pre- and post-event DEMs. The geomorphologic characteristics in the debris flow areas are discussed from the distribution of the estimated soil volumes.

Lithological mapping is one of the fundamental steps in the various mineral prospecting studies, because it forms the basis of the interpretation and validation of retrieved results. Therefore, this study exploited the multispectral ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and Landsat 8 data in order to map lithological units in the Bas Drâa inlier, at the Moroccan Anti atlas. This task was completed by using the principal component analysis (PCA), band ratios (BR) and support vector machine (SVM) classification. Overall accuracy and the kappa coefficient of SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological maps and previous works on the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 data in lithological units discrimination.

It seems to be obvious that precipitation has a major impact on greening during the rainy season in semi-arid regions. First results1 imply a strong dependence of NDVI on rainfall. Therefore it will be necessary to consider specific rainfall events besides the known ordinary annual cycle. Based on this fundamental idea, the paper will introduce the development of a rain adjusted vegetation index (RAVI). The index is based on the enhancement of the well-known normalized difference vegetation index (NDVI²) by means of TAMSAT rainfall data and includes a 3-step procedure of determining RAVI. Within the first step both time series were analysed over a period of 29 years to find best cross correlation values between TAMSAT rainfall and NDVI signal itself. The results indicate the strongest correlation for a weighted mean rainfall for a period of three months before the corresponding NDVI value. Based on these results different mathematical models (linear, logarithmic, square root, etc.) are tested to find a functional relation between the NDVI value and the 3-months rainfall period before (0.8). Finally, the resulting NDVI-Rain-Model can be used to determine a spatially individual correction factor to transform every NDVI value into an appropriate rain adjusted vegetation index (RAVI).

This paper focuses on the use of Unmanned Aerial Vehicles (UAVs) over the study area of Pissouri in Cyprus to document the sloping landscapes of the area. The study area has been affected by overgrazing, which has led to shifts in the vegetation patterns and changing microtopography of the soil. The UAV images were used to generate digital elevation models (DEMs) to examine the changes in microtopography. Next to that orthophotos were used to detect changes in vegetation patterns. The combined data of the digital elevation models and the orthophotos will be used to detect the occurrence of catastrophic shifts and mechanisms for desertification in the study area due to overgrazing. This study is part of the "CASCADE- Catastrophic shifts in dryland" project.

Increased anthropogenic activities over the year have remained a major factor of the Earth changing environment. This phenomenon has given rise to a number of environmental degraded sites that characterize the Nigeria's landscape. The human-induced elements include gully erosion, mangrove ecosystems degradation, desertification and deforestation, particularly in the south east, Niger Delta, north east and south west of Nigeria respectively, as well as river flooding/flood plain inundation and land degradation around Kainji lake area. Because of little or no effective management measures, the attendant environmental hazards have been extremely damaging to the infrastructures and socio-economic development of the affected area. Hence, a concerted effort, through integrated and space-based research, is being intensified to manage and monitor the environment in order to restore the stability, goods and services of the environment. This has justified Nigeria's investment in its space programme, especially the launch of NigeriaSat-1, an Earth observation micro-satellite in constellation with five (5) other similar satellites, Alsat-1, China DMC, Bilsat-1, DEMOS and UK DMC belonging to Algeria, China, Turkey, Spain and United Kingdom respectively. The use of data from these satellites, particularly NigeriaSat-1, in conjunction with associated technologies has proved to be very useful in understanding the influence of both natural and human activities on the Nigeria's ecosystems and environment. The results of some researches on specific applications of Nigerian satellites are presented in this paper. Appropriate sustainable land and water resources management in the affected areas, based on Nigeria‟s satellite data capture and integration, are also discussed.

Unmanned aerial vehicles (UAVs) for use in remote sensing offer simple and affordable observation from the air. Due to the decreasing size of the sensors, receivers and antennas, it is now possible to create integrated low-altitude airborne systems. Remote sensing technologies on a UAV platform are an efficient, non-invasive and low cost resource for the detection, documentation and monitoring of various environmental (both natural and cultural) features, in high resolution and quality. Different remote sensing techniques can be used alongside to the UAVs, such as field spectroscopy, multispectral cameras, infrared cameras and thermal cameras. This integration in terms of techniques and technologies, contributes further to more solid outcomes depending on the case study. The possibility of adding a variety of sensors to the basic UAV unit amplifies both the application cases and the results. Among the types of camera sensors that can be used with the UAVs are the visible spectrum cameras, the multi-spectral cameras and the hyperspectral cameras. These cameras can extract more detailed information since an entire spectrum is acquired from each pixel of the image. In this paper, various UAVs used for environmental and archaeological applications in different areas of Cyprus are presented.

Herdade da Contenda (HC), located in Moura municipality, Beja district (Alentejo province) in the south of Portugal (southwestern Iberia Peninsula), is a national hunting area with 5270ha. The development of an integrated system that aims to make the management of the natural and cultural heritage resources will be very useful for an effective management of this area. This integrated system should include the physical characterization of the territory, natural conservation, land use and land management themes, as well the cultural heritage resources. This paper presents a new tool for an integrated environmental management system of the HC, which aims to produce maps under a GIS open source environment (QGIS). The application is composed by a single button which opens a window. The window is composed by twelve menus (File, DRASTIC, Forest Fire Risk, Revised Universal Soil Loss Equation (RUSLE), Bioclimatic Index, Cultural Heritage, Fauna and Flora, Ortofoto, Normalizes Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Land Use Land Cover Cover (LULC) and Help. Several inputs are requires to generate these maps, e.g. DEM, geologic information, soil map, hydraulic conductivity information, LULC map, vulnerability and economic information, NDVI. Six buttons were added to the toolbar which allows to manipulate the information in the map canvas: Zoom in, Zoom out, Pan, Print/Layout and Clear. This integrated and open source GIS environment management system was developed for the HC area, but could be easily adapted to other natural or protected area. Despite the lack of data, the methodology presented fulfills the objectives.

Recently, the demand of forest resources monitoring technology on a large scale is growing, and spaceborne LiDAR is expected to provide a means for accurate monitoring. This study aims to clarify the potential of ICESat/GLAS spaceborne LiDAR for forest resources monitoring on a regional scale. The study areas were Hokkaido Island in Japan (cool-temperate forest), Borneo Island (tropical forest), and Siberia (boreal forest). Firstly, we conducted field measurements in Hokkaido and Borneo, and calculated the average canopy height (Lorey’s height) and the above-ground biomass (AGB) for each GLAS-footprint. Then, we developed some models to estimate canopy height and AGB from the GLAS waveform parameters based on the field measurement data. Next, we applied the developed models to the GLAS data in Hokkaido and Borneo. The average canopy height and AGB were 17.8 m and 119.4 Mg ha^-1, respectively, in Hokkaido, and 16.2 m and 190.2 Mg ha^-1, respectively, in Borneo. These results suggest that the tropical forest in Borneo has a higher biomass than the cool-temperate forest in Hokkaido. Furthermore, we applied the estimation model to the GLAS data acquired in Siberia. The average AGB was 86.2 Mg ha^-1, and it has decreased especially in Southern area of Western Siberia. This study showed that spaceborne LiDAR had an ability of forest resources monitoring on a regional scale, for each of boreal, cool-temperate, and tropical forests.

In the current study a Worldview-2 image was used for fusion quality assessment. The bundle image was collected on July 2014 over Araxos area in Western Peloponnese. Worldview-2 is the first satellite that collects at the same time a panchromatic (Pan) image and 8 band multispectral (MS) image. The Pan data have a spatial resolution of 0.46m while the MS data have a spatial resolution of 1.84m. In contrary to the respective Pan band of Ikonos and Quickbird that range between 0.45 and 0.90 micrometers the Worldview Pan band is narrower and ranges between 0.45 and 0.8 micrometers. The MS bands include four conventional visible and near-infrared bands common to multispectral satellites like Ikonos Quickbird, Geoeye Landsat-7 etc., and four new bands. Thus, it is quite interesting to investigate the assessment of commonly used fusion algorithms with Worldview-2 data. Twelve fusion techniques and more especially the Ehlers, Gram-Schmidt, Color Normalized, High Pass Filter, Hyperspherical Color Space, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, Pansharp2, PCA and Wavelet were used for the fusion of Worldview-2 panchromatic and multispectral data. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q and entropy difference were examined and the results are presented. The quality control was evaluated both in spectral and spatial domain.

The aim of this work is to detect and qualify natural karst depressions in the Aitoloakarnania Prefecture, Western Greece, using remote sensing data in conjunction with the Geographical Information Systems - GIS. The study area is a part of the Ionian geotectonic zone, and its geological background consists of the Triassic Evaporates. The Triassic carbonate breccias where formed as a result of the tectonic and orogenetic setting of the external Hellenides and the diaper phenomena of the Triassic Evaporates. The landscape characterized by exokarst features closed depressions in the Triassic carbonate breccias. At the threshold of this study, an in situ observation was performed in order to identify dolines and swallow holes. The creation of sinkholes, in general, is based on the collapse of the surface layer due to chemical dissolution of carbonate rocks. In the current study airphotos stereopairs, DSMs and GIS were combined in order to detect and map the karst features. Thirty seven airphotos were imported in Leica Photogrammetry Suite and a stereo model of the study area was created. Then in 3D view possible karst features were detected and digitized. Those sites were verified during the in situ survey. ASTER GDEM, SRTM DEM, high resolution airphoto DSM created from the Greek Cadastral and a DEM from digitized contours from the 1/50,000 topographic were also evaluated in GIS environment for the automatic detection of the karst depressions. The results are presented in this study.

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

In remote sensing, the change detection topic represents a broad field of research. If time series data is available, change detection can be used for monitoring applications. These applications require regular image acquisitions at identical time of day along a defined period. Focusing on remote sensing sensors, radar is especially well-capable for applications requiring regularity, since it is independent from most weather and atmospheric influences. Furthermore, regarding the image acquisitions, the time of day plays no role due to the independence from daylight.

Since 2007, the German SAR (Synthetic Aperture Radar) satellite TerraSAR-X (TSX) permits the acquisition of high resolution radar images capable for the analysis of dense built-up areas. In a former study, we presented the change analysis of the Stuttgart (Germany) airport. The aim of this study is the categorization of detected changes in the time series. This categorization is motivated by the fact that it is a poor statement only to describe where and when a specific area has changed. At least as important is the statement about what has caused the change. The focus is set on the analysis of so-called high activity areas (HAA) representing areas changing at least four times along the investigated period. As first step for categorizing these HAAs, the matching HAA changes (blobs) have to be identified. Afterwards, operating in this object-based blob level, several features are extracted which comprise shape-based, radiometric, statistic, morphological values and one context feature basing on a segmentation of the HAAs. This segmentation builds on the morphological differential attribute profiles (DAPs).

Seven context classes are established: Urban, infrastructure, rural stable, rural unstable, natural, water and unclassified. A specific HA blob is assigned to one of these classes analyzing the CovAmCoh time series signature of the surrounding segments. In combination, also surrounding GIS information is included to verify the CovAmCoh based context assignment. In this paper, the focus is set on the features extracted for a later change categorization procedure.

Road databases are an important part of geo data infrastructure. The knowledge about their characteristics and course is essential for urban planning, navigation or evacuation tasks. Starting from OpenStreetMap (OSM) shape-file data for street networks, we introduce an algorithm to enrich these available road maps by new maps which are based on other airborne sensor technology. In our case, these are results of our context-based urban terrain reconstruction process. We wish to enhance the use of road databases by computing additional junctions, narrow passages and other items which may emerge due to changes in the terrain. This is relevant for various military and civil applications.

Green space must be increased in the development of new cities as green space can moderate temperature in the cities. In this study we estimated the land surface temperature (LST) and established relationships between LST and land cover and various vegetation and urban surface indices in the Iskandar Malaysia (IM) region. IM is one of the emerging economic gateways of Malaysia, and is envisaged to transform into a metropolis by 2025. This change may cause increased temperature in IM and therefore we conducted a study by using Landsat 5 image covering the study region (2,217 km²) to estimate LST, classify different land covers and calculate spectral indices. Results show that urban surface had highest LST (24.49 °C) and the lowest temperature was recorded in, forest, rubber and water bodies ( 20.69 to 21.02°C). Oil palm plantations showed intermediate mean LST values with 21.65 °C. We further investigated the relationship between vegetation and build up densities with temperature. We extracted 1000 collocated pure pixels of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI), Urban Index (UI) and LST in the study area. Results show a strong and significant negative correlation with (R²= -0.74 and -0.79) respectively between NDVI, NDWI and LST . Meanwhile a strong positive correlation (R²=0.8 and 0.86) exists between NDBI, UI and LST. These results show the importance of increasing green cover in urban environment to combat any adverse effects of climate change.

Validating the accuracy of land cover products using a reliable reference dataset is an important task. Recently, the amount of ground truth data provided by volunteers has increased. Although ground truth data can provide information that can produce reliable reference data, the information is only correct within the observable landscape. It is necessary to scale up reference data derived from ground truth data to match the spatial resolution of the global land cover product. We propose a scale-up method that confirms expanding land cover characteristics for a target position using the occurrence ratio of pixels that meet the criteria for the target position to the total pixels for the target scale. The results of applying the scale-up method to test sites showed that the occurrence ratio method was a better judge of expanding target land cover types than the average method.

Water resource monitoring and management has been an important concern in the Philippines, considering that the country is archipelagic in nature and is exposed to a lot of disasters imposed by the global effects of climate change. The design and implementation of an effective management scheme relies heavily on accurate, complete, and updated water resource inventories, usually in the form of digital maps and geodatabases. With the aim of developing a detailed and comprehensive database of all water resources in the Philippines, the 3-year project “Development of the Philippine Hydrologic Dataset (PHD) for Watersheds from LiDAR Surveys” under the Phil-LiDAR 2 Program (National Resource Inventory), has been initiated by the University of the Philippines Diliman (UPD) and the Department of Science and Technology (DOST). Various workflows has already been developed to extract inland hydrologic features in the Philippines using accurate Light Detection and Ranging (LiDAR) Digital Terrain Models (DTMs) and LiDAR point cloud data obtained through other government-funded programs such as Disaster Risk and Exposure Assessment for Mitigation (DREAM) and Phil-LiDAR 1, supplemented with other remotely-sensed imageries and ancillary information from Local Government Units (LGUs) and National Government Agencies (NGAs). The methodologies implemented are mainly combinations of object-based image analysis, pixel-based image analysis, modeling, and field surveys. This paper presents the PHD project, the methodologies developed, and some sample outputs produced.

The objective of the present paper is to study and compare the headward erosion and deep erosion rates in the river systems of Asopos, Nemeas and Raizanis, which are in the wider area of the Nemea archaeological site, in the Peloponnese, Greece. The study area is a section of the NE part of the Peloponnese, specifically the zone between the villages of Velo and Vrachati. In the study area terraces appear, which have been mapped in detail and which correspond to isotopic stages of interglacial periods of the Pleistocene. The basic processes that act in our study area and have shaped the current relief is headward erosion and deep erosion, which are favoured by the dominant lithology and fault tectonic. The northern parts of the three river networks intersect sea at terraced levels, where the local terrace front backward retreat indicates the rate of headward erosion and the depth of the valleys at the terrace front indicates the rate of deep erosion. Thus we tried to calculate the rate of these two factors for each river system in order to understand the course of the phenomenon in time. In order to achieve our goal, we used geological and topographical maps of the area at a 1/50.000 scale. Mapinfo GIS software was used in order to make the necessary measurements. The results from the analysis of the topographic maps were compared with the results from freely available DSMs such as ASTER GDEM and SRTM DEM.

In the Wadi Biskra arid and semi-arid area, sustainable development is limited by land degradation, such as secondary salinization of soils. As an important high quality date production region of Algeria, it needs continuous monitoring of desertification indicators, since the bio-physical setting defines it as highly exposed to climate-related risks. For this particular study, for which little ground truth data was possible to acquire, we set up an assessment of appropriate methods for the identification and change detection of salt-affected areas, involving image interpretation and processing techniques employing Landsat imagery. After a first phase consisting of a visual interpretation study of the land cover types, two automated classification approaches were proposed and applied for this specific study: decision tree classification and principal components analysis (PCA) of Knepper ratios. Five of the indices employed in the Decision Tree construction were set up within the current study, among which we propose a salinity index (SMI) for the extraction of highly saline areas. The results of the 1984 to 2014 diachronic analysis of salt – affected areas variation were supported by the interpreted land cover map for accuracy estimation. Connecting the outputs with auxiliary bio-physical and socio-economic data, comprehensive results are discussed, which were indispensable for the understanding of land degradation dynamics and vulnerability to desertification. One aspect that emerged was the fact that the expansion of agricultural land in the last three decades may have led and continue to contribute to a secondary salinization of soils. This study is part of the WADIS-MAR Demonstration Project, funded by the European Commission through the Sustainable Water Integrated Management (SWIM) Program (www.wadismar.eu).

The Sundarbans, UNESCO affiliated world heritage site and world largest mangrove forest covering with (~10000) km² along the coastal areas of Bangladesh and India. Bangladesh is one of the most vulnerable countries due to sea level rise caused by global warming. In the present situation world mangrove forest are declining at alarming rate. The extensions of this mangrove forest continuously changing with the nature of Bay of Bengal coast. This paper presents the contrast result of erosional hazard and accretion of Sundarbans with its adjacent east coast of Bangladesh. This study based on images of Landsat MSS, Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), and Operational Land Imager (OLI) from Landsat 8. Landsat images from 1980 to 2014 were analyzed. The result shows the net erosion rate of Sundarbans is higher than its adjacent east coast. The study area has been divided into two phases - Phase I and Phase II. Phase I represents only Sundarbans and Phase II represents adjacent east coast. These two phases has equal longitudinal distance. Many small channels and river supply water and sediment into the Sundarbans. The adjacent east coast of Sundarbans lies on the Meghana estuary that experienced with huge amount of sediment flow. The Sundarbans has been facing erosional stage with a rate of net erosion about 3 km² y^-1 whereas the adjacent east coast facing an accretional stage with rate of accretion about 1 km²y^-1. The highest erosion rate of Phase I (Sundarbans) was 10.61 km²y^-1 and highest erosion of Phase II adjacent east coast was 18-km² y^-1 in the period of 1989-1999. The highest accretion rate in the Sundarbans was 6.48 km² y^-1 in the period of 1999-2006 and in the Phase II, accretion rate was 16.43 km² y^-1 in the period of 1980-1989. The lowest erosion rate was 3 km² y^-1 in Phase I and 2.11 km² y^-1 in Phase II in the period of 1999- 2006. The reworking area in phase I is about 37 km² and phase II is about 51 Km².So, water is highly active in adjacent east coast than Sundarbans. Though the mean sea level is increasing at a same rate in two different phases but the accretion and erosion rates are not same. From this study, it is established that Sundarbans has lost 113.31 km² of its land in the 34 years of study period whereas; adjacent east coast has gain about 35 km² lands. The rate of water discharges and sediment supply are not same in two different phases. It plays a vital role in erosion and accretion process. Therefore, Sundarbans are declining at frightening rate than its adjacent east coast.

Jharia coal-field holds unequivocal importance in the Indian context as it is the only source of prime coking coal in the country. The coalfield is also known for its infamous coal mine fires which have been burning since last more than a century. Haphazard mining over a century has led to eco-environmental changes to a large extent such as changes in vegetation distribution and widespread development of surface and subsurface fires. This article includes the spatiotemporal study of remote sensing derived eco-environmental parameters like vegetation index (NDVI), tasseled cap transformation (TCT) and temperature distribution in fire areas. In order to have an estimate of the temporal variations of NDVI over the years, a study has been carried out on two subsets of the Jharia coalfield using Landsat images of 1972 (MSS), 1992 (TM), 1999 (ETM+) and 2013 (OLI). To assess the changes in brightness and greenness over the year s, difference images have been calculated using the 1992 (TM) and 2013 (OLI) images. Radiance images derived from thermal bands have been used to calculate at-sensor brightness temperature over a 23 year period from 1991 to 2013. It has been observed that during the years 1972 to 2013, moderate to dense vegetation has decreased drastically due to the intense mining going on in the area. TCT images show the areas that have undergone changes in both brightness and greenness from 1992 to 2013. Surface temperature data obtained shows a constant increase from 1991 to 2013 apparently due to coal fires. The utility of remote sensing data in such EIA studies has been emphasized.

Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are often large in size, it’s necessary to develop an efficient algorithm for landslide and mudflow detection. Based on the theory of sparse representation and, we propose a novel automatic landslide and mudflow detection method in this paper, which combines multi-channel sparse representation and eight neighbor judgment methods. The whole process of the detection is totally automatic. We make the experiment on a high resolution image of ZhouQu district of Gansu province in China on August, 2010 and get a promising result which proved the effective of using sparse representation on landslide and mudflow detection.

The geologic context of the north of Tunisia is favorable to varied mining activities. Wastes quantities which were left over after smelting have a wide spread distribution. These immense quantities threaten soils, vegetation and above all human health. Reflectance spectroscopy provides information about targets between visible (VIS) and shortwave infrared (SWIR) (300–2500 nm) wavelengths. As mine wastes constitute a mixture of minerals, the analysis of the spectral behavior of mine wastes using reflectance spectroscopy is an alternative approach to traditional methods. Signatures resulting from measures reflectance are quantified according to the ratio between reflected energy and incident energy on a defined target. Mine wastes analysis by means of reflectance spectroscopy with respect to laboratory analysis have provided favorable results in detecting and mapping transformation processes in environmental systems, particularly in arid and semiarid climates. In this study we analyze the spectral behavior of mine wastes in the VIS and SWIR regions using hyperspectral reflectance spectroscopy supported by ground-truth based X ray diffraction XRD. Wastes samples collected in the field were measured in situ in order to determine their spectral reflectance properties. The wastes spectral signatures were normalized and compared to laboratory results. This study was performed in the environs of Jebel Ressas mine in the north of Tunisia which is considered as pilot site.

Monitoring and mapping complex urban features (e.g. roads and buildings) from remotely sensed data multispectral and hyperspectral has gained enormous research interest. Accurate ground truth allows for high quality assessment of classified images and to verify the produced map. Ground truth can be acquired from: field using the handheld Global Positioning System (GPS) device and from Images with high resolution extracted from Google Earth in additional to field. Ground truth or training samples could be achieved from VHR satellite images such as QuickBird, Ikonos, Geoeye-1 and Wordview images. Archived images are costly for researchers in developing countries. Images from GE with high spatial resolution are free for public and can be used directly producing large scale maps, in producing LULC mapping and training samples. Google Earth (GE) provides free access to high resolution satellite imagery, but is the quality good enough to map urban areas. Costal of the Red sea, Marsa Alam could be mapped using GE images. The main objective of this research is exploring the accuracy assessment of producing large scale maps from free Google Earth imagery and to collect ground truth or training samples in limited geographical extend. This research will be performed on Marsa Alam city or located on the western shore of the Red Sea, Red sea Governorate, Egypt. Marsa Alam is located 274 km south of Hurghada. The proposed methodology involves image collection taken into consideration the resolution of collected photographs which depend on the height of view. After that, image rectification using suitable rectification methods with different number and distributions of GCPs and CPs. Database and Geographic information systems (GIS) layers were created by on-screen vectorization based on the requirement of large scale maps. Attribute data have been collected from the field. The obtained results show that the planmetric accuracy of the produced map from Google Earth Images met map scale 10 000 according to (National Map Accuracy Standards).The collect ground truth or training samples from GE images and field help in accuracy assessment of classification process.

Jintang Island, located in Zhoushan archipelago New Area, China, covers an area of ~76.4 km². As the closest island to connecting the continental shore of Zhejiang and the eastern Zhoushan Island after the project of Zhoushan Tran-Oceanic Bridges, it has experienced great change during the past 40 years, and however little attention was paid to evaluate the coastline exploitation of Jintang Island. In this paper the coastline position was firstly extracted by combining histogram thresholding and band ratio techniques with visual interpretation supplemented, based on multitemporal medium-high resolution imagery from 1970 to 2011. And then the coastline exploitation degree for different periods was quantified from three index, coastline complexity, coastline artificial intensity and coastal use. The uncertainty for coastline extraction was controlled in one-pixel for each image. The results show that the position of coastline accreted seaward since 1970. As the natural coastline was straightened, the proportion of artificial coastline has surpassed the natural coastline after 1992. Until 2011, the dominant coastline type has been changed as artificial coastline, mainly covering urban/industrial and port coast. The calculated coastline exploitation degree of Jintang Island in 1970, 1992, 2003 and 2011 is 0.637, 0.719, 0.734 and 0.929 respectively, showing an uptrend. Part 1, 3, 4, 5, 7 and 8 of the total coastline are relatively higher than other parts, and this generally coincides with the distribution of deep ports. It indicates that natural conditions and human policies may contribute to increasing coastline exploitation from 1970 to 2011. And Jintang Island is facing great human intervention for reclaiming more coastal land to develop the marine economy.

Understanding spatio-temporal changes of urban environments is essential for regional and local planning and environmental management. With the rapid changes of Bucharest city in Romania during past decades, green spaces have been fragmented and dispersed causing impairment and dysfunction of these urban elements. The main goal of this study is to address these tasks in synergy with in-situ data and new analytical methods. Spatio- temporal monitoring of urban vegetation land cover changes is important for policy decisions, regulatory actions and subsequent land use activities. This study explored the use of time-series MODIS Terra/Aqua Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) and evapotranspiration (ET) data to provide vegetation change detection information for metropolitan area of Bucharest. Training and validation are based on a reference dataset collected from IKONOS high resolution remote sensing data. The mean detection accuracy for period 2002- 2014 was assessed to be of 87%, with a reasonable balance between change commission errors (20.24%), change omission errors (25.65%), and Kappa coefficient of 0.72. Annual change detection rates across the urban/periurban areas over the study period (2002–2014) were estimated at 0.79% per annum in the range of 0.46% (2002) to 0.77% (2014).Vegetation dynamics in urban areas at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system. Extracted green space areas were further analyzed quantitatively in relation with air quality data and extreme climate events. The results have been analyzed in terms of environmental impacts and future climate trends.

With the development of space-based technologies to measure surface geophysical parameters and deformation at the boundaries of tectonic plates and large faults, earthquake science has entered a new era. Using time series satellite data for earthquake prediction, it is possible to pursue the behaviors of earthquake precursors in the future and to announce early warnings when the differences between the predicted value and the observed value exceed the pre-define threshold value. Starting with almost one week prior to a moderate or strong earthquake a transient thermal infrared rise in LST of several Celsius degrees (oC) and the increased OLR values higher than the normal have been recorded around epicentral areas, function of the magnitude and focal depth, which disappeared after the main shock. Also are recorded associated geomagnetic and ionospheric distrurbances. Vrancea tectonic active zone in Romania is characterized by a high seismic hazard in European- Mediterranean region, being responsible of strong or moderate intermediate depth and normal earthquakes generation on a confined epicentral area. Based on recorded geophysical parameters anomalies was developed an integrated geospatial system for earthquake precursors assessment in Vrancea active seismic zone. This system integrates derived from time series MODIS Terra/Aqua, NOAA-AVHRR, ASTER, Landsat TM/ETM satellite data multi geophysical parameters (land surface temperature -LST, outgoing long-wave radiation- OLR, and mean air temperature- AT as well as geomagnetic and ionospheric data in synergy with in-situ data for surveillance and forecasting of seismic events.

Urbanization may be considered the most significant anthropogenic force that has brought about fundamental changes in urban land cover and landscape pattern around the globe, being one of the crucial issues of global change in the 21st century affecting urban ecosystem. In the physical climate system, land surface albedo determines the radiation balance of the surface and affects the surface temperature and boundary-layer structure of the atmosphere. Due to anthropogenic and natural factors, urban 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 land cover of Bucharest metropolitan area, Romania based on satellite remote sensing MODIS Terra/Aqua (Moderate Imaging Spectroradiometer) data over 2000–2014 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 urban land cover changes over the same investigated period have been used also IKONOS satellite data. Due to deforestation in the periurban areas albedo changes appear to be the most significant biogeophysical effect in temperate forests. As the physical climate system is very sensitive to surface albedo, urban/periurban vegetation systems could significantly feedback to the projected climate change modeling scenarios through albedo changes.

This paper presents a radiometric cross calibration of KOMPSAT-3 AEISS based on Landsat-8 OLI. Cross calibration between the two sensors using simultaneous image pairs, acquired during an underfly event over the Libya 4 pseudo invariant calibration site (PICS) site. The spectral profile of the target comes from the near-simultaneous EO-1 Hyperion data over these sites for apply Spectral Band Adjustment Factor (SBAF). The results indicate that the Top Of Atmosphere (TOA) reflectance measurements for KOMPSAT-3 agree with Landsat-8 to within 5% after the application of SBAF. To validate radiometric coefficient, comparison TOA reflectance executed in north Virginia, USA. The difference in TOA reflectance was calculated to within a maximum ±1.55%. There was a huge improvement when the standard deviation altered from 0.1 to 0.01, when applying the SBAF. The result of radiometric coefficient presented here appear to be a good standard for maintaining the optical quality of the KOMPSAT-3, for which prelaunch, onboard, and vicarious calibration data are lacking.

The Natural Resources Conservation Service Curve Number (NRCS-CN) method is widely used for predicting direct runoff from rainfall. It employs the hydrologic soil groups and landuse information along with period soil moisture conditions to derive NRCS-CN. This method has been well documented and available in popular rainfall-runoff models such as HEC-HMS, SWAT, SWMM and many more. The Sharply-Williams and Hank methods was used to adjust CN values provided in standard table of TR-55. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is used to derive slope map with spatial resolution of 30 m for Kuantan River Basin (KRB). The two investigated method stretches the conventional CN domain to the lower values. The study shows a successful application of remote sensing data and GIS tools in hydrological studies. The result of this work can be used for rainfall-runoff simulation and flood modeling in KRB.