This PDF file contains the front matter associated with SPIE Proceedings Volume 10428 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Change detection represents a broad field of research in SAR remote sensing, consisting of many different approaches. Besides the simple recognition of change areas, the analysis of type, category or class of the change areas is at least as important for creating a comprehensive result. Conventional strategies for change classification are based on supervised or unsupervised landuse / landcover classifications. The main drawback of such approaches is that the quality of the classification result directly depends on the selection of training and reference data. Additionally, supervised processing methods require an experienced operator who capably selects the training samples. This training step is not necessary when using unsupervised strategies, but nevertheless meaningful reference data must be available for identifying the resulting classes. Consequently, an experienced operator is indispensable. In this study, an innovative concept for the classification of changes in SAR time series data is proposed. Regarding the drawbacks of traditional strategies given above, it copes without using any training data. Moreover, the method can be applied by an operator, who does not have detailed knowledge about the available scenery yet. This knowledge is provided by the algorithm. The final step of the procedure, which main aspect is given by the iterative optimization of an initial class scheme with respect to the categorized change objects, is represented by the classification of these objects to the finally resulting classes. This assignment step is subject of this paper.

In the developing countries, urban areas are expanding rapidly. With the rapid developments, a short term monitoring of urban changes is important. A constant observation and creation of urban distribution map of high accuracy and without noise pollution are the key issues for the short term monitoring. SAR satellites are highly suitable for day or night and regardless of atmospheric weather condition observations for this type of study. The current study highlights the methodology of generating high-accuracy urban distribution maps derived from the SAR satellite imagery based on Convolutional Neural Network (CNN), which showed the outstanding results for image classification. Several improvements on SAR polarization combinations and dataset construction were performed for increasing the accuracy. As an additional data, Digital Surface Model (DSM), which are useful to classify land cover, were added to improve the accuracy. From the obtained result, high-accuracy urban distribution map satisfying the quality for short-term monitoring was generated. For the evaluation, urban changes were extracted by taking the difference of urban distribution maps. The change analysis with time series of imageries revealed the locations of urban change areas for short-term. Comparisons with optical satellites were performed for validating the results. Finally, analysis of the urban changes combining X-band, L-band and C-band SAR satellites was attempted to increase the opportunity of acquiring satellite imageries. Further analysis will be conducted as future work of the present study

Damage detection after natural disasters is one of the remote sensing tasks in which Synthetic Aperture Radar (SAR) sensors play an important role. Since SAR is an active sensor, it can record images at all times of day and in all weather conditions, making it ideally suited for this task. While with the newer generation of SAR satellites such as TerraSAR-X or COSMOSkyMed amplitude change detection has become possible even for urban areas, interferometric phase change detection has not been published widely. This is mainly because of the long revisit times of common SAR sensors leading to temporal decorrelation. This situation has changed dramatically with the advent of the TanDEM-X constellation, which can create single-pass interferograms from space at very high resolutions, avoiding temporal decorrelation almost completely. In this paper the basic structures that are present for any building in InSAR phases, i.e. layover, shadow, and roof areas, are examined. Approaches for their extraction from TanDEM-X interferograms are developed using simulated SAR interferograms. The extracted features of the building signature will in the future be used for urban change detection in real TanDEM-X High Resolution Spotlight interferograms.

The concept of remote sensing is to provide information about a wide-range area without making physical contact with this area. If, additionally to satellite imagery, images and videos taken by drones provide a more up-to-date data at a higher resolution, or accurate vector data is downloadable from the Internet, one speaks of sensor data fusion. The concept of sensor data fusion is relevant for many applications, such as virtual tourism, automatic navigation, hazard assessment, etc. In this work, we describe sensor data fusion aiming to create a semantic 3D model of an extremely interesting yet challenging dataset: An alpine region in Southern Germany. A particular challenge of this work is that rock faces including overhangs are present in the input airborne laser point cloud. The proposed procedure for identification and reconstruction of overhangs from point clouds comprises four steps: Point cloud preparation, filtering out vegetation, mesh generation and texturing. Further object types are extracted in several interesting subsections of the dataset: Building models with textures from UAV (Unmanned Aerial Vehicle) videos, hills reconstructed as generic surfaces and textured by the orthophoto, individual trees detected by the watershed algorithm, as well as the vector data for roads retrieved from openly available shapefiles and GPS-device tracks. We pursue geo-specific reconstruction by assigning texture and width to roads of several pre-determined types and modeling isolated trees and rocks using commercial software. For visualization and simulation of the area, we have chosen the simulation system Virtual Battlespace 3 (VBS3). It becomes clear that the proposed concept of sensor data fusion allows a coarse reconstruction of a large scene and, at the same time, an accurate and up-to-date representation of its relevant subsections, in which simulation can take place.

Large scale solar Photovoltaic (PV) deployment on existing building rooftops has proven to be one of the most efficient and viable sources of renewable energy in urban areas. As it usually requires a potential analysis over the area of interest, a crucial step is to estimate the geometric characteristics of the building rooftops. In this paper, we introduce a multi-layer machine learning methodology to classify 6 roof types, 9 aspect (azimuth) classes and 5 slope (tilt) classes for all building rooftops in Switzerland, using GIS processing. We train Random Forests (RF), an ensemble learning algorithm, to build the classifiers. We use (2 × 2) [m² ] LiDAR data (considering buildings and vegetation) to extract several rooftop features, and a generalised footprint polygon data to localize buildings. The roof classifier is trained and tested with 1252 labeled roofs from three different urban areas, namely Baden, Luzern, and Winterthur. The results for roof type classification show an average accuracy of 67%. The aspect and slope classifiers are trained and tested with 11449 labeled roofs in the Zurich periphery area. The results for aspect and slope classification show different accuracies depending on the classes: while some classes are well identified, other under-represented classes remain challenging to detect.

3D reconstruction and recognition of buildings and urban scenes is crucial for urban planning, disaster evaluation, historical, architectural and/or urban research and other photogrammetric tasks. The purpose of this research work is to evaluate 3D reconstruction of urban scenes by using the simplest workflow given by the optional IMAGINE UAV module for the ERDAS IMAGINE 2016 software. Digital elevation models and point clouds were extracted from RGB aerial images, which were captured by two SVS-Vistek nadir cameras with a resolution of 16 megapixel. The cameras were synchronized with a GPS/INS module for a direct georeferencing of the images. Additionally, the generated 3D models were imported to the Agiosoft PhotoScan application for a better visualization and to verify the focal length of the camera. The tridicon BuildingFinder software was used to improve the quality of the produced point clouds and for building recognition as well.

Motivated by politics and economy, the monitoring of the world wide ship traffic is a field of high topicality. To detect illegal activities like piracy, illegal fishery, ocean dumping and refugee transportation is of great value. The analysis of satellite images on the ground delivers a great contribution to situation awareness. However, for many applications the up-to-dateness of the data is crucial. With ground based processing, the time between image acquisition and delivery of the data to the end user is in the range of several hours. The highest influence to the duration of ground based processing is the delay caused by the transmission of the large amount of image data from the satellite to the processing centre on the ground. One expensive solution to this issue is the usage of data relay satellites systems like EDRS. Another approach is to analyse the image data directly on-board of the satellite. Since the product data (e.g. ship position, heading, velocity, characteristics) is very small compared to the input image data, real-time connections provided by satellite telecommunication services like Iridium or Orbcomm can be used to send small packets of information directly to the end user without significant delay. The AMARO (Autonomous real-time detection of moving maritime objects) project at DLR is a feasibility study of an on-board ship detection system involving a real-time low bandwidth communication. The operation of a prototype on-board ship detection system will be demonstrated on an airborne platform. In this article, the scope, aim and design of a flight experiment for an on-board ship detection system scheduled for mid of 2018 is presented. First, the scope and the constraints of the experiment are explained in detail. The main goal is to demonstrate the operability of an automatic ship detection system on board of an airplane. For data acquisition the optical high resolution DLR MACS-MARE camera (VIS/NIR) is used. The system will be able to send product data, like position, size and a small image of the ship directly to the user’s smart-phone by email. The time between the acquisition of the image data and the delivery of the product data to the end-user is aimed to be less than three minutes. For communication, the SMS-like Iridium Short Burst Data (SBD) Service was chosen, providing a message size of around 300 Bytes. Under optimal sending/receiving conditions, messages can be transmitted bidirectional every 20 seconds. Due to the very small data bandwidth, not all product data may be transmittable at once, for instance, when flying over busy ships traffic zones. Therefore the system offers two services: a query and a push service. With the query service the end user can explicitly request data of a defined location and fixed time period by posting queries in an SQL-like language. With the push service, events can be predefined and messages are received automatically, if and when the event occurs. Finally, the hardware set-up, details of the ship detection algorithms and the current status of the experiment is presented.

Thin clouds in the optical remote sensing data are frequent and in most of the cases don’t allow to have a pure surface data in order to calculate some indexes as Normalized Difference Vegetation Index (NDVI). This paper aims to evaluate the Automatic Cloud Removal Method (ACRM) algorithm over a high elevation city like Quito (Ecuador), with an altitude of 2800 meters above sea level, where the clouds are presented all the year. The ACRM is an algorithm that considers a linear regression between each Landsat 8 OLI band and the Cirrus band using the slope obtained with the linear regression established. This algorithm was employed without any reference image or mask to try to remove the clouds. The results of the application of the ACRM algorithm over Quito didn’t show a good performance. Therefore, was considered improving this algorithm using a different slope value data (ACMR Improved). After, the NDVI computation was compared with a reference NDVI MODIS data (MOD13Q1). The ACMR Improved algorithm had a successful result when compared with the original ACRM algorithm. In the future, this Improved ACRM algorithm needs to be tested in different regions of the world with different conditions to evaluate if the algorithm works successfully for all conditions.

Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels’ position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R²=0.85 and estimation of heading R²=0.92, computed as the average of R² values obtained for both optical and radar images.

Time series of satellite reflectance data have been widely used to characterize environmental phenomena, describe trends in vegetation dynamics and study climate change. However, several sensors with wide spatial coverage and high observation frequency are usually designed to have large field of view (FOV), which cause variations in the sun-targetsensor geometry in time-series reflectance data. In this study, on the basis of semiempirical kernel-driven BRDF model, a new semi-empirical model was proposed to normalize the sun-target-sensor geometry of remote sensing image. To evaluate the proposed model, bidirectional reflectance under different canopy growth conditions simulated by Discrete Anisotropic Radiative Transfer (DART) model were used. The semi-empirical model was first fitted by using all simulated bidirectional reflectance. Experimental result showed a good fit between the bidirectional reflectance estimated by the proposed model and the simulated value. Then, MODIS time-series reflectance data was normalized to a common sun-target-sensor geometry by the proposed model. The experimental results showed the proposed model yielded good fits between the observed and estimated values. The noise-like fluctuations in time-series reflectance data was also reduced after the sun-target-sensor normalization process.

Problem of remote sensing data harnessing for decision making in conflict territories is considered. Approach for analysis of socio-economic and demographic parameters with a limited set of data and deep uncertainty is described. Number of interlinked techniques to estimate a population and economy in crisis territories are proposed. Stochastic method to assessment of population dynamics using multi-source data using remote sensing data is proposed. Adaptive Markov’s chain based method to study of land-use changes using satellite data is proposed. Proposed approach is applied to analysis of socio-economic situation in Donbas (East Ukraine) territory of conflict in 2014-2015. Land-use and landcover patterns for different periods were analyzed using the Landsat and MODIS data . The land-use classification scheme includes the following categories: (1) urban or built-up land, (2) barren land, (3) cropland, (4) horticulture farms, (5) livestock farms, (6) forest, and (7) water. It was demonstrated, that during the period 2014-2015 was not detected drastic changes in land-use structure of study area. Heterogeneously distributed decreasing of horticulture farms (4-6%), livestock farms (5-6%), croplands (3-4%), and increasing of barren land (6-7%) have been observed. Way to analyze land-cover productivity variations using satellite data is proposed. Algorithm is based on analysis of time-series of NDVI and NDWI distributions. Drastic changes of crop area and its productivity were detected. Set of indirect indicators, such as night light intensity, is also considered. Using the approach proposed, using the data utilized, the local and regional GDP, local population, and its dynamics are estimated.

Land degradation is one of the causes of desertification of drylands in the Mediterranean. UAVs can be used to monitor and document the various variables that cause desertification in drylands, including overgrazing, aridity, vegetation loss, etc. This paper examines the use of UAVs and accompanying sensors to monitor overgrazing, vegetation stress and aridity in the study area. UAV images can be used to generate digital elevation models (DEMs) to examine the changes in microtopography as well as ortho-photos were used to detect changes in vegetation patterns. The combined data of the digital elevation models and the orthophotos can be used to identify the mechanisms for desertification in the study area.

The aim of this paper is to present the strategy and vision to upgrade the existing ERATOSTHENES Research Centre (ERC) established within the Cyprus University of Technology (CUT) into a sustainable, viable and autonomous Centre of Excellence (CoE) for Earth Surveillance and Space-Based Monitoring of the Environment, which will provide the highest quality of related services on the National, European and International levels. EXCELSIOR is a Horizon 2020 Teaming project which addresses a specific challenge defined by the work program, namely, the reduction of substantial disparities in the European Union by supporting research and innovation activities and systems in low performing countries. It also aims at establishing long-term and strategic partnerships between the Teaming partners, thus reducing internal research and innovation disparities within European Research and Innovation landscape. The proposed CoE envisions the upgrading of the existing ERC into an inspiring environment for conducting basic and applied research and innovation in the areas of the integrated use of remote sensing and space-based techniques for monitoring the environment. Environment has been recognized by the Smart Specialization Strategy of Cyprus as the first horizontal priority for future growth of the island. The foreseen upgrade will regard the expansion of this vision to systematic monitoring of the environment using Earth Observation, space and ground based integrated technologies. Such an approach will lead to the systematic monitoring of all three domains of the Environment (Air, Land, Water). Five partners have united to upgrade the existing ERC into a CoE, with the common vision to become a world-class innovation, research and education centre, actively contributing to the European Research Area (ERA). More specifically, the Teaming project is a team effort between the Cyprus University of Technology (CUT, acting as the coordinator), the German Aerospace Centre (DLR), the National Observatory of Athens (NOA), the German Leibniz Institute for Tropospheric Research (TROPOS) and the Cyprus Department of Electronic Communications of the Ministry of Transport, Communications and Works (DEC-MTCW).

Wetlands, one of the most productive ecosystems on Earth, perform myriad ecological functions and provide a host of ecological services. Despite their ecological and economic values, wetlands have experienced significant degradation during the last century and the trend continues. Hyperspectral sensors provide opportunities to map and monitor macrophyte species within wetlands for their management and conservation. In this study, an attempt has been made to evaluate the potential of narrowband spectroradiometer data in discriminating wetland macrophytes during different seasons. main objectives of the research were (1) to determine whether macrophyte species could be discriminated based on in-situ hyperspectral reflectance collected over different seasons and at each measured waveband (400-950nm), (2) to compare the effectiveness of spectral reflectance and spectral indices in discriminating macrophyte species, and (3) to identify spectral wavelengths that are most sensitive in discriminating macrophyte species. Spectral characteristics of dominant wetland macrophyte species were collected seasonally using SVC GER 1500 portable spectroradiometer over the 400 to 1050nm spectral range at 1.5nm interval, at the Bhindawas wetland in the state of Haryana, India. Hyperspectral observations were pre-processed and subjected to statistical analysis, which involved a two-step approach including feature selection (ANOVA and KW test) and feature extraction (LDA and PCA). Statistical analysis revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from visible to the near-infrared regions. The results suggest that hyperspectral data can be used discriminate wetland macrophyte species working as an effective tool for advanced mapping and monitoring of wetlands.

Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user’s accuracy (UA), producer’s accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.

Aceh had been the focus of an unprecedented international rehabilitation effort in response to the extreme SumatraAndaman earthquake and tsunami disaster on December 24, 2004. During this period, most researchers have contributed to better understanding what happened in the past, and what going to happen in the future. This paper is related to the environmental impact assessment of post-disaster recovery and reconstructions in Banda Aceh city of Indonesia. The indicators are based on the use of the moderate spatial resolution optical satellite sensor by assessing the impacts of land use and land cover change (LULC) on land surface temperature (LST). LULC classification and LST were derived and estimated utilizing visible and thermal infrared data of the Landsat-5 TM + Landsat 8 OLI within the period 2000 and 2015. The surface temperature-vegetation index space of LULC was established to investigate the impacts of land changes over LST sensitivity. The result demonstrated that the post-disaster recovery and reconstruction has had a significant impact to the LULC in Banda Aceh and its fringes. Dramatic LULC in Banda Aceh significantly increases the LST, the temporal trend of pixels space migrated from the dense vegetation-low temperature condition to the less dense vegetation-high temperature condition.

The aim of this paper is seasonal monitoring of floating reed islands dynamic in Srebarna Lake (Bulgaria), using SAR data. In order to study the seasonal dynamic of floating reed islands (such as absolute and relative movement) the only opportunity which provides high-tech methods based on space remote sensing was used. Sensors by suitable parameters for data registration for this type of unsystematic landscape units were used. SAR data (Synthetic Aperture) are powerful high-tech tool for monitoring from the ground objects. SAR data images are privileged to register data at any time of the day or night and in adverse weather conditions, which are the main limiting factor in optical images. Seasonal monitoring of floating reed islands using SAR data was performed - winter - when the water in the lake is frozen, then a relative movement of these islands was observed, spring - melting snow cover and rising water level in the Danube River and Srebarna Lake was observed, when the water level is raised. Obtained results give a quantitative assessment of the ecological dynamics of these types of specific habitats in Srebarna Lake. They show the movement of the islands through the seasons in the period of six mounts, the changes in their shape and size. Regular seasonal monitoring of the floating reed islands dynamic is very important for their preservation as a specific habitat.

Many geological applications like mapping, open quarry monitoring or even the study of tectonic structures, require a high resolution and a very accurate representation of the surface in order to minimize errors. High resolution satellite images or airphotos can provide the necessary basemap for such applications. Nevertheless these kinds of images are quite expensive and hard to get access to. Google Earth is easily accessible to the public, but the question that arises is, if those images can be as reliable as the original satellite images and if they can be used in their place for geological applications. The purpose of this study was to investigate Google Earth images reliability, measure their accuracy and find out if they can provide valid results and finally if they can be used for mapping applications. The region that was investigated is Markopoulo quarry in Attiki Peninsula Greece. Images were taken using Google Earth, one for each direction (North, South, East, and West) from 2004 to 2015. All the images were of the same magnification and same image quality. Subsequently, those images were georeferenced with the use of ArcGIS software. The georeference procedure was executed again using Erdas Imagine software for comparison purposes. A quickbird satellite image over the same area was orthorectified in Leica Photogrammetry Suite. From the orthorectified Quickbird image and the Google Earth images the road network was digitized and the derived vectors were compared in ARCGIS. The comparison showed remarkably low deviation which leads us to the conclusion that Google Earth images can be used as alternative basemap in many applications.

The coastal areas in the Patras Gulf suffer degradation due to the sea action and other natural and human-induced causes. Changes in beaches, ports, and other man made constructions need to be assessed, both after severe events and on a regular basis, to build models that can predict the evolution in the future. Thus, reliable spatial data acquisition is a critical process for the identification of the coastline and the broader coastal zones for geologists and other scientists involved in the study of coastal morphology. High resolution satellite data, airphotos and airborne Lidar provided in the past the necessary data for the coastline monitoring. High-resolution digital surface models (DSMs) and orthophoto maps had become a necessity in order to map with accuracy all the variations in costal environments. Recently, unmanned aerial vehicles (UAV) photogrammetry offers an alternative solution to the acquisition of high accuracy spatial data along the coastline. This paper presents the use of UAV to map the coastline in Rio area Western Greece. Multiple photogrammetric aerial campaigns were performed. A small commercial UAV (DJI Phantom 3 Advance) was used to acquire thousands of images with spatial resolutions better than 5 cm. Different photogrammetric software’s were used to orientate the images, extract point clouds, build a digital surface model and produce orthoimage mosaics. In order to achieve the best positional accuracy signalised ground control points were measured with a differential GNSS receiver. The results of this coastal monitoring programme proved that UAVs can replace many of the conventional surveys, with considerable gains in the cost of the data acquisition and without any loss in the accuracy.

In this paper we present monolithic implementations of tunable mechanical seismometers and accelerometers (horizontal, vertical and angular) based on the UNISA Folded Pendulum configuration, characterized by large measurement band 10⁻⁷ ÷ 1 kHz, sensitivity down to ≈ 10⁻¹⁵ m/√ Hz, high directivity > 10⁴ , low weight < 1.5 kg, dimensions < 10 cm, coupled to a large insensitivity to environmental noises and capability of operating in ultra high vacuum and cryogenic environments. Typical applications of this class of sensors are in the field of earthquake engineering, seismology, geophysics, civil engineering (buildings, bridges, dams, etc.), space (inertial guide).

This paper presents the integrated methods using UAVs and geodetic techniques to monitor ground deformation within the Choirokoitia UNESCO World Heritage Site in Cyprus. The Neolithic settlement of Choirokoitia, occupied from the 7th to the 4th millennium B.C., is one of the most important prehistoric sites in the eastern Mediterranean. The study is conducted under the PROTHEGO (PROTection of European Cultural HEritage from GeO-hazards) project, which is a collaborative research project funded in the framework of the Joint Programming Initiative on Cultural Heritage and Global Change (JPICH) – Heritage Plus in 2015–2018 (www.prothego.eu) and through the Cyprus Research Promotion Foundation. PROTHEGO aims to make an innovative contribution towards the analysis of geo-hazards in areas of cultural heritage, and uses novel space technology based on radar interferometry to retrieve information on ground stability and motion in the 400+ UNESCO's World Heritage List monuments and sites of Europe. The field measurements collected at the Choirokoitia site will be later compared with SAR data to verify micro-movements in the area to monitor potential geo-hazards. The site is located on a steep hill, which makes it vulnerable to rock falls and landslides.

Hydrocarbons are nonrenewable resources but today they are the cheaper and easier energy we have access and will remain the main source of energy for this century. Nevertheless, their exploration is extremely high-risk, very expensive and time consuming. In this context, satellite technologies for Earth observation can play a fundamental role by making hydrocarbon exploration more efficient, economical and much more eco-friendly. Complementary to traditional geophysical methods such as gravity and magnetic (gravmag) surveys, satellite remote sensing can be used to detect onshore long-term biochemical and geochemical alterations on the environment produced by invisible small fluxes of light hydrocarbons migrating from the underground deposits to the surface, known as microseepage effect. This paper describes two case studies: one in South Sudan and another in Mozambique. Results show how remote sensing is a powerful technology for detecting active petroleum systems, thus supporting hydrocarbon exploration in remote or hardly accessible areas and without the need of any exploration license.

Porosity is a fundamental property of sand deposits found in a wide range of landscapes, from beaches to dune fields. As a primary determinant of the density and permeability of sediments, it represents a central element in geophysical studies involving basin modeling and coastal erosion as well as geoaccoustics and geochemical investigations aiming at the understanding of sediment transport and water diffusion properties of sandy landscapes. These applications highlight the importance of obtaining reliable porosity estimations, which remains an elusive task, notably through remote sensing. In this work, we aim to contribute to the strengthening of the knowledge basis required for the development of new technologies for the remote monitoring of environmentally-triggered changes in sandy landscapes. Accordingly, we employ an in silico investigation approach to assess the effects of porosity variations on the reflectance of sandy landscapes in the visible and near-infrared spectral domains. More specifically, we perform predictive computer simulations using SPLITS, a hyperspectral light transport model for particulate materials that takes into account actual sand characterization data. To the best of our knowledge, this work represents the first comprehensive investigation relating porosity to the reflectance responses of sandy landscapes. Our findings indicate that the putative dependence of these responses on porosity may be considerably less pronounced than its dependence on other properties such as grain size and shape. Hence, future initiatives for the remote quantification of porosity will likely require reflectance sensors with a high degree of sensitivity.

Land surface phenology (LSP) can improve the characterisation of forest areas and their change processes. The aim of this work was: i) to characterise the temporal dynamics in Mediterranean Pinus forests, and ii) to evaluate the potential of LSP for species discrimination. The different experiments were based on 679 mono-specific plots for the 5 native species on the Iberian Peninsula: P. sylvestris, P. pinea, P. halepensis, P. nigra and P. pinaster. The entire MODIS NDVI time series (2000–2016) of the MOD13Q1 product was used to characterise phenology. The following phenological parameters were extracted: the start, end and median days of the season, and the length of the season in days, as well as the base value, maximum value, amplitude and integrated value. Multi-temporal metrics were calculated to synthesise the inter-annual variability of the phenological parameters. The species were discriminated by the application of Random Forest (RF) classifiers from different subsets of variables: model 1) NDVI-smoothed time series, model 2) multi-temporal metrics of the phenological parameters, and model 3) multi-temporal metrics and the auxiliary physical variables (altitude, slope, aspect and distance to the coastline). Model 3 was the best, with an overall accuracy of 82%, a kappa coefficient of 0.77 and whose most important variables were: elevation, coast distance, and the end and start days of the growing season. The species that presented the largest errors was P. nigra, (kappa= 0.45), having locations with a similar behaviour to P. sylvestris or P. pinaster.

Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.

In the task of damage detection based on SAR imagery, the handling of the textural similarity between heaps of debris surrounding damaged buildings and high vegetation poses to be a challenge. However, in previous work we showed that features exist that are sensitive to the small but distinct textural difference. Since, those analyses were based on a single data set containing only one phenological state of the vegetation, we have to analyze the stability of the textural feature used for separation. In this paper, based on one time series the variation of the SAR signature of high vegetation, especially forest areas, is studied due to phenological changes. The data are high resolution Spotlight amplitude images of TerraSAR-X, because similar data are used for damage detection. The considered textural features are statistical features of the first order as well as Haralick features. The evaluation is performed on non-overlapping patches in the forest areas and comprises the search of seasonal runs of the texture features. Additionally, the occurrence of deciduous and coniferous forests is analyzed to emphasize potential phenological differences between both species.

The demand for remotely sensed data is growing increasingly, due to the possibility of managing information about huge geographic areas, in digital format, at different time periods, and suitable for analysis in GIS platforms. However, primary satellite information is not such immediate as desirable. Beside geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical images. In terms of land cover, such a contamination is intended as missing information and should be replaced. Generally, image reconstruction is classified according to three main approaches, i.e. in-painting-based, multispectral-based, and multitemporal-based methods. This work relies on a multitemporal-based approach to retrieve uncontaminated pixels for an image scene. We explore an automatic method for quickly getting daytime cloudless and shadow-free image at moderate spatial resolution for large geographical areas. The process expects two main steps: a multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase, based on automatic selection of uncontaminated pixels from an image stack. The result is a composite image based on middle values of the stack, over a year. The assumption is that, for specific purposes, land cover changes at a coarse scale are not significant over relatively short time periods. Because it is largely recognized that satellite imagery along tropical areas are generally strongly affected by clouds, the methodology is tested for the case study of the Dominican Republic at the year 2015; while Landsat 8 imagery are employed to test the approach.

Rivers are the lifelines of our environment. For this reason they are always in the focus of environmental studies. For the assessment of the ecological status of rivers the member states of the European Union have developed different monitoring programs and approaches. The shadowing of a river affects the temperature of the water and also the energy balance of the water body. Therefore changes in temperature also influence the biological and chemical status of rivers and lakes. The main objective of this study is the simulation of the shadowing of a section of the river Freiberger Mulde for a full year. This ensures that effects of different sun positions over the year (azimuth, elevation) and also local topography conditions in the close environment of the river section are taken into account. For all analyses a digital surface model and a digital elevation model with a geometric resolution of 2 m is available. The result of this simulation is a raster layer which represents the theoretical annual hours of shadowing of the river section. The results indicate a decrease of illumination of partly more than 80 %. In future this information can be expanded by means of further affecting factors such as the consideration of the phenological status of deciduous trees in the riparian zone.

Recent studies aim to exploit vegetation hyperspectral signature as an indicator of pipeline leakages and natural oil seepages by detecting changes in reflectance induced by oil exposure. In order to assess the feasibility of the method at larger spatial scale, a study has been carried out in a greenhouse on two tropical (Cenchrus alopecuroides and Panicum virgatum) and a temperate (Rubus fruticosus) species. Plants were grown on contaminated soil during 130 days, with concentrations up to 4.5 and 36 g.kg^-1 for heavy metals and C₁₀-C₄₀ hydrocarbons respectively. Reflectance data (350-2500 nm) were acquired under artificial light from 1 to 60 days. All species showed an increase of reflectance in the visible (VIS, 400-750 nm) and short-wave infrared (SWIR, 1300-2500 nm) under experimental contaminants exposure. However, the responses were contrasted in the near-infrared (NIR, 750-1300 nm). 47 normalized vegetation indices were compared between treatments, and the most sensitive to contamination were retained. Same indices showed significant differences between treatments at leaf and plant scales. Indices related to plant pigments, plant water content and red-edge reflectance were particularly sensitive to soil contamination. In order to validate the selection of indices, hyperspectral measurements were performed outdoor at plant scale at the end of the experiment (130 days). Leaf samples were also collected for pigment analysis. Index selected at day 60 were still sensitive to soil contamination after 130 days. Significant changes in plant pigment composition were also observed. This study demonstrates the interest of hyperspectral data for oil exploration and environmental diagnosis.

Flooding is the most common and costly natural disaster around the world, causing the loss of human life and billions in economic and insured losses each year. In 2016, pluvial and fluvial floods caused an estimated 5.69 billion USD in losses worldwide with the most severe events occurring in Germany, France, China, and the United States. While catastrophe modeling has begun to help bridge the knowledge gap about the risk of fluvial flooding, understanding the extent of a flood – pluvial and fluvial – in near real-time allows insurance companies around the world to quantify the loss of property that their clients face during a flooding event and proactively respond. To develop this real-time, global analysis of flooded areas and the associated losses, a new methodology utilizing optical multi-spectral imagery from DigitalGlobe (DGI) WorldView satellite suite is proposed for the extraction of pluvial and fluvial flood extents. This methodology involves identifying flooded areas visible to the sensor, filling in the gaps left by the built environment (i.e. buildings, trees) with a nearest neighbor calculation, and comparing the footprint against an Industry Exposure Database (IE) to calculate a loss estimate. Full-automation of the methodology allows production of flood extents and associated losses anywhere around the world as required. The methodology has been tested and proven effective for the 2016 flood in Louisiana, USA.

The paper aims at presentation of SYeNERGY project, which is designed to develop the on-line platform applying satellite data in order to support various actors in the Energy Market in Poland. According to the Amendment of the Law on Renewable Energy Sources from 22.06.2016 r. in Poland, the recipients of the energy biomass – Energy Companies, are obliged to obey to the rules of local biomass (the biomass can be obtained from the distance of maximum 300 km in straight line from the CHP) and biomass’ sustainable acquisition (the biomass cannot be collected from the areas under protection of: NATURA 2000, Reservations, National and Regional Parks). The entire responsibility of obtaining the biomass according to the law is put on the recipients of the biomass – Energy Companies. This situation was found by the Institute of Geodesy and Cartography as a motivation for development of the system which would enable to efficiently check if the acquired biomass fulfill the requirements of local biomass and sustainable acquisition and would deliver the estimation of the biomass to be obtained. In the beginning of 2017, the agreement between the Institute of Geodesy and Cartography and PGE S.A. – one of the biggest energy companies in Poland was signed in order to conduct the pilot application of SYeNERGY platform in their everyday work.

Aimed at key problems the system of 1:5000 scale space stereo mapping and the shortage of the surveying capability of urban area, in regard of the performance index and the surveying systems of the existing domestic optical mapping satellites are unable to meet the demand of the large scale stereo mapping, it is urgent to develop the very high accuracy space photogrammetric satellite system which has a 1:5000 scale (or larger).The new surveying systems of double baseline stereo photogrammetric mode with combined of linear array sensor and area array sensor was proposed, which aims at solving the problems of barriers, distortions and radiation differences in complex ground object mapping for the existing space stereo mapping technology. Based on collinearity equation, double baseline stereo photogrammetric method and the model of combined adjustment were presented, systematic error compensation for this model was analyzed, position precision of double baseline stereo photogrammetry based on both simulated images and images acquired under lab conditions was studied. The laboratory tests showed that camera geometric calibration accuracy is better than 1μm, the height positioning accuracy is better than 1.5GSD with GCPs. The results showed that the mode of combined of one linear array sensor and one plane array sensor had higher positioning precision. Explore the new system of 1:5000 scale very high accuracy space stereo mapping can provide available new technologies and strategies for achieving demotic very high accuracy space stereo mapping.

Rivers, one of the most complex ecosystems are highly dynamic and vary spatially as well as temporally. Chlorophyll-a (Chl-a) is considered one of the primary indicators of water quality and a measure of river productivity, while turbidity in rivers is a measure of suspended organic matter. Monitoring of river water quality is quite challenging, demand tremendous efforts and resources. Numerous algorithms have been developed in the recent years for estimating environmental parameters such as chlorophyll-a and turbidity from remote sensing imagery. However, most of these algorithms were focused on the lentic ecosystems. There is a paucity of algorithms for rivers from which water quality variables can be estimated using remotely sensed imagery. The primary objective of our study is to develop algorithms based on Landsat 8 OLI imagery and in-situ observations for estimating of Chl-a and turbidity in the Upper Ganga river, India. Band reflectance images from multispectral Landsat-8 OLI pertaining to May and October 2016, and May 2017 were used for model development and validation along with near synchronous ground truth data. Algorithms based on Band 3 (R²= 0.73) proved to be the best applicable algorithm for estimating chlorophyll-a. The best algorithm for estimating turbidity was found to be log (B4/B5) (R²= 0.69) based on band combinations (individual band reflectance, band ratio, logarithmically transformed band reflectance and ratios) tested. The developed algorithms were used to generate maps showing the spatiotemporal variability of chlorophyll-a and turbidity concentration in the Upper Ganga river (Brijghat to Narora) which is also a Ramsar site.

Landslide monitoring can be performed using many different methods: Classical geotechnical measurements like inclinometer, topographical survey measurements with total stations or GNSS sensors and photogrammetric techniques using airphotos or high resolution satellite images. However all these methods are expensive or difficult to be developed immediately after the landslide triggering. In contrast airborne technology and especially the use of Unmanned Aerial Vehicles (UAVs) make response to landslide disaster easier as UAVs can be launched quickly in dangerous terrains and send data about the sliding areas to responders on the ground either as RGB images or as videos. In addition, the emergency response to landslide is critical for the further monitoring. For proper displacement identification all the above mentioned monitoring methods need a high resolution and a very accurate representation of the relief. The ideal solution for the accurate and quick mapping of a landslide is the combined use of UAV’s photogrammetry and GNSS measurements. UAVs have started their development as expensive toys but they currently became a very valuable tool in large scale mapping of sliding areas. The purpose of this work is to demonstrate an effective solution for the initial landslide mapping immediately after the occurrence of the phenomenon and the possibility of the periodical assessment of the landslide. Three different landslide cases from Greece are presented in the current study. All three landslides have different characteristics: occurred in different geomorphologic environments, triggered by different causes and had different geologic bedrock. In all three cases we performed detailed GNSS measurements of the landslide area, we generated orthophotos as well as Digital Surface Models (DSMs) at an accuracy of less than ±10 cm. Slide direction and velocity, mass balances as well as protection and mitigation measurements can be derived from the application of the UAVs. Those data in addition are accurate, cost- and time-effective.

Time series analysis of GPS (Global Positioning Systems) and InSAR (Interferometric Synthetic Aperture Radar) data are important tools for Earth’s surface deformation assessment, which can result from a wide range of geological phenomena like as earthquakes, landslides or ground water level changes. The aim of this paper was to identify several types of earthquake precursors that might be observed from geospatial data in Vrancea seismogenic region in Romania. Continuous GPS Romanian network stations and few field campaigns data recorded between 2005-2012 years revealed a displacement of about 5 or 6 millimeters per year in horizontal direction relative motion, and a few millimeters per year in vertical direction. In order to assess possible deformations due to earthquakes and respectively for possible slow deformations, have been used also time series Sentinel 1 satellite data available for Vrancea zone during October 2014 till October 2016 to generate two types of interferograms (short-term and medium- term). During investigated period were not recorded medium or strong earthquakes, so interferograms over test area revealed small displacements on vertical direction (subsidence or uplifts) of 5-10 millimeters per year. Based on GPS continuous network data and satellite Sentinel 1 results, different possible tectonic scenarios were developed. The localization of horizontal and vertical motions, fault slip, and surface deformation of the continental blocks provides new information, in support of different geodynamic models for Vrancea tectonic active region in Romania and Europe.

There are many types of natural gas fields including shale formations that are common especially in the St-Lawrence Valley (Canada). Since methane (CH4), the major component of shale gas, is odorless, colorless and highly flammable, in addition to being a greenhouse gas, methane emanations and/or leaks are important to consider for both safety and environmental reasons. Telops recently launched on the market the Hyper-Cam Methane, a field-deployable thermal infrared hyperspectral camera specially tuned for detecting methane infrared spectral features under ambient conditions and over large distances. In order to illustrate the benefits of this novel research instrument for natural gas imaging, the instrument was brought on a site where shale gas leaks unexpectedly happened during a geological survey near the Enfant-Jesus hospital in Quebec City, Canada, during December 2014. Quantitative methane imaging was carried out based on methane’s unique infrared spectral signature. Optical flow analysis was also carried out on the data to estimate the methane mass flow rate. The results show how this novel technique could be used for advanced research on shale gases.

Nowadays remote sensing is a well-established method and technique of providing data. The current development shows the availability of systems with very high geometric resolution for the monitoring of vegetation. At the same time, however, the value of temporally high-resolution data is underestimated, particularly in applications focusing on the detection of short-term changes. These can be natural processes like natural disasters as well as changes caused by anthropogenic interventions. These include economic activities such as forestry, agriculture or mining but also processes which are intended to convert previously used areas into natural or near-natural surfaces. The K¨onigsbr¨ucker Heide is a former military training site located about 30 km north of the Saxon state capitol Dresden. After the withdrawal of the Soviet forces in 1992 and after nearly 100 years of military use this site was declared as nature reserve in 1996. The management of the whole protection area is implemented in three different management zone. Based on MODIS-NDVI time series between 2000 and 2016 different developments are apparent in the nature development zone and the zone of controlled succession. Nevertheless, the analyses also show that short-term changes, so called breaks in the vegetation development cannot be described using linear trend models. The complete understanding of vegetation trends is only given if discontinuities in vegetation development are considered. Structural breaks in the NDVI time series can be found simultaneously in the whole study area. Hence it can be assumed that these breaks have a more natural character, caused for example by climatic conditions like temperature or precipitation. Otherwise, especially in the zone of controlled succession structural breaks can be detected which cannot be traced back to natural conditions. Final analyses of the spatial distribution of breakpoints as well as their frequency depending on the respective protection zone allow a detailed view to vegetation development in the K¨onigsbr¨ucker Heide.

The value of remote sensing data is particularly evident where an areal monitoring is needed to provide information on the earth’s surface development. The use of temporal high resolution time series data allows for detecting short-term changes. In Kogi State in Nigeria different vegetation types can be found. As the major population in this region is living in rural communities with crop farming the existing vegetation is slowly being altered. The expansion of agricultural land causes loss of natural vegetation, especially in the regions close to the rivers which are suitable for crop production. With regard to these facts, two questions can be dealt with covering different aspects of the development of vegetation in the Kogi state, the determination and evaluation of the general development of the vegetation in the study area (trend estimation) and analyses on a short-term behavior of vegetation conditions, which can provide information about seasonal effects in vegetation development. For this purpose, the GIMMS-NDVI data set, provided by the NOAA, provides information on the normalized difference vegetation index (NDVI) in a geometric resolution of approx. 8 km. The temporal resolution of 15 days allows the already described analyses. For the presented analysis data for the period 1981-2012 (31 years) were used. The implemented workflow mainly applies methods of time series analysis. The results show that in addition to the classical seasonal development, artefacts of different vegetation periods (several NDVI maxima) can be found in the data. The trend component of the time series shows a consistently positive development in the entire study area considering the full investigation period of 31 years. However, the results also show that this development has not been continuous and a simple linear modeling of the NDVI increase is only possible to a limited extent. For this reason, the trend modeling was extended by procedures for detecting structural breaks in the time series.

Every year people in Taiwan have been facing earthquake and typhoon nature disaster challenges which are seriously affecting the people's lives and property. During summer season typhoons bring abundant rainfall resulting in landslides and debris flows. Because the landslide attributes to several environmental factors, we could monitor and analyze endangered areas to prevent damages. In this study Taiwan Provincial Highway 14 branch at 34K~38K+500m is chosen, which has devastated by Earthquake 921 and Typhoon Mindulle. A decision analysis is conducted by utilizing combined ortho-photo and LiDAR Digital Elevation Model (DEM) data to classify landslide areas. The result showed that the performance of overall accuracy could be increased when terrain factors are considered.

Himalayan Glaciers are a major source of fresh water, and in places are the critical source of water for drinking, irrigation and hydroelectric power generation for South Asia. Modern water resource management requires understanding the volume of source available for a robust planning and development for the future.Glaciers are highly sensitive indicators to any climate change. Contemporary size and volume are critical factors for timely evaluation/assessment for both near-term and long-term changes in both temperature and precipitation and the cryosphere thereupon. Glacier area and surface morphology can be readily mapped from both satellites imagery and aerial photographs. With the help of remotely sensed data and GIS analysis, glacier surface areas have been mapped with mean spatial resolution of 10 meter. The surface mapping with such resolution is neither able to access the exact volume nor determine sensitivity of glacier to the recent climate. Most satellite maps are only two-dimensional mapping of crysophere, but three-dimensional maps of glacier hydrological systems are necessary for volumetric assessment and long-term planning. Therefore, the integration of survey instrument such as DGPS, Total Station with millimeter accuracy, and simultaneous simulation with Ground Penetrating Radar (GPR) surveys over the mapped conduit systems will help in accessing glacier mass accurately and define subsurface geometry for the 3-D modeling of glaciers for better understanding and estimation of water fresh resource. Further, the 3-D mapping of cryosphere will help us to access accurate the volume of fresh water in the Himalayan cryosphere, along with contemporary dynamics. We show an integrated approach to assess and quantify the Himalayan cryosphere by integrating such techniques for a better management and understanding of the Himalayan cryosphere to climatic parameters and management of future water requirements. However, it is an established fact that glaciers show varied sensitivity to climate over the time and space, both in growing and receding.Therefore, monitoring this fresh water resource is most essential for an agrarian country like India where demand for irrigation is great in the Great Plains. Present study will represent the seamless integration of field based 3-D Total station mapping of glacier snout of Gangotri Glacier, GPR Profiling of Glacier at selective locations, Volume estimation and annual change in the volume and its integration with the MODIS LST data in a way to access the present glacier sensitivity to climatic variability, as well as help model future scenario more accurately for robust management of this finite water resource.

Soil is a dominant factor of the terrestrial geosystems in semi-arid and dry sub-humid zones, particularly through its effect on biomass production. Due to the climate changes and industrial development, soil resources in these zones are prone to degradation. On the other hand, degradation processes cause changes in land cover. Remote sensing optical data are widely used in the process of determining land cover change whereas SAR data is suitable for determination of soil moisture dynamics. In the present study, Tasseled Cap Transform (TCT) and modified Change Vector Analysis (mCVA) techniques are applied to Landsat and Sentinel 2 data in order to be determined magnitude and direction of land cover changes in the semi-natural areas of Haskovo Region, Southeast Bulgaria. The study of the vector direction presents some distinct changes in the soil characteristics for the whole territory and significant changes in vegetation characteristics and moisture content in part of the semi-mountainous territories of the examined region. It has been found that the magnitude of those changes increases up to 50% in some of the territories under investigation. SAR data has been used to evaluate the relative soil moisture content in various soil differences and to trace its dynamics during growing season. In order to achieve this aim, Relative Soil Moisture Index (RSMI) is used. The index estimates the relative variation of volumetric soil moisture content in a given time period and enables determination of its change in relative values. On the basis of integrated application of aforementioned techniques, a model providing key information about the impact of soil moisture change and dynamics upon processes related to land cover change. The suggested model is appropriate for estimation of ecosystem services and functions delivered by landscapes in semi-arid and dry sub-humid zones.

Ecological land provides goods and services that have direct or indirect benefic to eco-environment and human welfare. In recent years, researches on ecological land have become important in the field of land changes and ecosystem management. In the study, a multi-scale classification scheme of ecological land was developed for land management based on combination of the land-use classification and the ecological function zoning in China, including eco-zone, eco-region, eco-district, land ecosystem, and ecological land-use type. The geographical spatial unit leads toward greater homogeneity from macro to micro scale. The term “ecological land-use type” is the smallest one, being important to maintain the key ecological processes in land ecosystem. Ecological land-use type was categorized into main-functional and multi-functional ecological land-use type according to its ecological function attributes and production function attributes. Main-functional type was defined as one kind of land-use type mainly providing ecological goods and function attributes, such as river, lake, swampland, shoaly land, glacier and snow, while multi-functional type not only providing ecological goods and function attributes but also productive goods and function attributes, such as arable land, forestry land, and grassland. Furthermore, a six-level grid encoding mode was proposed for modern management of ecological land and data update under cadastral encoding. The six-level irregular grid encoding from macro to micro scale included eco-zone, eco-region, eco-district, cadastral area, land ecosystem, land ownership type, ecological land-use type, and parcel. Besides, the methodologies on ecosystem management were discussed for integrated management of natural resources in China.

The objective of this work is to identify the potential zones for detailed mineral exploration studies in areas adjoining to a copper prospect using Remotely Sensed data sets. In this study visualization of ASTER data has been enhanced to highlight the mineral-rich areas using various remote sensing techniques such as colour composites and band ratios. VNIR region of ASTER is significant to detect iron oxides while, clay minerals, carbonates and chlorites have characteristic absorption in the SWIR wavelength region. Therefore, an attempt has been made to target the mineral abundant regions through ASTER data processing. Height based information was extracted using high-resolution ALOSDEM to analyse the topographical controls in the region considering the fact that mineral deposits often found associated with geological structures and geomorphological units. Gravity data was used to generate gravity anomaly map which gives clues about subsurface density differences. In this context, base metal ores may show anomalous (high) gravity values in comparison to the non-mineralised areas. Outputs from all the data sets were analysed and correlated with the geological map and available literature. Final validation of results has been done through proper ground checks and laboratory analysis of rock samples collected from the litho-units present in the study area. Based on this study some new areas have been successfully demarcated which may be potential for base metal exploration.

Water body is one of the most important natural elements in coastal zone. Water bodies in coast are subdivided into offshore sea, aquaculture ponds, inland water bodies, river and so on. Remote sensing is an effective tool to obtain coastal typical objects with high spatial resolution imageries. This paper aims at existing problems of object-based image analysis application to monitor resources and environment in coastal area. For object-based recognition for water body types, relevant works have been carried out by adding spatial semantic features to the extraction process. Through analyzing the spectral, spatial and texture features of water body, the rule set for extracting water body type is established based on the topological and contextual relationship between segments. The recognition method of water body types proposed in this paper gets rid of the traditional object-based classifications based on statistical law. Using prior knowledge to construct knowledge rules with spatial semantic information makes spatial distribution characteristics in coastal zone effective in improving the accuracy of type identification.

To perform an accurate interpretation of remote sensing images, it is necessary to extract information using different image processing techniques. Nowadays, it became usual to use image processing plugins to add new capabilities/functionalities integrated in Geographical Information System (GIS) software. The aim of this work was to develop an open source application to automatically process and classify remote sensing images from a set of satellite input data. The application was integrated in a GIS software (QGIS), automating several image processing steps. The use of QGIS for this purpose is justified since it is easy and quick to develop new plugins, using Python language. This plugin is inspired in the Semi-Automatic Classification Plugin (SCP) developed by Luca Congedo. SCP allows the supervised classification of remote sensing images, the calculation of vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) and other image processing operations. When analysing SCP, it was realized that a set of operations, that are very useful in teaching classes of remote sensing and image processing tasks, were lacking, such as the visualization of histograms, the application of filters, different image corrections, unsupervised classification and several environmental indices computation. The new set of operations included in the PI2GIS plugin can be divided into three groups: pre-processing, processing, and classification procedures. The application was tested consider an image from Landsat 8 OLI from a North area of Portugal.

Increased impervious surfaces pose significant threats to the hydrologic cycle of the Xiangjiang River basin as a consequence of urbanization. Quantifying the percentage of imperviousness within the Xiangjiang River basin is important to pollution control and watershed management. Per-pixel and sub-pixel methods have been widely used for analyzing impervious surface changes, but these methods are considered as complicated, computationally intensive, and sometimes subjective, especially when applied to a large geographic area. In this paper, normalized difference built-up index (NDBI), normalized difference impervious surface index (NDISI), normalized difference vegetation index (NDVI) and enhanced built-up and bareness index (EBBI) were respectively used to estimate impervious surfaces in Chang-ZhuTan region (CZT) of the Xiangjiang River basin, and a comparative analyses was conducted. Then the optimum spectral index was chosen to map the percentage of impervious surfaces for the study area. The results show that the spectral index of NDBI has the optimum estimation of large-scale impervious surfaces, and the percentage of imperviousness in CZT was 13.87%. The water quality in CZT was characterized as “protected”, indicating that water quality protection in the plain areas of CZT is imperative.

Satellite remote sensing is an important tool for spatio-temporal analysis and surveillance of NPP environment, thermal heat waste of waters being a major concern in many coastal ecosystems involving nuclear power plants. As a test case the adopted methodology was applied for 700x2 MW Cernavoda nuclear power plant (NPP) located in the South-Eastern part of Romania, which discharges warm water affecting coastal ecology. The thermal plume signatures in the NPP hydrological system have been investigated based on TIR (Thermal Infrared) spectral bands of NOAA AVHRR, Landsat TM/ETM+/OLI, and MODIS Terra/Aqua time series satellite data during 1990-2016 period. If NOAA AVHRR data proved the general pattern and extension of the thermal plume signature in Danube river and Black Sea coastal areas, Landsat TM/ETM and MODIS data used for WST (Water Surface Temperature) change detection, mapping and monitoring provided enhanced information about the plume shape, dimension and direction of dispersion in these waters. Thermal discharge from two nuclear reactors cooling is dissipated as waste heat in Danube-Black -Sea Channel and Danube River. From time-series analysis of satellite data during period 1990-2016 was found that during the winter season thermal plume was localized to an area of a few km of NPP, and the mean temperature difference between the plume and non-plume areas was about 1.7 oC. During summer and fall, derived mean temperature difference between the plume and non-plume areas was of about 1.3°C and thermal plume area was extended up to 5- 10 km far along Danube Black Sea Channel.

Built-up areas are the results of human activities. Not only are they the real reflection of human and society activities, but also one of the most important input parameters for the simulation of biogeochemical cycle. Therefore, it is very necessary to map the distribution of built-up areas and monitor their changes by using new technologies and methods at high spatiotemporal resolution. By combining technologies of GIS (Geographic Information System) and RS (Remote Sensing), this study mainly explored the expansion and driving factors of built-up areas at the beginning of the 21st century in Zhejiang Province, China. Firstly, it introduced the mapping processes of LULC (Land Use and Land Cover) based on the method which combined object-oriented method and binary decision tree. Then, it analyzed the expansion features of built-up areas in Zhejiang from 2000 to 2005 and 2005 to 2010. In addition to these, potential driving factors on the expansion of built-up areas were also explored, which contained physical geographical factors, railways, highways, rivers, urban centers, elevation, and slop. Results revealed that the expansions of built-up areas in Zhejiang from 2000 to 2005 and from 2005 to 2010 were very obvious and they showed high levels of variation in spatial heterogeneity. Except those, increased built-up areas with distance to railways, highways, rivers, and urban centers could be fitted with power function (y = a*x^b ), with minimum R² of 0.9507 for urban centers from 2000 to 2005; the increased permillages of built-up areas to mean elevation and mean slop could be fitted with exponential functions (y = a*e^bx), with minimum R² of 0.6657 for mean slop from 2005 to 2010. Besides, government policy could also impact expansion of built-up areas. In a nutshell, a series of conclusions were obtained through this study about the spatial features and driving factors of evolution of built-up areas in Zhejiang from 2000 to 2010.

Ozone (O₃) is unique among pollutants because it is not emitted directly into the air, and its results from complex chemical reactions in the atmosphere. O₃ can bring different effects for all the living on earth (either harm or protect), depending on where O₃ resides. This is the main reason why O₃ is such a serious environmental problem that is difficult to control and predict. The objective of this paper is to analyze the variations of total column O₃ measured by Brewer O₃ spectrophotometer over Global Atmosphere Watch Station (GAW) regional station, which is located at southwest of Peninsular Malaysia, Petaling Jaya. Total column O₃ observations in Petaling Jaya are studied for the period January 2008 to December 2008. Ozone shows seasonal variation with maximum (276.8 DU in May 2008) during pre-monsoon season and minimum (246.8 DU in January 2008) during northeast monsoon season. In addition, the monthly O₃ maps for the year of 2008 were obtained from the NASA-operated Giovanni portal to overview the distribution of total column O₃ in Peninsular Malaysia. For the upcoming studies, validation of ground measurements with satellite O₃ data and study of tropospheric O₃ over the study area is recommended.

Hyperspectral imaging is up-to-date promising technology widely applied for the accurate thematic mapping. The presence of a large number of narrow survey channels allows us to use subtle differences in spectral characteristics of objects and to make a more detailed classification than in the case of using standard multispectral data. The difficulties encountered in the processing of hyperspectral images are usually associated with the redundancy of spectral information which leads to the problem of the curse of dimensionality. Methods currently used for recognizing objects on multispectral and hyperspectral images are usually based on standard base supervised classification algorithms of various complexity. Accuracy of these algorithms can be significantly different depending on considered classification tasks. In this paper we study the performance of ensemble classification methods for the problem of classification of the forest vegetation. Error correcting output codes and boosting are tested on artificial data and real hyperspectral images. It is demonstrates, that boosting gives more significant improvement when used with simple base classifiers. The accuracy in this case in comparable the error correcting output code (ECOC) classifier with Gaussian kernel SVM base algorithm. However the necessity of boosting ECOC with Gaussian kernel SVM is questionable. It is demonstrated, that selected ensemble classifiers allow us to recognize forest species with high enough accuracy which can be compared with ground-based forest inventory data.

To verify data obtained by a satellite instrument a systematic calibration of the instrument is carried out. In addition to an internal calibration using on–board lamp or reflected solar radiation, the external calibration based on a comparison of radiance measurements above special ground test sites and calculated radiances is often employed. Radiances at the top of the atmosphere can be calculated using a radiative transfer model basing on measurement of the atmospheric properties and surface characteristics at the test sites. External calibration of hyperspectral instrument is sensitive to the spectral structure of absorbing and scattering of atmospheric species and, as a consequence, has a specific spectral structure of errors. We compared theoretical errors of a satellite hyperspectral instrument radiometric calibration using two test sites one of which is located in downcountry at 200 m a.s.l. and another one in highlands at 2000 m a.s.l. We suppose that both stations are equipped by the same set of instruments for measurements of the properties of the atmosphere and surface reflectance. The aerosol vertical profile and the aerosol phase function are supposed as not measured characteristics. The analysis is performed for an instrument with the spectral resolution of 1-8 nm which is typical for special regime of payload GSA of Russian satellite Resurs-P. The errors related with the atmospheric composition (including possible scenarios of the aerosol phase function and the aerosol vertical profile) and albedo measurement errors were theoretically examined. The errors strongly depend on aerosol loading. In case of low aerosol loading (corresponding to aerosol optical depth of 0.1 at 0 m a.s.l.) errors are less than 10% at both sites for all the wavelengths between 400 nm and 1000 nm with the exception of the absorption band of water vapor about 950 nm, where errors reach 35% at downcountry and 14% at highlands. For aerosol optical depth of 1 at 0 m a.s.l. the errors can reach 45% at downcountry and 18% at highlands.

The study of the interaction between vegetation development and climate factors is paramount for the management of protected natural areas. Data provided by remote-sensing satellites and derivative products, such as vegetation indices, permit the extraction of basic information regarding the functioning of vegetation masses and their interaction with certain environmental factors. This paper carries out an approach regarding the behaviour of radiation intercepted by aquatic macrophytes present in the Doñana National Park marshland, represented by the plant association Bolboschoenetum maritimi. Based on MODIS NDVI (Normalised Difference Vegetation Index) data, the temporal dynamics of these vegetation masses were studied over a 16-year period (2000–2015), as was their typical annual behaviour, thereby deriving different indicators for seasonal dynamics (NDVI-I, RREL, MAX, MIN, MMAX and MMIN), which help to understand the basic functional characteristics for this type of vegetation. Afterwards, different regression analyses were performed between precipitation and the indicators derived from the NDVI time series. The obtained results indicated that the examined association has a strong dependence on the marshland's flooding processes, requiring a minimum annual precipitation volume (350 mm/year) for proper flooding and vegetation growth development. Furthermore, a strong correlation (r² =0.70; <;0.05) was found between seasonal nature of the vegetation masses, measured via RREL, and precipitation, as well as slightly weaker relationships between precipitation and other indicators, such as the maximum and minimum annual NDVI (r² =0.43 and r² =0.61; p<0.05 and p<0.05, respectively).

Forest fires are regarded as one of the most threatening sources of disturbance for the property, infrastructure as well as ecosystems. The present study aimed at analyzing spectral information products derived from the Landsat–8 OLI sensor together with spectral indices to evaluate their ability to map burn scars and burn severity. In particular the study objectives were: (1) to identify the capability of OLI to burnt area mapping and burn severity, (2) to evaluate the contribution of several spectral indices to the overall accuracy (3) to assess post-fire effects such as flood risk and, (4) to investigate the vegetation re-growth in relation to the burn severity. As a case study, Chios Island was selected due to the recent fire event in the south-western part of the island (25/07/2016). Three multispectral Landsat-8 OLI images, acquired on 13/07/2016 (pre-fire), 15/09/2016 (post-fire) and 27/03/2017 (six months after the fire), were utilized. Several spectral indices were implemented to detect the burnt areas and assess the burn severity (Burn Area Index – BAI, Normalized Burn Ratio - NBR, Normalized Burn Ration + Thermal - NBRT), as well as to evaluate the vegetation conditions and re-growth six months after the fire event (Normalized Difference Vegetation Index - NDVI and the Normalized Difference Water Index - NDWI). Additionally, NBR index of pre- and post-fire images was calculated in a difference change detection procedure which estimates the Differenced Normalized Burn Ratio dNBR. Overall, a total burned area of 45,9 km² was delineated, and both burned severity map and vegetation recovery map were created and evaluated.

In this study the development and implementation of a geospatial database model for the management of multiscale datasets encompassing airborne imagery and associated metadata is presented. To develop the multi-source geospatial database we have used a Relational Database Management System (RDBMS) on a Structure Query Language (SQL) server which was then integrated into ArcGIS and implemented as a geodatabase. The acquired datasets were compiled, standardized, and integrated into the RDBMS, where logical associations between different types of information were linked (e.g. location, date, and instrument). Airborne data, at different processing levels (digital numbers through geocorrected reflectance), were implemented in the geospatial database where the datasets are linked spatially and temporally. An example dataset consisting of airborne hyperspectral imagery, collected for inter and intra-annual vegetation characterization and detection of potential hydrocarbon seepage events over pipeline areas, is presented. Our work provides a model for the management of airborne imagery, which is a challenging aspect of data management in remote sensing, especially when large volumes of data are collected.

Nepal is one of the poorest nations of the world and the Koshi Basin includes some of the poorest regions of this country. It’s farming system is subsistence agriculture, mainly rainfed, with crop productivity among the lowest in South Asia. Nepal is also severely impacted by climate changes, such as retreat of glaciers, rise in temperature, erratic rainfalls and increase in frequency of extreme weather. This paper describes the spatio-temporal evolution of cultivated land in Dudh Koshi during the last four decades (1970s-2010s), by mapping the farming of its four main cereals in the districts of Solukhumbu, Okhaldunga and Kothang from space. The analysis of satellite time series showed a 10% of increment in farmland from 1970s to 1990s, and about 60% in the following twenty years. With a shift of cropping to higher altitudes. Data belonging to of the second twenty years are strongly correlated with the population growth observed in the same period (0.97<R²<0.99) and consistent with the average daily caloric intake. Finding confirms the result of recent studies that agriculture practices once distributed in lowland areas have now spread to higher altitudes and seems to suggest that demographic and socioeconomic pressures are driving the expansion, while climatic and topographic parameters are just channeling the expansion. Apart from any policies that could change the tack, Dudh Koshi should be able to meet the increasing demand of cereals in the near future and climate seems not being a limiting factor for further development as it will be the availability of an irrigation system.