This PDF file contains the front matter associated with SPIE Proceedings Volume 11156, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Monitoring the near-shore environment is always a challenging task. A very detailed representation of the relief is necessary to understand and properly characterize many of coastal processes. Traditional in situ measurements with topographic equipment provide spatially sparse datasets and they could not map in detail the spatial variability in wave and current fields, the shallow water bathymetry and the beach morphology. As an alternative solution to the in situ measurements remote sensing data and particularly high resolution satellite data and airphotos have benn used in coastal monitoring in order to assess the morphological evolution of the coastline or to measure volume changes in the coastal area. More recently, the use of airborne LIDAR systems has been of high interest in coastal area mapping due to the very high accuracy that can be achieved but on the other hand, the very high campaign costs are dissuasive in the use of LIDAR over small study areas. As high-resolution digital surface models (DSMs) and orthophoto maps became a necessity in order to map with precision all the variations in coastal environments unmanned aerial vehicles (UAV) photogrammetry offers an alternative solution to the acquisition of high accuracy spatial data along the coastline. The UAVs present serious capabilities such as: almost real-time applicability, flexible survey planning, acquisition of high resolution data, low operational cost, and capability of data collection in difficult accessible environments. This paper presents the feasibility of the use of a small commercial UAV and an unmanned surface vehicle (USV) for coastal monitoring and shallow water mapping. Optical and acoustic remote sensing data were acquired and processed and the results are presented.

The article presents a model for assessing the information capabilities of multi-, hyperspectral satellite systems for Earth remote sensing in solving forest monitoring problems. The information capabilities of satellite system for Earth remote sensing means the ability to complete assigned tasks in time and quality. Assessment of information capabilities is divided into two parts. The first part is an assessment of the operational capabilities of the satellite system, that is, an expected time to complete the task. The second part is an assessment of solving the problems quality. Assessing of the information capabilities of satellite systems for Earth remote sensing allows to determine the appropriateness of including the task of monitoring territories (for example, forest monitoring) in the flight task for the satellite system, to develop measures to improve information capability in solving the tasks. The article also provides informational capability assessment obtained by the proposed method for the hyperspectral satellite system by NPO «Lepton» and Moscow Institute of Physics and Technology in solving the problem of classifying the species composition of deciduous and coniferous forest on a given territory. Such assessment is valid for similar satellite systems for Earth remote sensing when solving similar problems.

The instrument simulation of space-borne remote sensing systems is an important work for the adaptation and optimization of fundamental instrument parameters for a sensor and its observation conditions. The multispectral imaging simulator has been developed with the framework of GF-4 mission, which is a geostationary satellite in the national high-resolution earth observation system of China. The presented simulator consists of two processing modules producing GF-4 like digital number data in VNIR and MIR bands. The first processing module converts at sensor radiance to photons considering the spectral response, optical transmission, noise, modulation transfer function (MTF), et al. The second part of the simulation is electronic data processing module including an analogue-to-digital converter. The verification of the simulation is performed by comparing the real output of radiometric calibration in laboratory with simulated DN. Analysis of the final simulation data has shown the accurate and reliable performance of the established simulator enabling the system to support technical decision-making processes required for the development of the next generation geostationary satellite.

Greenhouse gas emissions caused by human activities remain one of the most important subjects of international discussions. The routine gas flaring has been estimated to be responsible for as much as 1% of global carbon dioxide emission per year. Gas flaring analysis is one of the key-interest subjects in remote sensing community. Some of the existing publications use remote sensing techniques with satellite imagery to derive information about flame temperature and further about to estimate the volume of the flared gas. The often missing element is an in-situ measurement analysis of gas flames, combining signals recorded by a camera on-ground and data on flared gas volume, temperature and gas type. In order to address this problem, an experiment was conducted, in which the gas flame was recorded by thermal cameras on-ground, simultaneously to an aerial survey and the gas flow was measured at the same time on-ground. The measurement setup was designed in cooperation between Institute of Optical Sensor Systems of German Aerospace Center (DLR) and German Federal Institute for Materials Research and Testing (BAM). Cameras recording in thermal and mid-wave IR wavelengths were used to record the burning gas on-ground and from the aerial survey. All the measurements have been compared and statistically analysed with respect to the recorded temperature. The purpose of the examination was to describe the signal changes in thermal imagery with respect to changes in energy, emitted by the burning process. This approach will allow for later calculation of the amount of energy in form of thermal radiation sent from the flame to the satellite.

Wildfires are one of the most widespread disturbances of forest ecosystems. Countries of the Mediterranean basin registered the largest number of fires and burned area in the last decade. Optical remote sensing, particularly Landsat images, has been commonly used to characterise forest disturbance and subsequent recovery for long time series. Time series techniques such as temporal segmentation algorithms have been developed to facilitate the understanding of postfire vegetation recovery dynamics. This study aims to extract the main types of natural recovery trajectories from a Large Forest Fire occurred in 1994 from a thermophilous pine forests (Pinus Halepensis and Pinus Pinaster) in the long-term (1994-2018). We built annual composites from Landsat Surface Reflectance images and calculated Tasseled-cap components, which are sensitive to canopy moisture and structure (Wetness - TCW) and percent vegetation cover (Angle – TCA). We evaluated fire severity and fire recovery relationship. The differenced Normalised Burn Ratio (dNBR) was used as a fire severity proxy, whereas recovery processes were assessed from spectral profiles using LandTrendr temporal segmentation algorithm. TCW and TCA were used as inputs to LandTrendr and the outputs of fitting were subsequently used to classify recovery types based on a k-means classification with the optimum number of clusters based on the Elbow Method. Groups of continuous positive recovery, non-continuous recovery and continuous recovery with slope changes were identified. The proposed method could be an approach to model the long-term recovery for the Mediterranean areas and help decisionmakers in determining which areas could not recover naturally.

Soil moisture (SM) is a key variable in controlling the water, carbon, and energy exchange processes of land atmosphere interface. One of the widely used approaches to retrieve soil moisture is based on satellite remote sensing technology. However, these spatiotemporally continuous soil moisture products retrieved from microwave remote sensing data are not able to meet the accuracy requirement of flood prediction and irrigation management due to the coarse spatial resolution. As one of the relatively new passive microwave products, The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product was retrieved from passive microwave brightness temperature data based on the Qp model. However, it has rarely been applied at the catchment and regional scale due to the coarse resolution with 25- km grid. In this study, the Fengyun-3B soil moisture product was downscaled from 25-km to 1-km based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The downscaling approach uses MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) to construct soil evaporative efficiency (SEE). The 1-km SM was then estimated based on the difference value of high resolution and average SEE in original FY3B pixel. The downscaling method was applied to every Fengyun-3B pixel in the Naqu area on the Tibetan Plateau to retrieve the downscaled 1-km resolution FY3B soil moisture product. The downscaling results were validated using the in-situ soil moisture from Soil Moisture/ Temperature Monitoring Network on the central Tibetan Plateau (TP-STMNS) in August 2015. The validation results revealed that the downscaling approach showed promising results. We can conclude that the downscaled FY3B SM product better characterize the spatial and temporal continuity and have higher consistency with validation soil moisture data. The approach proposed in this study are applicable to bare surface or sparse vegetation covered land surface.

Soil erosion constitute a major threat to human lives and assets worldwide, as well as a major environmental disturbance. The Revised Universal Soil Loss Equation (RUSLE) integrated with Geographical Information System (GIS) has been the most widely used model in predicting and mapping soil erosion loss. Remote sensing has particular utility for soil loss model applications, providing observations on several key aspects of Land use and Land cover (LULC) linked to the cover-management factor C of the RUSLE, over wide areas and in consistent and repeatable measurements. A free and open source GIS application coupled with remote sensing data was developed under QGIS software allowing to improve the C factor functionality: (i) automatically download satellite images; (ii) clip with the study case and; (ii) perform a supervised or unsupervised classification, in order to obtain the land cover classification and produce the final C map. One of the most efficient supervised classification algorithms is the Support Vector Machine (SVM). Random Forest (RF) is also an easy-to-use machine learning algorithm for supervised classification. The automation of this functionality was based in the R and SAGA software, both integrated in QGIS. To perform the supervised classification, SVM and RF methods were incorporated. The overall accuracy and Kappa values are also automatically obtained by the R script and GRASS algorithms, which allows to evaluate the result obtained. To perform the unsupervised classification K-means algorithm from SAGA was used. This updating in RUSLE application improve the results obtained for C factor and help us to obtain a most accurate estimation of RUSLE erosion risk map. The application was tested using Sentinel 2A images in two different periods, after and before the forest fire event in Coimbra region, Portugal. In the end, the three resulted maps from SVM, RF and K-means classification were compared.

In this work a project for the implementation of remote sensing research activities for the acquisition of new knowledge and encouraging the participation of the PhD students of Remote Sensing Systems /RSS/ Department at SRTI-BAS in these activities is presented. The goal of the project is collecting data through spectral measurements for land cover monitoring in a selected test region in Bulgaria and create an open access spectral database. The first task of the work to collecting spectral measurements data is related to the methodology of acquiring in-situ spectral data of land covers in test site. Methodology follows the next steps of 1) collecting samples and additional information; 2) laboratory and field spectrometric measurements; 3) spectral data verification. For the implementation of the steps the test region is selected meeting the following requirements: i) Offers a wide variety of objects from the adopted nomenclature; ii) Has spectral data from Earth Observation device systems; iii) Has the possibility to perform regular measurements with available spectrometric systems. According to the described conditions the test region around the town of Novi Iskar is chosen. In CORINE Land Cover database for this area the presence of 12 classes of land covers has been verified which has to be characterized in detail on the basis of the received data. Each one will be recorded in the created database which is the next project task. This will allow the data received in the experiments to be considered reliable and representative. For monitoring purposes the data could be interpolated for larger areas with similar land covers to trace the dynamics of objects using spectral data.

Information on land cover is very important variable not only affecting on human activities but also studying the functional and morpho-functional changes occurring in the earth. The goal of this study is an assessment of support vector machine (SVM) for land cover classification over South Korea using normalized difference vegetation index (NDVI) of geostationary ocean color imager (GOCI). We collected level-2 land cover maps in South Korea and defined the seven most common land cover types (urban, croplands, forest, grasslands, wetlands, barren, and water) in South Korea to assess SVM model and produce land cover map. To train SVM model, we decided 1,000 training samples per classes. In addition, We repeated 50 times random selection of training samples. In order to evaluate accuracy of SVM`s kernels, we selected four kernels; linear, polynomial, sigmoid, and radial basis function (RBF). The parameters of each kernel were determined by the grid-search method using cross validation approach. The best accuracy of four kernel is linear kernel, the overall accuarcy was calculated 71.592%.

Complete high resolution geochemical maps are strongly needed for mineral exploration; however, the previously proposed methods for making geochemical maps have low accuracy. In this research, we propose a new algorithm called sample density based mixture interpolation (SADBAMIN) for high resolution geochemical map completion using remote sensing data. In the SADBAMIN algorithm, first, according to the measured copper data density on the map, the map is classified into two parts: the area for training (T area) and the area waiting to be predicted (P area). The two areas are classified by the edge of the data point set’s alpha shape. In the T area, a triangle area among three neighbourhood points is interpolated by using the kriging model. Then, remote sensing data, including advanced spaceborne thermal emission and reflection radiometer (ASTER) data, digital elevation model (DEM) data, and geophysics (magnetic) data, and copper geochemical data at all measured and partial randomly selected interpolated points are applied as training data to construct a random forest regression model. By considering the relationship between interpolation reliability and distance, a penalty on data selection probability of going into training data is given. Finally, by inputting the remote sensing data in the P area to the model, the copper data in this area can be obtained, and the completed map comprises these two parts. We use 16,000 measured points, 10-fold cross-validation, and root mean squared error (RMSE) for model evaluation. We achieved an RMSE of 293 ppm, while the RMSE of the previously proposed method is 347 ppm.

Many archaeological sites and monuments of the world are at risk of natural disasters and potentially devastating natural events such as landsides, flood, fires and earthquakes, and effective risk reduction is only possible if all relevant threats are considered and analyzed. As opposed to single-hazard analyses, the examination of multiple hazards poses a range of additional challenges due to the differing characteristics of procedures. This refers to the evaluation of the hazard level, as well as to the vulnerability toward distinct processes, and to the arising risk level. As comparability of the single-hazard results is strongly required, an equivalent methodology has to be chosen that allows to estimate the overall hazard and subsequent risk level additionally to rank threats. The purpose of this paper is to provide a literature review of the two existing multi-hazards methods: Analytical Hierarchy process (AHP), and MmhRisk-HI (Model for multi-hazard Risk assessment with a consideration of Hazard Interaction) which used for several applications. A critical assessment of existing methods provides the opportunity to retrieve the main advantages and disadvantages of method. Furthermore, based on this critical assessment some of the methods will be implemented in different archaeological sites in Cyprus. Finally, the main attempt of the paper is to raise awareness on the benefits of advancements in EO technologies and of deriving products can bring to a more complete analysis and monitor natural threats on archaeological sites.

Innovative technologies provide an accurate, simple and cost-effective method of documenting cultural heritage sites and generating digital 3D models using novel techniques and innovative methods. These digital 3D models can then be saved in a central database that can be accessed by end users. The project “Digital unblocking of holy islands” proposes digital service requires the creation of digital infrastructure and its enrichment with culturally digital evidence and data, in order to serve as an information hub for the management and promotion of ecclesiastical cultural heritage. The internal and external digitization of ecclesiastical monuments will be carried out using several methods, including images from Unmanned Aerial Vehicles and photogrammetry. Hundreds of images from the monument will be taken using a UAV with an attached high-resolution camera. The images will then be processed through photogrammetry to provide a digital model of the church. The use of digital technology to document cultural heritage within a structure, creates a dynamic database and valuable resource to better understand the cultural heritage monument, as end-users will be able to access the information from the digital platform at any time. This research is supported by the project entitled: “Navigators of Cultural Heritage Digitization of Churches of Cyprus and Crete” referred as “Digital unblocking of holy islands” and is co-funded by the European Regional Development Fund (ERDF)and by national funds of Greece and Cyprus, under the Cooperation Programme “INTERREG V-A GreeceCyprus 2014-2020”

Landslides are type of natural geohazard interfering with many economical and social activities and causing serious damages on human life. It is ranked as a great disaster, threatening life, property and environment. Therefore, early prediction of landslide prone areas is vital. Variety of causative factors such as glaciers melting, excessive raining, mining, volcanic activities, active faults, earthquake, logging, erosion, urbanization, construction, and other human activities can trigger landslide occurrence. Then, identification of factors that directly influences the slide events is highly in demand. Some topographical, geological, and hydrological datasets (e.g., slope, aspect, geology, terrain roughness, vegetation index, distance to stream, distance to road, distance to fault, land use, precipitation, profile curvature, plan curvature) are considered to be effective conditioning factors. However, the importance of each factor differs from one study to another. This study investigates the effectiveness of four sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into four groups G1, G2, G3, and G4. Three machine learning algorithms namely, Random Forest (RF), Naive Bayes (NB), and Boosted Logistic Regression (LogitBoost) were constructed based on each dataset in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 227 landslide inventory datasets of the study area were used where 70% was used for training and 30% for testing. To this end, in the present research, the two main objectives were: 1) Investigation on effectiveness of 14 landslides conditioning factors (altitude, slope, aspect, total curvature, profile curvature, plan curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), distance to fault, distance to road, distance to stream, land use, and geology) by analyzing and determining the most important factors using variance-inflated factor (VIF), Pearson’s correlation and Chi-square techniques. Consequently, 4 categories of datasets were defined; first dataset included all 14 conditioning factors, second dataset included Digital Elevation Models (DEM) derivatives (morphometrice factors), third dataset was only based on 5 factors namely lithology, land use, distance to stream, distance to road, and distance to fault, and last dataset was included 8 factors selected using factor analysis and optimization. 2) Evaluate the sensitivity of each modeling technique (NB, RF and LogitBoost) to different conditioning factors using the area under curve (AUC). Eventually, RF technique using optimized variables (G4) performed well with AUC of 0.940 followed by LogitBoost (0.898) and NB (0.864).

In recent years urbanized areas have been expanding more and more into hillside areas, which increases the risk for landslides to destroy or gravely damage human settlements, industrial establishments and important infrastructure. Greece and in particular the northwestern tip of the Peloponnese Peninsula are often affected by landslides. A well-known and documented landslide south-east of Patras is located in that area and was chosen as the main focus for this study. The landslide collapsed and thus submerged the main road connecting the small village Moira and Patras on January 20, 2016. Immediately afterwards the geometry and geology of the affected area have been determined using GNSS Measurements, UAV Campaigns and DInSAR techniques. However, the velocity of the post-collapse movement has not been investigated. In order to make this determination two time series of SAR images acquired by the two Sentinel-1 satellites from an ascending and descending orbit were evaluated using the advanced InSAR technique of persistent scatterer interferometry. The result is a map of the mean velocities the identified persistent scatterers were experiencing in the time period from January 2017 to June 2019. The persistent scatterer density in the rural areas of the region was sufficient enough to identify the landslide within the results. Roughly 80 persistent scatterer with a velocity of more than ± 8 mm/a in the line of sight of the sensor were identified within the outlines of the landslide (approximately 750 m²), indicating that the landslide is experiencing post-collapse movement.

This study aims to produce landslide susceptibility map (LSM) using landslide conditioning attributes selected by different feature selection methods and compare predictive capability. Among the total 140 landslide locations, 98 locations (70%) were selected randomly for model training and remaining 42 locations (30%) were used to validate. Fourteen landslide conditioning attributes related to topography, hydrology, and forestry factors were considered. These factors were analyzed importance using four feature selection methods, such as information gain, gain ratio, Chi-squared, and filtered subset evaluator. From the results, the top seven attributes were selected and the LSMs were produced by random forest model. The results showed that the all LSMs had a prediction rate of more than 0.80 that yielded higher accuracy than the LSMs produced using all attributes. In addition, the LSM produced using attributes selected by gain ratio performed slightly better than another LSMs. These results indicate that the produced LSMs had good performance for predicting the spatial landslide distribution in the study area. In addition, selection of input attributes using feature selection methods was contributed to improve model performance. The produced LSMs could be helpful for establishing mitigation strategies and for land use planning in the study area.

The main problem for seismic precursors recognition is to extract useful information associated with tectonic activities and to eliminate the effects of non-tectonic factors. Pre-earthquake spatio-temporal developed geophysical, geodetically, and geochemical anomalies are controlled by various factors like as earthquake moment magnitude and its focal depth, geological setting, topography, land covers as well as climate and atmospheric conditions. In this paper, changes before and after some moderate Vrancea earthquakes in the crustal dynamics as well as in the land and atmospheric parameters (surface air temperature- AT and land surface temperature LST anomalies, have been investigated on the basis of timeseries geospatial (NOAA AVHRR and MODIS Terra/Aqua) and field data analysis for 2010-2018 period. Ground surface deformations have been detected through analysis of Synthetic Aperture Radar Interferometry (InSAR) radar satellite Sentinel 1 and high quality in-situ GPS monitoring data. The detected changes show strong evidence of coupling between lithosphere-land surface-atmosphere-ionosphere associated with the Vrancea’s earthquakes For some analyzed earthquakes, starting with ten days up to one week prior to a moderate earthquake a transient thermal infrared rise appeared in AT (2-10°C) and LST (20-30°K) higher than the normal values, function of the magnitude and focal depth, which disappeared after the main shock. Ground vertical surface displacements presented on interferometric deformation map are in the range of 4 cm for uplifts and subsidence. The joint analysis of geospatial, geophysical, and geological information is revealing new insights for Vrancea zone seismicity understanding.

Surface hydrocarbon detection is of interest both for environment monitoring as well as for exploration purposes. For an oil and gas company, being able to detect early the presence of surface hydrocarbon in the exploration process is an important information that will derisk the presence of a source rock and its maturity. These, alone, will not guaranty the success of exploration, which also requires a reservoir, a trap and a sealing cap rock, but they are some essential elements for the presence of an oil accumulation in the subsurface and for the success of subsequent exploration wells. Many papers have been published on surface hydrocarbon remote sensing direct detection; however, most are either for the offshore domain, or for onshore experimental experiments or environmental case studies. Why is onshore detection of naturally occurring hydrocarbons so difficult from remote sensing? In this paper we will explain why out of the lab onshore hydrocarbon detection is more difficult than offshore detection and how the spectral or spatial limited resolution of current satellite sensors are hampering this detection.

Machine learning algorithms (MLAs) have gained great importance in remote sensing-based applications, and also in mineral prospectivity mapping. Studies show that MLAs can outperform classical classification techniques. So, MLAs can be useful in the exploration of strategical raw materials like lithium (Li), which is used in consumer electronics and in the green-power industry. The study area of this work is the Fregeneda-Almendra region (between Spain and Portugal), where Li occurs in pegmatites. However, their smaller exposition can be regarded as a problem to the application of remote sensing methods. To overcome this, Support Vector Machine (SVM) and Random Forest (RF) algorithms were applied to. This study aims at: (i) comparing the performance accuracy in lithological mapping achieved by SVM and by RF; (ii) evaluating the sensitivity of both classifiers to class imbalance and; (iii) compare the results achieved with previously obtained results. For these, the same Level 1-C Sentinel-2 images (October 2017) were used. SVM showed slightly better accuracy, but RF was able to correctly classify a larger number of mapped Li-bearing pegmatites. The performance of the models was not equal for all classes, having all underperformed in some classes. Also, RF was affected by class imbalanced, while SVM prove to be more insensitive. The potential of this kind of approach in Li-exploration was confirmed since both algorithms correctly identified the presence of Li-bearing pegmatites in the three open-pit mines where they outcrop as well in areas where Li-pegmatites were mapped. Also, some of the areas classified as Li-bearing pegmatites are corroborated by the interest areas delimited in previous studies.

Spontaneous combustion of coal is a critical problem encountered by mining and thermal power industries. Usually, coal is stored in open area in the form of stockpiles in the coal mines and thermal power plants. In this paper, we have focused on localization of open-cast coal mines and coal stockpiles using satellite images to automate the entire process of prediction of spontaneous combustion in the coal stockpiles. We have used USGS Landsat-8 Satellite images, collected from various coal mines and thermal plants across the world. The satellite images consist of 11 bands including Red, Blue, Green, Near Infrared (NIR), and Shortwave Infrared (SWIR). Apart from the reflectance measurements obtained from these bands, we also use standard indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Normalized Difference Water Index (NDWI) as the features to train the models. The ground truth for the training dataset has been created by manually annotating these images for three classes: coal mines, coal stockpiles and water bodies. The Fully Convolutional Network (FCN) based U-Net architecture has been trained to develop two models to classify pixels between (A) Coal Mine and Water and (B) Coal Stockpile and Water. In this paper, we present an exhaustive experimental results to demonstrate the effective localization of coal mine and coal stockpiles using the proposed FCN based approach.

With our study presented at the SPIE conference “Earth Resources and Environmental Remote Sensing/GIS Applications” last year, we discussed a concept for change analysis in sequences of SAR sub-aperture images. The main aspect of this concept is to investigate an adaption of our approach for incoherent time series change analysis on such short-term time series data. In a first step, sub-aperture amplitude images of two maritime scenes were calculated leading to time series stacks being considered as input for our incoherent change detection method. As output, so-called ActivityMaps (AMs) were constructed aiming on a recognition of high activity areas. Focusing on short-term time series, such areas are caused by Ground Moving Targets (GMTs) which denote objects that were in motion during the image acquisition. With respect to the maritime scenes considered in this study, GMTs might be ships, cars, trucks, cranes with moving components, etc. It was observed, that GMTs show different signatures in the AMs, depending for example on their size and their velocity. In the paper at hand, we link to this previous study by discussing different features being reliable for a later categorization of the detected change objects. Moreover, it is investigated, whether High Activity Objects (HAOs) are of solely interest, or, if other change objects have to be included. The relevance of the discussed features to produce categories being clearly distinguishable from each other is tested by an unsupervised clustering procedure. As test data, a TerraSAR-X (TSX) Staring Spotlight (ST) Single Look Complex (SLC) image of Rotterdam (NED) was used.

Forests cover one third of Europe’s land and significantly contribute to the regional economy. Moreover, they play an essential role in climate regulation. Traditional inventory-based forest data update is often much lower than required. Remote Sensing is a valuable source for forest monitoring, as it provides periodic data on vegetation status. In this context, EU Horizon-2020 MySustainableForest project (MSF, Grant Agreement nº 776045) aims at developing remote sensingderived geo-information services for integrated forest management through a web service platform. An unsupervised method to obtain a forest mask over European forests using optical Sentinel-2 data was implemented. Kmeans algorithm was used for segmenting the images in clusters, which were subsequently assigned to a forest class depending on its overlap with the forest classes of ancillary land cover data. The resulting classification was refined applying a filter and a vegetation mask. The algorithm was tested over 16 sites representing Europe’s main biogeographic regions. A confusion matrix was built using points selected via photointerpretation. Validation metrics were computed from the confusion matrix. The results showed that it is possible to develop an automatic forest mask for Europe, (overall accuracy above 90%). Accuracies varied depending on forest characteristics. Best results were achieved in Boreal and Continental forests. Although the algorithm was tuned to consider the diversity of European forests, there is scope for improving the adaptability of MSF Forest Mask, mainly in the southern Mediterranean region, where the mixed effect of tree-grass formations hindered a better forest discrimination. These results may be of interest to forest and land managers and climate modellers.

The coastal areas of the Northwest Peloponnese suffer degradation due to the sea action and other natural and humaninduced 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. In the frame of INTERREG bilateral call, a project titled «Development of management Tools and diRectives for immediate protection of bIodiversity in coasTal areas affected by sea erOsion and establishment of appropriate eNvironmental control systems (TRITON) is being executed. Three laboratories of the University of Patras execute multidisciplinary monitoring surveys both onshore and offshore and the present paper describes those activities.

Marine Spatial Planning (MSP) is a critical tool for the economic, social and environmental sustainability of coastal and marine areas. MSP seeks to identify the various human economic activities in these zones and in various depths. In compliance with Directive 2014/89/EU of the European Parliament and Council of the 23rd of July 2014, which aims to establish a common framework where each member state identify the maritime space under its control. This was accomplished in Cyprus through the project “Cross-Border Cooperation for the Development of Maritime Spatial Planning (Thal-Chor)”, in short “Thal-Chor”, which was co-funded under the Interreg “Greece–Cyprus 2007–2013” framework. The methodology of the project used government data and bathymetry maps to create a GIS database which produced density maps. The Density maps identified a high concentration of activities near the Limassol district and around the ports of Cyprus and over 60 sea and land activities were analysed for conflicts and compatibilities. The further implementation of MPS will take place through the “Cross-Border Cooperation for Implementation of Maritime Spatial Planning (“Thal-Chor 2)”. During the project, satellite remote sensing and Unmanned Aerial Vehicles will be used to survey the coastal and marine areas that were identified on the density maps from the Thal-Chor project. SAR data from the Sentinel 1 satellite will provide the necessary data to verify the MSP activities in the first phase of the project. Such data will provide valuable information for the existing geo-spatial database for MSP for the implementation of integrated plans. This research is supported by the project entitled: “Cross-Border Cooperation for Implementation of Maritime Spatial Planning” referred as “THAL-CHOR 2” and is co-funded by the European Regional Development Fund (ERDF) and by national funds of Greece and Cyprus, under the Cooperation Programme “INTERREG V-A Greece-Cyprus 2014-2020”.

Oil spills, whether those come from tanker ships or offshore platforms, might potentially be disastrous for the environment as well as for the society both socially and economically. Generally, oil spills are spreading quite easily for hundreds of nautical miles at the sea damaging large areas and causing serious ecological disasters. Numerous accidents such as the Exxon Valdez accident in Alaska in 1989, the Prestige oil spill at the coast of Galicia, Spain in 2002 or the Deepwater Horizon platform oil spill accident in 2010 in the Gulf of Mexico have unfortunately demonstrated the dangerousness of an oil accident. Remote sensing constitutes an effective solution in oil mapping and monitoring. Specifically, Synthetic Aperture Radar (SAR) satellites have already proved their effectiveness in the specific research field. Oil identification and mapping constitutes a key point of the oil prevention research since is an essential step for the quantification of the oil volume and thereafter the development of a spread model. As case study of the specific work it was selected the Agia Zoni II oil spill, which resulted from the wreck of the Agia Zoni II tanker on September 10th, 2017. This work focuses on the best possible in terms of accuracy mapping of the Agia Zoni II oil spill using Sentinel-1 and Sentinel-2 imagery. The mapping was based on the application of three different processing approaches: a) using the oil spill tool, provided by SNAP software, b) using a semi-automatic methodology and c) by applying a sort of classification in Sentinel-1 images. The approaches were compared and evaluated regarding the quality (reliability) of the final product and the required processing time. The validation of the results of the oil spill mapping using different approaches implemented by the digitization of the affected areas utilizing Sentinel-2 data in ArcGIS environment.

This study investigates the effectiveness of using groundwater inventory data for groundwater spring potential mapping in the Haraz watershed located in Norther Iran. From a total of 917 groundwater inventory dataset, six random inventory scenarios of 917, 690, 450, 230, 92, and 46 were generated. We trained two learning classifiers, namely the Support Vector Machine (SVM) and Random Forest (RF) based on each scenario to determine which one(s) would be more suitable for spring potential mapping. In each of the scenarios, 70% of the dataset was used for training whereas 30% was used for testing. The end results (classified maps) for each classifier and their respective dataset were quantitatively assessed based on the Area under Curve (AUC) metric. The prediction accuracies for the spring potential maps being produced for each scenario ranged from 0.693 to 0.736 using the SVM, and 0.608 to 0.895 for RF. Our findings indicate that 46 random points of inventory data did not produce a desirable outcome. On the contrary, more points yield better results, i.e. 450 random points produced the highest ROC when using SVM (0.736) followed by 917 and 690 random points using RF (0.895 and 0.877, respectively).

A general decrease of wetland is observed at a global due to climate change and human activity of socio-economic developments. Landsat has been giving us the long record of the Earth's surface in moderate spatial resolution using a multispectral sensor, which can help us better to analyze and attribute the changes in wetland. This study use the multi-temporal observing data since 1979 from Landsat to reveal and evaluate the changes of long time in wetland in the studying area of Baiyangdian wetland in China which is surrounded by the farmland and rural residents. The results of studying could better understand the varying pattern and changing driven factors as to support the management, conservation and restoration of the wetland for government. We collected a total of 191 periods Landsat (L1T) cloud-free images, including MSS, TM, ETM+ and OIL images from the USGS from 1979 to 2017 in the studying area. Moreover, GF-2 data with spatial resolution of 4m in 2017, which is a series of observing land satellites with high spatial resolution released by the China Resources Satellite Applications Center, is also used to get the land cover and verification of results. Firstly, open waters, which is water body apparently without covered by aquatic plants in wetland, are extracted using the dynamic threshold method from 1984 to 2017 using the top of atmosphere reflectance (TOA) data of Landsat TM, ETM+ and OIL in NIR, SWIR and Green bands. The threshold values extracting water in NIR, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are tested through ROI data then the water pixels are extracted using the multiple thresholds for each period. The verification and optimizing the thresholds are carried by visual interpretation. Lastly we obtained the distribution of open water in a long time from 1984 to 2017. Secondly, we take a clustering analysis of multi-temporal NDVI data derived from the collected Landsat data using an iterative self-Organizing data (Isodata) algorithms. We investigate how the change of precipitation and artificial water recharge, which are main sources of water supply for wetland, affect the variations of open water during 33 years. As a result, it is found that the artificial water recharge is an indispensable water supply of keeping of the ecological water volume for the Baiyangdian wetland as the natural water supply from rivers and precipitation are dwindling. Moreover, the artificial uses, such as paddy field, golf course, extending of settlement and road etc., have been decreasing the water and induced the degeneration of aquatic plants in wetland. These results will not only increase our knowledge for the change of wetland but also provide the assistant decision-making of the ecological conservation of wetland for the management strategies.

The present study aims to assess agricultural drought using remote sensing based NDVI and soil moisture products in a drought prone river basin of India. The study is conducted in the Ghataprabha river basin which is a sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Gridded soil moisture data from Modern-Era Retrospective analysis for Research and Applications (MERRA) from 1980 to 2015 is considered to derive Standardized Soil moisture Index (SSI) at different time scales. The Vegetation Condition Index (VCI) was calculated from MODIS NDVI products from 2000-2013. The results of VCI and SSI indicated significant number of drought episodes during the study period while severe agricultural drought was observed during 2001-2003. A Good agreement between SSI and VCI was observed during drought year.

The landslides are one of the well-known natural hazards occurring on the North East Black Sea coast of Bulgaria. The previous researches that take into account the geological and meteorological peculiarities of this region confirmed that the geomorphological conditions in this region are extremely favorable for landslide formation. Two are the main drivers that are being responsible for activation of the landslide processes in the area investigated. The first one is the sea erosion and the other is the increasing groundwater level. The influence of those is highly aggravated by the construction activities that took place in the last decades and the lack of sewerage networks. Those findings are based on information provided by the national authority responsible for monitoring and filing the landslides in Bulgaria. This was the motivation for developing and implementing reliable and accurate method for operational monitoring of the landslide processes in the said area. For development of such method data from SAR instruments were used. In this specific study data from Sentinel-1 SAR mission were processed by the freely provided by ESA SNAP software. Final results are interferometric images (IFIs) providing information about the ground movements. In this paper we present results reflecting the subsidence in the area of a landslide located some 20km north of the Albena resort. In the last decade two events have caused damages to the infrastructure in the area - one occurred in January 2015 which was attributed to the heavy rains the previous summer and the other took place in mid of August 2018. Both have been studied by processing SAR data for the periods mentioned. The results obtained are considered reliable since they have been reaffirmed by geodetic surveys and other terrain measurements. The outcomes of this research will contribute to better understanding the ongoing slow movements of the Earth’s crust, and for forecasting and early warning of geological hazards.

Satellite images of the Earth acquired in the thermal infrared range contain valuable information about the properties of objects on the Earth's surface, the significance of which increases in the context of climate change. To make this information accessible to many people including laymen, it is necessary not only to extract it from images, but also to present it in an obvious visual form. The goal of this research is to identify the thermal radiation generated by rail transport, assess the stability of this radiation through the seasons, and develop a method for visualizing thermal anomalies generated by rail transport in the places where it accumulates. The technique is based on TIRS Landsat 8 images use, acquired in different seasons. The images for 18 selected transport nodes were used firstly to calculate surface temperature for each period. The resulted images of a thermal field were combined with the corresponding highresolution images to determine objects that contribute to the high-temperature anomalies at the nodes. The stability of thermal anomalies for each node was estimated by summarizing the raster thermal maps of different seasons. The comparative analysis of preprocessed thermal images of transport nodes, located in different climatic and economic conditions, shows that the thermal effect is much more evident if a node is located outside a city, whereas the latitudinal position of a node has a significantly smaller effect. Finally, the selected transport nodes were classified according to the degree of intensity and permanency during a year.

In this study, a seismic vulnerability of Gyeongju city, where the 9.12 Gyeongju earthquakes occurred, was analyzed and compared the prediction accuracy using frequency ratio (FR) and logistic regression (LR) models. The buildings damaged by the 9.12 Gyeongju earthquakes were used as dependent variables, of which the buildings were randomly selected data for training (70%) and validation (30%). The total eighteen seismic-related factors were used as independent variables as slope, elevation, groundwater level, distance to epicenter, distance to faults, peak ground acceleration (PGA), age of children, age of elderly, population density, building density, construction materials, number of floors, age of buildings, distance to police stations, distance to fire stations, distance to hospitals, distance to gas stations, and distance to road network. The spatial relationship between damaged buildings and seismic-related factors was analyzed using FR and LR models. The produced seismic vulnerability maps were classified into five zones, i.e., very high, high, moderate, low, and very low. The two maps validated and compared prediction accuracy using relative operating characteristic (ROC) curve and the areas under the curves (AUC). The validation results indicated that AUC value of FR seismic vulnerability map (73.1%) was about 3% higher than LR map (71.4%). The seismic vulnerability maps produced in this study could possibly be used to minimize damage caused by earthquakes and could be used as a reference when establishing policies.

Vegetation is the essential cornerstone of ecosystem cycling, Leaf area index (LAI) is a key parameter to characterize vegetation growth statue. In this study, Jiangsu province as an important coastal province was chosen as the study area, the finished product data of LAI with 500-meter resolution acquired from MODIS sensor were used to reflect the vegetation statue variation and assess the ecological environment. The variation of the mean LAIs in the whole year, in the withering period and in the flourishing period of 2005, 2008, 2011, 2014, 2017 were explored, their spatial distributions were mapped, the stability and trend of vegetation variation were assessed respectively based on the coefficient of variation (CV) and variation rate (VR), the future vegetation statue was simulated by integrating Cellular Automata model and Markov model. Results showed that the mean LAI values in above three periods of 2017 were respectively 0.82, 0.34 and 1.6. From 2005 to 2017, the variation of the mean LAI values was flat except that in flourishing period, their spatial distributions were similar at the same period, northern vegetation statue was better than that in the south especially in the flourishing period. The stability in the whole year was the best of three periods, that in suburban areas was generally better than that in urban areas. Stable trend dominated Jiangsu province all the time, the vegetation in the flourishing period was significantly fluctuant. The vegetation would generally show an improving trend in future six years after 2017.

This paper presents a one-dimensional scanning algorithm that allows improving the quality of images that were using aerial photography to monitor minerals and the state of the subsurface. Also, a software module was developed that implements the presented algorithm, the main advantages of which are the simplicity of the mathematical apparatus, the required number of low-resolution images, equal to two, to obtain a high-resolution image, and resistance to interference and noise.

Several scientific studies with different concept on the mapping of pegmatites have been done in Muiane and Naipa (Mozambique) region. However, none of the studies compare different satellite data and different remote sensing classification algorithms. This study aims to compare the land cover/use classification maps and their accuracies considered sentinel-2, aster, and Landsat OLI imagery. The algorithms employed to evaluate the pegmatites location at Naipa and muiane in alto ligonha pegmatite district were minimum distance (MinD), spectral angle mapper (SAM), and maximum likelihood (ML). The identified features of landscape characteristics selected includes 8 class (kaolinite; montmorillonite; water; built up; bare soil; grasslands; shrubs; isolated bush). The results showed that SAM and MinD algorithms are appropriate for mineralogical mapping validated with ground truth data and geological maps. A kappa index of 0.85 and an overall accuracy (OA) of 80% was obtained for SAM algorithm, and a kappa of 0,80 and OA of 90% for the MinD algorithm. The classification of the images using SAM and mind showed better results for the clays (kaolinite, montmorillonite) visible in both classifications, has also been tested unsupervised classifications or criteria determined by the geologist using an input training dataset in the case of supervised classifications.

Lithium (Li) is defined as an alkaline metal which does not exist in nature in its free form. Moreover, it has properties that make it possible to be applied in several manners, such as industrial use, especially in the ceramic and glass industries, as well as the battery industry which has had an increase in this element consumption. It is crucial to use less expensive and faster techniques, compared to classical and intrusive methods, to identify new Li deposits. Remote sensing as proved to be a powerful tool to identify areas with potential for Li exploration. The objective of this work is to apply several images processing techniques, such as RGB band combinations, Band Ratios and Principal Components Analysis (PCA) to identify potential areas for Li prospection in the pegmatite district of São João Del Rei, located in the south of the state of Minas Gerais, Brazil. In these areas, two study zones were defined: the zone A, with approximately 323 km² in the pegmatite district of São João Del Rei and the zone B with approximately 90 km² in the pegmatite district of Araçuaí. The results of the techniques applied in this study are very promising since, in addition to ease and low cost, these techniques can be applied to several locations. This approach is highly valuable for the Li mining industry.

In order to quantify the eff ect of urbanization and land cover changes on urban surface albedo change and radiative forcing impact on urban thermal environment MODIS land surface albedo (LSA) and land surface temperature (LST) products were used to investigate the magnitude of extreme climate and anthropogenic pressures. The main goal of this study was to develop an effective remote sensing-based methodological approach to investigate the possible occurrence and associated causes of gradual surface albedo trends in metropolitan area of Bucharest during 2000-2018 period. During summer time and heat waves periods urban land surface broadband albedo is a critical variable affecting Bucharest city climate. Analysis of time series MODIS Terra/Aqua data revealed the strong inverse relationship between LSA and LST during summer time in city area with negative impact on urban thermal environment. Broadband albedo, which measures urban surface properties depends also on the atmospheric conditions. In this study, were analyzed also the interannual variations in Urban Heat Island Intensity (UHI), derived from MODIS LST data and their relationships with vegetation urban indices NDVI/EVI, climate variability and urbanization. These findings stress the dependence of urban thermal environment of urban biogeophysical variables such as land surface albedo, urban density and morphology, surface properties, vegetation, bodies of water, industrial sites, transportation systems and infrastructures.

Using the world’s first ocean color sensor at a geostationary orbit (Geostationary Ocean Color Imager; GOCI), we examine the relationship between satellite-derived chlorophyll-a concentration and MH events over the East China Sea during recent summers from 2016 to 2018. MH events usually arise in July and August over the study domain. When compared with the average of three days before and after MH events, the chlorophyll-a concentration since MH event occurrence tends to decrease from GOCI satellite images. Previous studies mentioned that the increased sea surface temperature (SST) enhances the stratification in upper ocean surface layer. Strong stratification derives the weak upwelling and the limited supply of nutrients from the deep to surface. These preliminary results show a possibility of real-time application of the geostationary ocean color satellite images for an immediate change in marine ecosystem caused by the extreme ocean warming event.

Land cover relates to the biophysical characteristics of the Earth’s surface, identifying vegetation, water, bare soil or artificial infrastructure. Land cover mapping is essential for planning and managing natural resource, and for understanding distribution of habitats. Land cover classification for land cover mapping has been developed in a variety of ways. Among them, there are many attempts to classification land cover using deep learning techniques such as Convolutional Neural Network(CNN). CNN has been developed in many models, and semantic segmentation techniques that combining segmentation are also being announced. Among the Semantic Segmentation models developed until recently, SegNet has high accuracy and learning efficiency. We analyzed the availability of SegNet in the Land Cover classification. The study area was conducted in parts of South Chungcheongnam-do in South Korea. For the learning of the model, 2,000 data were constructed with the same size using the aerial image, and the constructed data was divided into training and validation data by 8 to 2. To solve the problem of class imbalance, which causes problems such as overfitting due to the difference in area per class, the weight value of each class was calculated using medium frequency balancing method. In order to calculate the hyper parameter optimization, the batch size was changed from 1 to 5 and the iteration was changed from 0 to 100,000 times Our experiments show that an overall accuracy (OA) of up to 85%, which confirmed the positive possibility of the semantic segmentation technique in the study of land cover classification.

Forests cover 36.5% of Spanish land. Natural and man-induced disturbances are causing important changes in species distribution. As Spanish National Forest Inventory is updated every 10 years, a more recurrent periodic data source providing information on species distribution is needed in order to predict changes in forest area and composition. Remote Sensing meets this demand, as it provides periodic and spatially continuous data on forest status. In this context, MySustainableForest (MSF) H2020 project aims at providing remote sensing-based geo-information services through a web service platform. One of MSF products is a classification of main forest types, whose preliminary development was tested over a 950 km2 area located in Northern Spain. A Random Forest model was trained with data delineated with the help of local forest data. The output was validated using stratified k-fold cross-validation. Validation metrics were computed from the confusion matrix for each class separately and for the total set of classes. Although overall metrics were high (OA = 95%; DC = 85.1%), they varied significantly for different classes (e.g., Fagus sylvatica was classified with higher accuracy than Pinus nigra, which was mainly confused with other Pinus species), showing that species with higher seasonal variations were easier to identify. Random Forest feature importance ranking showed that bands in the near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths were essential to discriminate forest species, since they explained more than 40% of the variations alone and 82% in combination with Red wavelength.

The purpose of this study is to collect ortho-images and point clouds acquired from UAVs in February and May to comprehensively assess whether they are suitable for time series offshore monitoring, such as volume change calculation and shoreline extraction. In February and May, UAV photogrammetry was performed at an altitude of 100 m using Zenmuse7 of Inspire-2 for the research area, and 245 chapters and 240 chapters were collected in ground sample resistance (GSD) 1.59 cm and 1.62 cm respectively. We obtained 40 and 21 ground control points (GCPs) that will be used for UAV photogrammetry and TLS surveying by using RTK-GNSS. The collected UAV images were treated as Pix4D mapper software. As a result, we deployed each point cloud and ortho-images in February and May. Image processing showed that the root mean square error (RMSE) in February was 0.015, 0.017, 0.040 m (x, y, z), and in May was 0.018, 0.015, and 0.035 (x, y, z).To verify accuracy, point clouds data collected with TLS surveying were collected. Using TLS point clouds and UAV point clouds (Feb, May), each DEM was deployed and the volume was calculated. In addition, a physical crosssection analysis was performed using 2 lines at the deployed TLS DEM, UAV DEM (2, 5 month). Finally, the coastline for the Imlang beach was extracted by applying the object based image segmentation technique obtained from UAV.

The positioning service of Quasi-Zenith Navigation Satellite System (QZSS“Michibiki”) has launched on November, 2018 in Japan involved by Cabinet Office, Government of Japan ^[1] . This study focused on a development of support system for snowplow operation which is combined with the real time positioning information acquired from GNSS including QZSS and the three dimensional road facility information acquired from mobile mapping system (MMS) equipped with digital photograph and laser devices. The system has been consisted of four components which are moving window displaying, recognition of road facility, guidance and alarm at real time processing for the snowplow operation corresponding to a vehicle speed. In addition, this study attempted a validation for the performance of the system in the test site of an expressway in the northern part of Japan. The precision of mapping of road facilities by means of MMS was less than 0.027 m in horizontal direction and less than 0.045 m in vertical direction, then point cloud data set was reconstructed into vector typed data set with attribute data for three dimensional landscape features including highway road facilities ^[7] . The vector type data was real-timely processed with QZSS down linked signals on a vehicle using a receiving device, AQLOC-VCX equipped with INS ^[6] . The validation on the official precision which are less than 12 cm of horizontal direction and less than 24 cm of vertical direction was performed by means of centimeter level augmentation service (CLAS) of QZSS which provide the corrected positioning information based on the existing continuously operating reference stations of GNSS provided from GSI in Japan ^[3] .

Satellites that capture large areas with high spatial and temporal resolution allow extensive analyses of water bodies and thus represent an ideal supplement to existing in situ point measurements. In the joint project WasMon-CT (Water Monitoring of Chlorophyll and Turbidity) the usability of satellite data for official monitoring of flowing waters and lakes was examined. The subproject at the Institute for Lake Research of the LUBW focused on satellite-based monitoring of chlorophyll a, an important indicator for water quality, in lakes. Freely available data from spatially reasonable high-resolution satellites, e.g. Sentinel-2, open up new possibilities for monitoring the water quality of a larger number of small lakes. The aim of the comprehensive validation study presented here was to get information about applicability and potential limitations of remote sensing techniques for different types of lakes. EOMAP processed the satellite data used in the validation (Sentinel-2/3, Landsat 7/8 and MODIS) by applying its Modular Inversion and Processing System MIP. Results extracted from satellite data between 2000 and 2017 were compared with in situ measurement data of about 20 lakes in Baden-Wuerttemberg, including Lake Constance, for water quality parameters such as chlorophyll a and Secchi depth. First results of the validation study show that in general the statistical values, e.g. annual mean values of in situ and remote sensing retrieved chlorophyll a and Secchi depth data, agree well, but some systematic differences occur. Further validation and data interpretation steps take into account methodical differences as well as time differences between in situ and satellite measurements.