Proceedings, 2018, ECRS-2 2018

Nowadays, nanosatellites are widely used in space technology due to their small size, ease of deployment, and relatively short development period. CubeSat specifications have been suggested as an effort to standardize nanosatellite mission design. Standardization opens the door for inter-CubeSat communications that can be used to form a CubeSat Cloud and mimic regular large multifunctional satellites with wide range of features, measurements, and sensing capabilities. In this paper, we introduce a Comprehensive CubeSat (CoCube, Gurgaon, India) online database. CoCube database focuses mainly on the different subsystems used during the design and implementation stages of existing CubeSat missions. Based on the lessons learned by comparing various CubeSat design alternatives and components’ structures and analyzing the best practices of CubeSat development, LibanSAT design is introduced. LibanSAT is a 1U CubeSat that serves two main objectives: (i) greenhouse gases observation and (ii) educational purposes. We benchmarked off-the-shelf subsystems from various suppliers and chose the most suitable for our target mission based on cost, size, weight, and power consumption. Finally, we introduce a new CubeSat security algorithm based on predefined anomaly detection baseline that serves as intrusion prevention system for the control channel. Full article

The RADARSAT Constellation Mission (RCM) is a future Canadian spaceborne Synthetic Aperture Radar (SAR) mission, with the purpose of supporting the operational use of SAR imagery for different Earth observation applications. The mission, through its three identical satellites, will provide average daily complete coverage of Canada’s land and oceans. In this paper, we provide an overview of the RCM and its characteristics and advancements over previous Canadian SAR missions. However, emphasis is given to the expected potential of the RCM in regard to environmental applications. Experimental results of environmental applications using simulated RCM data have shown promising potential for the mission. Full article

Flying in a non-Sun-synchronous orbit, RapidScat is the first scatterometer capable of measuring ocean vector winds over the full diurnal cycle, instead of observing a given location at a fixed time of day. The non-Sun-synchronous orbit also enables the overlap with other satellite instruments that have been flying in Sun-synchronous orbits. RapidScat covered the latitude range between ±51.6° and was operated on board the International Space Station between September 2014 and August 2016. This paper describes the process that combines RapidScat’s active and passive modes, simultaneously measuring both the radar surface backscatter (active mode) and the microwave emission determining the system noise temperature (passive mode). This work also presents the radiometric (passive mode) cross-calibration using the GPM (Global Precipitation Measurement) Microwave Imager (GMI) as a reference to eliminate the measurement biases of brightness temperature between a pair of radiometer channels that are operating at slightly different frequencies and incidence angles. Since the RapidScat operates at 13.4 GHz, and the closest GMI channel is 10.65 GHz, GMI brightness temperatures were normalized before the calibration. Normalization was based on the radiative transfer model (RTM) to yield an equivalent brightness temperature prior to the direct comparison with RapidScat. The seasonal and systematic biases were calculated for both polarizations as a function of geometry, atmospheric, and ocean brightness temperature models. The calculated biases may be used for measurement correction and for reprocessing of geophysical retrievals. Full article

Recently, with the development of remote sensing and computer techniques, automatic and accurate road extraction is becoming feasible for practical usage. Nowadays, accurate extraction of road information from satellite data has become one of the most popular topics in both remote sensing and transportation fields. It is very useful for applying this technique to fast map updating, construction supervision, and so on. However, as there is usually a huge volume of information provided by remote sensing data, an efficient method to refine the big volume of data is important in corresponding applications. We apply deep convolution network to perform an image segmentation approach, as a solution for extracting road networks from high resolution images. In order to take advantage of deep learning, we study the methods of generating representative training and testing datasets, and develop semi-supervised leaning skills to enhance the data scale. The extraction of the satellite images that are affected by color distortion is also studied, in order to make the method more robust for more applicational fields. The GF-2 satellite data is used for experiments, as its images may show optical distortion in small pieces. Experiments in this paper showed that, the proposed solution successfully identifies road networks from complex situations with a total accuracy of more than 80% in discriminable areas. Full article

This study deals with the integration of merging highly accurate precipitation estimates from the Global Precipitation Measurement (GPM) with sampling gap-free satellite observations from Meteosat 7 of the Meteosat First Generation (MFG) to develop a regional rainfall monitoring algorithm to monitor precipitation over India and nearby oceanic regions. For this purpose, we derived precipitation signatures from Meteosat observations to co-locate them against precipitation from GPM. A relationship was then established between rainfall and rainfall signature using observations from various rainy seasons. The relationship thus derived can be used to monitor precipitation over India and nearby oceanic regions. The performance of this technique was tested against rain gauges and global precipitation products including the Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Centre MORPHing (CMORPH), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Integrated Multi-satellitE Retrievals for GPM (IMERG). A case study is presented here to examine the performance of the developed algorithm in monitoring heavy rainfall during the flood event in Tamil Nadu in 2015. This is the first attempt to use near-real-time observations from GPM and MFG to monitor heavy precipitation over the Indian region. Due to its finer resolution and near-real-time availability, this technique can be used to monitor near-real-time flash floods. Full article

This paper deals with (both supervised and unsupervised) classification of multispectral Sentinel-2 images, utilizing the abundance representation of the pixels of interest. The latter pixel representation uncovers the hidden structured regions that are not often available in the reference maps. Additionally, it encourages class distinctions and bolsters accuracy. The adopted methodology, which has been successfully applied to hyperpsectral data, involves two main stages: (I) the determination of the pixel’s abundance representation; and (II) the employment of a classification algorithm applied to the abundance representations. More specifically, stage (I) incorporates two key processes, namely (a) endmember extraction, utilizing spectrally homogeneous regions of interest (ROIs); and (b) spectral unmixing, which hinges upon the endmember selection. The adopted spectral unmixing process assumes the linear mixing model (LMM), where each pixel is expressed as a linear combination of the endmembers. The pixel’s abundance vector is estimated via a variational Bayes algorithm that is based on a suitably defined hierarchical Bayesian model. The resulting abundance vectors are then fed to stage (II), where two off-the-shelf supervised classification approaches (namely nearest neighbor (NN) classification and support vector machines (SVM)), as well as an unsupervised classification process (namely the online adaptive possibilistic c-means (OAPCM) clustering algorithm), are adopted. Experiments are performed on a Sentinel-2 image acquired for a specific region of the Northern Pindos National Park in north-western Greece containing water, vegetation and bare soil areas. The experimental results demonstrate that the ad-hoc classification approaches utilizing abundance representations of the pixels outperform those utilizing the spectral signatures of the pixels in terms of accuracy. Full article

Considering that ground areas with intense compositional variability appear as mixed pixels in hyperspectral data, we focus on the mixing problem imposed by the various volcanic products found in the vicinity of Mt. Etna’s volcanic craters. Mt. Etna, which is one of the most active volcanoes globally, is a generator of diverse mineralogical environments. Therefore, the inherent abundant information of hyperspectral imagery of the volcanic edifice calls for the use of time-efficient and accurate spectral unmixing methods in order to unravel the data. Lava flows (LFs) and related products from the historical 1536–1669 era were selected based on their distinct spatial distribution and lava field segregation. Based on the selection of appropriate pixel representatives, distinct optimizing signal transformations were implemented, with the most dominant being the Fourier transform, in order to use the data in the linear least squares unmixing (LLSU) and bilinear unmixing (BLU) methods. We thus report the results of the historic lava flow characterization and respective abundance analysis qualitatively and quantitatively, evaluated through the structural similarity index (SSIM) of each method. Ultimately, method intercomparison provided the optimum selection for volcanic product segregation. Full article

The Canadian Ice Service (CIS, Ottawa, ON, Canada) is receiving hundreds of Synthetic Aperture Radar (SAR) images daily with almost a complete coverage of Canada navigable waters for the monitoring and mapping of seasonal sea and lake ice. In order to efficiently use and analyze such a large amount and wide areal extent of data, short-term (i.e., 12 h to a few days) highresolution mosaic products are of interest. Among these SAR images, Sentinel-1 data have been known to have an issue of border noise which needs to be removed before generating a seamless mosaic. A method using line-by-line scanning and filtering is proposed, which traces an extreme jump between two neighboring pixels along every scan line. The results show that this method can remove the noise precisely while retaining the rest of the valid data. For visual display, analysis, and interpretation, as done at the CIS, a tone-balanced smooth mosaic is of interest and value to ice analysts in displaying the overall ice distribution and in viewing and comparing cross-region ice conditions. To address this, a scene boundary match balancing method is developed. These shortterm mosaic products are proved very helpful in daily ice analysis and macroscopic ice drift measurement. Full article

Aquaculture is the farming of fish, crustaceans, molluscus, aquatic plants, algae, and other aquatic organisms. Aquaculture farming in coastal areas of India plays key role in the economy which contributes 1.1% of GDP. In Odisha, the aquaculture system exports 26% of its products to foreign countries. Artificial neural networks have a feature of pattern recognition, which uses a training dataset to identify patterns of any feature from satellite images. The term pattern recognition considers a wide range of information and processing problems of great practical significance. This study was ckarried out in two coastal districts, namely, Bhadrak and Kendrapada in Odisha state. Landsat-8 satellite data (OLI sensor) were used, and training sites were generated. The pattern recognition features of the neural network were used to extract aquaculture features from satellite images. We analyzed the areas that were converted to aquaculture from 2002 to 2017 using the neural network classification. There was a two-fold increase in aquaculture activities from 2002 to 2017 in the two coastal districts. The increases in aquaculture activities indicated that aquaculture plays an important role in the socio-economic developmental of coastal people. Full article

The ice flow velocity is a critical variable in understanding the glacier dynamics. The Synthetic Aperture Radar Interferometry (InSAR) is a robust technique to monitor Earth’s surface mainly to measure its topography and deformation. The phase information from two or more interferogram further helps to extract information about the height and displacement of the surface. We used this technique to derive glacier velocity for Polar Record Glacier (PRG), East Antarctica, using Sentinel-1 Single Look Complex images that were captured in Interferometric Wide mode. For velocity estimation, Persistent Scatterer interferometry (PS-InSAR) method was applied, which uses the time coherent of permanent pixel of master images and correlates to the same pixel of the slave image to get displacement by tracking the intensity of those pixels. C-band sensor of European Space Agency, Sentinel-1A, and 1B data were used in this study. Estimated average velocity of the PRG is found to be approximately ≈400 ma⁻¹, which varied from ≈100 to ≈700 ma⁻¹. We also found that PRG moves at ≈700 and 200 ma⁻¹ in the lower part and the upper inland area, respectively. Full article

Changes in the earth's surface significantly increase natural disasters, resulting in severe damage to man-made objects, such as roads, buildings, bridges, and so on. Radar techniques have advantages, such as lack of sensitivity to weather conditions, to night and day, and to cloud cover conditions, which can be used to identify, alert, and mitigate these damages. Because of the importance of these areas and the need to care for them, land-use classification, one of the important applications of remote sensing, is performed. Polarimetric synthetic aperture radar (PolSAR) images have many capabilities, having the scattering information on four polarized levels (HH, HV, VH and VV) and consequently depending on the shape and structure of the environment. In this study, unmaned aerial vehicle (UAVSAR) image is used. The support vector machine (SVM) model is a well-known classification method, able to run on different types of features and to distinguish classes that are not linearly separable. On the other hand, it is possible to use data mining methods to facilitate data analysis, like classifications. In this regards, it is recommended to use the random forest (RF) technique. The RF is one of the useful methods for data classification which uses a tree structure for decision-making. This method uses strategies to enhance the probability of reaching goals with conditional probability. In this study, by incorporating a variety of target decomposition methods in PolSAR images, images producing the land cover types were generated. Then, 70 features were obtained by applying the support vector machine (SVM), random forest (RF) , and K-nearest neighbor (KNN) classification methods. In order to estimate accuracy, the output of these methods was evaluated by reference data. Full article

Canopy cover (or vegetation cover) maps serve in irrigation management mainly to determine the primary evapotranspiration (ET) coefficient, as radiation interception and evaporative surface area are directly related to canopy cover. Crop size and development with time depends on water supply; therefore, crop canopy maps are tools for the detection of the spatial uniformity of irrigation systems. Several aerial scan campaigns were deployed in the Upper Galilee of Israel in the 2017 growing season to follow up and evaluate the irrigation uniformity and crop coefficients of peanuts and cotton by RGB scans of a Phantom 4 multirotor unmanned aerial vehicle (UAV). Foliage intensity and coverage were enhanced by a green-red vegetation index (GRVI), which is a normalized difference vegetation index (NDVI)-like process where the green channel replaced the near-infrared (NIR). The results demonstrated that the GRVI is suitable for the purpose of determining the vegetation cover. Furthermore, the GRVI yielded better results than the NDVI in recognizing phenological crop changes (especially senescence). Therefore, this research proves the applicability of a low-cost digital camera mounted on an easily accessible UAV for crop cover and actual, in-field, ET coefficients determination and irrigation uniformity evaluation. Full article

Limited research has studied the use of Lidar in mapping coastal geomorphology. The purpose of this project was to build on existing research and develop an automated modeling approach to classify the coastal geomorphology of barrier islands and test this at four sites in North Carolina. Barrier islands are shaped by natural coastal processes, such as storms and longshore sediment transport, as well as by human influences, such as beach nourishment and urban development. An automated geomorphic classification model was developed to classify Lidar data into 10 geomorphic types over four time-steps from 1998 to 2014. Tropical storms and hurricanes had the most influence on change and movement. On the developed islands, there was less influence of storms, owing to the inability of features to move because of coastal infrastructure. Beach nourishment was the dominant influence on developed beaches, because this activity ameliorated the natural tendency of an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island’s future response to changing environmental factors such as sea-level rise. The development of an automated model equips policy makers and coastal managers with information to make development and conservation decisions, and the model can be implemented at other barrier islands. Full article

Throughout the world, deforestation, degradation, and fragmentation threaten the integrity of tropical forests and the biodiversity that they contain. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened as unsustainable agricultural practices reduce its capacity to provide life-supporting ecosystem services. Deforestation data is necessary for forest managers to efficiently allocate resources and make decisions for proper conservation and resource management. This study utilized satellite imagery to map and analyze current forest cover and recent forest loss in southern Belize in order to identify the areas that are the most susceptible to future deforestation. A forest cover change analysis was conducted using a supervised classification of Landsat imagery and ground-truthed land cover points in Google Earth Engine. Then, a proximity-based model was used to predict where deforestation could occur in the future based on the drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests. The results of this study will be used in spatial conservation planning in order to strategically focus conservation efforts in the most threatened areas in southern Belize. The sites that were found to be most vulnerable to future deforestation will be locations for implementing law enforcement and compliance, sustainable agriculture, and community outreach. This method could be applied to conservation planning in other regions to prioritize the protection of threatened areas. Full article

A quantitative understanding of vegetation structure is vital to inform long-term protection and management of Australia’s vegetation communities. Although airborne light detection and ranging (LiDAR) systems are increasingly utilised to provide three-dimensional measures of vegetation structure at high spatial resolutions (1–10 m²), only limited studies characterise vertical vegetation structure using these datasets. This study assesses the capacity of high spatial resolution LiDAR data to accurately characterise the structural forms of Australian vegetation communities. Four study sites, each covering approximately 25 km², were selected to provide examples across a range of vegetation structural forms, from shrubland to tall closed forest. A novel vertical segmentation methodology was developed to process airborne LiDAR data from each study site at 1 or 2 m vertical and horizontal spatial resolutions. Ratios were applied to standardise point density values, prior to an exploratory analysis utilising multi-dimensional clustering algorithms to classify distinct vertical structure patterns. Comparisons were subsequently performed between the exploratory analysis results and established structural classifications for Australian vegetation communities. The use of the vertical segmentation technique was found to improve the identification of sub-canopy features in multi-story vegetation communities, particularly shrubs and herbaceous ground covers 0.5–4 m tall. The exploratory analysis results saw increased noise in structurally complex and dense vegetation communities due to reduced sub-canopy returns. Further development and application of vertical segmentation methods in multi-story vegetation communities should be evaluated because of their potential for targeted management and monitoring of vegetation communities and wildlife populations. Full article

Glaciers exhibit a wide range of surface facies that can be analyzed as proxies for mass balance studies. Along with hydrological implications, these are in turn quintessential indicators of climate change. Moderate-to-high-resolution (MHR) data for mapping glacier facies have been used previously; however, the use of very high-resolution (VHR) data for this purpose has not yet been fully exploited. This study uses WorldView-2 (WV-2) VHR data to classify available glacier surface facies on the Samudra Tapu glacier, located in the Himalayas. Traditional methods of facies classification using conventional multispectral data involve band rationing and/or supervised classification. This study explores glacier surface facies classification by using the unique bands available in the multispectral range of WV-2 to develop customized spectral index ratios (SIRs) within an object-oriented domain. The results of this object-oriented classification (OOC) are then compared with five popular supervised classification algorithms using error matrices to determine the classification accuracies. The overall accuracy achieved by the object-based image analysis (OBIA) approach is 97.14% (κ = 0.96), and the highest overall accuracy among the pixel-based classification methods is 74.28% (κ = 0.70). The present results show that the object-based approach is far more accurate than the pixel-based classification techniques. Further studies should test the robustness of the object-oriented domain for the classification of glacier surface facies using customized sensor-specific as well as transferable indices, and the resultant accuracies. Full article

Indian Space Research Organisation’s SCATSAT-1 is a continuity mission for Oceansat-2 Scatterometer. The sensor works in a Ku-band (13.515 GHz) similar to the one flown on-board Oceansat-2. It provides backscattering coefficient over the globe and wind vector data products over the oceans that are useful for weather forecasting, cyclone detection, and tracking services. Besides backscattering coefficient (sigma nought), two other important parameters, namely, Gamma nought (obtained from backscattering coefficient) and Brightness temperature (obtained from scatterometer noise measurement) are given as the Level-4 data products archived at the ISRO’s Meteorological & Oceanographic Satellite Data Archival Centre. We used these three parameters both in horizontal and vertical polarizations for the Antarctic region (South Polar) to perform, first, a principal component analysis. Then, we used the first three principal components explaining the largest variability in the data set to perform an unsupervised ISODATA clustering classification to estimate the regions of sea ice around Antarctica. The derived sea ice extent through this method is compared with other popular sea ice extent products available elsewhere. Full article

Changes in the dynamics of glaciers must be assessed, as they are important for sea level changes. Glacier velocity is the most important parameter used in glacier dynamics studies. Various image matching techniques, which are implemented in different domains, have been utilized to estimate the surface velocity of glaciers, since the first use of remote sensing technology. In this study, we derived the precise velocity of the Polar Record Glacier (PRG), east Antarctica, in recent years, using optical remote sensing. The secondary objective of the study was to comparatively test the accurate geospatial tools for velocity estimation. The study was first conducted on a single image pair, and four different tools were used for the estimation of the glacier velocity, which are the COSI-Corr (Co-registration of Optically Sensed Images and Correlation) tool in ENVI (Exelis Visual Information Solutions), the IMGRAFT (Image GeoRectification and Feature Tracking) in MATLAB, the IMCORR (Image correlation) feature tracking tool in SAGA-GIS, and the image correlation software CIAS. After evaluation of the four feature tracking tools, COSI-Corr yielded a pixel-level velocity with both magnitude and directions, while IMGRAFT provided the glacier speed without the directions. On the other hand, IMCORR yielded good results with respect to magnitude and directions of the glacier velocity, but the pixel-wise magnitude was not produced. CIAS also provided closely bundled velocity products without pixel-wise velocity. COSI-Corr and IMGRAFT were found to be the best of the four tools, and COSI-Corr is recommended for further studies to estimate the velocity of the PRG. Full article

The determination of evapotranspiration (ET) using ground-based meteorological data does not adequately capture the spatial patterns of mass and energy fluxes in mountainous areas. In this work, we evaluate the daily spatial distribution of ET over a mountainous watershed in southeastern Brazil by coupling Surface Energy Balance Algorithms for Land (SEBAL), a global solar radiation (GSR) model, and a gridded weather dataset (GWD). To estimate daily tilted GSR, we use the relation between terrain and sun angles over a 24-h integration time. Tests were performed in summer/wet (12 January 2015) and winter/dry (25 September 2015) periods to evaluate the seasonal differences in ET over tilted surfaces. The results indicated different spatial patterns of daily ET on the watershed in each period. In summer, ET was 9.8% higher on slopes facing South, while in winter, ET was 10.6% higher on slopes facing North and East. A high variability in daily ET was found on steeper slopes (above 45°) in both periods. The notable ET spatial heterogeneity indicates the complex partitioning of mass and energy fluxes from different terrain angles, which may influence hydroecological processes at the local scale. The presented approach allowed a more detailed capture of the spatial variability of ET in a mountainous watershed with scarce ground-based data. Full article

The study shows the variation in the most important climatic variables i.e., Net Surface Radiation (Rn), Temperature, Rainfall, Evapotranspiration (ET), etc. during 2000–2016 along the North–South transect across Jharkhand, Bihar and Eastern Nepal. The Tropical Rainfall Measuring Mission (TRMM) monthly average precipitation (0.25° × 0.25°), Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day average Land Surface Temperature (LST) product (1 km × 1 km), Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2) radiation (0.5° × 0.625°) and Global Land Data Assimilation System (GLDAS) reanalysis model data (0.25° × 0.25°) have been used to study and analysed the spatial variability and distribution of rainfall, surface temperature, energy fluxes and evapotranspiration, respectively. The results have shown that the overall annual average rainfall has a gradual decreasing trend. Results have suggested that the regions with low rainfall (<1000 mm) have to witness warmer temperature conditions (>43 °C). The east–west central line of the Bihar, along the river Ganga is found to be the line of division for the comparatively higher (towards south) and lower (towards north) temperature zones. The results for Rn have shown an overall increasing trend over the period of time. Nepal has a wider stretch of Rn concluded by its mountain topography followed by the Jharkhand (plateau) and Bihar (plain). ET values have also shown an increasing trend and the results are noticeable for western Bihar-Jharkhand. There is an upward latitudinal shifting of the low rainfall bands in both the pre-monsoon and monsoon conditions. Due to the lack of availability of ground truth data, we have to restrict with the remotely sensed dataset only. Full article

Satellite earth observation enables the monitoring of different types of natural hazards, contributing to the mitigation of their fatal consequences. In this paper, satellite Synthetic Aperture Radar (SAR) images are used to derive terrain deformation measurements. The images acquired with the ESA satellites Sentinel-1 are used. In order to fully exploit these images, two different approaches to Persistent Scatterer Interferometry (PSI) are used, depending on the characteristics of the study area and the available images. The main processing steps of the two methods, i.e.; the simplified and the full PSI approach, are described and applied over an area of 7500 km² located in Catalonia (Spain). The deformation velocity map and deformation time series are analysed in the last section of the paper. Full article

Pan Sharpening is an important part of the Remote Sensing science. Obtaining high spatial resolution data can be crucial in some studies. Sentinel-2 provides data of 10, 20 and 60 m, and it is a promising program for Earth observation studies. Although Sentinel-2 provides a high range of multispectral bands, the lack of panchromatic band disables the production of a set of fine-resolution (10 m) bands. However, few methods have been developed for increasing the spatial resolution of the 20 m bands up to 10 m. In this study, three different methods of producing panchromatic bands have been compared. The first method uses the closest higher spatial resolution band to the lowest spatial resolution band as a panchromatic band, the second method uses one single band as the panchromatic band produced as an average value out of all fine resolution bands, while the third method uses linear correlation for the selection of the panchromatic band. The 60 m bands have not been taken into consideration in this study. In order to compare these methods, three image fusion techniques from different fusion subsections (Component substitution—Intensity Hue Saturation IHS; Numerical method—High Pass Filter HPF; Hybrid Technique—Wavelet Principal Component WPC) have been applied on two Sentinel-2 images over the same study area, on different dates. For the accuracy assessment, both qualitative and quantitative analyses have been made. It has been concluded that using the average value of the visual and the near infrared bands can be accepted as a panchromatic band in the Sentinel-2 dataset. Full article

Synthetic aperture radar interferometry (InSAR) technology is one of the powerful tools to measure deformation and/or deposition on the ground’s surface. In addition to this, mass movement can also be monitored using InSAR techniques. The earthquake that occurred on November 12, 2017 in the south of Halabjah with a magnitude of 7.2 caused 350 people to lose their lives, and over 2500 people were injured. The aim of this study is to measure the deformation resulting from the earthquake using the “Interferometric Wide Swath”, which is one of the four display types of Sentinel-1 data. In order to carry out this process, two types of datasets were used, SRTM data and Sentinel-1 images acquired on November 7 and 19, 2017. In this study, VV polarization with the C band was used to generate an interferogram. During the study, SNAP 5.0 free image analysis and processing software by the European Space Agency (ESA). According to the obtained results, the minimum and maximum surface displacements were −0.45 and 0.49 m. When comparing the results with faults, the results are appropriate for the tectonic structures. Using InSAR technologies with open-source software and free data, it is possible to produce displacement maps just after earthquakes. Full article

High resolution elevation data is fundamental information for multiple applications in geomorphology spanning from visual analyses to modeling. Nowadays, gathering of high-quality elevation data relies on multiple sensors and technologies that can be mounted on terrestrial, aerial and satellite platforms. In the last few years, the Structure from Motion (SfM) algorithms have made the acquisition of high and very-high resolution elevation data from optical images acquired with high overlapping rates at virtually no cost possible. Such a feature made it possible to exploit remote sensing archival imagery to build historical topographic information with unprecedented detail. However, despite the increasing number of applications of SfM algorithms in the scientific literature, still little has been done in terms optimization and quality evaluation of the results. We have applied the SfM algorithm developed in the photogrammetric open source software MicMac to six black and white aerial photographs taken in 1954 at 1:33,000 in a mountainous and steep area in Central Italy. The aim of the experiment consists of a quantitative evaluation of the digital surface models obtained for different scanning settings. Full article

This study was carried out for Siberia using Terra/Modis satellite data (2002–2016), data of ground surveys on burned areas of different ages, long-term meteorological information, and numerical simulation results. On the basis of meteorological and wildfire databases, we evaluated the probability (~18%) of an extreme fire danger scenario that was found to occur every 8 ± 3 years in different parts of the region. Next, we used Fire Radiative Power (FRP) measurements to classify the varieties of burning conditions for each wildfire in the database. The classification of the annually burned forest area was obtained in accordance with the assessments of burning intensity ranges categorized by FRP. Depending on the fire danger scenario in Siberia, 47.04 ± 13.6% of the total wildfire areas were classified as low-intensity burning, 42.46 ± 10.50% as medium-intensity fire areas, and 10.50 ± 6.90% as high-intensity. Next, we calculated the amount of combusted biomass and the direct emissions for each wildfire, taking into account the variable intensity of burning within the fire polygons. The total annual emissions were also calculated for Siberia for the last 15 years, from 2002 to 2016. The average estimate of direct carbon emission was 83 ± 21 Tg/year, which is lower than the result (112 ± 25 Tg/year) we obtained using the standard procedure. Full article

Many works dealing with the problem of urban detection at large scale have been published, but very little attention has been paid to the investigation of the features’ relative importance. Feature selection is known to be an NP-hard problem, which means it can not be solved in polynomial time, but there are many heuristics suggested to approximate the solution. In this paper, a survey of the features used for large scale urban detection is presented, then the question of finding the best subset of features is investigated. Using Landsat scenes of five urban areas, most common features were extracted to represent the full feature set. Employing mutual information based ranking methods, Support Vector Machine (SVM) and Random Forest feature ranking, an importance score was assigned to each feature by each method. To aggregate the individual rankings of features, a two stage voting scheme was implemented to choose a subset of size N as the most relevant features. The most important features for all five cities taken together were listed. Full article

In this study, we have evaluated the use of unmanned air vehicle (UAV) photogrammetry for the monitoring of a wheat experiment under field conditions, filtered a digital surface model (DSM) to derive the wheat plant heights, and compared the results with the field measurements. The images were acquired with the use of a low-cost UAV Walkera QR350 and GoProHero3+ action camera in May 2015. In total, 477 images were acquired for quality assessment of the proposed method, and a reference dataset was collected with terrestrial fieldwork. For a comparison of field measurements with DSM-derived plant heights, the maximum calculated plant height in the plot was selected. The mean, median, and standard deviation were calculated as 4.66, 3.75, and 13.78 cm. Regarding the statistical t-test between the field measurements and plant heights from the DSM, the t-value was calculated as 1.82, and the p-value was 0.071. Because the t-value was larger than 0.50, the values between the traditional method and our approach were highly correlated. Full article

During the last decade, remote sensing methods have developed significantly. The technological progress in development of new sensors and techniques opened up a large scope of new applications including near-field data collecting using Unmanned Aerial Vehicles (UAVs). State-of-the-art UAV technologies provide advantages including cost-effectiveness and temporal flexibility. For our case study, we acquired the high-resolution UAV data over the archaeological site near Černouček, the Czech Republic. This site was discovered at the beginning of 1990s as a result of low altitude aerial reconnaissance carried out by the Institute of Archaeology, Czech Academy of Sciences. Two ditched enclosures were identified due to vegetation marks in late spring and early summer, as higher moisture and presence of some chemical constituents in the secondary infill of the ditches give better conditions for plants above them. In 2017, new UAV data (Red, Green and Blue: RGB and Red and Near-infrared data: Red + NIR) were acquired over the Černouček site in June to find out whether there are some other objects hidden under ground. Using the RGB data digital elevation models were derived while the Red + NIR data were used to compute vegetation indices (VI), further spatial filtering allowing enhancing the local anomalies in the VI values was employed. As a result, several small objects were detected and suggested for the further investigations. Full article

Ground control points (GCPs) are used in the process of indirectly georeferencing Unmanned Aerial Systems (UAS) images. A minimum of three ground control points (GCPs) is required but increasing the number of GCPs will lead to higher accuracy of the final results. The aim of this study is to provide the answer to the question of how many ground control points are necessary in order to derive high precision results. To obtain the results, an area of about 1 ha was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard at two different heights, 28 m and 35 m above ground, the camera being oriented in a nadiral position, and 50 ground control points were measured using a total station. In the first and the second scenario, the UAS images were processed using the Pix4D Mapper Pro software and 3DF Zephyr, respectively, by performing a full bundle adjustment process with the number being gradually increased from three GCPs to 40. The third test was made with 3DF Zephyr Pro software using a free-network approach in the bundle adjustment. Also, the point clouds and the mesh surfaces derived automatically after using the minimum and the optimum number of GCPs, respectively, were compared with a terrestrial laser scanner (TLS) point cloud. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. Full article

Clouds and cloud-shadow are a persistent problem in all optical satellite imagery. Plenty of methods have been suggested in the literature to address this problem, and reconstruct the missing part of the optical signal. In this work, three methods representative of different approaches to the cloud removal problem were compared. The first method is temporal fitting using Fourier series, which benefits from the temporal continuity of the signal. The second method uses sparse spectral unmixing to fill in the missing areas. The third method employs radiometric consistency as a tool to determine the missing part of the signal. These three methods were first presented and their theoretical background described, followed by a discussion of their implied assumptions and general performance. A set of experiments using Landsat 8 time series with diverse land cover types were conducted. The quantitative results of the three methods using simulated clouds are presented. Finally, some concluding remarks about the relative advantages of the three approaches are listed, in addition to some recommendations about their use. Full article

Quantifying and monitoring woody cover distribution in semiarid regions is challenging, due to their scattered distribution. Data mining has been widely used with remote sensing data for the information extraction of spectral and temporal data in the analysis of change detection. The main objective of this study was to characterize the land cover and use over the 2000–2010 time period for the Brazilian Caatinga seasonal biome using a temporal Normalized Difference Vegetation Index (NDVI) series and Geographic Object-Based Image Analysis (GEOBIA). For each of the target years NDVI images were derived from a Moderate Resolution Imaging Spectroradiometer (MODIS, MOD13Q1, at a 250 m spatial and 16-day temporal scale) sensor during the dry season to predict wood cover in the municipality of Buriti dos Montes, in the state of Piaui in the north-east region of Brazil (H13V09 tile). The images were automatically pre-processed and the GEOBIA approach was performed for image segmentation, spatial and spectral attribute extraction and labelling according to the following legend, tree cover (TC) and cropland/grass (CG), to obtain a classification using the decision tree supervised algorithm. Our results showed that the approach using GEOBIA presented a Kappa index of 0.58 and global accuracy (GA) of 0.81% and showed better accuracy for the tree cover. Finally, we recommend new studies adding others parameters strongly related to the vegetation of semiarid regions. Full article

Arable soils are subjected to the altering influence of agricultural and natural processes determining surface feedback patterns therefore affecting their ability to reflect light. However, remote soil mapping and monitoring usually ignore information on surface state at the time of data acquisition. Conducted research demonstrates the contribution of surface feedback dynamics to soil reflectance and its relationship with soil properties. Analysis of variance showed that the destruction surface patterns accounts for 71% of spectral variation. The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In the case of organic matter and medium and coarse sand particles, correlation decreases with the removement of surface structure. For particles of fine sand and coarse silt, grinding changes spectral areas of high correlation. Partial least squares regression models also demonstrated variations in complexity, R²cv and RMSEPcv. Field dynamics of surface feedback patterns of arable soils causes 22–46% of soil spectral variations depending on the growing season and soil type. The directions and areas of spectral changes seem to be soil-specific. Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of optical remote sensing data to ensure reliable and reproducible results. Full article

An urban heat island (UHI) is an urban area that is significantly warmer than its surrounding rural areas due to antropogenic activities. The urban area of the city of Skopje has been rising rapidly in the past decade. In this study, the effect of UHI is analyzed using Landsat 8 data in the summer period of 2013–2017 as a case study in Skopje, Macedonia. An algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data. In addition, the correlation between land surface temperature and the normalized difference vegetation index (NDVI) and the normalized difference build-up index (NDBI) were analyzed to explore the impacts of the green areas and the build-up land on the urban heat island. The results indicate that the effect of the urban heat island in Skopje is located in many sub-urban areas. The negative correlation between LST and NDVI indicates that the green area can weaken the effect on the urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of the urban heat island in the study area. Full article

Natural disasters and wars wreak havoc not only on individuals and critical infrastructure, but also leave behind ruined residential buildings and housings. The size, type and location of damaged houses are essential data sources for the post-disaster reconstruction process. Building damage detection due to war activities has not been thoroughly discussed in the literature. In this paper, an automated building damage detection technique that relies on both pre- and post-war aerial images is proposed. Building damage estimation was done using shadow information and Gray Level Co-occurrence Matrix features. Accuracy assessment applied over a Syrian war-affected zone near Damascus reveals the excellent performance of the proposed technique. Full article

Urban areas are important for city planning, security, traffic purposes, decision makers etc. Remotely sensed data are useful to detect urban areas either with active or passive systems. Each system has advantages and disadvantages. Passive images are mainly multispectral images and have rich information with their rich spectral resolution. In addition, they are affected by the atmospheric conditions, so there should not be clouds over the sensed region during data acquisition. On the other hand, SAR (Synthetic Aperture Radar) systems are not affected by the atmospheric conditions, but their spectral resolution is low, with mainly one-channel SAR systems. Also, the structure of passive images is completely different from that of multispectral images. Moreover, the geometrical and electrical properties of objects play an important role in the pixel values. In this study, a multispectral GOKTURK-2 MS (Multispectral) image and a SENTINEL 1A SAR image were used to detect urban buildings, using the advantages of both datasets. Firstly, the SVM (Support Vector Machines) method was applied to detect the buildings in the GOKTURK image. Then, the buildings were detected from the SAR image with the fuzzy logic approach. Finally, the buildings were detected by intersecting the results from both methods. The results from the SAR image could eliminate the false negative results from the GOKTURK-2 image. The study area was selected in Antalya province, Kepez district. The detected urban area was 288.353 m² in the selected study area. Full article

Post-earthquake analysis using radar interferometry has become a standard procedure for assessing earthquakes with significant damages. Sentinel-1 satellite provides 6-day revisiting time, and Sentinel-2 data has 5-day revisiting time and the same viewing angle that can enable the detection of changes in surface/land-cover after a major seismic event. Using Sentinel-2 alongside Sentinel-1 could bring new benefits when gathering spatial information about a post seismic event. In our study, we focused on analyzing a major earthquake, which occurred on 14 November 2016 with 7.8 magnitude near the city of Kaikōura, New Zealand, using both Sentinel-1 radar images and Sentinel-2 optical data. Hundreds of landslides were reported as a result of this earthquake. In addition, substantial land uplift was detected in some parts of the sea shore. Differential interferometry allowed us to estimate earthquake strength analyzing the distribution of absolute vertical displacement values. Sentinel-2 pre- and post-earthquake images were used in order to assess land-cover changes and automatically detect landslides, which occurred after the earthquake. Linking DInSAR results with Sentinel-2 change detection analysis helped us to get a more complex perspective on the earthquake impact, to create landslide inventory maps, and to subsequently develop workflows for quick post-event analysis. Full article

Urban development contributing to air pollution is one of the factors seriously affecting public health. Besides the traditional ground observation methods, the current space technology has been added to the monitoring and managing environment. This research used Landsat satellite image to detect PM10 from by the Aerosol Optical Thickness (AOT) method for Ho Chi Minh City area. The regression analysis was used for establishing the relationship between the PM10 data obtained at ground stations and AOT values from processed images in 2003. The analysis showed a good correlation coefficient (R = 0.95) for the case of AOT calculated from spectral reflective green band. The relative radiation normalization was carried out for satellite imaging in 2015 in order to simulate the spatial distribution of PM10 with the same regression function. The distribution for PM10 aerosol pollution is focused on the urban area, traffic booth and industrial zones. The results of this study provided a picture of general distribution for current pollution status and also supported the determining of specified polluted areas. This has provided helpful and good support for zoning and urban environmental management in accordance with urban development. Full article

Carbon Use Efficiency (CUE) is the ratio of net primary production (NPP) to gross primary production (GPP) and shows the capacity of terrestrial ecosystems to transfer carbon from the atmosphere to biomass. The aim of this study is to examine the CUE under the stress conditions using NPP/GPP data products from the MODIS (NASA) spectroradiometer for the period 2000– 2014. The drought reduced the CUE by 10 to 20% and, as a result, the region has shifted from a carbon sink to a carbon source. The stress affects mostly forest biomes, which are the lowest effective. The most significant impact on terrestrial ecosystems productivity and CUE are five-months-lasting droughts in terms of SPEI drought index. Drought also increases the variation of CUE. The degree of CUE reduction depends not only on drought strength, but also on its duration and the time of year when it occurs (perhaps phenophase). Further research is needed to understand the mechanisms of dependencies between CUE and the combination of high temperatures and water deficit conditions in terrestrial ecosystems. Full article

Accurate and rapid mapping of fire damaged areas is fundamental to support fire management, account for environmental loss, define planning strategies and monitor the restoration of vegetation. Under climate change conditions, drought severity may trigger tough fire regimes, in terms of number and dimension of fires. In order to deliver rapid information about areas damaged by fires, Burned Area Index (BAI), Normalized Burn Ratio (NBR), and their relative versions have been largely employed in the past to map burned areas from high resolution optical satellite data. The new MSI sensor aboard Sentinel-2 satellites carries more spectral information recorded in the red-edge spectral region, opening the way to the development of new indices for burned area mapping. This study present a newly developed Burned Area Index for Sentinel-2 (BAIS2), based on Sentinel-2 spectral bands, to detect burned areas at 20 m spatial resolution and the design of a processor developed to perform post-fire mapping using Sentinel-2 data. The new index has been tested on various study cases in Italy for summer 2017 fires, and results show a good performance of the index and highlighted critical issues related to the Sentinel-2 data processing. Full article

The Earth’s land-cover is exposed to several types of environmental change, caused by either human activities or natural disasters. On 11 March 2011, an earthquake occurred about 130 km off the east coast of Sendai City in Japan. This earthquake was followed by a huge tsunami, which caused devastating damages over wide areas in the eastern coastline of Japan. Due to the occurrence of natural disasters across the world, there is a strong need to develop an automated algorithm for the fast and accurate extraction of changing landscapes within the affected areas. Such techniques can accelerate the process of strategic planning and primary services to move people into shelters and to carry out damage assessments as well as risk management during a crisis. Therefore, a variety of change detection (CD) techniques has been previously developed, based on various requirements and conditions. However, the selection of the most suitable method for change detection is not easy in practice. To the best of our knowledge, there is no existing CD approach that is both optimal and applicable in the cases of using a variety of optical and radar remote sensing images. To resolve these problems, an automated CD method based on a support vector data description (SVDD) classifier is proposed. This method uses the information contents of radar and optical data simultaneously by decision-level fusing of the change maps obtained from these data. For evaluating the efficiency of the proposed method and extract the damaged areas, the 2011 Sendai tsunami was considered. Various optical and radar remote sensing images from before and after the 2011 Sendai tsunami acquired by IKONOS and Radarsat-2 were used. The results confirmed the fundamental role and potential of using both optical and radar data for natural hazard damage detection applications. Full article

Maize is the most commonly cultivated cereal in Africa in terms of land area and production. Low yields in this region are very often associated with issues related to low Nitrogen (N), such as low soil fertility or low fertilizer availability. Developing new maize varieties with high and reliable yields in actual field conditions using traditional crop breeding techniques can be slow and costly. Remote sensing has become an important tool in the modernization of field-based High Throughput Plant Phenotyping (HTPP), providing faster gains towards improved yield potential, adaptation to abiotic (water stress, extreme temperatures, and salinity) and biotic (susceptibility to pests and diseases) limiting conditions, and even quality traits. We evaluated the performance of a set of remote sensing indices derived from Red-Green-Blue (RGB) images and the performance of the field-based Normalized Difference Vegetation Index (NDVI) and SPAD as phenotypic traits and crop monitoring tools for assessing maize performance under managed low nitrogen conditions. Phenotyping measurements were conducted on maize plants at two different levels: on the ground and from an airborne UAV (Unmanned Aerial Vehicle) platform. For the RGB indices assessed at the ground level, the strongest correlations compared to yield were observed with Hue, GGA (Greener Green Area), and GA (Green Area) at the ground level, while GGA and CSI (Crop Senescence Index) were better correlated with grain yield at the aerial level. Regarding the field sensors, SPAD exhibited the closest correlation with grain yield, with a higher correlation when measured closer to anthesis. Additionally, we evaluated how these different HTPP data contributed to the improvement of multivariate estimations of crop yield in combination with traditional agronomic field data, such as ASI (Anthesis Silking Data), AD (Anthesis Data), and Plant Height (PH). All multivariate regression models with an R² higher than 0.50 included one or more of these three agronomic parameters as predictive parameters, but with RGB indices at both levels increased to R² over 0.60. As such, this research suggests that traditional agronomic data provide information related to grain yield in abiotic stress conditions, but that they may be potentially supplemented by RGB indices from either ground or UAV phenotyping platforms. Finally, in comparison to the same panel of maize varieties grown under optimal conditions, only 11% of the varieties that were in the highest yield-producing quartile under optimal N conditions remained in the highest quartile when grown under managed low N conditions, suggesting that specific breeding for low N tolerance can still produce gains, but that low N productivity is also not necessarily exclusive of high productivity in optimal conditions. Full article

In the coming decades, Sub-Saharan Africa faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. To increase maize yields, the main staple food in SSA, the selection of suitable genotypes has been explored using remote sensing tools. They may play a fundamental role in overcoming the limitations of data collection and processing in large scale phenotyping studies. We present the result of a study in which Red-Green-Blue and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. The measurements were conducted on seedlings at ground level and from an unmanned aerial vehicle platform. Most indexes were significantly affected by tillage conditions, increasing their values from CP to CA. Indexes derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indexes with yield were improved by applying a soil-mask derived from a NDVI threshold. Full article

The Ma-le’l Dunes are located at the upper end of the North Spit of Humboldt Bay, California and are home to a range of plant and animal species. The goal of this study was to determine which classification method was the most accurate in identifying dune features when performed on a large, diverse area. The data sources used for this study were an orthomosaic image (2017) with 14 cm spatial resolution and NAIP images (2012, 2014, and 2016) with 1 m spatial resolution. A DJI Mavic Pro Unmanned Aerial Vehicle (UAV) was used to fly a 31 acre plot of the Ma-le’l Dunes at a height of about 80 m. The images from this flight were used to create an orthomosaic image in AgisoftPhotoScan. The dune feature classes were compared with two images using supervised, unsupervised, and feature extraction classification methods and accuracy assessments were performed using 100 ground control points. The classified feature classes were beach grass, shore pine, sand, other vegetation, and water. Overall, the NAIP classified maps showed a higher accuracy for all classification methods than UAV classified maps, with 86% overall accuracy for the supervised classification. A feature extraction method showed a low accuracy for both NAIP (46%) and UAV ortho classified images (30%). Of the classified methods for the orthomosaic image, the unsupervised classification showed a high accuracy (44%). The Ma-le’l dune habitats are more heterogeneous and some classes were overlapping (i.e., beach grass and sand) due to high microtopographic variation of the dune, resulting in lower accuracy for the feature extraction method. Monitoring dune habitats and geomorphic changes over time with UAV images is important for implementing suitable management practices for species conservation and mitigating coastal vulnerabilities. Full article

With large volumes of data acquired every month, the Copernicus satellites provide essential information for analysing and monitoring our environment. However, technical and knowledge barriers may affect user’s uptake of such a wealth of information. The RUS (Research and User Support for Sentinel Core Products) Service (funded by the EC and managed by ESA) began operations in October 2017 and aims to support overcoming such issues. A scalable cloud environment offers the possibility to remotely store and process data by bringing data and associated processing closer to the user. An integral part of the solution is the exploitation and adaptation of the platform, Free and Open-Source Software (FOSS). In addition, technical and scientific support (including training sessions) are provided to simplify exploitation of Copernicus data. The RUS Service is specially addressed to users from Copernicus countries who are willing to discover and use Copernicus core products and datasets. Other users willing to access the Service should first liaise with RUS to check their eligibility. The service is free. Commercial and operational activities cannot be carried out through the RUS Service. Full article

In this paper, we develop algorithms for oil spill detection using radar remote sensing. The algorithms take into account both the mathematical and the physical modeling of the sea surface covered by oil slicks. We use the statistical characterization of the power reflectivity and its distribution under various oil thicknesses and electromagnetic wave frequencies. We first introduce a single frequency (SF) oil spill detector that uses single or multiple observations (SO or MO) of power reflection coefficients over several scanning iterations for the sea area. Then, using Monte Carlo simulations we address the correctness of this detector by choosing different frequencies. Results show the inability of this detector to effectively distinguish between oil slicks and oil-free slicks for the total range of possible thicknesses. Nevertheless, increasing the number of observations leads to an increase in the effectiveness of the detector. An upgrade of this detector is the dual-frequency (DF) detector using single and multiple observations where two electromagnetic frequencies are used at the same time. Performance analysis of this detector proves its ability to overcome the drawbacks of the first detector by providing accurate detection especially for multiple observations. Full article

In 2015, the Environment Agency—Abu Dhabi developed the extensive Abu Dhabi Habitat, Land Use, Land Cover Map based on very high resolution satellite imagery acquired between 2011 and 2013. This was the first integrated effort at such a scale. This information has greatly helped in environmental conservation and preservation activities along with future infrastructure planning. This map has created an excellent baseline and allows efficient monitoring. In this work, we aim to establish a framework for short term updates to the maps to quickly enable efficient planning. We make use of spectral–spatial approaches based on object-based image analysis to adapt the classification scheme. Training examples from the baseline maps and field surveys are used to train classifiers, such as support vector machines (SVM), to build the updated maps. Eventually, the goal is to develop a consistent classification approach and then adapt automatic change detection approaches to extend the baseline maps temporally. Full article