bluuebunny/crossref_metadata_2025 huggingface dataset:
HOW DOES SHANNON’S SOURCE CODING THEOREM FARE IN PREDICTION OF IMAGE COMPRESSION RATIO WITH CURRENT ALGORITHMS?
Authors: Cheng X. , Li Z.
Published: August 2020

Abstract. Images with large volumes are generated daily with the advent of advanced sensors and platforms (e.g., satellite, unmanned autonomous vehicle) of data acquisition. This incurs issues on the storage, processing, and transmission of images. To address such issues, image compression is essential and can be achieved by lossy and/or lossless approaches. With lossy compression, a high compression ratio can usually be achieved but the original data can never be completely recovered. On the other hand, with lossless compression, the original information is well reserved. Lossless compression is very desirable in many applications such as remote sensing, geological surveying. Shannon's source coding theorem has defined the theoretical limits of compression ratio. However, some researchers have discovered that some compression techniques have achieved a compression ratio that is higher than the theoretical limits. Then, two questions naturally arise, i.e., “When this happens?” and “Why this happens?”. This study is dedicated to giving answers to these two questions. Six algorithms are used to compress 1650 images with different complexities. The experimental results show that the generally acknowledged Shannon’s coding theorem is still good enough for predicting compression ratio by the algorithms with consideration of statistical information only, but not capable of predicting compression ratio by the algorithms with consideration of configurational information of pixels. Overall, this study indicates that new empirical (or theoretical) models for predicting lossless compression ratio can be built with metrics capturing configurational information.
UAS DATA ACQUISITION PROTOCOL FOR MARINE HABITAT MAPPING: AN ACCURACY ASSESSMENT STUDY
Authors: Doukari M. , Topouzelis K.
Published: August 2020

Abstract. Marine habitat mapping is essential for updating existing information, preserving, and protecting the marine environment. Unmanned Aerial Systems (UAS) are an important tool for monitoring and mapping coastal and marine environment because of their ability to provide very high-resolution aerial imagery.Environmental conditions have a critical role in marine mapping using UAS. This is due to the limitations of UAS surveys in coastal areas, i.e. the environmental conditions prevailing in the area. The limitations of weather and oceanographic conditions affecting the quality of marine data led to the creation of a UAS protocol for the acquisition of reliable marine information. The produced UAS Data Acquisition Protocol consists of three main categories: (i) Morphology of the study area, (ii) Environmental conditions, (iii) Flight parameters. These categories include the parameters that must be considered for marine habitat mapping.The aim of the present study is the accuracy assessment of the UAS protocol for marine habitat mapping through experimental flights. For the accuracy assessment of the UAS protocol, flights on different dates and environmental conditions were conducted, over a study area. The flight altitude was the same for all the missions, so the results were comparable. The high-resolution orthophoto maps derived from each date of the experiment were classified. The classification maps show several differences in the shape and size of the marine habitats which are directly dependent on the conditions that the habitats were mapped. A change detection comparison was conducted in pairs to examine the exact changes between the classified maps.The results emphasize the importance of the environmental conditions prevailing in an area during the mapping of marine habitats. The present study proves that the optimal flight conditions that are proposed of the UAS Data Acquisition protocol, respond to the real-world conditions and are important to be considered for an accurate and reliable mapping of the marine environment.
INVESTIGATIONS ON THE GLOBAL IMAGE DATASETS FOR THE ABSOLUTE GEOMETRIC QUALITY ASSESSMENT OF MSG SEVIRI IMAGERY
Authors: Kocaman S. , Debaecker V. , Bas S. , Saunier S. , Garcia K. , Just D.
Published: August 2020

Abstract. EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites), an intergovernmental organisation founded in 1986, supplies weather and climate-related satellite data, images and products throughout the year for EU Member States and other users worldwide. The optical Earth Observation satellites launched and operated by EUMETSAT, both current and planned ones, have different spatial, spectral and temporal resolutions; sensor models and acquisition geometries. While the number and the diversity of the satellite missions increase, the requirement of novel methods and up-to-date reference data for geometric accuracy assessment of the imagery also grows. This paper aims at reporting the results of a study investigating the availability for suitable satellite imagery to be employed as reference data for the geometric quality assessment (GQA) of MSG SEVIRI Level 1.5 image products. The reference datasets need to have superior spatial resolution, wide global coverage, and spectral compatibility with respect to the SEVIRI sensor, which has 12 spectral bands with 1 km and 3 km spatial resolutions. The SEVIRI sensor works with whiskbroom principle at a geostationary orbit and collects data at 5 minutes (rapid scan) and 15-minutes (full scan) intervals. Although preliminary investigations on reference data were performed by using images of different satellite sensors during the study, in-depth investigations were performed with MERIS global image mosaic and Landsat imagery. The progress and different problems observed in the images are reported here.
QUALITY ANALYSIS AND IMPROVEMENT OF FUNDAMENTAL GEOGRAPHIC NATIONAL CONDITIONS MONITORING RESULTS
Authors: Li M. , Chen H. P. , Tian Z. B. , Qiu B. , Xie W. J. , Chen Y. H.
Published: August 2020

Abstract. With the integration and unification of natural resources, the application fields and application levels of geographic national condition monitoring results have also expanded and improved, and the data quality requirements for national condition monitoring have become higher and higher, especially the results of fundamental geographic national conditions monitoring, the quality of the results is the basis of monitoring and the life of monitoring. Based on the recent practice in fundamental geographic national conditions monitoring, this paper summarizes the quality requirements of monitoring results, analyzes typical quality problems and quality influencing factors, and three measures for improving the quality of the results are proposed: one is a problem-oriented quality inspection method; the other is the key points of quality control in the fundamental national condition monitoring process; the third is the preliminary exploration of the application of big data in monitoring.
QUALITY INSPECTION AND EVALUATION OF SATELLITE ORTHOPHOTOS IN CHINA'S THIRD NATIONAL LAND SURVEY
Authors: Luo F. , Li C. , Yan Y.
Published: August 2020

Abstract. Satellite orthophotos are important data sources and results of China's third national land survey, and their quality profoundly affects the accuracy of the survey results. This article reviews the quality requirements and quality models of satellite orthophotos for the purpose of land survey in conjunction with the quality inspection and evaluation of satellite orthophotos covering the entire land area of China, and analyzes the shortcomings of current orthophotos quality evaluation methods. On the basis of this method, a multilevel fuzzy comprehensive evaluation method was introduced to test the orthophotos quality evaluation, and the concepts of maximum membership effectiveness index and evaluation grade probability were used to constrain the evaluation results. The results prove that the method is more effective than the current method true and accurate.
BROCELIANDE: A COMPARATIVE STUDY OF ATTRIBUTE PROFILES AND FEATURE PROFILES FROM DIFFERENT ATTRIBUTES
Authors: Merciol F. , Pham M.-T. , Santana Maia D. , Masse A. , Sannier C.
Published: August 2020

Abstract. Morphological attribute profiles (APs) are among the most effective spatial-spectral methods to perform multilevel image description based on hierarchical tree-based representation. They have been widely applied to the processing and characterization of remote sensing images, in particular to tackle classification task, in the literature. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from both inclusion and partition trees. The motivation of this article is to conduct a comparative study of APs and FPs using different attributes including some novel ones that have not been used in the literature. We also present our developed library called Broceliande, which proposes efficient implementation of APs and FPs to perform remote sensing image classification, with various choices of tree structures as well as attributes. We perform our experiments on two high resolution optical image data sets and provide comparative results of APs and FPs, showing and confirming their effectiveness to describe and classify remote sensing images.
INVESTIGATING THE PERFORMANCE OF RANDOM FOREST AND SUPPORT VECTOR REGRESSION FOR ESTIMATION OF CLOUD-FREE NDVI USING SENTINEL-1 SAR DATA
Authors: Mohite J. D. , Sawant S. A. , Pandit A. , Pappula S.
Published: August 2020

Abstract. The current study focuses on the estimation of cloud-free Normalized Difference Vegetation Index (NDVI) using the Synthetic Aperture Radar (SAR) observations obtained from Sentinel-1 (A and B) sensor. South-West Summer Monsoon over the Indian sub-continent lasts for four months (mid-June to mid-October). During this time, optical remote sensing observations are affected by dense cloud cover. Therefore, there is a need for methodology to estimate state of vegetation during the cloud cover. The crops considered in this study are Paddy (Rice) from Punjab and Haryana, whereas Cotton, Turmeric, and Banana from Andhra Pradesh, India. We have considered, observations of Sentinel-1 and Sentinel-2 sensors with the same overpass day and non-cloudy pixels for each crop. We used Google Earth Engine to extract surface reflectance for the Sentinel-2 and Ground Range Detected (GRD) backscatter for Sentinel-1. The Red and NIR bands of Sentinel 2 were used to estimate NDVI. Sentinel-1 based VV, and VH backscatter was used for estimation of Normalized Ratio Procedure between Bands (NRPB). Regression analysis was performed by using NDVI as an independent variable, and VV, VH, NRPB, and radar incidence angle as dependant variables. We evaluated the performance of Linear regression with tuned Support Vector Regression (SVR) as well as tuned Random Forest Regression (RFR) using the independent data. Results showed that the RFR produced the lowest RMSE for all the crops in the study. The average RMSE using the RFR was 0.08, 0.09, 0.11, and 0.10 for Rice, Cotton, Banana, and Turmeric, respectively. Similarly, we have obtained R2 values of 0.79, 0.76, 0.69, and 0.71 for the same crops using the RFR. A model with 80 trees produced the best results for Rice and Cotton, whereas the model with 90 trees produced the best results for Banana and Turmeric. Analysis with NDVI threshold of 0.25 showed improved R2 and RMSE. We found that for grown crop canopy, SAR based NDVI estimates are reasonably matching with the optical NDVI. A good agreement was observed between the actual and estimated NDVI using the tuned RFR model.
VALIDATION OF LIDAR SURVEY DATA BY COMPARISON OF SEVERAL UNCERTAINTY MODELS
Authors: Seube N.
Published: August 2020

Abstract. This paper introduce a new method for validating the precision of an airborne or a mobile LiDAR data set. The proposed method is based on the knowledge of an a Combined Standard Measurement Uncertainty (CSMU) model which describes LiDAR point covariance matrix and thus uncertainty ellipsoid. The model we consider includes timing errors and most importantly the incidence of the LiDAR beam. After describing the relationship between the beam incidence and other variable uncertainty (especially attitude uncertainty), we show that we can construct a CSMU model giving the covariance of each oint as a function of the relative geometry between the LiDAR beam and the point normal. The validation method we propose consist in comparing the CSMU model (predictive a priori uncertainty) t the Standard Deviation Alog the Surface Normal (SDASN), for all set of quasi planr segments of the point cloud. Whenever the a posteriori (i.e; observed by the SDASN) level of uncertainty is greater than a priori (i.e; expected) level of uncertainty, the point fails the validation test. We illustrate this approach on a dataset acquired by a Microdrones mdLiDAR1000 system.
THE OVERSEAS GEOMETRIC ACCURACY VALIDATION OF ZY-3 SATELLITES IMAGES
Authors: Xue Y. , Tang X. , Gao X. , Yue Q. , Kainz W. , Chanthabouathong V. , Lv S.
Published: August 2020

Abstract. With China’s first stereo imaging satellite ZY-3’s successful launch and two satellites networking operation, space remote sensing becomes an important means of data acquisition for survey and mapping, and geographic information updating in China.The updating period of 4D products in China is shortened by one third, the geographic information updating capability was improved more than 2 times.After more than eight years of stable and continued earth observation, ZY-3 statellites data coverage has gradually achieved more than half of the earth land which enables the overseas service for global geospatial application. Focusing on the Chinese domestic mapping applications and services, the data of ZY-3 and other domestic land observation satellites can be real-time pushed and distributed across China through the satellite cloud service platform, and worldwide for facilitating the "Belt and Road" initiative, which is promoted through the GEOSS as well for global application.This paper presents the overseas geometric accuracy evaluation and validation of ZY-3 satellites images, which was carried out systematically for the first time abroad and complements with China’s mapping evaluation practices to provide a comprehensive technical guidelines for global mapping application. The direct location accuracy of ZY-3 satellites sensor corrected image products were further evaluated and verified cooperating with the University of Vienna, Austria. The location deviations in mountain area and in flat area were analyzed and compared to check the deviation pattern and validate their consistency with the results in China.Utilizing China’s ZY-3 satellites images ranging from 2012–2018, the DSM was generated automatically based on a semiglobal optimization method (Yue et al. 2016) using self-developed software SDP without ground control points but using SRTM data as elevation reference to improve the geometric accuracy. The result shows the elevation accuracy of DSM is 3.19 m (RMSE), satisfying the local requirement for 10m interval contour mapping.The planar accuracy of DOM generated without ground control points utilizing ZY-3 satellites images within 2012–2015 is 2.46 m (RMSE). With 128 orthorectified aerial photo slots provided by NGD of Laos as reference, the planar accuracy of the DOM generated utilizing ZY-3 satellites images from 2016–2018 is 1.65 m (RMSE), which can satisfy the local 1:25000 planar accuracy requirement and overlay with the local 1:2000 transportation data exactly. The overseas geometric accuracy evaluation and validation of ZY-3 satellites images provides a systematic and practical guidance for future global applications by complementing with china’s experience, which would be beneficial to overseas users in acknowledging the usage of ZY-3 satellites data and help to promote it’s global applications.
QUALITY INSPECTION OF SATELLITE IMAGERY BLOCK ADJUSTMENT WITHOUT GCP
Authors: Zhao Y. , Zhao H. T. , Guo J. , Tu H. J. , Yin S.
Published: August 2020

Abstract. The current status of insufficient research on production technology and quality inspection methods for uncontrolled mapping of block adjustment results for global project. This paper proposes a set of quality inspection process and technical methods for large-scale production, lack of field checkpoints and difficult to verify in the field based on the analysis of the characteristics of the results: First, analyze the quality elements and inspection contents to be inspected, determine the inspection methods for different inspection contents; Then, extract items that can be automatically checked or need to be verified the production process data, establish inspection rules, implement batch automatic inspection and production data verification for some check items; study the inspection methods for the remaining items and implement human-computer interaction check. Finally, 4 test-fields were checked and the results showed that the technical route proposed in this paper is feasible, and the quality evaluation results are objective and reliable.
ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING
Authors: Amarsaikhan D.
Published: August 2020

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.
RELATIONSHIP BETWEEN RICE RESIDUE BURNING AND INCREASING AIR POLLUTION IN NORTH-WEST INDIA
Authors: Bhadauriya S. , Chaudhary N. , Mamatha S. , Ray S. S.
Published: August 2020

Abstract. Punjab and Haryana are two major Rice-producing states of India. They generate high amount of rice residue every year and these residues are burnt in the months of October and November to clear the fields for the next sowing, i.e. Wheat. Residue burning in these two states is considered to be a major factor for the pollution conditions persisting in Delhi, the capital of the country, during October and November. In this study, we aim to analyse the role of stubble burning on Pollution. The approach aimed at a) Determination of rice straw contingent to open burning in the states of Punjab and Haryana, b) Determine and quantify the air pollutant emissions from rice residue contingent to open burning and c) Compare them with the air pollution of Delhi. Also, in order to analyse the various reasons for the increasing pollution in Delhi, Aerosol Parameters like Aerosol Optical Depth, Angstrom Exponent and Single Scattering Albedo were also studied along with auxiliary data like Temperature, Wind Directions, Wind Trajectories, MODIS Fire Counts and CPCB Pollution Data. In this study, we found that not only residue burnings of Punjab and Haryana, but also dust storms from far beyond these states influence the pollution levels in Delhi, especially in the case of Particulate Matter less than 10.
CUSTOMIZED WEBGIS SOLUTIONS FOR EXPOSOMICS
Authors: Casella V. , Franzini M. , Rocca M. T. , Pogliaghi A. , Fiscante N. , Raso L. , Sapio F.
Published: August 2020

Abstract. Exposomics is a science aiming at quantifying the effects on human health of all the factors influencing it, but genetic ones. They include environment, food, mobility habits and cultural factors. The percentage of the world’s population living in the urban areas is projected to increase in the next decades. Rising industrialization, urbanization and heterogeneity are leading to new challenges for public health and quality of life in the population. The prevalence of conditions such as asthma and cardiovascular diseases is increasing due to a change in lifestyle and air quality. This enlightens the necessity of targeted interventions to increase citizens’ quality of life and decrease their health risks. Within the EU H2020 PULSE project, a multi-technological system to assist the population in the prevention and treatment of asthma and type 2 diabetes has been developed. The system created in PULSE features several parts, such as a personal App for the citizens, a set of air quality sensors, a WebGIS and dashboards for the public health operators. Citizens are directly involved in an exchange paradigm in which they send their own data and receive feedbacks and suggestions about their health in return. The WebGIS is a very distinguishing element of the PULSE technology and the paper illustrates its main functionalities focusing on the distinguishing and innovative features developed.
PREDICTING DAILY PM2.5 USING WEIGHTED LONG SHORT-TERM MEMORY NEURAL NETWORK MODEL
Authors: Fan H. , Yang M. , Xiao F. , Zhao K.
Published: August 2020

Abstract. Over the past few decades, air pollution has caused serious damage on public health, thus making accurate predictions of PM2.5 crucial. Due to the transportation of air pollutants among areas, the PM2.5 concentration is strongly spatiotemporal correlated. However, the distribution of air pollution monitoring sites is not even, making the spatiotemporal correlation between the central site and surrounding sites varies with different density of sites, and this was neglected by most existing methods. To tackle this problem, this study proposed a weighted long short-term memory neural network extended model (WLSTME), which addressed the issue that how to consider the effect of the density of sites and wind condition on the spatiotemporal correlation of air pollution concentration. First, several the nearest surrounding sites were chosen as the neighbour sites to the central station, and their distance as well as their air pollution concentration and wind condition were input to multi-layer perception (MLP) to generate weighted historical PM2.5 time series data. Second, historical PM2.5 concentration of the central site and weighted PM2.5 series data of neighbour sites were input into LSTM to address spatiotemporal dependency simultaneously and extract spatiotemporal features. Finally, another MLP was utilized to integrate spatiotemporal features extracted above with the meteorological data of central site to generate the forecasts future PM_2.5 concentration of the central site. Daily PM_2.5 concentration and meteorological data on Beijing–Tianjin–Hebei from 2015 to 2017 were collected to train models and evaluate the performance. Experimental results with 3 other methods showed that the proposed WLSTME model has the lowest RMSE (40.67) and MAE (26.10) and the highest p (0.59). This finding confirms that WLSTME can significantly improve the PM2.5 prediction accuracy.
SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 TIME SERIES FOR POPLAR PLANTATIONS MONITORING AT LARGE SCALE
Authors: Hamrouni Y. , Paillassa É. , Chéret V. , Monteil C. , Sheeren D.
Published: August 2020

Abstract. The current context of availability of Earth Observation satellite data at high spatial and temporal resolutions makes it possible to map large areas. Although supervised classification is the most widely adopted approach, its performance is highly dependent on the availability and the quality of training data. However, gathering samples from field surveys or through photo interpretation is often expensive and time-consuming especially when the area to be classified is large. In this paper we propose the use of an active learning-based technique to address this issue by reducing the labelling effort required for supervised classification while increasing the generalisation capabilities of the classifier across space. Experiments were conducted to identify poplar plantations in three different sites in France using Sentinel-2 time series. In order to characterise the age of the identified poplar stands, temporal means of Sentinel-1 backscatter coefficients were computed. The results are promising and show the good capacities of the active learning-based approach to achieve similar performance (Poplar F-score ≥ 90%) to traditional passive learning (i.e. with random selection of samples) with up to 50% fewer training samples. Sentinel-1 annual means have demonstrated their potential to differentiate two stand ages with an overall accuracy of 83% regardless of the cultivar considered.
MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING
Authors: Liu C. , Zhou X. , Zhou Y. , Akbar A.
Published: August 2020

Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This paper proposes a multi-spectral water quality monitoring method based on UAV platform, which can quickly monitor an entire urban water area and conduct multi-temporal observation for key indices of water quality within one day. It is helpful to find and locate the polluted areas which affect the water environment quickly. Also, it can show the changes of water quality on the time axis. The result can provide a decision-making basis for water environment treatment.
EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON
Authors: Andrade R. B. , Costa G. A. O. P. , Mota G. L. A. , Ortega M. X. , Feitosa R. Q. , Soto P. J. , Heipke C.
Published: August 2020

Abstract. Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods.
EVALUATION OF UNet AND UNet++ ARCHITECTURES IN HIGH RESOLUTION IMAGE CHANGE DETECTION APPLICATIONS
Authors: Bousias Alexakis E. , Armenakis C.
Published: August 2020

Abstract. Change detection applications from satellite imagery can be a very useful tool in monitoring human activities and understanding their interaction with the physical environment. In the past few years most of the recent research approaches to automatic change detection have been based on the application of Deep Learning techniques and especially on variations of Convolutional Neural Network architectures due to their great representational capacity and their state-of-the-art performance in visual tasks such as image classification and semantic segmentation. In this work we train and evaluate two CNN architectures, UNet and UNet++, on a change detection task using Very High-Resolution satellite images collected at two different time epochs. We also examine and analyse the effect of two different loss functions, a combination of the Binary Cross Entropy Loss with the Dice Loss, and the Lovász Hinge loss, both of which were specifically designed for semantic segmentation applications. Finally, we experiment with the use of data augmentation as well as deep supervision techniques to evaluate and quantify their contribution in the final classification performance of the different network architectures.
REMOTE SENSING TIME SERIES ANALYSIS AIMED AT DETECTING AND MONITORING BRAZILIAN IRON ORE MINING DISASTERS
Authors: Centeno J. A. S. , Kishi R. T. , Mitishita E.
Published: August 2020

Abstract. Iron mining is one of the main activities of the State of Minas Gerais (Brazil). However, this activity offers significant environmental risks and may harm the local population. In 2019, the Brazil experienced large environmental disaster related to iron mining in the municipality of Brumadinho. The collapse of mining waste water Dam caused the mud spill composed of the mixture of water and tailings minerals that traveled from the dam through the network of local drainage. In this work, it is analyzed the environmental impact of the dam's collapse with the help of remote sensing image using two spectral bands (red and near infrared). The comparative study carried out by analysing Landsat OLI of several months enables evaluating the effect of the event on the water resources and also monitor the evolution of the situation by comparing multiple images. In the first step, direct digital values are measured along the rivers to compare the sediment load in each date, which allows monitoring the situation of the rivers. In a second step, the image series is combined using binary encoding to visualize changes in the catchment. The study shows that the Paraopeba and Córrego do Feijão rivers suffered a great impact from the dam break and that they are slowly recovering from the tragic event.
UAV IMAGING AT RGB FOR CROP CONDITION MONITORING
Authors: Choroś T. , Oberski T. , Kogut T.
Published: August 2020

Abstract. Modern techniques such as precision agriculture tasks are provided to intentional fertilization, pesticide dosing or simply watering the crops. These tasks need to be continuously monitored. One of known method for analyzing the crops conditions is calculating the vegetation indexes. This paper focuses on purpose of using images made with UAV equipped with ordinary non-metric digital RGB camera. The methods had been taken revealed easy to use and cost effective. We present an experiment which attend to distinguish different crops conditions on two test fields sowed with wheat and rape. For this purpose, two different RGB based vegetation indexes were analyzed. The results of calculated indexes shown how crops differs in each stage of vegetation. During the first stage (germinating) the plants are green and average TGI is low. It increases at second stage (flowering) because of plant flowers, which partly cover the leaves. At last stage (ripening) TGI decreases, so plants are still green but starting to dry and change their color.
TRANSFORMATION OF A VILLAGE: CASE OF WAD AL ABBAS, SENNAR STATE, SUDAN
Authors: Elkamali M. , Yagoub M. M.
Published: August 2020

Abstract. Understanding socioeconomic changes associated with the conversion of rural areas to urban and the factors behind them are essential for future prospects. Theoretical debates on the topic have flourished. However, empirical case studies, particularly from developing nations, are limited. This study attempts to fill the gap through a case study of a village in Sudan. The development of Wad al Abbas village is investigated using remote sensing techniques employing free Landsat satellite images. The study used the Object-Based Image Analysis (OBIA) approach to detect the village sprawl between 1987 and 2018. Results showed significant built-up growth (84.5% increase) between 1987 and 1998 and this is linked to the natural increase in population, diversification of income as a result of a change in jobs from farming to trade, migration of a large number of its residents in and outside the country, and attraction of administrative and governmental institutions. Although Wad al Abbas starts to have a town feature in terms of population size and other metrics, still there are many challenges facing this transformation. These include water, education, health, sewage system, solid waste, drainage system, landslide, water channel for the agricultural scheme (canal), the meandering of the Blue Nile, and inner road network. High-resolution satellite images could be used to help in the investigation of these challenges. Recommendations are made on how to deal with these challenges. The lesson learned from this study is the value of the use of the space (free satellite images) to document changes that couldn’t be monitored especially in developing countries where historical maps or aerial photographs are not available.
EVALUATION OF SPATIO-TEMPORAL ASPECTS OF LAND USE AND LAND COVER CHANGES IN NAGALAND, NORTH-EAST, INDIA
Authors: Hiese N. , Hiese Z. , Katiry D. , Medo T. , Hiese M.
Published: August 2020

Abstract. Land use is a dynamic phenomenon, changing with time and space. Land use/land cover (LULC) information and its periodic changes has become crucial to carry out the prediction to the dynamical change of land use. This study analyzed the spatial and temporal changes in land use that has taken place in Nagaland over the last 10 years (2005–2016). It has been observed that the dynamic change in land use and land cover has been mainly caused by the traditional practice of jhum cultivation, also known as shifting cultivation in Nagaland, which constitute about 81% of the total agricultural land. As a result, there is a decline in forest area by 593.87 km2 (5.66%) from 2005–2006 to 2015–2016. Concurrently, abandoned jhum land and scrubland has increased by 11.72% and 24.89%, demonstrating the decreased in jhum/ shifting cultivation cycle. The loss of forest in the last decade was attributed to ever increasing population, putting pressure on demand of jhum/ shifting cultivation and other anthropogenic activities. The degradation of forest is ever increasing, which calls for intervention of appropriate technology and holistic approach to address this issue.
ASSESSING THE CONTRIBUTION OF SPECTRAL AND TEMPORAL FEATURES FOR ANNUAL LAND COVER AND CROP TYPE MAPPING
Authors: Karakizi C. , Tsiotas I. A. , Kandylakis Z. , Vaiopoulos A. , Karantzalos K.
Published: August 2020

Abstract. Freely available satellite image time-series are currently the most exploited data towards land cover mapping. In this work we assess the contribution of spectral and temporal features for the detailed, i.e., with more than thirty classes, land cover and crop type mapping based on annual Sentinel-2 data. As a baseline we employed a datacube consisting of spectral features, i.e., spectral bands and indices from one tile of Sentinel-2A data for the year 2016. Then we formed two different datacubes of reduced dimensions, containing either spectrotemporal or temporal features and performed the same experiments in order to assess their contribution. For the second dataset only spectral features that fulfilled certain temporal conditions were retained, reducing by 40% the initial datacube dimensionality. The third dataset was formed only of temporal features resulting to a reduction of 50%. A random forest classifier was employed for the classification procedure and standard accuracy metrics for the validation. All experiments resulted into high overall accuracy rates of over 90% while rates for average F-score metric exceeded 78% in all cases. The quantitative and qualitative validation indicated that the baseline dataset modestly outperformed the other two of spectrotemporal and temporal features. Insights regarding the influence of spectral differentiation among classes and the impact of their sample size, on the per-class performance are further discussed. The importance of spatial independency for training and testing sets was also demonstrated highlighting the need of following best practises during validation in order to deliver a realistic estimation of the produced map accuracy.
“3S” TECHNOLOGIES AND APPLICATION FOR DYNAMIC MONITORING SOIL AND WATER LOSS IN THE YANGTZE RIVER BASIN, CHINA
Authors: Li C. , Yao J. , Li R. , Zhu Y. , Yao H. , Zhang P. , Wei D. , Zhao S. , Li Y. , Wu Y.
Published: August 2020

Abstract. For China, which has many big rivers, there is an urgent need for efficient dynamic monitoring technology of water and soil loss. However, there are some problems in the current 3S (RS, GIS and GPS) technology for dynamic monitoring water and soil loss. This paper takes the Yangtze River Basin as an example to innovate and optimize the key technologies of the remote sensing interpretation of the water and soil loss dynamic monitoring of the Yangtze River Basin, and overcome the major technical difficulties in the remote sensing interpretation of the dynamic monitoring of water and soil loss. The key technologies include: 1) The establishment of a field investigation platform based on Internet and UAV (Unmanned Aerial Vehicle) for remote sensing interpretation; 2) Near real-time evaluating key factors of soil and water loss based on UAV photogrammetry and digital terrain analysis; 3) Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for remote sensing images pre-processing; 4) An object-oriented land use change update quality control method supported by multi-PC and GIS; and an object-oriented remote sensing image classification system based on random forest, deep learning and transfer learning; 5) Improvement of quantitative change detection method for image vegetation and three-dimensional topography. The results have been successfully applied in the remote sensing interpretation of the dynamic monitoring of water and soil loss in the national key prevention and control area of the Yangtze River Basin. They have been provided a scientific reference for the development planning of The Yangtze River Economic Zone.
HIGH-RESOLUTION SAR COHERENT CHANGE DETECTION IN URBAN ENVIRONMENT
Authors: Manzoni M. , Monti-Guarnieri A. , Molinari M. E.
Published: August 2020

Abstract. The identification of changes in urban settlements is of great interest for damage assessment after natural disasters, cadastral mapping and monitoring urban development and illegal activities.Radar-based remote sensing from space-borne platforms is quite useful in this scenario and Synthetic Aperture Radar (SAR) data is attractive due to its wide coverage, the day and night all-weather availability, and the sensitivity to slight changes in the scene structure. In this context, the launch of the European Space Agency (ESA) constellation Sentinel-1 has played a significant role: the exact repetition of the acquisition geometry, the repeated illumination and the sensitivity to centimetric changes thanks to the C-Band (5.4GHz) radar payload make Sentinel-1 the perfect instrument to monitor urban settlements.Coherent Change Detection (CCD) techniques are able to detect even the finest change in the structure of a target, so small to be comparable with the wavelength. This sensibility is an advantage, but turns into a drawback especially in an urban environment where every subtle change may cause an unwanted detection.This paper tackles the problem of the huge amount of triggered detections over urbanized areas with a combination of a high-resolution coherent multi-change detection technique and Geospatial Information System (GIS) post-processing. The final result is a map of buildings that are changed in the scene due to relevant variation of their structure. In this contribution, the complete workflow is explained, and a preliminary validation is carried out by means of a set of images gathered by Sentinel-1 and a set of optical images over the city of Manchester.
A NOVEL INDEX FOR TEMPORAL STABILITY ANALYSIS IN SPACE AND TIME OF SAR-DERIVED SCENES
Authors: Molinari M. E. , Monti-Guarnieri A. , Manzoni M.
Published: August 2020

Abstract. Detecting temporal changes is one of the most important applications of satellite sensors. In recent years, the increasing availability of regular time-series of SAR imagery, provided by the Sentinel-1 mission of the European Space Agency (ESA), has drawn increasing attention to these techniques, especially in earth environment monitoring and risk management. Within this paper, a coherent change detection analysis for evaluating the risk due to movements of dunes and sand sheets in desertic areas is proposed. To this purpose, we introduce a novel, coherence-based index, named Temporal Stability Index (TSI), that is suited for characterizing the percentage of stability of a target with time. TSI maps can be generated over areas as wide as hundreds of kilometers, in a short time, and mostly by exploiting available software tools (plus some simple coding). The information provided is complementary to the average of the short-term coherence, here shown. Results of analysis performed on two desertic regions (the United Arab Emirates and Egypt) document the usefulness of TSI for the identification of dune movements and areas subject to sand accumulation, supporting risk mitigation measures.
SENTINEL-1 AND SENTINEL-2 TIME SERIES PROCESSING CHAINS FOR CYCLONE IMPACT MONITORING IN SOUTH WEST INDIAN OCEAN
Authors: Mouquet P. , Alexandre C. , Rasolomamonjy J. , Rosa J. , Catry T. , Révillion C. , Rakotondraompiana S. , Pennober G.
Published: August 2020

Abstract. Monitoring the spatial footprint of cyclone impacts by remote sensing offers great potential for assessing the extent of damage and monitoring the resilience of the affected territories. For this purpose, as part of the Renovrisk-Impact project, we have developed two change detection processing chains based on optical (Sentinel-2) and SAR (Sentinel-1) data. These chains have been used to track different events in different regions of the world. In this article we focus on two study sites in Madagascar: the city of Miandrivazo, which was heavily affected by severe rainfall from Cyclone AVA in January 2018, and more recently the town of Marovoay which suffered a major disaster following the passage of tropical storm DIANE in January 2020. The obtained results were evaluated and compared with the Copernicus Emergency Mapping Service product, showing good consistency with this product and between them. These results confirm the potential of these Sentinel data and the developed processing chains for monitoring the impacts of cyclones, but also open up prospects for longer-term monitoring.
DYNAMICS OF AGRICULTURAL EXPANSION IN AREAS OF THE BRAZILIAN SAVANNA BETWEEN 2000 AND 2019
Authors: Pereira P. R. M. , Bolfe E. L. , Rodrigues T. C. S. , Sano E. E.
Published: August 2020

Abstract. The information survey and the land use and land cover (LULC) change monitoring are essential to understand the changes in the landscape and their impacts on the environment. The Brazilian savanna (Cerrado) constitutes the second largest biome in Brazil and is highly relevant because of its rich biodiversity. The Cerrado in the Maranhão State is facing a high conversion rate of its natural vegetation into agricultural systems because of the agricultural development policies. This article aims to analyze and quantify the LULCC in the Chapadinha microregion, Maranhão State, Brazil, by comparing Landsat satellite images from 2000, 2009, and 2019. The Chapadinha microregion covers an area of more than 14,000 km2 and, since 2000, presents significant spatial transformations related to agricultural expansion. The goal of this expansion is the economic development based on agricultural commodities (mainly soybean monoculture) for exportation. The study area is the third microregion in terms of agricultural production in Maranhão. The satellite image interpretation showed a reduction of more than 800 km2 of natural vegetation.
HIERARCHICAL APPROACH FOR DETECTING CHANGES WITH THE USE OF DIFFERENT PYRAMID LEVELS IN DENSE IMAGE MATCHING
Authors: Pilarska M.
Published: August 2020

Abstract. Many cities order spatial data systematically, in particular aerial nadir images and orthophotomaps. However, only the orthoimages and orthophotomaps are usually used by the city administration, particularly in spatial planning. Some of the users are not aware of the possibilities as to how the aerial images can be used. Spatial data users, who may not be specialists in photogrammetry, are sometimes not aware that it is possible to obtain 3D information from 2D images as a point cloud. The idea of dense image matching (DIM) is well-known and described in the field of photogrammetry. Although dense image matching is a time- and memory-consuming process, this does not present a major drawback with modern computing. Images for the test area – Warsaw – are characterised by Ground Sampling Distance (GSD) equal to 8 cm. These images can be successfully used in change detection processes, comparing the dense image matching point cloud from two different dates. What is important while considering land cover change detection, is that it is not necessary to generate a detailed and high-density point cloud, e.g. in order to detect changes in buildings. The main idea of the article is to present the possibility of using higher levels of images pyramid in dense image matching within the change detection process as a way to optimize the processing time and point cloud accuracy. Which level of pyramid is needed to detect different changes in urban land cover will also be discussed.
BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING
Authors: Seydi S. T. , Hasanlou M.
Published: August 2020

Abstract. Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena to promote better decision making. The bi-temporal hyperspectral imagery has a high potential for the detection of surface changes. However, the extraction of changes from bi-temporal hyperspectral imagery due to special content of data, and environment conditions (atmospheric condition), change into challenging task. To this end, this research proposed a change detection framework based on deep learning using bi-temporal hyperspectral imagery. The proposed framework is applied in two main steps: (1) predict phase that the change areas highlighted from no-change areas using image differencing algorithm (ID), (2) decision phase that it decides for detecting change pixels based on 3D convolution neural network (CNN). The efficiency of the presented method is evaluated using Hyperion multi-temporal hyperspectral imagery. To evaluate the performance of the proposed method, two bi-temporal hyperspectral Hyperion with a variety of land cover classes were used. The results show that the proposed method has high accuracy and low false alarms rate: overall accuracy is more than 95%, and the kappa coefficient is greater than 0.9 and the miss-detection is lower than 10% and the false rate is lower than 4%.