Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of "dark" and "bright" appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection

Приложение для мониторинга затопления территории при обрушении плотины в Лаосе в 2018 г. на основе данных SAR Sentinel-1A и объектно-ориентированного метода

Flood disasters seriously threaten the survival and development of human beings. Monitoring the changes of water bodies during floods and estimating the affected area is essential for comprehensive and accurate analysis of disaster information. Recently, radar satellite data has been increasingly used for flood monitoring, since in this case, cloudiness is not an obstacle to estimating the flood area. In this paper Sentinel-1 ground range detected (GRD) data was selected to estimate the inundated area after the Xe-Pian Xe-Namnoy Dam breach in Laos at the end of July 2018. The flooded Hinlat area and the Xe-Pian Xe-Namnoy reservoir were selected as the study area for flood inundation extent monitoring, because this area is characterized by bare land, agricultural land, and residential land with complex topography and geomorphology. The study area is located in the Bolaven Plateau, is a highland region in southern Laos. One of the reasons for the flooding of the study area is an elevation difference between upper reaches and downstream of the river. Several reaches with a convex profile and knickpoints because of the geologic control when draining the plateau represent the undeveloped longitudinal profile of the Vang Ngao River. The main channel of the Vang Ngao River is dug into Mesozoic fluvial sandstones, which resist scouring by the flood. The eCognition software is used to organize the process of extracting information about the flood zone. The object-oriented approach and the threshold method are combined to extract information about the reservoir. First, SNAP software is used to pre-process Sentinel-1A SAR images. Then, the eCognition multi-scale segmentation method is used to determine the best segmentation scale, for iterative testing and comparative analysis of experimental results, taking into account the characteristics of the object and a priori knowledge. After sensitivity analysis of the flooded area image features and other features, the VH-polarized backscattered mean features were selected to construct a knowledge base for flooded area extraction to differentiate water and non-water bodies. At the same time, the modified bare soil index (MBI) and the terrain relief were combined to remove the influence of bare land and mountain shadow on the extraction results to achieve the 2018 dam collapse flood monitoring in Laos. Finally, the extent and area of the affected region were analyzed and the changes of water bodies before and after the disaster were mapped. The study shows that the monitoring results of Sentinel-1A SAR data are more consistent with the actual situation and have significant advantages in flood hazard monitoring and assessment, which can effectively carry out large-scale flood inundation extent monitoring

Remote sensing technologies for the assessment of marine and coastal ecosystems

Abstract This chapter reviews the Remote Sensing (RS) technologies that are particularly appropriate for marine and coastal ecosystem research and management. RS techniques are used to perform analysis of water quality in coastal water bodies; to identify, characterize and analyze river plumes; to extract estuarine/coastal sandy bodies; to identify beach features/patterns; and to evaluate the changes and integrity (health) of the coastal lagoon habitats. For effective management of these ecosystems, it is essential to have satellite data available and complementary accurate information about the current state of the coastal regions, in addition to well-informed forecasts about its future state. In recent years, the use of space, air and ground-based RS strategies has allowed for the rapid data collection, Image processing (Pixel-Based and Object-Based Image Analysis (OBIA) classification) and dissemination of such information to reduce vulnerability to natural hazards, anthropic pressures, and to monitoring essential ecological processes, life support systems and biological diversityinfo:eu-repo/semantics/submittedVersio

X-laineala tehisava-radari rakendused keskkonnakaugseireks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Tehisava-radar on lennukitel ja satelliitidel kasutatav maa- ja veepinna kaugseire instrument. Tehisava-radarid töötavad raadio- ja mikrolainete piirkonnas lainepikkustel 1 m kuni 3 cm ning on tundlikud uuritavate objektide struktuurile ja elektrilistele omadustele. Käesolevas doktoritöös on uuritud X-laineala tehisava-radari rakendusi üleujutuste kaardistamiseks metsas ja rohumaade parameetrite tuvastamisel. 2010 aasta kevadel läbi viidud katsed kinnitasid X-laineala sobivust üleujutuste kaardistamiseks parasvöötmelises Põhja-Euroopa metsas raagus aastaajal. Varem arvati, et X-laineala metsa läbitavus pole piisav vee tuvastamiseks võrastiku all. Mõõdeti ka X-laineala HH-VV polarimeetrilise kanali eelist HH kanali ees üleujutuste tuvastamisel. Leiti, et HH-VV kanal pakub 0,2 kuni 1,6 dB kõrgemat üleujutatud ja üleujutamata metsa eristamist tagasihajumise järgi kui HH kanal. 2011 suvel Matsalu rohumaadel läbi viidud katsed näitasid X-laineala tehisava-radarite sobivust värskelt niidetud alade tuvastamisel. Värskelt niidetud ja koristamata heinaga rohumaadel ilmnes iseäralik dominantse alfa parameetri kasv 10 kraadilt 25 kraadini.Synthetic Aperture Radar (SAR) is a land and water surface remote sensing instrument typically used on aeroplanes and satellites. SARs work in radio and microwave spectral regions with wavelengths from 1 m to 3 cm and are sensitive to sensed objects structure and electrical properties. In the current thesis X-band SAR applications for flood mapping in forest and grassland parameters retrieval are tested. The tests done during spring 2010 have proven X-band SAR suitability for flood detection in Northern European temperate forest during leaf-off season. Before this work it was commonly believed that X-band SAR forest penetration is not enough to detect water under forest canopy. The improvement of using HH-VV polarimetric channel over conventional HH for flood detection in forest was measured. HH-VV channel provided 0.2 to 1.6 dB higher flooded vs non-flooded forest backscatter based distinction than conventional HH channel. In grasslands X-band SAR was able to reveal the areas with freshly cut uncollected grass according to the tests carried out in Matsalu grasslands in summer 2011. The regions with freshly cut uncollected grass corresponded to dominant alpha parameter of about 25 degrees, whereas for other grassland states the same parameter was around 10 degrees

Delineation of Surface Water Features Using RADARSAT-2 Imagery and a TOPAZ Masking Approach over the Prairie Pothole Region in Canada

The Prairie Pothole Region (PPR) is one of the most rapidly changing environments in the world. In the PPR of North America, topographic depressions are common, and they are an essential water storage element in the regional hydrological system. The accurate delineation of surface water bodies is important for a variety of reasons, including conservation, environmental management, and better understanding of hydrological and climate modeling. There are numerous surface water bodies across the northern Prairie Region, making it challenging to provide near-real-time monitoring and in situ measurements of the spatial and temporal variation in the surface water area. Satellite remote sensing is the only practical approach to delineating the surface water area of Prairie potholes on an ongoing and cost-effective basis. Optical satellite imagery is able to detect surface water but only under cloud-free conditions, a substantial limitation for operational monitoring of surface water variability. However, as an active sensor, RADARSAT-2 (RS-2) has the ability to provide data for surface water detection that can overcome the limitation of optical sensors. In this research, a threshold-based procedure was developed using Fine Wide (F0W3), Wide (W2) and Standard (S3) modes to delineate the extent of surface water areas in the St. Denis and Smith Creek study basins, Saskatchewan, Canada. RS-2 thresholding results yielded a higher number of apparent water surfaces than were visible in high-resolution optical imagery (SPOT) of comparable resolution acquired at nearly the same time. TOPAZ software was used to determine the maximum possible extent of water ponding on the surface by analyzing high-resolution LiDAR-based DEM data. Removing water bodies outside the depressions mapped by TOPAZ improved the resulting images, which corresponded more closely to the SPOT surface water images. The results demonstrate the potential of TOPAZ masking for RS-2 surface water mapping used for operational purposes

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Métodos de classificação de imagens de satélite para delineamento de banhados

As Áreas Úmidas (AUs) são ecossistemas de importância global, que apresentam altos níveis de diversidade ecológica e produtividade primária e secundária. Os Banhados são um tipo de AU, característicos nos estados do Sul do Brasil, no Uruguai e na Argentina. O delineamento e classificação desses ecossistemas é uma tarefa árdua, dada as características estruturais hidrológicas, de solos, de cobertura vegetal e espectrais. No estado Rio Grande do Sul os Banhados são considerados Áreas de Preservação Permanente, porém, não há um inventário e tampouco um delineamento desses ambientes. Deste modo, o objetivo destatese é comparar diferentes métodos baseados em sensoriamento remoto ativo e passivo e aprendizado de máquina(AP)para o delineamento de Banhados. Para isto, utilizamos três abordagens: i) aplicação de índices espectrais de sensoriamento remoto e árvore de decisão; ii) integração de imagens SAR de dupla e quádrupla polarização em bandas C e L e árvore de decisão; e, iii) análise multisensor (ativo e passivo), Geobia e diferentes classificadores. Nossos resultados mostram que os índices espectrais de sensoriamento remoto apresentaram acurácias entre 77,9% e 95,9%; a aplicação de imagens SAR resultou em acurácias entre 56,1% e 72,9%, ambos pelo algoritmo Árvore de Decisão. Para a abordagem multisensor utilizando Geobia e diferentes classificadores, as acurácias variaram entre 95,5% e 98,5%, sendo que, o k-NN foi o algoritmo que apresentou maior acurácia entre os modelos avaliados, demonstrando o potencial da análise multisensor (ativo e passivo) e doaprendizado de máquinapara o delineamento e classificação de Banhados. Adotamos como estudo de caso um Banhado localizado no Sul do Brasil, porém recomendamos que devido as semelhanças hidrológicas, estruturais e espectrais desses ambientes, essas metodologias possam ser aplicadas em outras áreas de Banhados (marshes).Wetlands are ecosystems of global importance, with high levels of ecological diversity and primary and secondary productivity.Marshes are a type of wetland characteristic of the southernBrazil, Uruguay and Argentina.The delineationand classification of these ecosystems is an arduous task, given the hydrological structure, soil, vegetation and spectral characteristics.In the Rio Grande do Sul state, marshesare considered Permanent Preservation Areas, however, there is no inventory and no delineationof these environments.Thus, the aim of this thesis is to compare different active and passive remote sensing based methodsand machine learningfor the delineationof marshes. For this, we use three approaches: i) application of spectral indices of remote sensing and decision tree; ii) integration of dual and quad-poll SAR images in C and L-bands and decision tree, and iii) multisensor analysis (active and passive), Geobia and different classification methods. Our results show that the spectral indexes of remote sensing presented accuracy between 77.9% and 95.9%; the application of SAR images resulted in accuracy between 56.1% and 72.9%, both using the Decision Tree algorithm. For the multisensor approach using Geobia and different classifiers, the accuracy varied between 95.5% to 98.5%, k-NN was the algorithm that showed greater accuracy among the models evaluated, demonstrating the potential of the multisensor analysis (activeand passive) and machine learningfor marshesdelineation and classification. Our study was carried out in a marsh located in the southernBrazil, however due to the hydrological, structural and spectral similarities of these environments, the methodologies can be applied in other marshes area

Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research