A SAR FINE AND MEDIUM SPATIAL RESOLUTION APPROACH FOR MAPPING THE BRAZILIAN PANTANAL

The objective of this research was to utilize a dual season set of L-band (ALOS/PALSAR) and C-band (RADARSAT-2 and ENVISAT/ASAR) imagery, a comprehensive set of ground reference data, and a hierarchical object-oriented approach to 1) define the diverse habitats of the Lower Nhecolândia subregion of the Pantanal at both a fine spatial resolution (12.5 m), and a relatively medium spatial resolution (50 m), thus evaluating the accuracy of the differing spatial resolutions for land cover classification of the highly spatially heterogeneous subregion, and, 2) to define on a regional scale, using the 50 m spatial resolution imagery, the wetland habitats of each of the hydrological subregions of the Pantanal, thereby producing a final product covering the entire Pantanal ecosystem. The final classification maps of the Lower Nhecolândia subregion were achieved at overall accuracies of 83% and 72% for the 12.5 m and 50 m spatial resolutions, respectively, defining seven land cover classes. In general, the highest degree of confusion for both fine and medium resolution Nhecolândia classifications were related to the following issues: 1) scale of habitats, for instance, capões, cordilheiras, and lakes, in relation to spatial resolution of the imagery, and 2) variable flooding patterns in the subregion. Similar reasons were attributed to the classification errors for the whole Pantanal. A 50 m spatial resolution classification of the entire Pantanal wetland was achieved with an overall accuracy of 80%, defining ten land cover classes. Given the analysis of the comparison of fine and relatively medium spatial resolution classifications of the Lower Nhecolândia subregion, the authors concluded that significant improvements in accuracy can be achieved with the finer spatial resolution dataset, particularly in subregions with high spatial heterogeneity in land cove

Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land‐ and water‐related applications in coastal zones. Compared to optical satellites, cloud‐cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all‐weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud‐prone tropical and sub‐tropical climates. The canopy penetration capability with long radar wavelength enables L‐band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change‐induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L‐band SAR data for geoscientific analyses that are relevant for coastal land applications

A comparison of data mining techniques and multi-sensor analysis for inland marshes delineation

Inland Marsh (IM) is a type of wetland characterized by the presence of non-woody plants as grasses, reeds or sedges, with a water surface smaller than 25% of the area. Historically, these areas have been suffering impacts related to pollution by urban, industrial and agrochemical waste, as well as drainage for agriculture. The IM delineation allows to understand the vegetation and hydrodynamic dynamics and also to monitor the degradation caused by human-induced activities. This work aimed to compare four machine learning algorithms (classification and regression tree (CART), artificial neural network (ANN), random forest (RF), and k-nearest neighbors (k-NN)) using active and passive remote sensing data in order to address the following questions: (1) which of the four machine learning methods has the greatest potential for inland marshes delineation? (2) are SAR features more important for inland marshes delineation than optical features? and (3) what are the most accurate classification parameters for inland marshes delineation? To address these questions, we used data from Sentinel 1A and Alos Palsar I (SAR) and Sentinel 2A (optical) sensors, in a geographic object-based image analysis (GEOBIA) approach. In addition, we performed a vectorization of a 1975 Brazilian Army topographic chart (first official document presenting marsh boundaries) in order to quantify the marsh area losses between 1975 and 2018 by comparing it with a Sentinel 2A image. Our results showed that the method with the highest overall accuracy was k-NN, with 98.5%. The accuracies for the RF, ANN, and CART methods were 98.3%, 96.0% and 95.5%, respectively. The four classifiers presented accuracies exceeding 95%, showing that all methods have potential for inland marsh delineation. However, we note that the classification results have a great dependence on the input layers. Regarding the importance of the features, SAR images were more important in RF and ANN models, especially in the HV, HV + VH and VH channels of the Alos Palsar I L-band satellite, while spectral indices from optical images were more important in the marshes delineation with the CART method. In addition, we found that the CART and ANN methods presented the largest variations of the overall accuracy (OA) in relation to the different parameters tested. The multi-sensor approach was critical for the high OA values found in the IM delineation (> 95%). The four machine learning methods can be accurately applied for IM delineation, acting as an important low-cost tool for monitoring and managing these environments, in the face of advances in agriculture, soil degradation and pollution of water resources due to agrochemical dumping

Classificação da cobertura da terra na planície de inundação do Lago Grande de Curuai (Amazônia, Brasil) utilizando dados multisensor e fusão de imagens

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea

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

Development of a bi-national Great Lakes coastal wetland and land use map using three-season PALSAR and landsat imagery

Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20–30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha, Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior)

Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data

The intense research of the last decades in the field of flood monitoring has shown that microwave sensors provide valuable information about the spatial and temporal flood extent. The new generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally high-resolution detection of the earth's surface and its environmental changes. This opens up new possibilities for accurate and rapid flood monitoring that can support operational applications. Due to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of new algorithms, which on the one hand enable precise and computationally efficient flood detection and on the other hand can process a large amounts of data. In order to capture the entire extent of the flood area, it is essential to detect temporary flooded vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to extract information from under the vegetation cover. Due to multiple backscattering of the SAR signal between the water surface and the vegetation, the flooded vegetation areas are mostly characterized by increased backscatter values. Using this information in combination with a continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based patterns for temporary flooded vegetation can be identified. This combination of information provides the foundation for the time series approach presented here. This work provides a comprehensive overview of the relevant sensor and environmental parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their benefits, limitations, methodological trends and potential research needs for this area are identified and assessed. The focus of the work lies in the development of a SAR and time series-based approach for the improved extraction of flooded areas by the supplementation of TFV and on the provision of a precise and rapid method for the detection of the entire flood extent. The approach developed in this thesis allows for the precise extraction of large-scale flood areas using dual-polarized C-band time series data and additional information such as topography and urban areas. The time series features include the characteristic variations (decrease and/or increase of backscatter values) on the flood date for the flood-related classes compared to the whole time series. These features are generated individually for each available polarization (VV, VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was performed by Z-transform for each image element, taking into account the backscatter values on the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image elements. The time series features constitute the foundation for the hierarchical threshold method for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time series data for the individual flood-related classes was analyzed and evaluated. The results showed that the dual-polarized time series features are particularly relevant for the derivation of TFV. However, this may differ depending on the vegetation type and other environmental conditions. The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods show the effectiveness of the method presented here in terms of classification accuracy. Theiv supplementary integration of temporary flooded vegetation areas and the use of additional information resulted in a significant improvement in the detection of the entire flood extent. It could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood extent in each of study areas. The transferability of the approach due to the application of a single time series feature regarding the derivation of open water areas could be confirmed for all study areas. Considering the seasonal component by using time series data, the seasonal variability of the backscatter signal for vegetation can be detected. This allows for an improved differentiation between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter signal can be assigned to changes in the environmental conditions, since on the one hand a time series of the same image element is considered and on the other hand the sensor parameters do not change due to the same acquisition geometry. Overall, the proposed time series approach allows for a considerable improvement in the derivation of the entire flood extent by supplementing the TOW areas with the TFV areas

Flood mapping in vegetated areas using an unsupervised clustering approach on Sentinel-1 and-2 imagery

The European Space Agency's Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available

Inundações em múltiplas escalas na América do Sul : de áreas úmidas a áreas de risco

South America hosts some of the major river systems on Earth, often associated with large floodplains that are inundated every year, such as the Pantanal and many Amazon wetlands. Interfluvial wetland complexes are also found across the continent, with particular geomorphic settings and unique savanna or grassland vegetation. South American wetlands can provide distinctive ecosystem services such as biodiversity supporting, food provision and flood attenuation. On the other hand, humans have settled around wetlands for millennia, benefiting from all resources they provide, and have adapted to its flood regime as well adapted its landscape, defining what has been called human-water systems. Yet, an increasing number of South American people have been negatively affected by extreme floods. Moving from continental to local scales, this thesis invites the readers to a journey across major South American wetland systems and their unique hydrological dynamics, under the light of the satellite era and the breakthrough advances on hydrologic-hydrodynamic modeling in the last decades. This work is founded on the proposition of a continental wetland research agenda, and based on a comparative hydrology approach. Floods are studied through both natural wetland processes and hazard dimensions. The first part presents a set of studies on the Amazon basin wetlands, from the development of 1D and 2D models to simulate hydrological processes in contrasting wetland types in the Negro river basin to the basin-wide intercomparison of 29 inundation products and assessment of long-term inundation trends. While most wetland studies have been conducted over the central Amazon floodplains, major knowledge gaps remain for understanding the hydrological dynamics of interfluvial areas such as the Llanos de Moxos and Negro savannas, where the inundation is less predictable and shallower. The second part of the thesis leverages satellite-based datasets of multiple hydrological variables (water levels, total water storage, inundation extent, precipitation and evapotranspiration) to address the hydrology of 12 large wetland systems in the continent. It shows the major differences among river floodplains and interfluvial wetlands on the water level annual amplitude, time lag between precipitation and inundation, and evapotranspiration dynamics. Finally, the third part addresses the flood hazard component of human-wetland interactions through large-scale assessments of flood hazard dynamics and effects of built infrastructure (dams) on flood attenuation. The dynamics of the great 1983 floods, one of the most extreme years ever recorded in the continent, is assessed with a continental hydrological model. Then, the capabilities of continental models to simulate the river-floodplain-reservoir continuum that exists across large river basins are assessed with case studies for major river basins affected by human intervention (Itajaí-Açu and upper Paraná river basins in Brazil). While this thesis enlightens some relevant hydrological processes regarding South American floods and their positive and negative effects to human societies and ecosystems in general, major knowledge gaps persist and provide great research opportunities for the near future. The launching of many hydrology-oriented satellite missions, and an ever-growing computational capacity, make the continental hydrology agenda related to wetlands and floods a great research topic for the upcoming years.A América do Sul abriga alguns dos maiores sistemas hídricos do planeta, frequentemente associados a grandes planícies de inundação, como o Pantanal e várias áreas da Amazônia. Áreas úmidas (AU’s) interfluviais são também encontrados no continente, com características geomorfológicas particulares, e vegetações de savana e gramíneas únicas. As AU’s da América do Sul provêm diversos serviços ecossistêmicos, como suporte à biodiversidade, provisão de alimento e atenuação de cheias. Humanos têm se estabelecido ao redor de AU’s por milênios, se beneficiando dos recursos providos por elas. Eles se adaptaram ao seu regime de inundação, e adaptaram sua paisagem, definindo o que tem sido chamado de sistemas sociedade-água. Por outro lado, um número crescente de pessoas têm sido negativamente afetado por cheias extremas. Da escala continental à local, esta tese convida o leitor a uma jornada através de importantes AU’s da América do Sul e suas particulares dinâmicas de inundação, sob a luz da era dos satélites e dos grandes avanços em modelagem hidrológica-hidrodinâmica das últimas décadas. Este trabalho é baseado na proposta de uma escala continental de pesquisa sobre AU’s, e é baseado em uma abordagem de hidrologia comparativa. Inundações são estudadas em múltiplas dimensões, de processos de AU’s naturais à questão do perigo para humanos. A primeira parte apresenta uma série de estudos sobre as AU’s da bacia amazônica, desde o desenvolvimento de modelos 1D e 2D para simular processos hidrológicos em tipos contrastantes de AU’s na bacia do Rio Negro, até a intercomparação de 29 produtos de inundação e avaliação de tendências de inundações de longo prazo para a escala da bacia amazônica. Enquanto a maioria dos estudos de AU’s foi conduzida nas várzeas do rio Amazonas, importantes lacunas do conhecimento permanecem para a compreensão da dinâmica hidrológica de áreas interfluviais como Llanos de Moxos e as savanas do rio Negro, onde a inundação é menos previsível e mais rasa. A segunda parte da tese utiliza dados oriundos de satélites relacionados a múltiplas variáveis hidrológicas (níveis d’água, armazenamento total de água, extensão de áreas inundadas, precipitação e evapotranspiração) para estudar a hidrologia de 12 grandes sistemas de AU’s do continente. São destacadas as grandes diferenças entre planícies de inundação e AU’s interfluviais em termos de amplitude anual de níveis d’água, defasagem entre precipitação e inundação, e dinâmica de evapotranspiração. Por fim, a última parte da tese aborda o componente de perigo de inundação das interações sociedade-água através de avaliações em grande escala da dinâmica de inundação e dos efeitos de infraestruturas construídas (como barragens) na atenuação de cheias. A dinâmica das grandes cheias de 1983, um dos anos mais extremos já registrados no continente, é avaliada com um modelo hidrológico continental. Depois, a capacidade de modelos continentais para simular o continuum entre rios, planícies de inundação e reservatórios que existe em grandes bacias hidrográficas é avaliada com estudos de casos para importantes bacias afetadas pela intervenção humana (bacia dos rios Paraná e Itajaí-Açu). Enquanto esta tese avança a compreensão de relevantes processos hidrológicos relacionados a inundações na América do Sul em múltiplas escalas, bem como seus efeitos positivos e negativos nas sociedades humanas e ecossistemas em geral, importantes lacunas do conhecimento persistem e fomentam importantes oportunidades de pesquisa futuras. O lançamento de várias missões satelitais orientadas a hidrologia, e uma cada vez mais crescente capacidade computacional, faz da agenda continental de hidrologia relacionada a AU’s e inundações um grande tópico de pesquisa para os próximos anos