Utilização de imagens SAR na classificação de formações florestais brasileiras

Brazil has a large territorial area with a large cover of vegetation and several forest typologies with different physiognomies. It is necessary to map the forest areas in the country in order to know the spatial distribution and the dynamics of each forest formation. An efficient and reliable way to obtain this information is using remote sensing techniques, and radar – SAR - imaging can be applied, which in turn has been the focus of many researchers. Thus, the objective of the present study is to gather scientific productions related to the use of radar images applied to the mapping of different forests in Brazil, analyzing the most recent approaches and classification techniques. There was a significant application of SAR images in forests of the Amazon biome, mainly for the detection of deforestation. The images of the ALOS/PALSAR L-band radar system were the most used in the mapping of forest typologies, associated to several classifier algorithms, such as: Iterated Conditional Modes, Maximum Likelihood and random forest. The data types worked in the classifications varied according to the polarimetric capacity of each image, with emphasis on the greater use of backscattering coefficients and attributes extracted from matrix decompositions. It was also observed that most studies related SAR data to those obtained by optical sensors. Therefore, the present study made it possible to gather several applications of classification techniques for the discrimination of forest formations in Brazil using microwave imaging, indicating the potentiality of the various classifiers with SAR images, and showing that radar systems are an important technology that is being used for mapping forests in the country.O Brasil tem uma vasta área territorial com várias tipologias florestais compostas por diferentes fisionomias. É necessário o mapeamento das áreas de florestas no país, com o intuito de se conhecer sua distribuição espacial, bem como de avaliar sua dinâmica de expansão ou redução. Uma forma eficiente e confiável de se obter tais informações se dá por meio de técnicas de sensoriamento remoto, podendo ser aplicado o imageamento por radar (micro-ondas), que por sua vez tem sido o foco de muitos pesquisadores. Sendo assim, o objetivo do presente estudo é reunir as produções científicas relacionadas à utilização de imagens de radar aplicadas ao mapeamento das diferentes florestas no Brasil, analisando as mais recentes abordagens e técnicas de classificação. Notou-se uma significativa aplicação de imagens SAR em florestas do bioma Amazônia, principalmente para a detecção do desmatamento. As imagens do sistema do radar de banda L do ALOS/PALSAR foram as mais utilizadas nos mapeamentos das tipologias florestais, associadas a vários algoritmos classificadores, tais como: Iterated Conditional Modes, Máxima Verossimilhança e random forest. Os tipos de dados trabalhados nas classificações variaram de acordo com a capacidade polarimétrica de cada imagem, com destaque à maior utilização dos coeficientes de retroespalhamento e atributos extraídos das decomposições de suas matrizes. Observou-se ainda que a maioria dos trabalhos relacionaram os dados SAR com os obtidos por sensores ópticos. Portanto, o presente estudo possibilitou reunir várias aplicações de técnicas de classificação para a discriminação de diferentes formações florestais no Brasil utilizando o imageamento por micro-ondas, indicando a potencialidade dos vários classificadores nos dados SAR, mostrando que os sistemas de radar são uma importante tecnologia utilizada para o mapeamento de florestas no país

Mapping the surface water storage variation in densely impounded semi-arid NE Brazil with satellite remote sensing approach

Surface water bodies provide vital support to the society and fundamentally affect ecosystems in various manners. Precise knowledge of the spatial extent of surface water bodies (e.g. reservoirs) as well as of the quantity of water they store is necessary for efficient water deployment and understanding of the local hydrology. Remote sensing provides broad opportunities for surface water mapping. The main objectives of this thesis are: 1) delineating surface water area of partly vegetated water bodies only from remote sensing data without field data input; 2) obtaining the surface water storage, and 3) analyzing its spatio-temporal variations for northeastern (NE) Brazil as a representative for a densely dammed semi-arid region. At first, I investigated the potential of digital elevation models (DEMs) generated from TanDEM-X data, which were acquired during the low water level stage, for reservoirs’ bathymetry derivation. I found that the accuracy of such DEMs can reach one meter, both in the absolute and relative respects. It has shown that DEMs derived from TanDEM-X data have great potentials for representing the reservoirs’ bathymetry of temporally dried-out reservoirs. Subsequently, I targeted at developing a method for mapping the water surface beneath canopy independent of field data for further delineation of the effective water surface. Instead of the commonly used backscattering coefficients, I investigated the capability of the Gray-Level Co-Occurrence Matrix (GLCM) texture index to distinguish different types of Radar backscattering taking place in (partly) vegetated reservoirs. This experiment demonstrated that different types of backscattering at the vegetated water surface show distinct statistical characteristics on GLCM variance derived from TerraSAR-X satellite time series data. Furthermore, with the threshold established based on the statistics of the sub-populations dominated by different types of backscattering, the vegetated water surfaces were effectively mapped, and the effective water surface areas were further delineated with an accuracy of 77% to 95%. ii Based on the investigation of the DEMs generated from TanDEM-X data, I derived the formerly unknown bathymetry for 2 105 reservoirs of various sizes in four representative regions of an overall area of 10 000 km2. The spatial distributions of surface water storage capacities in the four regions were subsequently extracted from the combination of the reservoir bathymetry and the water surface extents provided by RapidEye satellite time series. Furthermore, the spatio-temporal variations of surface water storage were derived for the four representative regions on an annual basis in the period of 2009-2017. This study showed that 1) The density of reservoirs in NE Brazil amounts to 0.04-0.23 reservoirs per km2, the corresponding water surface and surface water storage are 1.18-4.13 ha/km2 and 0.01-0.04 hm3 m/km², respectively; 2) On the spatial unit of 5×5 km2, the surface water storage in the region constantly decreased due to a prolonged drought with a rate of 105 m3/year from 2009 to 2017, with a slight increase from 2016 to 2017 in a few reservoirs; 3) Local precipitation deficit controls the variation of the overall surface water storage in the region. In this thesis I demonstrated the great potential of the great potential of SAR and optical satellite time series data for hydrological applications. The method I developed for delineating the effective water extent from the vegetated reservoirs has shown high potential transferability for other similar regions. The data gaps of bathymetry and surface waters storage capacity were filled for 2 105 reservoirs in NE Brazil. The results of the spatio-temporal variations of surface water storage in four representative regions from 2009-2016 can support future water management and improve hydrological prediction in NE Brazil

Radar satellite imagery for humanitarian response. Bridging the gap between technology and application

This work deals with radar satellite imagery and its potential to assist of humanitarian operations. As the number of displaced people annually increases, both hosting countries and relief organizations face new challenges which are often related to unclear situations and lack of information on the number and location of people in need, as well as their environments. It was demonstrated in numerous studies that methods of earth observation can deliver this important information for the management of crises, the organization of refugee camps, and the mapping of environmental resources and natural hazards. However, most of these studies make use of -high-resolution optical imagery, while the role of radar satellites is widely neglected. At the same time, radar sensors have characteristics which make them highly suitable for humanitarian response, their potential to capture images through cloud cover and at night in the first place. Consequently, they potentially allow quicker response in cases of emergencies than optical imagery. This work demonstrates the currently unused potential of radar imagery for the assistance of humanitarian operations by case studies which cover the information needs of specific emergency situations. They are thematically grouped into topics related to population, natural hazards and the environment. Furthermore, the case studies address different levels of scientific objectives: The main intention is the development of innovative techniques of digital image processing and geospatial analysis as an answer on the identified existing research gaps. For this reason, novel approaches are presented on the mapping of refugee camps and urban areas, the allocation of biomass and environmental impact assessment. Secondly, existing methods developed for radar imagery are applied, refined, or adapted to specifically demonstrate their benefit in a humanitarian context. This is done for the monitoring of camp growth, the assessment of damages in cities affected by civil war, and the derivation of areas vulnerable to flooding or sea-surface changes. Lastly, to foster the integration of radar images into existing operational workflows of humanitarian data analysis, technically simple and easily-adaptable approaches are suggested for the mapping of rural areas for vaccination campaigns, the identification of changes within and around refugee camps, and the assessment of suitable locations for groundwater drillings. While the studies provide different levels of technical complexity and novelty, they all show that radar imagery can largely contribute to the provision of a variety of information which is required to make solid decisions and to effectively provide help in humanitarian operations. This work furthermore demonstrates that radar images are more than just an alternative image source for areas heavily affected by cloud cover. In fact, what makes them valuable is their information content regarding the characteristics of surfaces, such as shape, orientation, roughness, size, height, moisture, or conductivity. All these give decisive insights about man-made and natural environments in emergency situations and cannot be provided by optical images Finally, the findings of the case studies are put into a larger context, discussing the observed potential and limitations of the presented approaches. The major challenges are summarized which need be addressed to make radar imagery more useful in humanitarian operations in the context of upcoming technical developments. New radar satellites and technological progress in the fields of machine learning and cloud computing will bring new opportunities. At the same time, this work demonstrated the large need for further research, as well as for the collaboration and transfer of knowledge and experiences between scientists, users and relief workers in the field. It is the first extensive scientific compilation of this topic and the first step for a sustainable integration of radar imagery into operational frameworks to assist humanitarian work and to contribute to a more efficient provision of help to those in need.Die vorliegende Arbeit beschäftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur Unterstützung humanitärer Einsätze. Die jährlich zunehmende Zahl an vertriebenen oder geflüchteten Menschen stellt sowohl Aufnahmeländer als auch humanitäre Organisationen vor große Herausforderungen, da sie oft mit unübersichtlichen Verhältnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von Flüchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlässliche Angaben über Anzahl und Aufenthaltsort der Geflüchteten und ihrer natürlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, während bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfügen gerade Radarsatelliten über Eigenschaften, die hilfreich für humanitäre Einsätze sein können, allen voran ihre Unabhängigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in Krisenfällen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur Unterstützung humanitärer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen für ausgewählte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche Ansprüche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und räumlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von Flüchtlingslagern zur Abschätzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von Flüchtlingslagern aufgezeigt. Darüber hinaus werden existierende oder erprobte Ansätze für die Anwendung im humanitären Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von Flüchtlingslagern, zur Ermittlung von Schäden an Gebäuden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende Abläufe oder Arbeitsroutinen in der humanitären Hilfe anhand technisch vergleichsweise einfacher Ansätze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestützte Kartierung von entlegenen Gebieten zur Unterstützung von Impfkampagnen, die Identifizierung von Veränderungen in Flüchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und Komplexitätsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten für die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstützen. Darüber hinaus wird in dieser Arbeit deutlich, dass Radardaten für humanitäre Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von Oberflächen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, Größe oder Höhe, sind sie optischen Daten überlegen und daher für viele Anwendungsbereiche im Kontext humanitärer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukünftiger Entwicklungen und Herausforderungen diskutiert. So versprechen neue Radarsatelliten und technologische Fortschritte im Bereich der Datenverarbeitung großes Potenzial. Gleichzeitig unterstreicht die Arbeit einen großen Bedarf an weiterer Forschung, sowie an Austausch und Zusammenarbeit zwischen Wissenschaftlern, Anwendern und Einsatzkräften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein für eine langfristige Integration von Radardaten in operationelle Abläufe dienen, um humanitäre Arbeit zu unterstützen und eine wirksame Hilfe für Menschen in Not ermöglichen