The work is aimed at finding appropriate methods for observing changes in the status of forest vegetation and its evaluation in the years 1992-2015. The satellite images of the Low Tatras are analyzed by using Time Series technology. Specifically, the images Landsat 4, 5, 7 and 8, for which it is necessary to perform a calibration and an adjustment of input data values to realize the individual vegetation indices, due to the fact that the images are captured by different sensors with different radiometric resolution. From this perspective, the work deals with the possibilities of normalized relative radiometric corrections and search for a particular type of appropriate compensation for Landsat CDR images. Calibrated data sets are evaluated by Time Series of different vegetation indices. The resulting values are evaluated in relation with the occurrence of forest disturbances, eg. wind storms, biological and other pests. The final part is discussion of the results, evaluating the test methods of calibration and suitability of vegetation indices for observing the state of calamity. The App is created for generating the Time Series of Landsat images CDR and for preparing RRN datasets. Key words: Time Series, radiometric correction, atmospheric correction, Landsat CDR, vegetation indices,...

Les particules en suspension dans l'eau de mer incluent les sédiments, le phytoplancton, le zooplancton, les bactéries, les virus et des détritus. Ces particules sont communément appelés matière en suspension (MES). Dans les eaux côtières, la MES peut parcourir de longues distances et être transportée verticalement à travers la colonne d'eau sous l'effet des vents et des marées favorisant les processus d'advection et de resuspension. Ceci implique une large variabilité spatio-temporelle de MES et quasiment impossible à reconstituer à travers les mesures traditionnelles des concentrations de MES [MES], par filtration de l'eau de mer à bord de bateaux. La [MES] peut être obtenue à partir de capteurs optiques enregistrant la diffusion et déployés soit de manière in-situ, soit à partir d'un satellite dans l'espace. Depuis la fin des années 70, par exemple, les satellites "couleur de l'eau" permettent d'établir des cartes de [MES] globales. La fréquence d'une image par jour pour la mer di Nord de ces capteurs polaires représente un obstacle non négligeable pour l'étude de variabilité de la [MES] dans les eaux côtières où la marée et les vents engendrent des variations rapides au cours de la journée. Cette limitation est d'autant plus importante pour les régions avec une couverture nuageuse fréquente. Les méthodes in-situ à partir d'un navire autonome ou d'une plateforme amarrée permettent d'enregistrer des données en continu mais leur couverture spatiale reste néanmoins limitée. Ce travail a pour objectif de mettre en avant les techniques de mesures in-situ et satellite de la [MES] en se concentrant principalement sur deux points. Premièrement, d'acquérir une meilleure connaissance de la variabilité de la relation entre la [MES] et la lumière diffuse, et deuxièmement, d'établir des cartes de [MES] dans la mer du Nord avec le capteur géostationnaire météorologique Européen (SEVIRI) qui donne des images chaque 15 minutes.La variabilité de la relation entre la [MES] et la lumière diffuse est étudiée à l'aide d'une banque de données in-situ. Nous démontrons que la [MES] est le mieux estimée à partir des mesures dans l'intervalle rouge du spectre de lumière rétro-diffuse. Par ailleurs, la relation entre la [MES] et la rétrodiffusion est gouvernée par la composition organique/inorganique des particules, ce qui représente des possibilités d'amélioration pour les algorithmes d'estimation de [MES] à partir de la couleur de l'eau. Nous démontrons aussi qu'avec SEVIRI il est possible d'estimer la [MES], la turbidité et le coefficient d'atténuation, deux variables étroitement liées à la [MES], avec généralement une bonne précision. Bien qu'il y ait d'importantes incertitudes dans les eaux claires, cette réussite est remarquable pour un capteur météorologique initialement conçu pour le suivi des nuages et des masses glaciaires, cibles beaucoup plus brillantes que la mer! Ce travail démontre pour la première fois que la variabilité de la [MES] à l'échelle temporelle des marées dans les eaux côtières au sud de la mer du Nord peut être capturée et mesurée par le biais de la télédétection de la couleur de l'eau ; ce qui ouvre des opportunités pour le monitoring de la turbidité et pour la modélisation des écosystèmes. Le premier capteur géostationnaire couleur de l'eau a été lancé en juin 2012, donnant des images multispectrale des eaux coréennes chaque heure. D'autres capteurs vont probablement suivre dans l'avenir, couvrant le reste des eaux du globe. Ce travail nous permet donc de préparer, de façon optimale, l'arrivée de ces capteurs qui vont révolutionner l'océanographie optique. / Particles suspended in seawater include sediments, phytoplankton, zooplankton, bacteria, viruses, and detritus, and are collectively referred to as suspended particulate matter, SPM. In coastal waters, SPM is transported over long distances and in the water column by biological, tide or wind-driven advection and resuspension processes, thus varying strongly in time and space. These strong dynamics challenge the traditional measurement of the concentration of SPM, [SPM], through filtration of seawater sampled from ships. Estimation of [SPM] from sensors recording optical scattering allows to cover larger temporal or spatial scales. So called ocean colour satelittes, for example, have been used for the mapping of [SPM] on a global scale since the late 1970s. These polar-orbiting satellites typically provide one image per day forthe North Sea area. However, the sampling frequency of these satellites is a serious limitation in coastal waters where [SPM] changes rapidly during the day due to tides and winds.Optical instruments installed on moored platforms or on under-water vehicles can be operated continuously, but their spatial coverage is limited. This work aims to advance in situ and space-based optical techniques for [SPM] retrieval by investigating the natural variability in the relationship between [SPM] and light scattering by particles and by investigating whether the European geostationary meteorological SEVIRI sensor, which provides imagery every 15 minutes, can be used for the mapping of [SPM] in the southern North Sea. Based on an extensive in situ dataset, we show that [SPM] is best estimated from red light scattered in the back directions (backscattering). Moreover, the relationship between [SPM]] and particulate backscattering is driven by the organic/inorganic composition of suspended particles, offering opportunities to improve [SPM] retrieval algorithms. We also show that SEVIRI successfully retrieves [SPM] and related parameters such as turbidity and the vertical light attenuation coefficient in turbid waters. Even though uncertainties are considerable in clear waters, this is a remarkable result for a meteorological sensor designed to monitor clouds and ice, much brighter targets than the sea! On cloud free days, tidal variability of [SPM] can now be resolved by remote sensing for the first time, offering new opportunities for monitoring of turbidity and ecosystem modelling. In June 2010 the first geostationary ocean colour sensor was launched into space which provides hourly multispectral imagery of Korean waters. Other geostationary ocean colour sensors are likely to become operational in the (near?) future over the rest of the world's sea. This work allows us to maximally prepare for the coming of geostationary ocean colour satellites, which are expected to revolutionize optical oceanography. / De in zeewater aanwezige zwevende materie zoals sedimenten, fytoplankton, zooplankton, bacteriën, virussen en detritus, worden collectief "suspended particulate matter" (SPM) genoemd. In kustwateren worden deze deeltjes over lange afstanden en in de waterkolom getransporteerd door biologische processen of wind- of getijdenwerking, waardoor SPM sterk varieert in ruimte en tijd. Door deze sterke dynamiek wordt de traditionele bemonstering van de concentratie van SPM, [SPM], door middel van filtratie van zeewaterstalen aan boord van schepen ontoereikend. Optische technieken die gebruik maken van de lichtverstriioongseigenschappen van SPM bieden een gebieds- of tijdsdekkend alternatief. Zogenaamde "ocean colour" satellieten bijvoorbeeld leveren beelden van o.a. [SPM] aan het zeeoppervlak op globale schaal sinds eind 1970, met een frequantie van één beeld per dag voor de Noordzee. Deze frequentie is echter onvoldoende in onze kustwateren waar [SPM] drastisch kan veranderen in enkele uren tijd. Optische instrumenten aan boord vann schepen of op onderwatervoertuigen kunnen continu meten, maar de gebiedsdekking is deperkt. Dit werk heeft tot doel de lichtverstriioongseigenschappen van SPM te karakterizeren en te onderzoeken of de Europese geostationaire weersatelliet, die elk kwartier een beeld geeft, kan worden gebruikt voor de kartering van [SPM] in de zuidelijke Noordzee. Op basis van een grote dataset van in situ metingen tonen wij aan dat [SPM] het nauwkeurigst kan worden bepaald door de meting van de verstrooiing van rood licht in achterwaartse richtingen (terugverstrooiing). Bovendien blijkt de relatie tussen [SPM] en terugverstrooiing afhankelijk van de organische-anorganische samenstelling van zwenvende stof, wat mogelijkhenden biedt tot het verfijnen van teledetectiealgoritmen voor [SPM]. Voorts tonen woj aan dat de Europese weersatelliet, SEVIRI, successvol kan worden aangewend voor de kartering van [SPM] en gerelateerde parameters zoals troebelheid en lichtdemping in de waterkolom. Hoewel met grote meetonzekerheid in klaar water toch een opmerkelijk resultaat voor een sensor die ontworpen werd voor detectie van wolken en ijs! Op wolkenvrije dagen wordt hierdoor de getijdendynamiek van [SPM] in de zuidelijke Noordzee voor het eerst detecteerbaar vanuit de ruimte, wat nieuwe mogelijkheden biedt voor de monitoring van waterkwaliteit en verbetering van ecosysteellodellen. Sinds juni 2010 is de eerste geostationaire ocean colour satelliet een feit : elk uur een multispectraal beeld van Koreaanse wateren. Vermoedelijk zullen er in de (nabije?) toekomst meer volgen over Europa en Amerika. Dit werk laat toe ons maximaal voor te bereiden op te komst van zo'n satellieten, waarvan verwacht wordt dat zij een nieuwe revolutie in optische oceanografie zullen ontketenen.

Orientador: Rubens Augusto Camargo Lamparelli / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-09T05:37:08Z (GMT). No. of bitstreams: 1 Mercante_Erivelto_D.pdf: 2989061 bytes, checksum: 75872d2f0b562b1a4ee7798a720abb60 (MD5) Previous issue date: 2007 / Resumo: Estudos e pesquisas referentes ao acompanhamento da produção agropecuária têm um peso determinante e estratégico na economia do país. Nos últimos anos, essas pesquisas vêm sofrendo grandes transformações para se tornarem menos subjetivas. A associação das técnicas de sensoriamento remoto e métodos estatísticos podem proporcionar uma visão sinóptica das áreas semeadas, gerando, assim informações sobre a área plantada das culturas e a variabilidade existente nelas. Entretanto, a utilização dos dados provenientes de imagens de satélites está condicionada, principalmente, às propriedades de refletância e absorção dos componentes da superfície e pelo comportamento da atmosfera. Dentre as culturas de grande valor econômico, a soja (Glycine max (L) Merrill.) se destaca como um dos principais produtos da agricultura brasileira, assumindo grande importância econômica nas exportações. O estado do Paraná se destaca como um dos maiores produtores agrícolas do país, e a sua economia é baseada principalmente na agricultura voltada para a produção de grãos. Neste contexto, o objetivo da pesquisa foi estudar a relação entre o comportamento espectral da cultura de soja com a ao longo de seu ciclo de desenvolvimento, gerando informações e metodologias para auxiliar no acompanhamento da produção e estimativa de área da cultura na região Oeste do Paraná. As áreas monitoradas abrangem 36 municípios e duas áreas agrícolas comerciais localizadas próximas ao município de Cascavel/PR. Foram utilizadas imagens do satélite Landsat 5/TM (cena órbita/ponto 223/77) e imagens do satélite Terra sensor MODIS (produto MOD13Q1), caracterizando, assim, a passagem de escala entre as imagens dos dois sensores com resoluções espaciais diferentes. Dados de produtividade da cultura foram coletados nas escalas local (para as duas áreas monitoradas) e regional junto à Secretária de Agricultura e Abastecimento do Paraná ¿ SEAB (para os 36 municípios). A cultura da soja foi monitorada nas safras 2003/2004 e 2004/2005 utilizando imagens dos satélites. Para a utilização das imagens do satélite Landsat 5/TM de forma multitemporal foram realizados ainda os procedimentos de correção atmosférica e normalização de imagens. No intuito de caracterizar a resposta espectral da biomassa da cultura da soja e a sua relação com a produtividade final, geraram-se imagens referentes aos índices de vegetação NDVI e GVI. Foram realizadas análises de correlação e regressão entre dados de produtividade (variável predita) e dados espectrais (variável preditora) oriundos dos índices de vegetação (NDVI e GVI) em nível municipal (36 municípios) e local (duas áreas). Os resultados obtidos com a correção atmosférica e a normalização de imagens são coerentes quanto ao comportamento espectral dos alvos, vegetação e solo. Após o mapeamento das áreas com a cultura da soja (¿máscaras de soja¿), por meio das imagens temporais do Landsat 5/TM, foi possível realizar a passagem de escala para o sensor MODIS. O comportamento espectral da cultura se mostrou diferente para as imagens Landsat 5/TM com os tratamentos de refletância aparente, de superfície e de normalização. Por meio dos gráficos dos perfis espectrais traduzidos pelos índices NDVI e GVI foi possível acompanhar o ciclo de desenvolvimento da cultura da soja. As melhores correlações e regressões lineares entre os parâmetros espectrais e a produtividade final ocorreram quando considerado todo o ciclo de desenvolvimento da cultura. Quanto aos índices de vegetação utilizados NDVI e GVI, observou-se que o GVI teve comportamento com menor variação quando analisado os resultados das regressões. Em síntese, os resultados demonstraram que utilizando somente técnicas de modelagem estatística com dados espectrais foi possível estimar em até 85% a variabilidade encontrada na produtividade da soja / Abstract: Studies and researches referring to attendance of the agropecuary production have a determinant strategic importance in the economy of the country. In the last years, those researches have been suffering great transformations to become less subjectives. The association of the remote sensing technique and statistic methods can provide a synoptic view of the seeded areas, in this manner producing information about the planted area of the crops and the variability on them. However, the use of data from the images of satellite is stipulated mainly to the reflectivity properties and absorption of the surface components and also for the atmosphere behaviour. Amongst the crops of great economic value, the soybean (Glycine max (L) Merrill.) emphasizes as one of the main product of the Brazilian agriculture, assuming big economic importance in the exportations. The Paraná state stands out as one of the biggest agricultural producers of the country, and its economy is based mainly towards to the production of grains. In this context, the aim of the research was study the relation between the spectral behaviour of the soybean crop with the end yield along its development cycle, producing information and methodologies to assist the attendance of the production and area estimation of the western Paraná region crop. The areas monitored reach 36 municipal districts and two agricultural commercial areas located near of Cascavel/PR. It was used images from satellite Landsat 5/TM (orbit/point 223/77) and images of the satellite Terra sensor MODIS (product MOD13Q1), then characterizing the crossing scale between the images from both sensors with different spatial resolution. Crop yield data were collected, in the local scale (to both monitored areas) and in the regional scale together with the Secretary of Agriculture (SEAB) (to the 36 municipal districts). Through the satellites images, the soybean crop was monitored in the 2003/2004 and 2004/2005 harvests. For the use of images from the satellite Landsat 5/TM of multitemporal form was realized the proceeding of atmospheric correction and image normalization. In the aim of distinguishing the biomass spectral answer of the soybean crop and its relation to the end yield, it was created images referring to vegetation indexes NDVI and GVI. It was accomplished analyses of correlation and regression between the yield data (variable predict) and spectral data (variable predictor) derived from vegetation indexes (NDVI and GVI) in municipal level (36 municipal districts) and local (two areas). The results obtained with the atmospheric correction and image normalization presented coherent as to the spectral behaviour of the vegetation and soil target. After the area mapping with the soybean crop (soybean mask) by the temporary images of Landsat 5/TM, it was possible to realize the cross-scale to the MODIS sensor. The spectral behaviour of the crop was showed different in the Landsat 5/TM images to the apparent reflectance usage, surface and normalization. By mean of the graphics of spectral profile translated from the NDVI and GVI indexes was possible follow the development cycle of the soybean crop. The best correlations and linear regressions between the spectral parameters and the end yield will occur when it is considered all the cycle of the crop development. For the matters of vegetation indexes NDVI and GVI used, it was observed that the GVI had a less variable behaviour when analysed the results of regressions. In the sum up, the results showed that using only statistical modelling techniques with spectral data was possible estimate 85% of variability found in the soybean yield / Doutorado / Planejamento e Desenvolvimento Rural Sustentável / Doutor em Engenharia Agrícola

La fluorescence chlorophyllienne induite par le soleil est une nouvelle façon de suivre l'évolution de la végétation et le cycle global du carbone. Grâce au modèle simulé et aux expériences sur le terrain et aéroportée, la recherche multi-échelles de méthode de détection de la fluorescence de la chlorophylle induite par le soleil est développé dans cette thèse. Les principales conclusions et innovations sont les suivantes : 1. Les expériences de contrôle en eau du maïs montrent que la fluorescence passive peut être utilisée pour détecter le stress hydrique des culture. L'analyse de la réponse de la fluorescence et de la température montre que la fluorescence est très sensible au stress hydrique précoce. 2. Après avoir analysé les effets de la température, de l'angle zénithal solaire et du rendement quantique de la fluorescence sur la détermination de la fluorescence, nous proposons une méthode d’obtention de la fluorescence qualitative basée sur l'indice de réflectance. 3. L’analyse des effets de la détermination de la fluorescence aéroportée nous a permis de montrer que l'angle zénithal solaire et la hauteur du capteur aéroporté sont les facteurs importants qui influent sur la détermination de la fluorescence induite par le soleil. / Sun-induced chlorophyll fluorescence is a new way to monitor the vegetation change and global carbon cycle. Through the model simulated analysis, the pot experiment and the airborne flying experiment, the research on detecting the multi-scale sun-induced chlorophyll fluorescence is developed in this dissertation. The main conclusions and innovations are as follows: 1. The maize water control experiments demonstrate that the passive fluorescence can be used to detect the crop water stress, and the analysis of the different responses of the fluorescence and temperature illustrates that the fluorescence is much sensitive to the early water stress. 2. Analyze the effects of temperature, sun zenith angle and fluorescence quantum efficiency on the qualitative fluorescence retrieval, and propose a qualitative fluorescence retrieval method based on the reflectance index. 3. Analyze the effects of airborne fluorescence retrieval, and obtain that sun zenith angle and airborne sensor height are the important factors to affect the sun-induced fluorescence retrieval from the simulated analysis and airborne flying experiment.

Made available in DSpace on 2016-03-15T19:35:57Z (GMT). No. of bitstreams: 1 Edith Tueros Cuadros.pdf: 4357925 bytes, checksum: 7a9a7c9700e9ab1b8e24a65ab089b198 (MD5) Previous issue date: 2016-02-02 / Fundação de Amparo a Pesquisa do Estado de São Paulo / Cosmic rays are strongly influenced by solar, geomagnetic and atmospheric phenomena. CARPET detector, conceived for cosmic rays observation with energies in the range between 105 - 1012 eV, is an important tool for the study of these phenomena. The Earth s atmosphere conditions are also affected by changes in the cosmic rays flux, therefore, cosmic rays characterization is important to define physical and chemical conditions of our atmosphere. To characterize the cosmic rays flux variations, detected on the ground, prior elimination of atmospheric pressure and temperature effects on ground level is needed, thus, data recorded by meteorological instruments on CASLEO were used for that corrections. To eliminate the effect of temperature through the whole vertical atmosphere it was applied the integral and the mass-average temperature method by using vertical temperature profiles. Both methods were tested using CARPET-TEL data for the year 2009, this data were previously corrected by pressure influences. The mass-average temperature method shows a better response when comparing the corrected CARPET date with neutron monitor observations .The whole cosmic rays flux data analysis, for CARPET-TEL data corrected by integral method (for the period 2006/04/01 - 2014/06/30), shows an anti-correlation with sunspot number and a clear seasonal variation after 2008. Two Forbush decreases (FD), were detected by CARPET which were produced by geo-effective CMEs. Both FD onsets coincided with the interplanetary shock instant, which preceded intense geomagnetic storms. With these results, we can reinforce that CARPET is an important tool to study long and short term cosmic rays behavior, because it has a similar response to experiments that operates at atmosphere particle energy ranges. / Os raios cósmicos são fortemente influenciados pelos fenômenos solares, geomagnéticos e atmosféricos. O detector CARPET, concebido para a observação dos raios cósmicos com energias na faixa compreendida entre 105 e 1012 eV, é uma ferramenta importante para o estudo desses fenômenos. As condições da nossa atmosfera são também afetadas pelas variações no fluxo de raios cósmicos que chegam a Terra, portanto sua caracterização é importante para se definir as condições físicas e químicas da mesma. Para caracterizar as variações do fluxo de raios cósmicos a partir de observações no solo é necessário a eliminação prévia da influência dos efeitos da pressão e temperatura atmosférica na superfície, para tal foram utilizados dados meteorológicos obtidos no CASLEO. Para a eliminação do efeito da temperatura ao longo de toda atmosfera foram utilizados o método integral e o método de temperatura ponderada pela massa que utilizam perfis verticais de temperatura. Os dois métodos foram testados para o ano de 2009 com os dados do canal TEL do CARPET previamente corrigidos pela pressão, sendo que o método de temperatura ponderada pela massa apresentou uma resposta melhor ao se comparar os dados corrigidos com os dados observados com monitores de nêutrons. A análise de todos os dados do fluxo raios cósmicos do canal TEL do CARPET (1/04/2006 a 30/06/2014) corrigidos pelo método integral, mostra a anti-correlação com o número de manchas solares, e uma variação sazonal pronunciada após 2008. Foram detectados dois decréscimos Forbush (FD) produzidos por CMEs geo-efetivos. O início de ambos FD coincidiu com o instante do choque interplanetário, que antecedeu tempestades geomagnéticas intensas. Com estes resultados reforçamos que o CARPET é uma ferramenta importante para estudo das modulações de raios cósmicos de longo e curto prazo porque apresenta uma resposta semelhante com os experimentos que fazem observações em outras faixas de energia.

Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.

Die Fernerkundung war in den vergangenen Jahren von einschneidenden Umbrüchen gekennzeichnet, die sich besonders in der stark gestiegenen geometrischen Bodenauflösung der Sensoren und den damit einhergehenden Veränderungen der Verarbeitungs- und Auswertungsverfahren widerspiegeln. Sehr hoch auflösende Satellitenbilddaten - definiert durch eine Auflösung zwischen einem halben und einem Meter - existieren seit dem Start von IKONOS Ende 1999. Etwa im selben Zeitraum wurden extrem hoch auflösende digitale Flugzeugkameras (0,1 bis 0,5 m) entwickelt. Dieser Arbeit liegen IKONOS-Daten mit einer Auflösung von einem (panchromatischer Kanal) bzw. vier Metern (Multispektraldaten) zugrunde. Bedingt durch die Eigenschaften sehr hoch aufgelöster Bilddaten (z. B. Detailgehalt, starke spektrale Variabilität, Datenmenge) lassen sich bisher verfügbare Standardverfahren der Bildverarbeitung nur eingeschränkt anwenden. Die Ergebnisse der in dieser Arbeit getesteten Verfahren verdeutlichen, dass die Methoden- bzw. Softwareentwicklung mit den technischen Neuerungen nicht Schritt halten konnte. Einige Verfahren werden erst allmählich für sehr hoch auflösende Daten nutzbar (z. B. atmosphärisch-topographische Korrektur). Die vorliegende Arbeit zeigt, dass Daten dieses Auflösungsbereiches mit bisher verwendeten pixelbasierten, statistischen Klassifikationsverfahren nur unzulänglich ausgewertet werden können. Die hier untersuchte Anwendung von Bildsegmentierungsmethoden hilft, die Nachteile pixelbasierter Verfahren zu überwinden. Dies wurde durch einen Vergleich pixel- und segmentbasierter Klassifikationsverfahren belegt. Im Rahmen einer Segmentierung werden homogene Bildbereiche zu Regionen verschmolzen, welche die Grundlage für die anschließende Klassifikation bilden. Hierzu stehen über die spektralen Eigenschaften hinaus Form-, Textur- und Kontextmerkmale zur Verfügung. In der verwendeten Software eCognition lassen sich diese Klassifikationsmerkmale zudem auf Grundlage des fuzzy-logic-Konzeptes in einer Wissensbasis (Entscheidungsbaum) umsetzen. Ein Vergleich verschiedener, derzeit verfügbarer Segmentierungsverfahren zeigt darüber hinaus, dass sich mit der genutzten Software eine hohe Segmentierungsqualität erzielen lässt. Der wachsende Bedarf an aktuellen Geobasisdaten stellt für sehr hoch auflösende Fernerkundungsdaten eine wichtige Einsatzmöglichkeit dar. Durch eine gezielte Klassifikation der Bilddaten lassen sich Arbeitsgrundlagen für die hier betrachteten Anwendungsfelder Landschaftsplanung und Landschaftsökologie schaffen. Die dargestellten Beispiele von Landschaftsanalysen durch die segmentbasierte Auswertung von IKONOS-Daten zeigen, dass sich eine Klassifikationsgüte von 90 % und höher erreichen lässt. Zudem können die infolge der Segmentierung abgegrenzten Landschaftseinheiten eine Grundlage für die Berechnung von Landschaftsstrukturmaßen bilden. Nationale Naturschutzziele sowie internationale Vereinbarungen zwingen darüber hinaus zur kontinuierlichen Erfassung des Landschaftsinventars und dessen Veränderungen. Fernerkundungsdaten können in diesem Bereich zur Etablierung automatisierter und operationell einsatzfähiger Verfahren beitragen. Das Beispiel Biotop- und Landnutzungskartierung zeigt, dass eine Erfassung von Landnutzungseinheiten mit hoher Qualität möglich ist. Bedingt durch das Auswertungsverfahren sowie die Dateneigenschaften entspricht die Güte der Ergebnisse noch nicht vollständig den Ansprüchen der Anwender, insbesondere hinsichtlich der erreichbaren Klassifikationstiefe. Die Qualität der Ergebnisse lässt sich durch die Nutzung von Zusatzdaten (z. B. GIS-Daten, Objekthöhenmodelle) künftig weiter steigern. Insgesamt verdeutlicht die Arbeit den Trend zur sehr hoch auflösenden digitalen Erderkundung. Für eine breite Nutzung dieser Datenquellen ist die weitere Entwicklung automatisierter und operationell anwendbarer Verarbeitungs- und Analysemethoden unerlässlich. / In recent years remote sensing has been characterised by dramatic changes. This is reflected especially by the highly increased geometrical resolution of imaging sensors and as a consequence thereof by the developments in processing and analysis methods. Very high resolution satellite imagery (VHR) - defined by a resolution between 0.5 and 1 m - exists since the start of IKONOS at the end of 1999. At about the same time extreme high resolution digital airborne sensors (0.1 till 0.5 m) have been developed. The basis of investigation for this dissertation is IKONOS imagery with a resolution of one meter (panchromatic) respectively four meters (multispectral). Due to the characteristics of such high resolution data (e.g. level of detail, high spectral variability, amount of data) the use of previously available standard methods of image processing is limited. The results of the procedures tested within this work demonstrate that the development of methods and software was not able to keep up with the technical innovations. Some procedures are only gradually becoming suitable for VHR data (e.g. atmospheric-topographic correction). Additionally, this work shows that VHR imagery can be analysed only inadequately using traditional pixel-based statistical classifiers. The herein researched application of image segmentation methods helps to overcome drawbacks of pixel-wise procedures. This is demonstrated by a comparison of pixel and segment-based classification. Within a segmentaion, homogeneous image areas are merged into regions which are the basis for the subsequent classification. For this purpose, in addition to spectral features also formal, textural and contextual properties are available. Furthermore, the applied software eCognition allows the definition of the features for classification based on fuzzy logic in a knowledge base (decision tree). An evaluation of different, currently available segmentation approaches illustrates that a high segmentation quality is achievable with the used software. The increasing demand for geospatial base data offers an important field of application for VHR remote sensing data. With a targeted classification of the imagery the creation of working bases for the herein considered usage for landscape planning and landscape ecology is possible. The given examples of landscape analyses using a segment-based processsing of IKONOS data show an achievable classification accuracy of 90 % and more. The landscape units delineated by image segmentation could be used for the calculation of landscape metrics. National aims of nature conservation as well as international agreements constrain a continuous survey of the landscape inventory and the monitoring of its changes. Remote sensing imagery can support the establishment of automated and operational methods in this field. The example of biotope and land use type mapping illustrates the possibility to detect land use units with a high precision. Depending on the analysis method and the data characteristics the quality of the results is not fully equivalent to the user?s demands at the moment, especially concerning the achievable depth of classification. The quality of the results can be enhanced by using additional thematic data (e.g. GIS data, object elevation models). To summarize this dissertation underlines the trend towards very high resolution digital earth observation. Thus, for a wide use of this kind of data it is essentially to further develop automated and operationally useable processing and analysis methods.

La disponibilité de nouveaux moyens d’acquisition en télédétection, satellitaire (PLEIADES, HYPXIM), aéroportée ou par drone (UAV) à très haute résolution spatiale ouvre la voie à leur utilisation pour l’étude de milieux complexes telles que les villes. En particulier, la connaissance de la ville pour l’étude des îlots de chaleur, la planification urbaine, l’estimation de la biodiversité de la végétation et son état de santé nécessite au préalable une étape de classification des matériaux qui repose sur l’utilisation de l’information spectrale accessible en télédétection hyperspectrale 0,4-2,5μm. Une des principales limitations des méthodes de classification réside dans le non traitement des zones à l’ombre. Des premiers travaux ont montré qu’il était possible d’exploiter l’information radiative dans les ombres des bâtiments. En revanche, les méthodes actuelles ne fonctionnent pas dans les ombres des arbres du fait de la porosité de leur couronne. L’objectif de cette thèse vise à caractériser les propriétés optiques de surface à l’ombre de la végétation arborée urbaine au moyen d’outils de transfert radiatif et de correction atmosphérique. L’originalité de ce travail est d’étudier la porosité d’un arbre via la grandeur de transmittance de la couronne. La problématique a donc été abordée en deux temps. Premièrement, la caractérisation de la transmittance d’un arbre isolé a été menée avec l’utilisation de l’outil DART à travers la mise en œuvre d’un plan d’expériences et d’études de sensibilité qui ont permis de la relier à des paramètres biophysiques et externes. Une campagne de mesures terrain a ensuite été réalisée afin d’évaluer son estimation à partir de différents niveaux de modélisation de l’arbre, dont un modèle réel acquis par mesures lidar terrestre. Deuxièmement, une nouvelle méthode de correction atmosphérique 3D adaptée à la végétation urbaine, ICARE-VEG, a été développée à partir des résultats précédents. Une campagne aéroportée et de mesures terrain UMBRA a été dédiée à sa validation. Ses performances comparées à d’autres outils existants ouvrent de larges perspectives pour l’interprétation globale d’une image par télédétection et pour souligner la complexité de modéliser des processus physiques naturels à une échelle spatiale très fine. / The new advances in remote sensing acquisitions at very high spatial resolution, either spaceborne (PLEIADES, HYPXIM), airborne or unmanned aerial vehicles borne, open the way for the study of complex environments such as urban areas. In particular, the better understanding of urban heat islands, urban planning, vegetation biodiversity, requires the knowledge of detailed material classification mapsbased on the use of spectral information brought by hyperspectral imagery 0.4-2.5μm. However, one of the main limitations of classification methods relies on the absence of shadow processing. Past studies have demonstrated that spectral information was possible to be extracted from shadows cast by buildings. But existing methods fail in shadows cast by trees because of their crown porosity. The objective of this thesis aims to characterize surface optical properties in urban tree shadows by means of radiative transfer and atmospheric correction tools. The originality of this work is to study the tree crown porosity through the analysis of the tree crown transmittance. Therefore, the issue has been divided into two parts. Firstly, an experimental design with the use of DART tool has been carried out in order to examine the relationships between the transmittance of an isolated tree and different biophysical and external variables. Then, the estimation of the tree crown transmittance has been assessed with several tree 3D modelling strategies derived from reference terrestrial lidar acquisitions. Secondly, a new atmospheric correction method appropriate to the processing of tree shadows, ICARE-VEG, was implemented fromthese previous results. An airborne and field campaign UMBRA was dedicated to its validation. Moreover, its performances was compared to other existing tools. Finally, the conclusions open large outlooks to the overall interpretation of remote sensing images and highlight the complexity to model physical natural processes with finer spatial resolutions.

Die Fernerkundung war in den vergangenen Jahren von einschneidenden Umbrüchen gekennzeichnet, die sich besonders in der stark gestiegenen geometrischen Bodenauflösung der Sensoren und den damit einhergehenden Veränderungen der Verarbeitungs- und Auswertungsverfahren widerspiegeln. Sehr hoch auflösende Satellitenbilddaten - definiert durch eine Auflösung zwischen einem halben und einem Meter - existieren seit dem Start von IKONOS Ende 1999. Etwa im selben Zeitraum wurden extrem hoch auflösende digitale Flugzeugkameras (0,1 bis 0,5 m) entwickelt. Dieser Arbeit liegen IKONOS-Daten mit einer Auflösung von einem (panchromatischer Kanal) bzw. vier Metern (Multispektraldaten) zugrunde. Bedingt durch die Eigenschaften sehr hoch aufgelöster Bilddaten (z. B. Detailgehalt, starke spektrale Variabilität, Datenmenge) lassen sich bisher verfügbare Standardverfahren der Bildverarbeitung nur eingeschränkt anwenden. Die Ergebnisse der in dieser Arbeit getesteten Verfahren verdeutlichen, dass die Methoden- bzw. Softwareentwicklung mit den technischen Neuerungen nicht Schritt halten konnte. Einige Verfahren werden erst allmählich für sehr hoch auflösende Daten nutzbar (z. B. atmosphärisch-topographische Korrektur). Die vorliegende Arbeit zeigt, dass Daten dieses Auflösungsbereiches mit bisher verwendeten pixelbasierten, statistischen Klassifikationsverfahren nur unzulänglich ausgewertet werden können. Die hier untersuchte Anwendung von Bildsegmentierungsmethoden hilft, die Nachteile pixelbasierter Verfahren zu überwinden. Dies wurde durch einen Vergleich pixel- und segmentbasierter Klassifikationsverfahren belegt. Im Rahmen einer Segmentierung werden homogene Bildbereiche zu Regionen verschmolzen, welche die Grundlage für die anschließende Klassifikation bilden. Hierzu stehen über die spektralen Eigenschaften hinaus Form-, Textur- und Kontextmerkmale zur Verfügung. In der verwendeten Software eCognition lassen sich diese Klassifikationsmerkmale zudem auf Grundlage des fuzzy-logic-Konzeptes in einer Wissensbasis (Entscheidungsbaum) umsetzen. Ein Vergleich verschiedener, derzeit verfügbarer Segmentierungsverfahren zeigt darüber hinaus, dass sich mit der genutzten Software eine hohe Segmentierungsqualität erzielen lässt. Der wachsende Bedarf an aktuellen Geobasisdaten stellt für sehr hoch auflösende Fernerkundungsdaten eine wichtige Einsatzmöglichkeit dar. Durch eine gezielte Klassifikation der Bilddaten lassen sich Arbeitsgrundlagen für die hier betrachteten Anwendungsfelder Landschaftsplanung und Landschaftsökologie schaffen. Die dargestellten Beispiele von Landschaftsanalysen durch die segmentbasierte Auswertung von IKONOS-Daten zeigen, dass sich eine Klassifikationsgüte von 90 % und höher erreichen lässt. Zudem können die infolge der Segmentierung abgegrenzten Landschaftseinheiten eine Grundlage für die Berechnung von Landschaftsstrukturmaßen bilden. Nationale Naturschutzziele sowie internationale Vereinbarungen zwingen darüber hinaus zur kontinuierlichen Erfassung des Landschaftsinventars und dessen Veränderungen. Fernerkundungsdaten können in diesem Bereich zur Etablierung automatisierter und operationell einsatzfähiger Verfahren beitragen. Das Beispiel Biotop- und Landnutzungskartierung zeigt, dass eine Erfassung von Landnutzungseinheiten mit hoher Qualität möglich ist. Bedingt durch das Auswertungsverfahren sowie die Dateneigenschaften entspricht die Güte der Ergebnisse noch nicht vollständig den Ansprüchen der Anwender, insbesondere hinsichtlich der erreichbaren Klassifikationstiefe. Die Qualität der Ergebnisse lässt sich durch die Nutzung von Zusatzdaten (z. B. GIS-Daten, Objekthöhenmodelle) künftig weiter steigern. Insgesamt verdeutlicht die Arbeit den Trend zur sehr hoch auflösenden digitalen Erderkundung. Für eine breite Nutzung dieser Datenquellen ist die weitere Entwicklung automatisierter und operationell anwendbarer Verarbeitungs- und Analysemethoden unerlässlich. / In recent years remote sensing has been characterised by dramatic changes. This is reflected especially by the highly increased geometrical resolution of imaging sensors and as a consequence thereof by the developments in processing and analysis methods. Very high resolution satellite imagery (VHR) - defined by a resolution between 0.5 and 1 m - exists since the start of IKONOS at the end of 1999. At about the same time extreme high resolution digital airborne sensors (0.1 till 0.5 m) have been developed. The basis of investigation for this dissertation is IKONOS imagery with a resolution of one meter (panchromatic) respectively four meters (multispectral). Due to the characteristics of such high resolution data (e.g. level of detail, high spectral variability, amount of data) the use of previously available standard methods of image processing is limited. The results of the procedures tested within this work demonstrate that the development of methods and software was not able to keep up with the technical innovations. Some procedures are only gradually becoming suitable for VHR data (e.g. atmospheric-topographic correction). Additionally, this work shows that VHR imagery can be analysed only inadequately using traditional pixel-based statistical classifiers. The herein researched application of image segmentation methods helps to overcome drawbacks of pixel-wise procedures. This is demonstrated by a comparison of pixel and segment-based classification. Within a segmentaion, homogeneous image areas are merged into regions which are the basis for the subsequent classification. For this purpose, in addition to spectral features also formal, textural and contextual properties are available. Furthermore, the applied software eCognition allows the definition of the features for classification based on fuzzy logic in a knowledge base (decision tree). An evaluation of different, currently available segmentation approaches illustrates that a high segmentation quality is achievable with the used software. The increasing demand for geospatial base data offers an important field of application for VHR remote sensing data. With a targeted classification of the imagery the creation of working bases for the herein considered usage for landscape planning and landscape ecology is possible. The given examples of landscape analyses using a segment-based processsing of IKONOS data show an achievable classification accuracy of 90 % and more. The landscape units delineated by image segmentation could be used for the calculation of landscape metrics. National aims of nature conservation as well as international agreements constrain a continuous survey of the landscape inventory and the monitoring of its changes. Remote sensing imagery can support the establishment of automated and operational methods in this field. The example of biotope and land use type mapping illustrates the possibility to detect land use units with a high precision. Depending on the analysis method and the data characteristics the quality of the results is not fully equivalent to the user?s demands at the moment, especially concerning the achievable depth of classification. The quality of the results can be enhanced by using additional thematic data (e.g. GIS data, object elevation models). To summarize this dissertation underlines the trend towards very high resolution digital earth observation. Thus, for a wide use of this kind of data it is essentially to further develop automated and operationally useable processing and analysis methods.