A quantificação de biomassa e carbono em florestas requer a aplicação de métodos adequados para se obter estimativas confiáveis de seus estoques. Neste sentido, o objetivo deste trabalho foi avaliar a aplicação de alguns métodos utilizados para a predição e estimação dessas variáveis em florestas nativas e restauradas da Mata Atlântica. Para isso, um primeiro capítulo aborda o uso do índice de vegetação NDVI como ferramenta auxiliar no inventário de estoques de biomassa em áreas de restauração florestal. Diferentes métodos de amostragem foram comparados em termos de precisão e conservadorismo das estimativas. Os resultados demonstraram que o NDVI apresentou adequada correlação com a biomassa estimada nas parcelas do inventário florestal instaladas em campo, sendo viável sua aplicação, seja para auxiliar na determinação de estratos, na aplicação da amostragem estratificada, seja como variável suplementar na utilização de um estimador de regressão relacionando-o à biomassa, no procedimento da amostragem dupla. Este último método, possibilitou minimizar as incertezas acerca das estimativas, valendo-se de uma intensidade amostral reduzida, fato que torna seu uso interessante, principalmente aos estudos em escala ampla, de modo a aumentar a confiabilidade das quantificações de estoques de carbono presentes na biomassa florestal, a custos de inventário reduzido. Um segundo capítulo discute a abordagem metodológica utilizada para inferir sobre a qualidade de modelos preditivos quando da seleção de modelos concorrentes para a aplicação em estudos de biomassa de florestas nativas. Para tanto, seis modelos considerando diferentes combinações de variáveis preditoras, incluindo diâmetro, altura total e alguma informação relativa à densidade da madeira, foram construídos a partir de dados de uma amostra de 80 árvores. As equações de predição de biomassa seca geradas por estes modelos foram avaliadas quanto à sua qualidade de ajuste e desempenho de aplicação. Neste segundo caso, aplicando-as aos dados de outra amostra composta por 146 árvores presentes em nove parcelas destrutivas instaladas em diferentes estágios sucessionais da floresta, de modo a possibilitar a avaliação dos vieses preditivos. No intuito de se verificar as discrepâncias nas estimativas de biomassa devido à aplicação das diferentes equações de predição de biomassa, as equações desenvolvidas, junto a outras disponíveis na literatura, foram aplicados aos dados de um inventário florestal realizado na área estudada. O estudo confirma a natureza empírica destas equações, atentando para a necessidade de prévia avaliação de seu desempenho de predição antes de sua aplicação, em especial, das ajustadas com amostras de outras florestas, expondo alguns dos principais fatores associados às causas de incertezas nas quantificações dos estoques de biomassa nos estudos realizado em florestas nativas. / The biomass and carbon quantification requires the application of appropriate methods to obtain reliable estimates of their stocks in natural and planted forests. The aim of this study was to evaluate different applicable methods to estimate biomass in both, natural and restored Atlantic Forests. The first chapter discusses the use of the vegetation index (NDVI) as an auxiliary tool in the inventory of biomass stocks in forest restoration areas. Different sampling methods were compared in terms of its accuracy and conservativeness. The results shown an adequate correlation between the vegetation index and the measured biomass, making the NDVI applicable either as supporting decision tool to define strata in the stratified sampling or as a predictor in the double sampling procedure. The last method allowed to the minimization of the uncertainties related to the biomass estimation combined to the reduction of sampling efforts. It makes the approach very interesting, especially in the context of large-scale surveys. The second chapter discusses the methodological approach used to evaluate the quality of predictive models applied to biomass studies in natural forests. For this, six models were fitted from 80 sample trees, using different combinations of predictor variables, such as, total height and information of wood density. The predictive equations generated by the models were evaluated according to their quality of fit and prediction performance. In order to evaluate its prediction performance, the equations were applied to the dataset of another 146 sample trees measured in nine destructive sample plots. The plots were located in different forest successional stages allowing the evaluation of model predictive bias among the stages. A third step of the analysis was the application of literature equations to a dataset of a forest inventory conducted in the study area, in order to verify the discrepancies in the estimates due to the use of these different models. The study confirms the empirical nature of the biomass equations and the need of previous evaluation in terms of prediction performance. This conclusion is even more relevant when we consider the equations that were obtained from other forests types, exposing some of the key factors associated to the causes of uncertainty in the biomass estimation applied to natural forests.

Made available in DSpace on 2017-07-21T14:19:31Z (GMT). No. of bitstreams: 1 MIKAMI, M M.pdf: 3402794 bytes, checksum: cb934aeb85ce1c45338450c4767a924d (MD5) Previous issue date: 2017-03-06 / The use of remotely piloted aircrafts (RPA) in precision agriculture (PA) is constantly evolving being a process comprised of the following steps: objective determination, capture and processing of images and the analisys of the obtained data. Although there is software and hardware made for those steps, the challange remains being the integration of the collected data, its reliability, and the intepretation of the resulting data for decision making. This work proposes a software architeture to make use of RPA on PA, allowing to perform all steps of the process using the Domain-Driven Design and Command and Query Responsability Segregation architectural patterns. The architecture, when used by researchers, allows the integration of new image processing modules, making use of case-based reasoning (CBR) for its evaluation. The architecture was evaluated in a case study under the following aspects: the reliability of the image processing methods and the adequacy of the CBR method when determining the best image processing algorithm for the specified objective. The results of the image processing algorithm for estimate the Normalized Difference Vegetation Index (NDVI) were compared to the results obtained by the field equipment (Greenseeker) and by processing made with the Pix4D software. Both the software and the equipment produced similar results, with a difference of about 7%. When doing the simulation for the best algorithm to be automaticaly used by the end user, the software correctly selected the algorithm with the highest probability of generating a correct outcome. In addition to the technical benefits, the developed architecture allows the results of field experiments analysis, associated with the algorithms used, to feed the knowledge base of the software so that it generates better parametrizations in the executions made by the end user. In addition to the technical benefits, the developed architecture allows the results of field experiments, associated with the used algorithms, to feed the knowledge base of the softwares that it generates better parametrizations when executed by the end user. The developed architecture in this work made possible integrating the several steps when using RPAs in PA, making easier its use by resaerchers and end users. The joint use by researchers and end users in the same environment could be an interesting alterative to publish new and better methods for image processing, giving to the end users more reliable results and more information about the crops. / O uso de aeronaves remotamente pilotadas (RPA) na agricultura de precisão (AP) está em constante evolução, sendo um processo que compreende as etapas de: determinação do objetivo, captura e processamento das imagens, e análise dos dados obtidos. Apesar de existirem alguns softwares e hardwares para essas etapas, a dificuldade ainda reside na integração dos dados coletados, sua confiabilidade, e na interpretação das informações resultantes para tomada de decisão. Este trabalho propõe uma arquitetura de software para o uso de RPA na AP que permite a execução de todas as etapas do processo utilizando os padrões arquiteturais Domain-Driven Design e Command e Query Responsability Segregation. Esta arquitetura, quando utilizada por pesquisadores, permite a integração de novos módulos de processamento de imagem, utilizando raciocínio baseado em casos (RBC) para sua avaliação. A arquitetura foi avaliada em um estudo de caso sob os seguintes aspectos: a confiabilidade dos métodos de processamento de imagem implementados e a adequação do método de RBC ao determinar o melhor algoritmo de processamento de imagem para objetivos específicos. O resultado do algoritmo de processamento de imagem para estimativa do índice de vegetação por diferença normalizada (NDVI) foi comparado aos resultados obtidos em equipamento de campo (Greenseeker) e pelo processamento via software Pix4D. Foi verificado que tanto os softwares quanto o equipamento geraram valores similares, com diferença média de 7%. Nas simulações de escolha do melhor algoritmo a ser utilizado automaticamente pelo usuário final, o software selecionou o algoritmo correto indicando para o mesmo a maior probabilidade de acerto. Além dos benefícios técnicos, a arquitetura desenvolvida permite que resultados de análises de experimentos em campo, associados aos algoritmos utilizados, alimentem a base de conhecimento do software para que o mesmo gere melhores parametrizações nas execuções feitas pelo usuário final. A arquitetura desenvolvida neste trabalho permitiu a integração das diversas etapas que envolvem o uso de RPA na AP, facilitando o uso por pesquisadores e usuários finais. A utilização conjunta de pesquisadores e usuários finais em um mesmo ambiente, pode ser uma alternativa interessante para publicação de novos e melhores métodos de processamento de imagem, fornecendo aos usuários finais mais informações sobre sua cultura e resultados mais confiáveis.

Esse trabalho tem como objetivo compreender as relações entre a Land Surface Temperature (LST), Normalized Difference Vegetation Índex (NDVI) e os padrões do uso da terra do município de São Paulo no período de 1985 a 2010. Analisou-se 15 bairros, nos quais foram extraídas 45 amostras aleatórias de diferentes padrões de uso da terra; subdivididas em baixo padrão, médio padrão e médio alto padrão. Com o aporte de geotecnologia, foi feita a extração dos dados das imagens de satélite Landsat 5 (TM) e das Ortofotos do ano de 2010. O comportamento das amostras variou de acordo como os diferentes perfis dos grupos analisados. O grupo de baixo padrão foi o que apresentou as maiores amplitudes térmicas, ausência de arborização urbana atreladas a um baixo padrão construtivo. O grupo de médio padrão é caracterizado pela predominância de área verticalizada e apresenta uma arborização urbana escassa em meio a uma malha urbana consolidada. O grupo de médio alto padrão foi o que mais apresentou arborização urbana, distribuída de forma homogênea na maioria das amostras, portanto foi o grupo que teve baixas amplitudes térmicas e o índice de Normalized Difference Vegetation Index (NDVI) com pouca variação. Os testes mostraram fortes correlações negativas entre as amostras de Land Surface Temperature (LST) e o índice de Normalized Difference Vegetation Index (NDVI), sendo -0,58 em 1985, -0,43 em 2004 e -0,82 em 2010. Os diferentes padrões de uso da terra, relacionados à temperatura de superfície, e o índice de vegetação, aliado à preocupação com o planejamento ambiental, deve resultar na melhoria da qualidade de vida da população. Esta pesquisa faz parte do Projeto Temático processo FAPESP 08/58161 -1, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change. / This study aims to understand the relationship between Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and the patterns of land use in the municipality of São Paulo, from 1985 to 2010. A totoal of 45 random samples were extracted from the 15 districts used in this study, with different patterns of land use which were subdivided into three different clases: low-end, middle and middle-high. Geospatial approaches allowed the extraction of satellite image data from Landsat 5 data (TM) and from Orthophotos from 2010. The behavior of the samples varied accordingly to the different group profiles. The low-end group presented the highest thermal amplitudes and more significant absence of urban vegetation linked, both to low urbanization and construction standards. The average standard group is characterized by the predominance of vertical buildings and lacks urban trees amidst a consolidated urban landscape. The average-high standard group displayed the highest concentration of green urban areas, distributed homogeneously in most samples, so this group presented low variations both in temperature amplitude and in the Normalized Difference Vegetation Index (NDVI). The correlation tests showed strong negative correlations between samples of Land Surface Temperature (LST) and the NDVI samples, of -0.58 in 1985, -0.43 in 2004 and -0.82 in 2010. Understanding the relations between the different patterns of land use, surface temperature and the NDVI (with due concern for environmental planning) is an important step in the identification and rehabilitation of enviromentally. This research is part of the Thematic Project FAPESP 08/58161 -1 process, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.

This work was written as a cumulative doctoral thesis based on reviewed publications. Climate projections are mainly based on the results of numeric simulations from global or regional climate models. Up to now processes between atmosphere and land surface are only rudimentarily known. This causes one of the major uncertainties in existing models. In order to reduce parameterisation uncertainties and to find a reasonable description of sub grid heterogeneities, the determination and evaluation of parameterisation schemes for modelling require as many datasets from different spatial scales as possible. This work contributes to this topic by implying different datasets from different platforms. Its objective was to analyse the spatial heterogeneity of land surface parameters and energy flux densities obtained from both satellite observations with different spatial and temporal resolutions and in-situ measurements. The investigations were carried out for two target areas in Germany. First, satellite data for the years 2002 and 2003 were analysed and validated from the LITFASS-area (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study). Second, the data from the experimental field sites of the FLUXNET cluster around Tharandt from the years 2006 and 2007 were used to determine the NDVI (Normalised Difference Vegetation Index for identifying vegetated areas and their "condition"). The core of the study was the determination of land surface characteristics and hence radiant and energy flux densities (net radiation, soil heat flux, sensible and latent heat flux) using the three optical satellite sensors ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spektroradiometer) and AVHRR 3 (Advanced Very High Resolution Radiometer) with different spatial (30 m – 1 km) and temporal (1 day – 16 days) resolution. Different sensor characteristics and different data sets for land use classifications can both lead to deviations of the resultant energy fluxes between the sensors. Thus, sensor differences were quantified, sensor adaptation methods were implemented and a quality analysis for land use classifications was performed. The result is then a single parameterisation scheme that allows for the determination of the energy fluxes from all three different sensors. The main focus was the derivation of the latent heat flux (L.E) using the Penman-Monteith (P-M) approach. Satellite data provide measurements of spectral reflectance and surface temperatures. The P-M approach requires further surface parameters not offered by satellite data. These parameters include the NDVI, Leaf Area Index (LAI), wind speed, relative humidity, vegetation height and roughness length, for example. They were derived indirectly from the given satellite- or in-situ measurements. If no data were available so called default values from literature were taken. The quality of these parameters strongly influenced the exactness of the radiant- and energy fluxes. Sensitivity studies showed that NDVI is one of the most important parameters for determination of evaporation. In contrast it could be shown, that the parameters as vegetation height and measurement height have only minor influence on L.E, which justifies the use of default values for these parameters. Due to the key role of NDVI a field study was carried out investigating the spatial variability and sensitivity of NDVI above five different land use types (winter wheat, corn, grass, beech and spruce). Methods to determine this parameter not only from space (spectral), but also from in-situ tower measurements (broadband) and spectrometer data (spectral) were compared. The best agreement between the methods was found for winter wheat and grass measurements in 2006. For these land use types the results differed by less than 10 % and 15 %, respectively. Larger differences were obtained for the forest measurements. The correlation between the daily MODIS-NDVI data and the in-situ NDVI inferred from the spectrometer and the broadband measurements were r=0.67 and r=0.51, respectively. Subsequently, spatial variability of land surface parameters and fluxes were analysed. The several spatial resolutions of the satellite sensors can be used to describe subscale heterogeneity from one scale to the other and to study the effects of spatial averaging. Therefore land use dependent parameters and fluxes were investigated to find typical distribution patterns of land surface properties and energy fluxes. Implying the distribution patterns found here for albedo and NDVI from ETM+ data in models has high potential to calculate representative energy flux distributions on a coarser scale. The distribution patterns were expressed as probability density functions (PDFs). First results of applying PDFs of albedo, NDVI, relative humidity, and wind speed to the L.E computation are encouraging, and they show the high potential of this method. Summing up, the method of satellite based surface parameter- and energy flux determination has been shown to work reliably on different temporal and spatial scales. The data are useful for detailed analyses of spatial variability of a landscape and for the description of sub grid heterogeneity, as it is needed in model applications. Their usability as input parameters for modelling on different scales is the second important result of this work. The derived vegetation parameters, e.g. LAI and plant cover, possess realistic values and were used as model input for the Lokalmodell of the German Weather Service. This significantly improved the model results for L.E. Additionally, thermal parameter fields, e.g. surface temperature from ETM+ with 30 m spatial resolution, were used as input for SVAT-modelling (Soil-Vegetation-Atmosphere-Transfer scheme). Thus, more realistic L.E results were obtained, providing highly resolved areal information. / Die vorliegende Arbeit wurde auf der Grundlage begutachteter Publikationen als kumulative Dissertation verfasst. Klimaprognosen basieren im Allgemeinen auf den Ergebnissen numerischer Simulationen mit globalen oder regionalen Klimamodellen. Eine der entscheidenden Unsicherheiten bestehender Modelle liegt in dem noch unzureichenden Verständnis von Wechselwirkungsprozessen zwischen der Atmosphäre und Landoberflächen und dem daraus folgenden Fehlen entsprechender Parametrisierungen. Um das Problem einer unsicheren Modell-Parametrisierung aufzugreifen und zum Beispiel subskalige Heterogenität in einer Art und Weise zu beschreiben, dass sie für Modelle nutzbar wird, werden für die Bestimmung und Evaluierung von Modell-Parametrisierungsansätzen so viele Datensätze wie möglich benötigt. Die Arbeit trägt zu diesem Thema durch die Verwendung verschiedener Datensätze unterschiedlicher Plattformen bei. Ziel der Studie war es, aus Satellitendaten verschiedener räumlicher und zeitlicher Auflösung sowie aus in-situ Daten die räumliche Heterogenität von Landoberflächenparametern und Energieflussdichten zu bestimmen. Die Untersuchungen wurden für zwei Zielgebiete in Deutschland durchgeführt. Für das LITFASS-Gebiet (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study) wurden Satellitendaten der Jahre 2002 und 2003 untersucht und validiert. Zusätzlich wurde im Rahmen dieser Arbeit eine NDVI-Studie (Normalisierter Differenzen Vegetations Index: Maß zur Detektierung von Vegetationflächen, deren Vitalität und Dichte) auf den Testflächen des FLUXNET Clusters um Tharandt in den Jahren 2006 und 2007 realisiert. Die Grundlage der Arbeit bildete die Bestimmung von Landoberflächeneigenschaften und daraus resultierenden Energieflüssen, auf Basis dreier optischer Sensoren (ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spectroradiometer) und AVHRR 3 (Advanced Very High Resolution Radiometer)) mit unterschiedlichen räumlichen (30 m – 1 km) und zeitlichen (1 – 16 Tage) Auflösungen. Unterschiedliche Sensorcharakteristiken, sowie die Verwendung verschiedener, zum Teil ungenauer Datensätze zur Landnutzungsklassifikation führen zu Abweichungen in den Ergebnissen der einzelnen Sensoren. Durch die Quantifizierung der Sensorunterschiede, die Anpassung der Ergebnisse der Sensoren aneinander und eine Qualitätsanalyse von verschiedenen Landnutzungsklassifikationen, wurde eine Basis für eine vergleichbare Parametrisierung der Oberflächenparameter und damit auch für die daraus berechneten Energieflüsse geschaffen. Der Schwerpunkt lag dabei auf der Bestimmung des latenten Wärmestromes (L.E) mit Hilfe des Penman-Monteith Ansatzes (P-M). Satellitendaten liefern Messwerte der spektralen Reflexion und der Oberflächentemperatur. Die P-M Gleichung erfordert weitere Oberflächenparameter wie zum Beispiel den NDVI, den Blattflächenindex (LAI), die Windgeschwindigkeit, die relative Luftfeuchte, die Vegetationshöhe oder die Rauhigkeitslänge, die jedoch aus den Satellitendaten nicht bestimmt werden können. Sie müssen indirekt aus den oben genannten Messgrößen der Satelliten oder aus in-situ Messungen abgeleitet werden. Stehen auch aus diesen Quellen keine Daten zur Verfügung, können sogenannte Standard- (Default-) Werte aus der Literatur verwendet werden. Die Qualität dieser Parameter hat einen großen Einfluss auf die Bestimmung der Strahlungs- und Energieflüsse. Sensitivitätsstudien im Rahmen der Arbeit zeigen die Bedeutung des NDVI als einen der wichtigsten Parameter in der Verdunstungsbestimmung nach P-M. Im Gegensatz dazu wurde deutlich, dass z. B. die Vegetationshöhe und die Messhöhe einen relativ kleinen Einfluss auf L.E haben, so dass für diese Parameter die Verwendung von Standardwerten gerechtfertigt ist. Aufgrund der Schlüsselrolle, welche der NDVI in der Bestimmung der Verdunstung einnimmt, wurden im Rahmen einer Feldstudie Untersuchungen des NDVI über fünf verschiedenen Landnutzungstypen (Winterweizen, Mais, Gras, Buche und Fichte) hinsichtlich seiner räumlichen Variabilität und Sensitivität, unternommen. Dabei wurden verschiedene Bestimmungsmethoden getestet, in welchen der NDVI nicht nur aus Satellitendaten (spektral), sondern auch aus in-situ Turmmessungen (breitbandig) und Spekrometermessungen (spektral) ermittelt wird. Die besten Übereinstimmungen der Ergebnisse wurden dabei für Winterweizen und Gras für das Jahr 2006 gefunden. Für diese Landnutzungstypen betrugen die Maximaldifferenzen aus den drei Methoden jeweils 10 beziehungsweise 15 %. Deutlichere Differenzen ließen sich für die Forstflächen verzeichnen. Die Korrelation zwischen Satelliten- und Spektrometermessung betrug r=0.67. Für Satelliten- und Turmmessungen ergab sich ein Wert von r=0.5. Basierend auf den beschriebenen Vorarbeiten wurde die räumliche Variabilität von Landoberflächenparametern und Flüssen untersucht. Die unterschiedlichen räumlichen Auflösungen der Satelliten können genutzt werden, um zum einen die subskalige Heterogenität zu beschreiben, aber auch, um den Effekt räumlicher Mittelungsverfahren zu testen. Dafür wurden Parameter und Energieflüsse in Abhängigkeit der Landnutzungsklasse untersucht, um typische Verteilungsmuster dieser Größen zu finden. Die Verwendung der Verteilungsmuster (in Form von Wahrscheinlichkeitsdichteverteilungen – PDFs), die für die Albedo und den NDVI aus ETM+ Daten gefunden wurden, bietet ein hohes Potential als Modellinput, um repräsentative PDFs der Energieflüsse auf gröberen Skalen zu erhalten. Die ersten Ergebnisse in der Verwendung der PDFs von Albedo, NDVI, relativer Luftfeuchtigkeit und Windgeschwindigkeit für die Bestimmung von L.E waren sehr ermutigend und zeigten das hohe Potential der Methode. Zusammenfassend lässt sich feststellen, dass die Methode der Ableitung von Oberflächenparametern und Energieflüssen aus Satellitendaten zuverlässige Daten auf verschiedenen zeitlichen und räumlichen Skalen liefert. Die Daten sind für eine detaillierte Analyse der räumlichen Variabilität der Landschaft und für die Beschreibung der subskaligen Heterogenität, wie sie oft in Modellanwendungen benötigt wird, geeignet. Ihre Nutzbarkeit als Inputparameter in Modellen auf verschiedenen Skalen ist das zweite wichtige Ergebnis der Arbeit. Aus Satellitendaten abgeleitete Vegetationsparameter wie der LAI oder die Pflanzenbedeckung liefern realistische Ergebnisse, die zum Beispiel als Modellinput in das Lokalmodell des Deutschen Wetterdienstes implementiert werden konnten und die Modellergebnisse von L.E signifikant verbessert haben. Aber auch thermale Parameter, wie beispielsweise die Oberflächentemperatur aus ETM+ Daten in 30 m Auflösung, wurden als Eingabeparameter eines Soil-Vegetation-Atmosphere-Transfer-Modells (SVAT) verwendet. Dadurch erhält man realistischere Ergebnisse für L.E, die hochaufgelöste Flächeninformationen bieten.

This work was written as a cumulative doctoral thesis based on reviewed publications. Climate projections are mainly based on the results of numeric simulations from global or regional climate models. Up to now processes between atmosphere and land surface are only rudimentarily known. This causes one of the major uncertainties in existing models. In order to reduce parameterisation uncertainties and to find a reasonable description of sub grid heterogeneities, the determination and evaluation of parameterisation schemes for modelling require as many datasets from different spatial scales as possible. This work contributes to this topic by implying different datasets from different platforms. Its objective was to analyse the spatial heterogeneity of land surface parameters and energy flux densities obtained from both satellite observations with different spatial and temporal resolutions and in-situ measurements. The investigations were carried out for two target areas in Germany. First, satellite data for the years 2002 and 2003 were analysed and validated from the LITFASS-area (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study). Second, the data from the experimental field sites of the FLUXNET cluster around Tharandt from the years 2006 and 2007 were used to determine the NDVI (Normalised Difference Vegetation Index for identifying vegetated areas and their "condition"). The core of the study was the determination of land surface characteristics and hence radiant and energy flux densities (net radiation, soil heat flux, sensible and latent heat flux) using the three optical satellite sensors ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spektroradiometer) and AVHRR 3 (Advanced Very High Resolution Radiometer) with different spatial (30 m – 1 km) and temporal (1 day – 16 days) resolution. Different sensor characteristics and different data sets for land use classifications can both lead to deviations of the resultant energy fluxes between the sensors. Thus, sensor differences were quantified, sensor adaptation methods were implemented and a quality analysis for land use classifications was performed. The result is then a single parameterisation scheme that allows for the determination of the energy fluxes from all three different sensors. The main focus was the derivation of the latent heat flux (L.E) using the Penman-Monteith (P-M) approach. Satellite data provide measurements of spectral reflectance and surface temperatures. The P-M approach requires further surface parameters not offered by satellite data. These parameters include the NDVI, Leaf Area Index (LAI), wind speed, relative humidity, vegetation height and roughness length, for example. They were derived indirectly from the given satellite- or in-situ measurements. If no data were available so called default values from literature were taken. The quality of these parameters strongly influenced the exactness of the radiant- and energy fluxes. Sensitivity studies showed that NDVI is one of the most important parameters for determination of evaporation. In contrast it could be shown, that the parameters as vegetation height and measurement height have only minor influence on L.E, which justifies the use of default values for these parameters. Due to the key role of NDVI a field study was carried out investigating the spatial variability and sensitivity of NDVI above five different land use types (winter wheat, corn, grass, beech and spruce). Methods to determine this parameter not only from space (spectral), but also from in-situ tower measurements (broadband) and spectrometer data (spectral) were compared. The best agreement between the methods was found for winter wheat and grass measurements in 2006. For these land use types the results differed by less than 10 % and 15 %, respectively. Larger differences were obtained for the forest measurements. The correlation between the daily MODIS-NDVI data and the in-situ NDVI inferred from the spectrometer and the broadband measurements were r=0.67 and r=0.51, respectively. Subsequently, spatial variability of land surface parameters and fluxes were analysed. The several spatial resolutions of the satellite sensors can be used to describe subscale heterogeneity from one scale to the other and to study the effects of spatial averaging. Therefore land use dependent parameters and fluxes were investigated to find typical distribution patterns of land surface properties and energy fluxes. Implying the distribution patterns found here for albedo and NDVI from ETM+ data in models has high potential to calculate representative energy flux distributions on a coarser scale. The distribution patterns were expressed as probability density functions (PDFs). First results of applying PDFs of albedo, NDVI, relative humidity, and wind speed to the L.E computation are encouraging, and they show the high potential of this method. Summing up, the method of satellite based surface parameter- and energy flux determination has been shown to work reliably on different temporal and spatial scales. The data are useful for detailed analyses of spatial variability of a landscape and for the description of sub grid heterogeneity, as it is needed in model applications. Their usability as input parameters for modelling on different scales is the second important result of this work. The derived vegetation parameters, e.g. LAI and plant cover, possess realistic values and were used as model input for the Lokalmodell of the German Weather Service. This significantly improved the model results for L.E. Additionally, thermal parameter fields, e.g. surface temperature from ETM+ with 30 m spatial resolution, were used as input for SVAT-modelling (Soil-Vegetation-Atmosphere-Transfer scheme). Thus, more realistic L.E results were obtained, providing highly resolved areal information. / Die vorliegende Arbeit wurde auf der Grundlage begutachteter Publikationen als kumulative Dissertation verfasst. Klimaprognosen basieren im Allgemeinen auf den Ergebnissen numerischer Simulationen mit globalen oder regionalen Klimamodellen. Eine der entscheidenden Unsicherheiten bestehender Modelle liegt in dem noch unzureichenden Verständnis von Wechselwirkungsprozessen zwischen der Atmosphäre und Landoberflächen und dem daraus folgenden Fehlen entsprechender Parametrisierungen. Um das Problem einer unsicheren Modell-Parametrisierung aufzugreifen und zum Beispiel subskalige Heterogenität in einer Art und Weise zu beschreiben, dass sie für Modelle nutzbar wird, werden für die Bestimmung und Evaluierung von Modell-Parametrisierungsansätzen so viele Datensätze wie möglich benötigt. Die Arbeit trägt zu diesem Thema durch die Verwendung verschiedener Datensätze unterschiedlicher Plattformen bei. Ziel der Studie war es, aus Satellitendaten verschiedener räumlicher und zeitlicher Auflösung sowie aus in-situ Daten die räumliche Heterogenität von Landoberflächenparametern und Energieflussdichten zu bestimmen. Die Untersuchungen wurden für zwei Zielgebiete in Deutschland durchgeführt. Für das LITFASS-Gebiet (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study) wurden Satellitendaten der Jahre 2002 und 2003 untersucht und validiert. Zusätzlich wurde im Rahmen dieser Arbeit eine NDVI-Studie (Normalisierter Differenzen Vegetations Index: Maß zur Detektierung von Vegetationflächen, deren Vitalität und Dichte) auf den Testflächen des FLUXNET Clusters um Tharandt in den Jahren 2006 und 2007 realisiert. Die Grundlage der Arbeit bildete die Bestimmung von Landoberflächeneigenschaften und daraus resultierenden Energieflüssen, auf Basis dreier optischer Sensoren (ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spectroradiometer) und AVHRR 3 (Advanced Very High Resolution Radiometer)) mit unterschiedlichen räumlichen (30 m – 1 km) und zeitlichen (1 – 16 Tage) Auflösungen. Unterschiedliche Sensorcharakteristiken, sowie die Verwendung verschiedener, zum Teil ungenauer Datensätze zur Landnutzungsklassifikation führen zu Abweichungen in den Ergebnissen der einzelnen Sensoren. Durch die Quantifizierung der Sensorunterschiede, die Anpassung der Ergebnisse der Sensoren aneinander und eine Qualitätsanalyse von verschiedenen Landnutzungsklassifikationen, wurde eine Basis für eine vergleichbare Parametrisierung der Oberflächenparameter und damit auch für die daraus berechneten Energieflüsse geschaffen. Der Schwerpunkt lag dabei auf der Bestimmung des latenten Wärmestromes (L.E) mit Hilfe des Penman-Monteith Ansatzes (P-M). Satellitendaten liefern Messwerte der spektralen Reflexion und der Oberflächentemperatur. Die P-M Gleichung erfordert weitere Oberflächenparameter wie zum Beispiel den NDVI, den Blattflächenindex (LAI), die Windgeschwindigkeit, die relative Luftfeuchte, die Vegetationshöhe oder die Rauhigkeitslänge, die jedoch aus den Satellitendaten nicht bestimmt werden können. Sie müssen indirekt aus den oben genannten Messgrößen der Satelliten oder aus in-situ Messungen abgeleitet werden. Stehen auch aus diesen Quellen keine Daten zur Verfügung, können sogenannte Standard- (Default-) Werte aus der Literatur verwendet werden. Die Qualität dieser Parameter hat einen großen Einfluss auf die Bestimmung der Strahlungs- und Energieflüsse. Sensitivitätsstudien im Rahmen der Arbeit zeigen die Bedeutung des NDVI als einen der wichtigsten Parameter in der Verdunstungsbestimmung nach P-M. Im Gegensatz dazu wurde deutlich, dass z. B. die Vegetationshöhe und die Messhöhe einen relativ kleinen Einfluss auf L.E haben, so dass für diese Parameter die Verwendung von Standardwerten gerechtfertigt ist. Aufgrund der Schlüsselrolle, welche der NDVI in der Bestimmung der Verdunstung einnimmt, wurden im Rahmen einer Feldstudie Untersuchungen des NDVI über fünf verschiedenen Landnutzungstypen (Winterweizen, Mais, Gras, Buche und Fichte) hinsichtlich seiner räumlichen Variabilität und Sensitivität, unternommen. Dabei wurden verschiedene Bestimmungsmethoden getestet, in welchen der NDVI nicht nur aus Satellitendaten (spektral), sondern auch aus in-situ Turmmessungen (breitbandig) und Spekrometermessungen (spektral) ermittelt wird. Die besten Übereinstimmungen der Ergebnisse wurden dabei für Winterweizen und Gras für das Jahr 2006 gefunden. Für diese Landnutzungstypen betrugen die Maximaldifferenzen aus den drei Methoden jeweils 10 beziehungsweise 15 %. Deutlichere Differenzen ließen sich für die Forstflächen verzeichnen. Die Korrelation zwischen Satelliten- und Spektrometermessung betrug r=0.67. Für Satelliten- und Turmmessungen ergab sich ein Wert von r=0.5. Basierend auf den beschriebenen Vorarbeiten wurde die räumliche Variabilität von Landoberflächenparametern und Flüssen untersucht. Die unterschiedlichen räumlichen Auflösungen der Satelliten können genutzt werden, um zum einen die subskalige Heterogenität zu beschreiben, aber auch, um den Effekt räumlicher Mittelungsverfahren zu testen. Dafür wurden Parameter und Energieflüsse in Abhängigkeit der Landnutzungsklasse untersucht, um typische Verteilungsmuster dieser Größen zu finden. Die Verwendung der Verteilungsmuster (in Form von Wahrscheinlichkeitsdichteverteilungen – PDFs), die für die Albedo und den NDVI aus ETM+ Daten gefunden wurden, bietet ein hohes Potential als Modellinput, um repräsentative PDFs der Energieflüsse auf gröberen Skalen zu erhalten. Die ersten Ergebnisse in der Verwendung der PDFs von Albedo, NDVI, relativer Luftfeuchtigkeit und Windgeschwindigkeit für die Bestimmung von L.E waren sehr ermutigend und zeigten das hohe Potential der Methode. Zusammenfassend lässt sich feststellen, dass die Methode der Ableitung von Oberflächenparametern und Energieflüssen aus Satellitendaten zuverlässige Daten auf verschiedenen zeitlichen und räumlichen Skalen liefert. Die Daten sind für eine detaillierte Analyse der räumlichen Variabilität der Landschaft und für die Beschreibung der subskaligen Heterogenität, wie sie oft in Modellanwendungen benötigt wird, geeignet. Ihre Nutzbarkeit als Inputparameter in Modellen auf verschiedenen Skalen ist das zweite wichtige Ergebnis der Arbeit. Aus Satellitendaten abgeleitete Vegetationsparameter wie der LAI oder die Pflanzenbedeckung liefern realistische Ergebnisse, die zum Beispiel als Modellinput in das Lokalmodell des Deutschen Wetterdienstes implementiert werden konnten und die Modellergebnisse von L.E signifikant verbessert haben. Aber auch thermale Parameter, wie beispielsweise die Oberflächentemperatur aus ETM+ Daten in 30 m Auflösung, wurden als Eingabeparameter eines Soil-Vegetation-Atmosphere-Transfer-Modells (SVAT) verwendet. Dadurch erhält man realistischere Ergebnisse für L.E, die hochaufgelöste Flächeninformationen bieten.

L'objectif de ce travail est de montrer la vulnérabilité des paysages forestiers en relation avec la variabilité climatique à l'échelle de la Tanzanie d’une part et d'analyser les dynamiques forestières, afin de réaliser une étude prospective des dynamiques de l'occupation des sols dans les réserves forestières de Pugu et de Kazimzumbwi d'autre part. L'analyse des données TRMM sur la période 2001-2013 a permis de mettre en évidence une variabilité saisonnière et interannuelle des précipitations à l'échelle du pays. Les cartes de précipitations interannuelles ont permis de distinguer les années à faible pluviométrie, les années à forte pluviométrie et les années à pluviométrie intermédiaire. Cette analyse a également permis de mettre en évidence 11 types de régimes pluviométriques marqués par des modes de variabilité saisonnière différents à l'échelle de la Tanzanie. Des oppositions existent entre les régimes pluviométriques de la région centrale savanicole marquées par des hauteurs annuelles faibles, un nombre de mois secs important (7 mois) et plus affectée par la variabilité interannuelle des précipitations d'une part et d’autre part les régions forestières du nord, sud et l'est plus humides et présentant des faibles déficits des hauteurs pluviométriques interannuelles. La sensibilité de la phénologie végétale à la variabilité pluviométrique a été analysé par l'étude des relations spatio-temporelles entre l'indice de végétation normalisé NDVI-MODIS et la pluviométrie (pluie TRMM). Les cartes de corrélations pluie/NDVI mettent en évidence une opposition entre les régions sèches du centre marquée par des paysages de savane fortement vulnérables à la variabilité pluviométrique et les régions du sud de forêts humides de montagnes et des régions côtières de forêts de mangroves réagissant peu à cette variabilité pluviométrique. Dans les régions de savanes du centre l'intensité de la dépendance pluie/NDVI est mesurée par un coefficient de corrélation de 0.70. Un suivi de l'analyse des pressions humaines sur les réserves forestières a été réalisé à partir de l'exemple des forêts de Pugu et de Kazimzumbwi sur la période 1995-2015 à partir de l'imagerie SPOT 6 (haute résolution) et LANDSAT. Les classifications de l’occupation du sol ont été réalisées à partir de la méthode orientée objet. Le bilan forestier montre que, des deux réserves forestières, seule la réserve de Pugu conserve encore près de la moitié de sa surface en forêt en 2015 (55% dont 32 % de forêt dense). À l'inverse la réserve de Kazimzumbwi ne contient que 5 % de forêt dense de sa superficie. Sur l'ensemble de la période étudiée (1995-2014), la sous-période 2009-2014 a été la plus critique en terme de perte de forêt. En effet, en l’espace de 5 ans les réserves forestières de Pugu et Kazimzumbwi ont presque perdu le double de leur superficie. Partant du constat d'une vulnérabilité accrue des pressions humaines dans les réserves, une analyse multicritère a permis d'identifier les zones de fortes et faibles pressions humaines. Les zones les plus vulnérables restent celles situées à proximité des axes de communication et des villes. Ainsi, les réserves forestières sont plus vulnérables dans leurs parties est, proches des routes principales et des grands centres urbains comme Pugu et Kisarawé. L'utilisation d’un modèle pour une modélisation prospective en 2050 a nécessité l’intégration des variables explicatives des changements observés et des cartes d'occupation du sol de 1995 et 2014. Le modèle est validé à partir d’une carte prédite et d’une carte réelle. Le résultat montre une simulation exacte à 72 %. Le modèle prévoit ainsi, à l’horizon 2050 une expansion et densification des surfaces artificialisées notamment à la périphérie nord-est de la réserve de Pugu et au sud dans la réserve de Kazimzumbwi. Cette croissance des surfaces artificialisées entraînera un recul important des surfaces forestières existantes à l’intérieur des réserves. / The objective of this work is on one hand to show the vulnerability of forest landscapes in relation to climate variability at the scale of Tanzania and on the other hand to analyze forest dynamics in order to carry out a prospective study of the dynamics of land use in the forest reserves of Pugu and Kazimzumbwi. Analysis of the TRMM data over the period from 2001 to 2013 has allowed revealing a seasonal and inter-annual variability in precipitation across the country. The inter-annual precipitation maps have made it possible to distinguish the years with low rainfall (2003, 2005, 2012 ), the years of high rainfall (2002, 2007, 2006, 2011) and the years of intermediate rainfall (2001, 2007, 2008, 2009, 2013). It has also help to distinguish 11 types of rainfall regimes marked by different patterns of seasonal variability at the scale of Tanzania. There are oppositions between the rainfall regimes of the central savannah region on one hand marked by low annual heights over an important period of seven dry months, also more affected by inter-annual variability, and the northern, southern and eastern forest regions are more humid and presenting low deficits of heights inter-annual rainfall. The sensitivity of plant phenology to rainfall variability has been analyzed by the examination of the spatio-temporal relationships between the standardized vegetation index NDVI-MODIS and rainfall (rain TRMM). The rain / NDVI correlation maps show an opposition between the dry regions of the center marked by savannah landscapes highly vulnerable to rainfall variability and the southern regions of moist forests, mountains and coastal regions, mangrove forests Reacting poorly to this rainfall variability. In the savannah regions of the center, the intensity of rain / NDI dependence is measured by a correlation coefficient of 0.70. A monitoring of the analysis of human pressures on forest reserves was carried out using the example of the Pugu and Kazimzumbwi forests during the period 1995-2015 using SPOT 6 (high resolution) and LANDSAT imagery. The land use classifications were realized from the object oriented method. The forest review shows that in 2015 (55% of which 32% is dense forest), from the two reserves only the reserve of Pugu still preserves nearly the half of its surface in forest while the reserve forest of Kazimzumbwi contains only 5% of its area. Over the entire period studied, the sub-period 2009-2014 was the most critical in terms of forest loss. In fact, within five years the forest reserves of Pugu and Kazimzumbwi have almost lost the double of their area. Based on the increased vulnerability of human pressures in the Pugu and Kazimzumbwi forest reserves and their periphery, a multicriteria analysis has made it possible to identify areas of high and low human pressures. The most vulnerable areas remain those located close to the communication axes and cities. Consequently forest reserves are more vulnerable in their eastern parts, close to major roads and major urban centers such as Pugu and Kazimzumbwi. The use of a model for prospective modeling in 2050 has required the integration of the explanatory variables of the observed changes and the land use maps of 1995 and 2014. The model is validated from a predicted map and a real map. The result shows an exact simulation at 72%, based on this hypothesis of an increase in anthropogenic human pressures on the two forest reserves over time; we have predicted the land use map of 2050 under the effect of explanatory variables. This prospective modeling therefore envisages, by 2050, an expansion and densification of artificial surfaces, notably at the north-eastern periphery of the reserve of Pugu and on the south in the kazimzumbwi reserve. This growth in artificial surfaces will result in a significant decline in existing forest areas within reserves.

Made available in DSpace on 2014-12-17T15:55:03Z (GMT). No. of bitstreams: 1 HenriqueRD_DISSERT.pdf: 2642675 bytes, checksum: dc5266cdb82cb1eb696b408a316b8e91 (MD5) Previous issue date: 2013-02-22 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The processes of occupation and evolution of natural environments as a result of a disorderly process of implementing economic practices agrosilvopastoris play today an important role in the degradation process of changing the landscape and natural resources of the semiarid Northeast. The Serra de Santana has natural elements important to the state of Rio Grande do Norte as the source of the Potengi. Therefore, this study aimed to analyze the degree of degradation in the municipality of Cerro Cora - RN. We used satellite images Landsat-5 and census data for the year 2008. The method made use of geotechnology which includes land use, NDVI, rainfall, livestock and erodibility in the evaluation of environmental degradation, as well as satellite images of Landsat TM-5, in the years 1984, 1995 and 2008, letters of NDVI, census data regarding the socioeconomic obtained from IBGE. The results showed that the absolute majority in the municipality of Cerro Cora has a low to medium susceptibility, which together represent 63.92% of the municipality, with a regression of disturbed areas and the areas of agriculture, and a recovery of the areas of Caatinga , coming to occupy currently 92% of the municipal territory. A Geographic Information System is indispensable to environmental monitoring of Cerro Cora / RN / Os processos de ocupa??o e evolu??o dos ambientes naturais em decorr?ncia de um processo desordenado de implementa??o de pr?ticas econ?micas agrosilvopastoris, desempenham at? hoje um papel determinante de degrada??o no processo de mudan?a da paisagem e dos recursos naturais do Semi?rido Brasileiro. A Serra de Santana apresenta elementos naturais importantes para o estado do Rio Grande do Norte como a nascente do rio Potengi. Para tanto, o presente trabalho teve como objetivo analisar o grau de degrada??o no munic?pio de Cerro Cor? - RN. Foram utilizadas imagens de sat?lite Landsat-5 e dados censit?rios referentes ao ano de 2008. Como m?todo fez-se uso de geotecnologias que contemplam uso do solo, NDVI, precipita??o, pecu?ria e erodibilidade na avalia??o da degrada??o ambiental, bem como imagens do sat?lite Landsat TM-5, nos anos de 1984, 1995 e 2008, cartas de NDVI, dados censit?rio referentes ? dados socioecon?micos obtidos no IBGE. Os resultados mostraram que em sua maioria absoluta o munic?pio de Cerro Cor? apresenta uma susceptibilidade de baixa ? m?dia, que juntos representam 63,92% do munic?pio, havendo uma regress?o das ?reas antropizadas e das ?reas de agricultura, e uma recupera??o das ?reas de Caatinga, chegando esta a ocupar atualmente 92% do territ?rio municipal. Um sistema de informa??o Geogr?fica torna-se indispens?vel no monitoramento ambiental de Cerro Cor?/RN

Esse trabalho tem como objetivo compreender as relações entre a Land Surface Temperature (LST), Normalized Difference Vegetation Índex (NDVI) e os padrões do uso da terra do município de São Paulo no período de 1985 a 2010. Analisou-se 15 bairros, nos quais foram extraídas 45 amostras aleatórias de diferentes padrões de uso da terra; subdivididas em baixo padrão, médio padrão e médio alto padrão. Com o aporte de geotecnologia, foi feita a extração dos dados das imagens de satélite Landsat 5 (TM) e das Ortofotos do ano de 2010. O comportamento das amostras variou de acordo como os diferentes perfis dos grupos analisados. O grupo de baixo padrão foi o que apresentou as maiores amplitudes térmicas, ausência de arborização urbana atreladas a um baixo padrão construtivo. O grupo de médio padrão é caracterizado pela predominância de área verticalizada e apresenta uma arborização urbana escassa em meio a uma malha urbana consolidada. O grupo de médio alto padrão foi o que mais apresentou arborização urbana, distribuída de forma homogênea na maioria das amostras, portanto foi o grupo que teve baixas amplitudes térmicas e o índice de Normalized Difference Vegetation Index (NDVI) com pouca variação. Os testes mostraram fortes correlações negativas entre as amostras de Land Surface Temperature (LST) e o índice de Normalized Difference Vegetation Index (NDVI), sendo -0,58 em 1985, -0,43 em 2004 e -0,82 em 2010. Os diferentes padrões de uso da terra, relacionados à temperatura de superfície, e o índice de vegetação, aliado à preocupação com o planejamento ambiental, deve resultar na melhoria da qualidade de vida da população. Esta pesquisa faz parte do Projeto Temático processo FAPESP 08/58161 -1, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change. / This study aims to understand the relationship between Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and the patterns of land use in the municipality of São Paulo, from 1985 to 2010. A totoal of 45 random samples were extracted from the 15 districts used in this study, with different patterns of land use which were subdivided into three different clases: low-end, middle and middle-high. Geospatial approaches allowed the extraction of satellite image data from Landsat 5 data (TM) and from Orthophotos from 2010. The behavior of the samples varied accordingly to the different group profiles. The low-end group presented the highest thermal amplitudes and more significant absence of urban vegetation linked, both to low urbanization and construction standards. The average standard group is characterized by the predominance of vertical buildings and lacks urban trees amidst a consolidated urban landscape. The average-high standard group displayed the highest concentration of green urban areas, distributed homogeneously in most samples, so this group presented low variations both in temperature amplitude and in the Normalized Difference Vegetation Index (NDVI). The correlation tests showed strong negative correlations between samples of Land Surface Temperature (LST) and the NDVI samples, of -0.58 in 1985, -0.43 in 2004 and -0.82 in 2010. Understanding the relations between the different patterns of land use, surface temperature and the NDVI (with due concern for environmental planning) is an important step in the identification and rehabilitation of enviromentally. This research is part of the Thematic Project FAPESP 08/58161 -1 process, \"Assessment of Impacts and Vulnerability to Climate Change in Brazil and strategies for Adaptation options\", Component 5: Vulnerability of the metropolitan region of São Paulo to climate Change.

A quantificação de biomassa e carbono em florestas requer a aplicação de métodos adequados para se obter estimativas confiáveis de seus estoques. Neste sentido, o objetivo deste trabalho foi avaliar a aplicação de alguns métodos utilizados para a predição e estimação dessas variáveis em florestas nativas e restauradas da Mata Atlântica. Para isso, um primeiro capítulo aborda o uso do índice de vegetação NDVI como ferramenta auxiliar no inventário de estoques de biomassa em áreas de restauração florestal. Diferentes métodos de amostragem foram comparados em termos de precisão e conservadorismo das estimativas. Os resultados demonstraram que o NDVI apresentou adequada correlação com a biomassa estimada nas parcelas do inventário florestal instaladas em campo, sendo viável sua aplicação, seja para auxiliar na determinação de estratos, na aplicação da amostragem estratificada, seja como variável suplementar na utilização de um estimador de regressão relacionando-o à biomassa, no procedimento da amostragem dupla. Este último método, possibilitou minimizar as incertezas acerca das estimativas, valendo-se de uma intensidade amostral reduzida, fato que torna seu uso interessante, principalmente aos estudos em escala ampla, de modo a aumentar a confiabilidade das quantificações de estoques de carbono presentes na biomassa florestal, a custos de inventário reduzido. Um segundo capítulo discute a abordagem metodológica utilizada para inferir sobre a qualidade de modelos preditivos quando da seleção de modelos concorrentes para a aplicação em estudos de biomassa de florestas nativas. Para tanto, seis modelos considerando diferentes combinações de variáveis preditoras, incluindo diâmetro, altura total e alguma informação relativa à densidade da madeira, foram construídos a partir de dados de uma amostra de 80 árvores. As equações de predição de biomassa seca geradas por estes modelos foram avaliadas quanto à sua qualidade de ajuste e desempenho de aplicação. Neste segundo caso, aplicando-as aos dados de outra amostra composta por 146 árvores presentes em nove parcelas destrutivas instaladas em diferentes estágios sucessionais da floresta, de modo a possibilitar a avaliação dos vieses preditivos. No intuito de se verificar as discrepâncias nas estimativas de biomassa devido à aplicação das diferentes equações de predição de biomassa, as equações desenvolvidas, junto a outras disponíveis na literatura, foram aplicados aos dados de um inventário florestal realizado na área estudada. O estudo confirma a natureza empírica destas equações, atentando para a necessidade de prévia avaliação de seu desempenho de predição antes de sua aplicação, em especial, das ajustadas com amostras de outras florestas, expondo alguns dos principais fatores associados às causas de incertezas nas quantificações dos estoques de biomassa nos estudos realizado em florestas nativas. / The biomass and carbon quantification requires the application of appropriate methods to obtain reliable estimates of their stocks in natural and planted forests. The aim of this study was to evaluate different applicable methods to estimate biomass in both, natural and restored Atlantic Forests. The first chapter discusses the use of the vegetation index (NDVI) as an auxiliary tool in the inventory of biomass stocks in forest restoration areas. Different sampling methods were compared in terms of its accuracy and conservativeness. The results shown an adequate correlation between the vegetation index and the measured biomass, making the NDVI applicable either as supporting decision tool to define strata in the stratified sampling or as a predictor in the double sampling procedure. The last method allowed to the minimization of the uncertainties related to the biomass estimation combined to the reduction of sampling efforts. It makes the approach very interesting, especially in the context of large-scale surveys. The second chapter discusses the methodological approach used to evaluate the quality of predictive models applied to biomass studies in natural forests. For this, six models were fitted from 80 sample trees, using different combinations of predictor variables, such as, total height and information of wood density. The predictive equations generated by the models were evaluated according to their quality of fit and prediction performance. In order to evaluate its prediction performance, the equations were applied to the dataset of another 146 sample trees measured in nine destructive sample plots. The plots were located in different forest successional stages allowing the evaluation of model predictive bias among the stages. A third step of the analysis was the application of literature equations to a dataset of a forest inventory conducted in the study area, in order to verify the discrepancies in the estimates due to the use of these different models. The study confirms the empirical nature of the biomass equations and the need of previous evaluation in terms of prediction performance. This conclusion is even more relevant when we consider the equations that were obtained from other forests types, exposing some of the key factors associated to the causes of uncertainty in the biomass estimation applied to natural forests.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Significant processes of conversion in the land use patterns have been verified in the State of Rio Grande do Sul due to the incorporation of forest areas during the last years. In this conception, this work aims to establish the methodological guidelines to analyze the dynamics of these patterns in the Serras do Sudeste and Campanha Meridional, which are micro regions located in the southern part of the State. Thematic maps from the years 2000, 2004 and 2008 were elaborated through the product NDVI (Normalized Difference Vegetation Index) from the MODIS sensor in a way to relate this index to the land use classes. The maps that we elaborated were used to drive a dynamic model, which made it possible to quantify, through the use of Markov model, the conversion rates between classes. The simulation designed using the probabilistic method weighs-of-evidence, and allowed to assess the role of influential variables in the forest cover changes of both micro regions. The main results are simulations based on the paradigm of Cellular Automata (CA), in which the forest areas are quantified and spatially distributed until the year 2016. The predictions modeled, based on the considered variables, indicate that the forest will expand over spaces previously allocated to agricultural activities and to extensive grazing of cattle, either with the introduction of exotic species or by means of regeneration. The forest cover in Serras do Sudeste will increase from 8,6% to 16,2% by the end of the period. In Campanha Meridional the increase will be from 11,1% to 12,5% and, in both micro regions, the expansion will tend to stabilize within the observed period. / Processos significativos de conversão nos padrões de uso da terra têm sido verificados no Rio Grande do Sul devido à incorporação de áreas florestais nos últimos anos. Nessa concepção, este trabalho tem como objetivo estabelecer diretrizes metodológicas para analisar a dinâmica desses padrões nas Serras do Sudeste e Campanha Meridional, microrregiões pertencentes à Metade Sul do Estado. Mapas temáticos dos anos de 2000, 2004 e 2008 foram elaborados a partir do produto NDVI (Índice de Vegetação por Diferença Normalizada) do sensor MODIS, de forma a relacionar este índice às classes de uso da terra. Os mapas elaborados serviram para alimentar um modelo dinâmico, o qual possibilitou quantificar, através de matrizes Markovianas, as taxas de conversão entre as classes. A simulação foi concebida através do método probabilístico pesos de evidência, o qual permitiu inferir sobre a contribuição das variáveis influentes nas mudanças na cobertura florestal das duas microrregiões. Os principais resultados constituem simulações baseadas no paradigma de Autômatos Celulares (AC), nas quais são quantificadas e distribuídas espacialmente as áreas florestais até o ano de 2016. Os prognósticos modelados, com base nas variáveis analisadas, indicam que a floresta deve ocupar espaços anteriormente destinados às atividades agrícolas e ao pastoreio extensivo do gado, tanto pela introdução de espécies exóticas como pelo estabelecimento da regeneração. A cobertura florestal na microrregião Serras do Sudeste passará de 8,6% para 16,2% ao fim do período. Na Campanha Meridional o acréscimo será de 11,1% para 12,5%, sendo observadas, nas duas microrregiões, tendências à estabilidade dessa expansão.