Análise espaço-temporal de queimadas em áreas nativas de cerrado : RPPN Serra do Tombador, GO

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2012.O Cerrado é o segundo maior bioma brasileiro, atrás apenas da Amazônia e é considerada a savana que apresenta a maior biodiversidade. Com o avanço da fronteira agrícola nas últimas décadas, cerca de 49% do bioma já foi alterado, sendo convertido para atividades antrópicas. A região Nordeste do Estado de Goiás apresenta uma das maiores e mais preservadas regiões de Cerrado, e possui diversas Unidades de Conservação- UCs. Dentre elas está a Reserva Particular Patrimônio Natural (RPPN) Reserva Natural Serra do Tombador (RNST), que é a maior reserva privada localizada em Goiás (8.902 ha), e a quarta maior presente no Cerrado. As queimadas que frequentemente ocorrem no Brasil Central nas épocas de estiagem contribuem para o aumento das taxas de desmatamento e influenciam na distribuição e densidade das diferentes fisionomias presentes no Cerrado, favorecendo as espécies que são mais resistentes ao fogo. O uso de imagens obtidas por sensores orbitais é cada vez mais comum para estudos ambientais, incluindo identificação de cicatrizes deixadas pelos incêndios nas vegetações. Compreender o comportamento das queimadas por meio de estudos espaço-temporais, mapeando a quantidade e as extensões das cicatrizes deixadas pelo fogo, pode auxiliar os gestores das UCs a melhor planejarem o manejo das reservas e os planos de ações de combate a incêndios descontrolados. O presente estudo teve como objetivo mapear as queimadas que ocorreram na RNST e em suas imediações no período de 2001 a 2010, e assim identificar o seu comportamento. Foram utilizadas técnicas de classificação supervisionada, aplicando o algoritmo Mahalanobis em amostras coletadas em cada imagem. Ao longo do período estudado 69% da área estudada sofreu com a passagem do fogo. O ano que maior apresentou a maior quantidade de polígonos mapeados e maior área queimada foi 2004, seguidos por 2001, 2007 e 2010. Também foram identificadas áreas nas quais as queimadas são muito recorrentes, indicando locais que merecem especial atenção. As fisionomias que mais sofreram com as queimadas foram as Formações Savânicas, concentrando 89% do total de queimadas. __________________________________________________________________________________ ABSTRACTThe Cerrado biome, the Brazilian savanna, second largest in Brazil after the Amazon, is considered to be the savanna with the highest biodiversity in the world. With the expansion of agriculture in the last decades, about 49% of the Cerrado has been transformed by human activity. The northeast region of Goiás state has one of the largest and best preserved regions in the Cerrado, and is home to several protected areas (PAs). Among them, the PRNP Serra Tombador Natural Preserve - RNST, which is the largest private preserve located in Goiás, and the fourth largest in the biome. The frequent fires that occur in Central Brazil in times of drought contribute to increasing rates of deforestation, and influence the distribution and density of the different Cerrado physiognomies, favoring the species that are most fire-resistant. The use of orbital sensor images is increasing in the area of environmental studies, including as a means to identifying fire-related scars in the vegetation. Understanding the behavior of the burning through multitemporal studies, and mapping the amount and extensions of the scars, can help managers better plan the preserves´ PA management and the action plans to combat uncontrolled wildfires. The present study aimed to map the fires that occurred in RNST and its surroundings in the period between 2001 and 2010, thus identifying their behavior. The technique used in this study was supervised classification through the application of the Mahalanobis algorithm in samples collected from each image. During the years covered by the study 69% of the areas analyzed had experienced fires. The year with the highest amount of mapped polygons and the largest area burned was 2004, followed by 2001, 2007 and 2010. The study also identified areas where fires are frequently recurrent, indicating places that deserve special attention. The physiognomies that suffered most fires were the savannas formations, where 89% of all fires occurred

Is there a direct and temporary relationship between fire persistence and hospitalization due to respiratory diseases? : analysis of the scenario of Palmas and the Lajeado’s APA, Tocantins, between the years of 2012 and 2018

A poluição do ar é uma das consequências das queimadas, em razão da quantidade do material particulado e compostos químicos que são liberados para a atmosfera, durante o processo de combustão. Em atenção a isto, morbidades respiratórias são rotineiras, associadas à intensidade e à persistência do incêndio. De 2008 a 2018, o DATASUS registrou, em média, 9450 internações por doenças respiratórias no Tocantins, pouco sabido, entretanto, se existe uma relação entre o total de internações e a persistência de queimadas. Desta forma, o objetivo do trabalho foi correlacionar mensalmente os registros de internação por doenças respiratórias com a persistência de queimadas no município de Palmas e na APA do Lajeado, Tocantins, durante o período de estiagem entre os anos de 2012 e 2018. A análise compreendeu as médias e anomalias de cada variável, resultando em valores de internação díspar ao esperado. Concluiu-se que é preferível considerar estudos do particulado emitido, da circulação atmosférica e de procedimentos em unidades básicas de saúde para explicar a relação de doenças respiratórias por consequência das queimadas.Air pollution is one of the consequences of fires, because of the amount of particulate matter and chemical compounds that are released into the atmosphere during the combustion process. In this regard, respiratory morbidities are routine, associated with the intensity and persistence of the fire. From 2008 to 2018, DATASUS registered, on average, 9450 hospitalizations for respiratory diseases in Tocantins, although it isn't known, however, whether there is a relation between the total admissions and the persistence of fires. Thus, the objective of this study was to correlate the monthly hospitalization records for respiratory diseases with the persistence of burnings in the municipality of Palmas and in the environmental protection area of Lajeado, Tocantins, during the drought period between 2012 and 2018. The analysis comprised the averages and anomalies of each variable, resulting in hospitalization values different from those expected. It was concluded that it's preferable to consider studies of emitted particles, atmospheric circulation and procedures in basic health units to explain the relation of respiratory diseases due to fires

Three Decades of Anthropogenic Fire Activity in a Neotropical Agricultural Frontier

In this dissertation, I evaluated the spatiotemporal dynamics of anthropogenic fire activity in a neotropical rainforest-savanna agricultural frontier. Given its fast and affordable nature, fire is used around the globe as a cost-effective way to clear and manage lands. This scenario is especially common in tropical regions experiencing high deforestation rates. The Amazon-Cerrado transition zone has been subject to the highest deforestation rates in Brazil over the last four decades. In this ecotone, fire is mainly used to clear natural vegetation lands and to manage encroachment of shrubs and trees in pasturelands; often, these fires spread accidentally. Nonetheless, the dynamics of the human-fire interaction are still not fully understood. To assess this relationship at a fine-scale, I developed a semi-automatic burned area mapping algorithm in Google Earth Engine that applies spectral mixture analysis to time-series of Landsat imagery: Burned Area Spectral Mixture Analysis (BASMA). Using BASMA, I generated annual burned area maps for a 32-year time-series (1985 to 2017), testing whether spectral mixture analysis is a robust means for mapping fire scars that is stable over time and space. Results showed that BASMA successfully identified char fractions and delineated burned area in Landsat imagery for a 36 million hectares study region. Accuracy assessment performed against independent burned area products returned high Dice coefficients (0.86 on average) demonstrating that BASMA is an effective algorithm to map fire scars for a large extent over long time-series analysis. The pyric transition, as proposed by Pyne (2001), suggests that human-driven fire activity increases at early stages of human occupation in a given region, and then decreases as the area is further industrialized. In my third chapter, I developed a conceptual model that combines the BASMA-derived burned area maps with a land use and land cover (LULC) database produced by the MapBiomas Project to evaluate the validity of the pyric transition hypothesis. Merging these two fine-scale datasets spanning a long time-series allowed me to quantitatively characterize spatiotemporal changes in fire activity occurring in parallel to human occupation dynamics. Two pyric phase transitions were observed: from ‘wildland anthropogenic fire’ to ‘agricultural anthropogenic fire’, and then to ‘fire suppression and wildfires’ phase. In my final chapter, I evaluated spatiotemporal patterns of traditional burnings in remnants areas of the Cerrado biome within four indigenous lands, assessing whether fire frequency can be modeled by statistical models. Three probability distribution models were tested: continuous and discrete two-parameter Weibull and the discrete lognormal. Results agreed with previous studies, finding a mean fire interval of 3 years, similar to metrics estimated in other protected areas of the Cerrado. However, the parameters estimated for the probability distribution models showed that the study area does not have a homogeneous fire regime, indicating that further studies must be conducted to better quantify the fire return interval in these fire-prone ecosystems. Thus, we suggest that subdividing the fire frequency modelling by each of the indigenous lands could potentially return better results. Ultimately, this dissertation clearly demonstrates the advantages of having fine-scale burned area products covering long time-series over large extents

Three Decades of Anthropogenic Fire Activity in a Neotropical Agricultural Frontier

In this dissertation, I evaluated the spatiotemporal dynamics of anthropogenic fire activity in a neotropical rainforest-savanna agricultural frontier. Given its fast and affordable nature, fire is used around the globe as a cost-effective way to clear and manage lands. This scenario is especially common in tropical regions experiencing high deforestation rates. The Amazon-Cerrado transition zone has been subject to the highest deforestation rates in Brazil over the last four decades. In this ecotone, fire is mainly used to clear natural vegetation lands and to manage encroachment of shrubs and trees in pasturelands; often, these fires spread accidentally. Nonetheless, the dynamics of the human-fire interaction are still not fully understood. To assess this relationship at a fine-scale, I developed a semi-automatic burned area mapping algorithm in Google Earth Engine that applies spectral mixture analysis to time-series of Landsat imagery: Burned Area Spectral Mixture Analysis (BASMA). Using BASMA, I generated annual burned area maps for a 32-year time-series (1985 to 2017), testing whether spectral mixture analysis is a robust means for mapping fire scars that is stable over time and space. Results showed that BASMA successfully identified char fractions and delineated burned area in Landsat imagery for a 36 million hectares study region. Accuracy assessment performed against independent burned area products returned high Dice coefficients (0.86 on average) demonstrating that BASMA is an effective algorithm to map fire scars for a large extent over long time-series analysis. The pyric transition, as proposed by Pyne (2001), suggests that human-driven fire activity increases at early stages of human occupation in a given region, and then decreases as the area is further industrialized. In my third chapter, I developed a conceptual model that combines the BASMA-derived burned area maps with a land use and land cover (LULC) database produced by the MapBiomas Project to evaluate the validity of the pyric transition hypothesis. Merging these two fine-scale datasets spanning a long time-series allowed me to quantitatively characterize spatiotemporal changes in fire activity occurring in parallel to human occupation dynamics. Two pyric phase transitions were observed: from ‘wildland anthropogenic fire’ to ‘agricultural anthropogenic fire’, and then to ‘fire suppression and wildfires’ phase. In my final chapter, I evaluated spatiotemporal patterns of traditional burnings in remnants areas of the Cerrado biome within four indigenous lands, assessing whether fire frequency can be modeled by statistical models. Three probability distribution models were tested: continuous and discrete two-parameter Weibull and the discrete lognormal. Results agreed with previous studies, finding a mean fire interval of 3 years, similar to metrics estimated in other protected areas of the Cerrado. However, the parameters estimated for the probability distribution models showed that the study area does not have a homogeneous fire regime, indicating that further studies must be conducted to better quantify the fire return interval in these fire-prone ecosystems. Thus, we suggest that subdividing the fire frequency modelling by each of the indigenous lands could potentially return better results. Ultimately, this dissertation clearly demonstrates the advantages of having fine-scale burned area products covering long time-series over large extents

A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought

Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach

A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought

Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach

Geographic Object-Based Image Analysis Framework for Mapping Vegetation Physiognomic Types at Fine Scales in Neotropical Savannas

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (<10 m) can improve map accuracy in these heterogeneous landscapes. Traditional pixel-based classification methods have proven problematic for fine spatial resolution data due to increased within-class spectral variability. Geographic Object-Based Image Analysis (GEOBIA) is a robust alternative method to overcome these issues. We developed a systematic GEOBIA framework accounting for both spectral and spatial features to map Cerrado structural types at 5-m resolution. This two-step framework begins with image segmentation and a Random Forest land cover classification based on spectral information, followed by spatial contextual and topological rules developed in a systematic manner in a GEOBIA knowledge-based approach. Spatial rules were defined a priori based on descriptions of environmental characteristics of 11 different physiognomic types and their relationships to edaphic conditions represented by stream networks (hydrography), topography, and substrate. The Random Forest land cover classification resulted in 10 land cover classes with 84.4% overall map accuracy and was able to map 7 of the 11 vegetation classes. The second step resulted in mapping 13 classes with 87.6% overall accuracy, of which all 11 vegetation classes were identified. Our results demonstrate that 5-m spatial resolution imagery is adequate for mapping land cover types of savanna structural elements. The GEOBIA framework, however, is essential for refining land cover categories to ecological classes (physiognomic types), leading to a higher number of vegetation classes while improving overall accuracy