Dynamic of Fires in the Cerrado of Maranhão State, North-Eastern Brazil

Fires have a central role in carbon emissions in tropical regions, directly affecting the carbon cycle and the health of the population. In the Cerrado, fire occurs naturally and by human influence, used in the productive process of agriculture and livestock. In this context the aim of this paper was to characterize the spatiotemporal dynamics of fires in the Brazilian Savannas, also known as Cerrado, in the state of Maranhão (North-eastern Brazil). We used datasets from remote sensing and INMET weather stations. Data were processed and tabulated. Spatial and regression analyses were performed. Our results evidenced that the fires are modulated by the seasonality of the rainfall regime, being able to be influenced also by variations of temperature and moisture of the air. This region was affected in 2007 and 2012 by droughts that caused the expressive increase in the number of fires. In this region, deforestation is not directly linked to the occurrence of fires. The savanna formations were identified as the most susceptible to the occurrence of fires, since they account for a large part of the recurrences and occurrences of fires in this region.As queimadas têm um papel central nas emissões de carbono nas regiões tropicais, afetando diretamente o ciclo do carbono e a saúde da população. No Cerrado, o fogo ocorre naturalmente e por influência humana, utilizado no processo produtivo da agricultura e pecuária. Nesse contexto o objetivo deste trabalho foi caracterizar a dinâmica espaço-temporal das queimadas no Cerrado do estado do Maranhão. Para isso, foram utilizados dados derivados de produtos de sensoriamento remoto e de estações meteorológicas do INMET. Os dados foram processados e tabulados. Análises espaciais e de regressão foram realizadas. Os resultados evidenciaram que as queimadas são moduladas pela sazonalidade do regime de chuva, podendo ser influenciada também por variações de temperatura e umidade do ar. Essa região foi afetada nos anos de 2007 e 2012 por secas que ocasionaram o aumento expressivo no número de queimadas. Nessa região, o desmatamento não está ligado diretamente à ocorrência de queimadas. A vegetação de formações savânicas foram identificadas como as mais suscetíveis à ocorrência de queimadas, por totalizarem grande parte das recorrências e ocorrências de queimadas nessa região

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Avaliação dos dados de chuva mensal para a região Amazônica oriundos do satélite Tropical Rainfall Measuring Mission (TRMM) produto 3b43 versões 6 e 7 para o período de 1998 a 2010

The estimation of components of the water cycle by satellite remote sensing plays an important role particularly in areas where the densities of ground level meteorological stations are sparse. In this study, we evaluated the accuracy of versions 6 and 7 of the product 3B43 from the Tropical Rainfall Measuring Mission (TRMM) satellite. The data derived from the TRMM satellite was compared with 33 rainfall gauge stations, derived from the National Institute for Meteorology (INMET). Data was selected to cover different parts of the Brazilian Legal Amazon. The data used for this study covers monthly time series from January 1998 to December 2010. A sample in the TRMM data was acquired for the location of each field station, and statistical analysis were carried out. The results showed that the TRMM product 3B43 tends to overestimate low rainfall and underestimate high rainfall. Both versions of the TRMM product explain 76% of the rainfall variability measured by the rain gauges stations (p<0.001). No clear spatial bias was observed for areas where the product presents higher Root Mean Square Error (RMSE) in relation to the rain gauges. Nonetheless, there seems to be a better performance of the TRMM in areas with higher seasonality, in the cerrado region. The next steps will be to include more field data for covering larger areas of the cerrado, and also include the other regions of the Brazilian biomes.Pages: 6743-675

Combining satellite-derived products and geo-spatial health information for assessing the impacts of drought conditions on children's respiratory diseases in the Brazilian Amazon

It is generally acknowledged that climate change will have an impact on human health, however within a region as vast as the Amazon; research on the topic is scarce. This study analyses the impacts of the 2005 drought on respiratory diseases in children under five years of age. Satellite observations of rainfall, active fires and aerosol were used to assess the extent of the drought, and hospitalisation data was used to establish general trends and changes during the drought. We identified critical areas of the 2005 drought, and established the peak period of respiratory diseases over a ten year period (2001-2010).Throughout the basin respiratory diseases for children under five years of age peaked at the end of the wet season. The 2005 drought affected south-west Amazonia; during this period increases in active fires were recorded. In Acre in 2005 the peak of respiratory diseases shifted to later in the year corresponding with the peak of the drought. Critical areas of drought were explored in more detail at a micro-region and municipality level. Aerosol is the main driver influencing respiratory disease hospitalisations during July, August, September period of 2005. During this period hospitalisations for respiratory diseases in children under five years of age increased 54% in Acre, 99% in Rio Branco, & 195% in Porto Acre in relation to the ten year mean.Pages: 8568-857

Analysis of the floristic and phytosociologic composition of Tapajós National Forest with geographic support of satellite images.

The objective of this work was to analyze the distribution of the vegetation cover of several portions of the Tapajos National Forest (TNF) in the State of Pará, through phytosociologic and floristic attributes supported by satellite images in primary (PF) and secondary (SF) forest areas. For this it was sampled 35 transects of 10 m ´ 250 m in PF areas of high and low plateau, including areas disturber by activities selective logging areas and 29 transects of 10 m ´ 100 m in SF areas in several regrowth stages. In each activities of these transects it was surveyed dendrometric information such as DBH (Diameter of Breast Height), total height (TH), and commercial height (CH), besides the location of the individual trees within the samples. The diameters considered for primary and secondary forest areas were 10 cm and 3 cm, respectively. It was inventoried 7666 individuals (6607 dicot trees or shrubs and 1059 palm trees) in a 11.65 ha total sample area distributed over different regions of the TNF. 190 species of trees, shrubs, and palm trees distributed among 153 genera and 46 families were identified in the primary and secondary forest areas. In the PF and SF areas, it was found a diversity index of Shannon-Wiener (H’) of 4.44 and 4.09 nits.indivíduos-1, respectively, indicating a high biological diversity for these two forest types. Using multivariate analysis, it was possible to conclude that exist a structural and floristic difference in the North, Central and South portion of the National Forest. The SF areas presented a large environmental heterogeneity, making difficult to perform the clustering process of their succession stages. Through this work, it was possible to conclude that the support of ETM+/Landsat and RADARSAT-1 images optimized the sampling process of TNF, end allowed to perform the spatial analysis of the regions with higher phytosociologic and floristic differentiation of the National Forest

Queimadas na Amazônia Oriental em anos de Seca Extrema: fontes de combustível e propágulo de incêndios florestais

This paper aims to identify the fuel sources to burn scars and the sources of ignition responsible for forest fires that occur in the Eastern Amazon region (Pará State) during extreme drought scenarios. To reach this goal, were used deforestation maps provided by PRODES, a land use map from 2008 by the TerraClass project and maps of burn scars that occurred in 2005 and 2010. PRODES and TerraClass data were combined to generate 2005 and 2010 land use maps. Then, these maps were overlaid with 2005 and 2010 burn scar maps. It was observed that grasslands used as pastures were the major source of fuel for fires in the Eastern Amazon. This land use concentrates more than 30% of the burned area in 2005 and 2010, with a total burned area of 21 500 km2 and 43 000 km2, respectively. Burning in Forest areas was also representative in 2005 and 2010, with 24% and 27% of the burned area, respectively. Regarding the sources of ignition in forest fires, and considering only the burnt areas within a 2 km buffer distance from forest edges, burning in pastures was the main propagation source of fire in Forest areas. These results show that the environmental policies of "zero deforestation" do not eliminate the global contribution of Amazon greenhouse gases; it is necessary to create policies for more sustainable and less predatory land management than the use of fire.Pages: 6230-623

3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion

Urban environments are regions in which spectral variability and spatial variability are extremely high, with a huge range of shapes and sizes, and they also demand high resolution images for applications involving their study. Due to the fact that these environments can grow even more over time, applications related to their monitoring tend to turn to autonomous intelligent systems, which together with remote sensing data could help or even predict daily life situations. The task of mapping cities by autonomous operators was usually carried out by aerial optical images due to its scale and resolution; however new scientific questions have arisen, and this has led research into a new era of highly-detailed data extraction. For many years, using artificial neural models to solve complex problems such as automatic image classification was commonplace, owing much of their popularity to their ability to adapt to complex situations without needing human intervention. In spite of that, their popularity declined in the mid-2000s, mostly due to the complex and time-consuming nature of their methods and workflows. However, newer neural network architectures have brought back the interest in their application for autonomous classifiers, especially for image classification purposes. Convolutional Neural Networks (CNN) have been a trend for pixel-wise image segmentation, showing flexibility when detecting and classifying any kind of object, even in situations where humans failed to perceive differences, such as in city scenarios. In this paper, we aim to explore and experiment with state-of-the-art technologies to semantically label 3D urban models over complex scenarios. To achieve these goals, we split the problem into two main processing lines: first, how to correctly label the fa&ccedil;ade features in the 2D domain, where a supervised CNN is used to segment ground-based fa&ccedil;ade images into six feature classes, roof, window, wall, door, balcony and shop; second, a Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) workflow is used to extract the geometry of the fa&ccedil;ade, wherein the segmented images in the previous stage are then used to label the generated mesh by a &ldquo;reverse&rdquo; ray-tracing technique. This paper demonstrates that the proposed methodology is robust in complex scenarios. The fa&ccedil;ade feature inferences have reached up to 93% accuracy over most of the datasets used. Although it still presents some deficiencies in unknown architectural styles and needs some improvements to be made regarding 3D-labeling, we present a consistent and simple methodology to handle the problem

Proposing an improved routine for quantifying and forecasting fire incidence in Amazonia using remote sensing information

Tropical forest fires are one of the most pressing environmental problems of the 21st century. In Amazonia, fire frequency has increased during the last decade as a consequence of climate variability and prolonged dry seasons (1998, 2005 and 2010 droughts) and human activities. To contribute with the monitoring and mitigation of these fire events, the objective of this paper is to propose a new routine for predicting fire probabilities in Amazonia using information derived from satellite products and field data. Our method is based on weights-of-evidence statistics, which allows the calculation of fire probabilities based on the explanatory power of the driving variables. It takes advantage of the wide range of freely available spatially-explicit products, on deforestation, degradation, land cover, fire incidence, rainfall and sea surface temperature. The modelling routine is divided in 4 stages: (1) Spatial Planning, (2) Anticipated Temporal Action, (3) Immediate Temporal Action, and (4) Monitoring. In this paper we present the model concept and some examples of the input datasets. Our method is based on the state of our knowledge on the drivers of fire occurrence, to produce a method that is scientifically relevant for analysing and predicting fire patterns, but also relevant for policy applications for preventing and mitigating fire problems in Amazonia. By combining historical and short-term drivers of the temporal and spatial variation of fire incidence in a unique model, we expect to significantly improve prediction power of fire occurrence.Pages: 6783-679