Suchergebnisse

RESUMO O uso do fogo e seus impactos são crescentes na Amazônia. Neste estudo, avaliou-se a problemática das queimadas e incêndios florestais no contexto da proteção e defesa civil objetivando avaliar o número de registros de situação de emergência ou estado de calamidade pública declarados ante o aumento da ocorrência de queimadas e incêndios florestais na Amazônia. Fez-se uma análise histórica para quantificar mudança na exposição da população a queimadas, incêndios florestais e qualidade do ar. Em seguida, uma visão da crise dos incêndios florestais na Amazônia em 2019, em que um dos principais impactos imediatos foi baixa qualidade do ar associada a esses eventos é apresentada. As análises sugerem que existe um negligenciamento em registrar oficialmente situações de emergência e calamidade pública associados à baixa qualidade do ar, oriundos de queimadas e incêndios florestais, principalmente na região norte do País. Mediante acompanhamento da crise entre os meses de julho e agosto de 2019, devido à situação crítica da ocorrência de queimadas e incêndios florestais reportados até o final de 2019 no sudoeste da Amazônia, entende-se que há possibilidade desse quadro ser considerado um desastre.

O objetivo deste artigo é fornecer informação sobre a detecção em tempo real da área desflorestada (DETER - Detecção em Tempo Real do Desflorestamento na Amazônia Brasileira) utilizando imagens do MODIS de alta resolução temporal. O DETER é parte do projeto operacional que estima a taxa anual do desflorestamento na Amazônia Brasileira (PRODES - Projeto de Monitoramento do Desflorestamento na Amazônia). Um método para detecção rápida do desflorestamento foi delineado para dar suporte à fiscalização do uso da terra na região. A fim de avaliar o método proposto foi selecionada uma área teste que cobre uma cena do Landsat ETM+ (227/68) localizada no Estado do Mato Grosso. Para este propósito uma série multitemporal de imagens refletância de superfície do MODIS (MOD09) e de imagens ETM+ correspondentes ao período de junho a outubro de 2002 foi analisada. Foi verificado que pequenas áreas de desflorestamento (menores do que 15 ha) foram detectadas pelas imagens MODIS com menor acurácia quando comparado com as imagens do ETM+. À medida que as áreas desflorestadas aumentam os resultados do MODIS e do ETM+ tendem a convergir. Este procedimento mostrou ser adequado para detectar e monitorar operacionalmente as áreas desflorestadas e vem sendo utilizado pelo governo desde 2004 como parte do Plano de Ação para a Prevenção e Controle do Desmatamento na Amazônia Brasileira. ; The objective of this paper is to provide near real time information about deforestation detection (DETER) in the entire Brazilian Amazon using MODIS high temporal resolution images. It is part of the operational deforestation monitoring project to estimate the annual deforestation rate in the Brazilian Amazon (PRODES). A rapid deforestation detection method was designed to support land use policies in this region. In order to evaluate the proposed method a test site was selected covering a Landsat ETM+ scene (227/68) located in Mato Grosso State. For this purpose a multitemporal series of MODIS surface reflectance images (MOD09) and the corresponding ETM+ images from June to October 2002 were analyzed. It was found that small deforested areas (lower than 15 ha) were detected by MODIS images with lower accuracy when compared with ETM+ images. As the deforested areas increase MODIS and ETM+ results tend to converge. This procedure showed to be adequate to operationally detect and monitor deforested areas and has been used since 2004 as part of a government plan to control the Amazon deforestation. ; The objective of this paper is to provide near real time information about deforestation detection (DETER) in the entire Brazilian Amazon using MODIS high temporal resolution images. It is part of the operational deforestation monitoring project to estimate the annual deforestation rate in the Brazilian Amazon (PRODES). A rapid deforestation detection method was designed to support land use policies in this region. In order to evaluate the proposed method a test site was selected covering a Landsat ETM+ scene (227/68) located in Mato Grosso State. For this purpose a multitemporal series of MODIS surface reflectance images (MOD09) and the corresponding ETM+ images from June to October 2002 were analyzed. It was found that small deforested areas (lower than 15 ha) were detected by MODIS images with lower accuracy when compared with ETM+ images. As the deforested areas increase MODIS and ETM+ results tend to converge. This procedure showed to be adequate to operationally detect and monitor deforested areas and has been used since 2004 as part of a government plan to control the Amazon deforestation.

The Brazilian government annually assesses the extent of deforestation in the Legal Amazon for a variety of scientific and policy applications. Currently, the assessment requires the processing and storing of large volumes of Landsat satellite data. The potential for efficient, accurate, and less data-intensive assessment of annual deforestation using data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution is evaluated. Landsat-derived deforestation estimates are compared to MODIS-derived estimates for six Landsat scenes with five change-detection algorithms and a variety of input data—Surface Reflectance (MOD09), Vegetation Indices (MOD13), fraction images derived from a linear mixing model, Vegetation Cover Conversion (MOD44A), and percent tree cover from the Vegetation Continuous Fields (MOD44B) product. Several algorithms generated consistently low commission errors (positive predictive value near 90 and identified more than 80% of deforestation polygons larger than 3 ha. All methods accurately identified polygons larger than 20 ha. However, no method consistently detected a high percent of Landsat-derived deforestation area across all six scenes. Field validation in central Mato Grosso confirmed that all MODIS-derived deforestation clusters larger than three 250-m pixels were true deforestation. Application of this field-validated method to the state of Mato Grosso for 2001–04 highlighted a change in deforestation dynamics; the number of large clusters (>10 MODIS pixels) that were detected doubled, from 750 between August 2001 and August 2002 to over 1500 between August 2003 and August 2004. These analyses demonstrate that MODIS data are appropriate for rapid identification of the location of deforestation areas and trends in deforestation dynamics with greatly reduced storage and processing requirements compared to Landsat-derived assessments. However, the MODIS-based analyses evaluated in this study are not a replacement for high-resolution analyses that estimate the ...

A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this local knowledge via sketch maps in the context of flooding. The Sketch Map Tool enables not only the visualization of this local knowledge and analyses of OpenStreetMap data quality but also the communication of the results of these analyses in an understandable way. Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas.

A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this local knowledge via sketch maps in the context of flooding. The Sketch Map Tool enables not only the visualization of this local knowledge and analyses of OpenStreetMap data quality but also the communication of the results of these analyses in an understandable way. Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas.