Use of spatial regression models in the analysis of burnings and deforestation occurrences in forest region, Amazon, Brazil

Salame, Camil Wadih; Queiroz, Joaquim Carlos Barbosa; Rocha, Gilberto de Miranda; Amin, Mario Miguel; Rocha, Edson Paulino da

doi:10.1007/s12665-015-4865-x

Cited by 8 publications

(9 citation statements)

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“…However, in the context of recent Brazilian livestock dynamics, the benefits appear to be less expressive than the impacts, since in 2010, several municipalities located in the expansion zone presented HDI (human development Index- [51]) levels smaller than, or close to, the general levels of the retraction zone in 2000 (Figure 10). Even with HDI increases of these municipalities in the same period, the livestock migration to the north shifted the cattle ranchers away from consumer markets, both internal and external, consolidating them in a portion of the territory with a lack of infrastructure and inducing various environmental impacts [52,53]. One of the probable causes of this process was the pressure for already opened lands in the central south of Brazil, due to the production demand of other commodities, such as soybeans, sugarcane and wood.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016

Parente

Ferreira

2018

Remote Sensing

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Abstract:The pasturelands areas of Brazil constitute an important asset for the country, as the main food source for the world's largest commercial herd, representing the largest stock of open land in the country, occupying~21% of the national territory. Understanding the spatio-temporal dynamics of these areas is of fundamental importance for the goal of promoting improved territorial governance, emission mitigation and productivity gains. To this effect, this study mapped, through objective criteria and automatic classification methods (Random Forest) applied to MODIS (Moderate Resolution Imaging Spectroradiometer) images, the totality of the Brazilian pastures between 2000 and 2016. Based on 90 spectro-temporal metrics derived from the Red, NIR and SWIR1 bands and distinct vegetation indices, distributed between dry and wet seasons, a total of 17 pasture maps with an approximate overall accuracy of 80% were produced with cloud-computing (Google Earth Engine). During this period, the pasture area varied from~152 (2000) to~179 (2016) million hectares. This expansion pattern was consistent with the bovine herd variation and mostly occurred in the Amazon, which increased its total pasture area by~15 million hectares between 2000 and 2005, while the Cerrado, Caatinga and Pantanal biomes showed an increase of~8 million hectares in this same period. The Atlantic Forest was the only biome in which there was a retraction of pasture areas throughout this series. In general, the results of this study suggest the existence of two relevant moments for the Brazilian pasture land uses. The first, strongly supported by the opening of new grazing areas, prevailed between 2000 and 2005 and mostly occurred in the Deforestation Arc and in the Matopiba regions. From 2006 on, the total pasture area in Brazil showed a trend towards stabilization, indicating a slight intensification of livestock activity in recent years.

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Section: Discussionmentioning

confidence: 99%

Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016

Parente

Ferreira

2018

Remote Sensing

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“…Ainda, destacam que esses resultados revelam a necessidade de estudos associados com informações socioambientais para a identificação de quais fatores podem estar favorecendo a ocorrência do fogo. Valores positivos do Índice de Moran, segundo Salame et al (2016), indicam similaridade entre áreas (células) que estão espacialmente próximas entre si, enquanto, que valores negativos revelam dissimilaridade, relacionados à ocorrência de eventos. Para que tais afirmações possam ser feitas, é aplicado um teste de probabilidade, em que valores não significativos indicam ausência de autocorrelação espacial, ou seja, aleatoriedade na ocorrência dos eventos analisados (KAMPEL et al, 2000).…”

Section: Discussionunclassified

“…Para que tais afirmações possam ser feitas, é aplicado um teste de probabilidade, em que valores não significativos indicam ausência de autocorrelação espacial, ou seja, aleatoriedade na ocorrência dos eventos analisados (KAMPEL et al, 2000). Por esta análise, realizada para as áreas queimadas no estado do Pará, Salame et al (2016) observaram Índices de Moran positivos e significativos quanto à ocorrência de queimadas entre os anos de 1999 a 2004. Ainda, relatam que o resultado demonstrou que municípios com alta (ou baixa) ocorrência de queimadas estão próximos a municípios com as mesmas observações e que não ocorreram de forma aleatória.…”

Section: Discussionunclassified

Caracterização De Áreas Queimadas No Estado Do Tocantins No Ano De 2014

Carvalho

Batista

Coelho

et al. 2017

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ResumoO objetivo desta pesquisa foi caracterizar áreas queimadas no Tocantins, no ano de 2014, por meio de análise exploratória e de componentes principais, com uso de imagens de satélite dos meses de julho a outubro. Foram utilizadas bandas captadas por sensores do satélite Landsat-8: infravermelho médio (1,57-1,65 µm), infravermelho próximo (0,85-0,88 µm) e vermelho (0,64-0,67 µm), na composição RGB, com resolução espacial de 30 m. Foi realizado o processamento e interpretação visual das imagens para vetorização das áreas queimadas, que foi cruzada com bases de dados (precipitação média anual, pedologia, temperatura média anual, bacia hidrográfica, declividade, áreas protegidas, uso da terra e limites municipais), disponibilizadas pela Secretaria de Planejamento e Orçamento do Tocantins, e dados territoriais e socioeconômicos dos municípios tocantinenses. Estes últimos foram utilizados na análise de Componentes Principais (CP) que extraiu cinco CP que explicam 81,74% da variância dos dados. Esta análise indicou que a área queimada apresentou maior relação positiva com a área do município, quantidade de focos de calor, área de pastagens naturais e florestas. Pelas análises espaciais, nos locais de maior ocorrência de queimadas (regiões do Jalapão e da Ilha do Bananal), bem como no estado todo, a ocorrência de queimadas não pôde ser considerada aleatória. Foi possível concluir que além das condições ambientais intrínsecas do Cerrado, municípios com maiores áreas queimadas estiveram associados com maior Produto Interno Bruto (PIB) da agropecuária e menor PIB de serviços e indústria e menor número de habitantes. Palavras-chave: Cerrado; distribuição espacial; fogo; imagens Landsat. AbstractCharacterization of wildfires in Tocantins State on 2014 season. This aim of this research was to characterize the burned areas in the Tocantins, 2014 season, by the exploratory and principal component analysis, with use of satellite images between July and October. They were used the bands of sensors of Landsat-8 satellite: shortwave infrared (1.57-1.65 µm), near infrared (0.85-0.88 µm) and red (0.64-0.67 µm), in the RGB composition, with a spatial resolution of 30 meters. It was conducted the processing and visual interpretation of the images, for the vectorization of the burned area. These data were crossed with seven databases (annual average precipitation, pedology, annual average temperature, watershed, slope, protected area, land use and municipal boundaries), available on Secretaria de Planejamento e Orçamento do Tocantins, and with the territory and socioeconomic data of the Tocantins cities. These last data were used in principal component analysis (PCA) that were extracted five PC (data variance explained was 81.74%). These analyses revealed that burned area had shown highest positive relationship with city area, number of heat spots, natural pasture and forest areas. By the spatial analysis the burned area occurrence, in the Tocantins State (include regions with highest burned area, as Jalapão e Bannal island), canno...

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“…Econometric models have been increasingly used with the aim of assessing the impacts of anthropic activities on the environment (Arraes et al, 2012;Diniz et al, 2009;Chiu, 2012;Scrieciu, 2007;Cheng et al, 2018;Salame et al, 2016;Zhang et al, 2017;Amare et al, 2017;Schmook & Vance, 2009). Through such approaches, it is possible to identify the factors that significantly contribute to the environmental changes, being extremely useful for management purposes, enabling simulations of possible future scenarios, and guiding priority efforts.…”

Section: Review Of Econometric Models Towards Decision-making Processmentioning

confidence: 99%

Deforestation in Legal Amazon: A Panel Data Analysis of Potential Interferers

Gomes¹,

Canella²,

Branco³

et al. 2020

JMS

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The knowledge on the significant factors that lead to environmental changes can be an attractive tool for directing priority actions of management, sustainability and impact minimization. In this regard, this work suggests the use of panel data analysis in environmental assessments, proposing a panel data regression model for the context of the Amazon forest, aiming to evaluate the role of primary activities over deforestation in Legal Amazon between 1988 and 2018. For this, the deforested areas in Legal Amazon were assessed regarding the potential explanatory variables: (i) area intended for soybean cultivation; (ii) area intended for palm oil cultivation; (iii) cattle ranching; and (iv) firewood and wood extraction. The model developed in this work evidenced cattle ranching and palm oil cultivation as significant factors for the increase of deforested areas, as well as the contribution of other factors besides primary activities in Amazon deforestation from 1988 to 2018. These results are in accordance with the literature, evidencing the applicability and assertiveness of the proposed method. This approach can help decision-makers of several other fields of environmental management. Additionally, this work also assessed the evolution of deforestation rates from 1988 to 2018, as well as possible regionalities and temporal trends in Legal Amazon deforestation. Statistically significant upward trends in deforestation rates in Amazonas, Mato Grosso, Pará, and Rondônia since 2012 were noticed. The spatial homogeneity in deforestation reinforces the need for effective oversight in Amazon.

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Use of spatial regression models in the analysis of burnings and deforestation occurrences in forest region, Amazon, Brazil

Cited by 8 publications

References 2 publications

Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016

Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016

Caracterização De Áreas Queimadas No Estado Do Tocantins No Ano De 2014

Deforestation in Legal Amazon: A Panel Data Analysis of Potential Interferers

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