Space-time dynamics of deforestation in Brazilian Amazônia

Alves, Diógenes Salas

doi:10.1080/01431160110096791

Cited by 169 publications

(119 citation statements)

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“…These results are presented for the deforested polygons <25 ha and ≥25 ha. After aggregation, the deforested polygons with <25 ha decreased in number and, consequently, in area (54,544 ha; −33%); while the total area of deforested polygons with ≥25 ha increased 16.9% after aggregation (Table 2), agreeing with [4,32]. Table 2 further shows that the total deforested area, before and after aggregation, is practically the same, corresponding to about 486,000 ha.…”

Section: Resultssupporting

confidence: 74%

“…However, smaller areas of deforestation that begin in specific spots are very often not isolated events that occur in a single year, but gradually increase through deforestation of adjacent areas in following years, thus forming larger deforested areas [4,21,32]. Therefore, annual deforested polygons of <25 ha were also monitored when the sum of the annual and adjacent deforested polygons became ≥25 ha.…”

Section: Preprocessing Of Deforested Polygons From the Prodes Projectmentioning

confidence: 99%

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Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative

et al. 2012

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Abstract:The Soy Moratorium is an initiative to reduce deforestation rates in the Amazon biome based on the hypothesis that soy is a deforestation driver. Soy planted in opened areas after July 24th, 2006 cannot be commercialized by the associated companies to the Brazilian Association of Vegetable Oil Industries (ABIOVE) and the National Association of Cereal Exporters (ANEC), which represent about 90% of the Brazilian soy market. The objective of this work is to present the evaluation of the fourth year of monitoring new soy plantations within the Soy Moratorium context. With the use of satellite images from the MODIS sensor, together with aerial survey, it was possible to identify 147 polygons with new soy plantations on 11,698 ha. This soy area represents 0.39% of the of the total deforested area during the moratorium, in the three soy producing states of the Amazon biome, and 0.6% of the cultivated soy area in the Amazon biome, indicating that soy is currently a minor deforestation driver. The quantitative geospatial information provided by an effective monitoring approach is paramount to the implementation of a governance OPEN ACCESSSustainability 2012, 4 1075 process required to establish an equitable balance between environmental protection and agricultural production.

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Section: Resultssupporting

confidence: 74%

Section: Preprocessing Of Deforested Polygons From the Prodes Projectmentioning

confidence: 99%

Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative

et al. 2012

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“…It was also interesting to note that the polygon class with the largest increase in number of polygons was the >100 ha class. These data confirms the practice of successive and adjacent deforestation as described by Alves [41] and Aguiar et al [42]. Table 3 also shows that the total deforested area did not vary substantially with the fusion process.…”

Section: Selecting the Prodes Polygonssupporting

confidence: 76%

The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images

Rudorff

Adami

Aguiar³

et al. 2011

Remote Sensing

150

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The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the MOD13Q1 product and TM/Landsat-5 images followed by aerial survey and field inspection. In the 2009/2010 crop year, 6.3 thousand ha of soybean (0.25% of the total deforestation) were identified in areas deforested during the moratorium period. The use of remote sensing satellite images reduced by almost 80% the need for aerial survey to identify soybean planting and allowed monitoring of all deforested areas greater than 25 ha. It is still premature to attribute the recent low deforestation rates in the Amazon biome to the Soy Moratorium, but the initiative has certainly exerted an inhibitory effect on the soybean frontier expansion in this biome.Remote Sens. 2011, 3 186

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“…Entretanto, as causas presentes do desmatamento na Amazônia são diversas, frequentemente inter-relacionadas (SOARES-FILHO et al, 2005), podem variar significativamente com o tempo (FAMINOW, 1998;ALVES, 2007b) e mostrar diferentes associações com diversos padrões de paisagem (ALVES, 2002;ALVES, 2011). Em particular, as atividades agrícolas têm seguido diferentes processos, que incluem distintos tipos de uso e intensificação da terra -rotação, degradação e abandono de terras (HECHT et al, 1988;FAMINOW, 1998;ALVES, 2007b;COSTA, 2008;ALVES et al, 2009).…”

Section: Introductionunclassified

“…Para diferenciar diferentes padrões de cobertura e uso da terra na Amazônia e suas mudanças, dados de sensoriamento remoto têm sido largamente utilizados como fonte principal para diferentes métodos de mapeamento (LU et al, 2004;ALVES et al, 2009 TARDIN et al, 1980;SHIMABUKURO et al, 1982;SANO et al, 1989;SKOLE;TUCKER, 1993;MORAN et al, 1994;ALVES e SKOLE, 1996;ALVES, 2002;ALVES et al, 2003;BATISTELLA et al, 2003;FIDALGO et al, 2003;PONZONI;REZENDE, 2004;BATISTELLA;MORAN, 2005;ALVES, 2007a;ALVES et al, 2009), auxiliando a delimitação e quantificação de áreas de diferentes usos e coberturas da terra e compreensão de sua dinâmica.…”

Section: Introductionunclassified

Avaliação de técnicas de classificação digital de imagens landsat em diferentes padrões de cobertura da terra em Rondônia

et al. 2012

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e Fábio Bueno de Lima 5 RESUMO -Dados de sensoriamento remoto têm sido largamente utilizados para classificação da cobertura e uso da terra, em particular graças à aquisição periódica de imagens de satélite e à generalização dos sistemas de processamento digital de imagens, que oferecem uma variedade de algoritmos de classificação de imagens. Este trabalho teve por objetivo avaliar alguns dos métodos mais comuns de classificações supervisionadas e não supervisionadas para imagens do sensor TM do satélite Landsat-5, em três áreas com diferentes padrões de paisagem em Rondônia: (1) áreas de fazendas de "Médio porte", (2) assentamentos no padrão "Espinha de peixe" e (3) áreas de contato entre floresta e "Cerrado". A comparação com um mapa de referência baseado na estatística Kappa produziu indicadores de desempenho bons ou superiores (melhores resultados -K-médias: k = 0,68; k = 0,77; k = 0,64 e MaxVer: k = 0,71; k = 0,89; k = 0,70, respectivamente nas três áreas citadas), para os algoritmos utilizados. Os resultados indicaram que a escolha de um algoritmo deve considerar tanto a capacidade de discriminar várias assinaturas espectrais em diferentes padrões de paisagem quanto a relação custo/benefício decorrente das várias etapas do trabalho dos operadores que elaboram um mapa de cobertura e uso da terra. Este trabalho apontou a necessidade de esforço mais sistemático de avaliação prévia de várias opções de execução de um projeto específico antes de se iniciar o trabalho de elaboração de um mapa de cobertura e uso da terra.Palavras-chave: Cobertura florestal, Classificação e Rondônia. k=0.77; k=0.64 and MaxVer: k=0.71; k=0.89; k=0.70 CLASSIFICATION TECHNIQUES FOR LANDSAT TM IMAGERY UNDER DIFFERENT LANDSCAPE PATTERNS IN RONDÔNIA ABSTRACT -Remotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. The aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in Rondônia, including (1) areas of mid-size farms, (2) "fish-bone" settlements and (3) a gradient of forest and Cerrado (Brazilian savannah). Comparison with a reference map based on the kappa statistics resulted in good to superior indicators

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Space-time dynamics of deforestation in Brazilian Amazônia

Cited by 169 publications

References 0 publications

Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative

Remote Sensing Images to Detect Soy Plantations in the Amazon Biome—The Soy Moratorium Initiative

The Soy Moratorium in the Amazon Biome Monitored by Remote Sensing Images

Avaliação de técnicas de classificação digital de imagens landsat em diferentes padrões de cobertura da terra em Rondônia

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