Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation

Podest, E.; Saatchi, S. S.

doi:10.1080/01431160110093000

Cited by 63 publications

(39 citation statements)

References 12 publications

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“…Researchers have therefore turned increasingly to cloud-penetrating radar imagery provided by such satellite platforms as the Japanese Earth Resources Satellite (JERS-1) and the European Remote Sensing Satellite (ERS) as alternatives to study tropical forest cover. Sgrenzaroli et al (2002) and Podest and Saatchi (2002) reported acceptable forest classification accuracies and thus recommend synthetic aperture radar (SAR) imagery for upscaling deforestation estimates to the continental scale due to its all-weather capability. The potential of microwave energy to penetrate through smoke and clouds have been utilized for mapping fire, fire scars and tree damage (French et al 1999;Couturier et al 2001;Cochrane 2003).…”

Section: Medium Resolution Remote Sensing Instruments and Their Applimentioning

confidence: 98%

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

2010

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Since the launch of the first satellite in 1972, ecologists have been equipped with new tools to address the degradation of tropical forests, previously limited by field-based methods. This article is a review of the state of remote sensing technology in characterizing the degradation of tropical forest. The factors responsible for the structural and functional degradation of the tropical forest and its likely impacts are described in view of generating remote sensing based inputs. In order to assess the degradation and utility of geo-informatics tools, 32 parameters are identified. The research developments at different levels of information extraction from the historic to recent periods are elaborated, and future challenges are predicted. The article concludes that an additional momentum of research is required to answer many unresolved questions of tropical forest degradation.

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Section: Medium Resolution Remote Sensing Instruments and Their Applimentioning

confidence: 98%

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

2010

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“…Many texture measures have been developed (Haralick et al, 1973;He & Wang, 1990;Unser, 1995;Riou & Seyler, 1997). In previous research, texture measures were mainly used for LULC classification (Franklin & Peddle, 1989;Marceau et al, 1990;Augusteijn et al, 1995;Franklin et al, 2000;ndi Nyoungui et al, 2002;Podest & Saatchi, 2002). Of the many texture measures, the grey-level cooccurrence matrix (GLCM) may be the most common texture used for improving LULC classification (Marceau et al, 1990;Franklin et al, 2000;ndi Nyoungui et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Batistella

2005

Acta Amaz.

149

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Many texture measures have been developed and used for improving land-cover classification accuracy, but rarely has research examined the role of textures in improving the performance of aboveground biomass estimations. The relationship between texture and biomass is poorly understood. This paper used Landsat Thematic Mapper (TM) data to explore relationships between TM image textures and aboveground biomass in Rondônia, Brazilian Amazon. Eight grey level co-occurrence matrix (GLCM) based texture measures (i.e., mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation), associated with seven different window sizes (5x5, 7x7, 9x9, 11x11, 15x15, 19x19, and 25x25), and five TM bands (TM 2, 3, 4, 5, and 7) were analyzed. Pearson's correlation coefficient was used to analyze texture and biomass relationships. This research indicates that most textures are weakly correlated with successional vegetation biomass, but some textures are significantly correlated with mature forest biomass. In contrast, TM spectral signatures are significantly correlated with successional vegetation biomass, but weakly correlated with mature forest biomass. Our findings imply that textures may be critical in improving mature forest biomass estimation, but relatively less important for successional vegetation biomass estimation. KEYWORDStexture, aboveground biomass, TM image, correlation, Amazon Explorando texturas de imagens TM e suas relações com estimativas de Biomassa em Rondônia. (i.e., média, variância, homogeneidade, contraste, dissimilaridade, entropia, segundo momento e correlação), associadas com sete diferentes tamanhos de janela (5x5, 7x7, 9x9, 11x11, 15x15, 19x19 e 25x25) e cinco bandas TM (TM 2, 3, 4, 5 RESUMO Muitas medidas de textura têm sido desenvolvidas e utilizadas para melhorar a acurácia de classificações de cobertura das terras, mas raramente têm-se avaliado a importância dessas medidas em estimativas de biomassa. Este trabalho utilizou dados Landsat TM para explorar as relações entre texturas de imagens TM e biomassa em Rondônia, Amazônia. Foram analisadas oito medidas de textura baseadas em matrizes de co-ocorrência de tons de cinza

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“…Local statistics characterize the moments of a neighbourhood of individual pixels in a particular region of an image. The method has been widely used for the quantification of texture in SAR images (Soares et al, 1997;Kurvonen and Hallikainen, 1999;Podest and Saatchi, 2002). In this work, we have estimated the local statistics-based textural measure, namely the 'coefficient of variation' (cv = √ V M , where, V and M are the variance and the mean) for quantification of meso and macro textures.…”

Section: Ravine Density Mapmentioning

confidence: 99%

Interferometric SAR for characterization of ravines as a function of their density, depth, and surface cover

Chatterjee

Saha

Kumar

et al. 2009

ISPRS Journal of Photogrammetry and Remote Sensing

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Application of multiscale texture in classifying JERS-1 radar data over tropical vegetation

Cited by 63 publications

References 12 publications

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

The progress on remote sensing technology in identifying tropical forest degradation: a synthesis of the present knowledge and future perspectives

Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon

Interferometric SAR for characterization of ravines as a function of their density, depth, and surface cover

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