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

Abstract

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

Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation

“…For the AGB estimation, reduction of spatial heterogeneity of forest stand structures is regarded as an effective way to improve the AGB estimation accuracy [30]. Image texture measures are often used for this purpose, especially when forest sites have complex forest stand structures [31,52]. GLCM-based texture measures are the most common approach to producing textural images [6,21,25,26,31,52].…”

“…Image texture measures are often used for this purpose, especially when forest sites have complex forest stand structures [31,52]. GLCM-based texture measures are the most common approach to producing textural images [6,21,25,26,31,52]. In this research, we examined mean, angular second moment, contrast, correlation, dissimilarity, entropy, homogeneity, and variance with different window sizes (e.g., 3ˆ3, 5ˆ5, .…”

“…Various methods have been explored to reduce the impacts of data saturation in Landsat imagery on AGB estimation accuracy (see the review paper by Lu et al [6]). Vegetation indices and textures are often used [21,[25][26][27][49][50][51][52][53]. In the moist tropical region of the Brazilian Amazon, Lu et al [51] compared different vegetation indices-e.g., normalized difference vegetation index (NDVI), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI)-from Landsat Thematic Mapper (TM) imagery and found that the vegetation indices including shortwave infrared spectral bands (SWIR) have higher correlation with AGB than others when the forest stand structure is complex, but the vegetation indices including near-infrared wavelength (NIR) improved correlations with AGB in relatively simple forest stand structures.…”

“…Lu and Batistella [52] examined the relationships between AGB and grey-level co-occurrence matrix-based (GLCM) textural images from Landsat TM imagery in the Brazilian Amazon and found that textural images were especially useful for primary forest AGB estimation due to its complex forest stand structures. Incorporation of textures into spectral responses has proven useful in improving AGB estimation performance because textures can reduce the impacts of heterogeneity [30].…”

Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests

“…A direct physical relationship between aboveground biomass and optical remote sensing data does not exist; hence, the latter only provide indirect estimates of aboveground biomass (Chopping et al 2008;Hyde et al 2007). Different approaches have been utilized with varying degrees of success to estimate biomass from optical remote sensing data, for example, establishing the relationships between biomass and vegetation indices (Peddle et al 2001;Sader et al 1989), spectral bands (Boyd et al 1999;Foody et al 2003;Steininger 2000), image texture (Lu 2005), and combinations of texture and spectral information (Lu and Batistella 2005). Vegetation reflectance and/or reflectance derivatives (for example, vegetation indices, texture, etc.)…”