20
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Exploring TM image texture and its relationships with biomass estimation in Rondônia, Brazilian Amazon Translated title: Explorando texturas de imagens TM e suas relações com estimativas de biomassa em Rondônia

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Translated abstract

          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 (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 e 7). Índices de correlação de Pearson foram utilizados para analisar as relações entre textura e biomassa. Esta pesquisa indica que a maioria das medidas de textura são pouco correlacionadas com biomassa de vegetação secundária, mas algumas medidas de textura têm correlação significativa com a biomassa de formações florestais maduras. Ao contrário, assinaturas espectrais de bandas TM são significativamente correlacionadas com a biomassa de vegetação secundária, mas fracamente correlacionadas com a biomassa de florestas maduras. Os resultados indicam que medidas de textura são importantes em estimativas de biomassa de floresta madura, mas relativamente menos importantes para estimativas de biomassa de vegetação secundária.

          Related collections

          Most cited references32

          • Record: found
          • Abstract: found
          • Article: not found

          Texture classification and segmentation using wavelet frames.

          M. Unser (1995)
          This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l(2) and that it has a fast iterative algorithm. A texture is characterized by a set of channel variances estimated at the output of the corresponding filter bank. Classification experiments with l(2) Brodatz textures indicate that the discrete wavelet frame (DWF) approach is superior to a standard (critically sampled) wavelet transform feature extraction. These results also suggest that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, and the like). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is also provided. Finally, the DWF feature extraction technique is incorporated into a simple multicomponent texture segmentation algorithm, and some illustrative examples are presented.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Evaluation Of The Grey-level Co-occurrence Matrix Method For Land-cover Classification Using Spot Imagery

                Bookmark

                Author and article information

                Contributors
                Role: ND
                Role: ND
                Journal
                aa
                Acta Amazonica
                Acta Amaz.
                Instituto Nacional de Pesquisas da Amazônia (Manaus )
                1809-4392
                June 2005
                : 35
                : 2
                : 249-257
                Affiliations
                [1 ] Indiana University United States
                [2 ] Embrapa Brazil
                Article
                S0044-59672005000200015
                10.1590/S0044-59672005000200015
                5adafd16-b4c9-42a0-9cef-e9cdfcddf627

                http://creativecommons.org/licenses/by/4.0/

                History
                Product

                SciELO Brazil

                Self URI (journal page): http://www.scielo.br/scielo.php?script=sci_serial&pid=0044-5967&lng=en
                Categories
                AGRONOMY
                BIOLOGY
                ZOOLOGY

                General life sciences,Animal science & Zoology,Horticulture
                TM image,texture,textura,biomassa,imagens TM,correlação,Amazônia,aboveground biomass,correlation,Amazon

                Comments

                Comment on this article