Contrasting the potential of Fourier transformed ordination and gray level co-occurrence matrix textures to model a tropical swamp forest’s structural and diversity attributes

Abstract

Several remote sensing proxies, such as image texture, have been used to describe the spatial variation of different tropical forest’s attributes at medium scales. Fourier transformed ordination (FOTO) and gray level co-occurrence matrix (GLCM) are two texture methods frequently used in these studies but seldom compared. Therefore, the objective of this study was to compare the potential of these metrics to describe and predict structural and diversity attributes of a tropical swamp forest located in southeast Mexico. FOTO and GLCM textures were extracted from the panchromatic (Pan) band, the near-infrared (NIR) and red (R), of a very high spatial resolution (VHSR) image. Six different types of models that incorporated between one and three texture variables were constructed to describe each forest attribute. Simpson (R2 = 0.71) and Shannon (R2 = 0.67) diversity indices followed by the aboveground biomass (R2 = 0.65) were the attributes that obtained models with a higher goodness-of-fit values. In terms of its modeling potential, GLCM greatly surpassed FOTO textures. Our results show that VHSR textures (especially R- and NIR-derived GLCM metrics) allow for a reliable estimation of important tropical swamp forest attributes associated with its diversity and structure.