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
Por:
Vidal Solorzano, Jonathan, Alberto Gallardo-Cruz, Jose, Javier Gonzalez, Edgar, Peralta-Carreta, Candelario, Hernandez-Gomez, Matias, Fernandez-Montes de Oca, Ana, Gerardo Cervantes-Jimenez, Luis
Publicada:
19 jul 2018
Categoría:
Earth and planetary sciences (miscellaneous)
Resumen:
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 (R-2 = 0.71) and
Shannon (R-2 = 0.67) diversity indices followed by the aboveground
biomass (R-2 = 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. (C) 2018 Society of Photo-Optical
Instrumentation Engineers (SPIE)
Filiaciones:
Ctr Cambio Global & Sustentabilidad Sureste AC, Centenario Inst Juarez S-N, Villahermosa, Mexico
Univ Nacl Autonoma Mexico, Dept Ecol & Recursos Nat, Fac Ciencias, Mexico City, DF, Mexico
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