Predicting old-growth tropical forest attributes from very high resolution (VHR)-derived surface metrics
Por:
Solorzano, Jonathan V., Meave, Jorge A., Alberto Gallardo-Cruz, J., Gonzalez, Edgar J., Luis Hernandez-Stefanoni, Jose
Publicada:
1 ene 2017
Categoría:
Earth and planetary sciences (miscellaneous)
Resumen:
Old-growth tropical forests are increasingly vanishing worldwide.
Although the accurate quantification of tropical old-growth forests
attributes is essential to understand, manage, and conserve their high
diversity and biomass, conducting this task over large areas and at fine
detail is not only expensive and time consuming, but also often
practically impossible. This calls for the search for more efficient
alternatives, particularly those based on remote sensing. In this study,
we evaluate the potential of several surface metrics (tone and texture)
extracted from very high resolution (VHR) satellite imagery to model the
structural and diversity attributes of a tropical dry forest (TDF) in
southern Mexico. We constructed simple linear models that used each
forest attribute as dependent variables, and the tone and texture
metrics extracted from several bands, the panchromatic (resolution = 0.5
m), red (R), infrared, and two vegetation indices (normalized difference
vegetation index (NDVI), enhanced vegetation index (EVI); resolution = 2
m), of a VHR image (GeoEye-1) as predictive variables. The significance
of the models including one, two, two and its interaction, and three
image metrics was evaluated by comparing them with null models. The
structural characteristics of the TDF (basal area (BA), mean height,
stem density) showed the highest modelling potential, with the
goodness-of-fit (R-2) values ranging from 0.58 to 0.66. Conversely, no
significant models were obtained for total crown area (TCA) and all
diversity attributes. Our results show that remote-sensing metrics
detect the spatial variation in the structural attributes of this
old-growth TDF better than they detect the variation in its diversity.
Our ability to model forest attributes at large scales at fine detail
(sampling plots <0.2 ha) can be much improved by combining the use of
VHR imagery with an array as wide as possible of the image surface
metrics, including both tone and texture.
Filiaciones:
Univ Nacl Autonoma Mexico, Fac Ciencias, Dept Ecol & Recursos Nat, Mexico City, DF, Mexico
Ctr Cambio Global & Sustentabilidad Sureste AC, Villahermosa, Mexico
Ctr Invest Cient Yucatan AC, Unidad Recursos Nat, Merida, Mexico
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