Mapping species density of trees,shrubs and vines in a tropical forest,using field measurements,satellite multiespectral imagery and spatial interpolation |
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Authors: | J Luis Hernández-Stefanoni Juan Manuel Dupuy |
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Institution: | (1) Centro de Investigación Científica de Yucatán A.C., Unidad de Recursos Naturales, Calle 43 # 130. Colonia Chuburná de Hidalgo, Mérida, Yucatán, C.P. 97200, México |
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Abstract: | We estimated the number of species in a tropical forest landscape in Quintana Roo, Mexico, based on the relationship between
reflectance values of satellite imagery and field measurements of plant species density (mean number of species per plot).
Total species density as well as that of tree, shrub and vine species were identified from 141 sampling quadrats (16543 individuals
sampled). Spatial prediction of plant diversity was performed using universal kriging. This approach considered the linear
relationship between plant species density and reflectance values of Thematic Mapper™, as well as the spatial dependence of
the observations. We explored the linear relationships between spectral properties of TM bands and the species density of
trees, shrubs and vines, using regression analysis. We employed Akaike Information Criterion (AIC) to select a set of candidate
models. Based on Akaike weights, we calculated model-averaged parameters. Linear regression between number of species and
reflectance values of TM bands yielded regression residuals. We used variogram analysis to analyze the spatial structure of
these residuals. Results show that accounting for spatial autocorrelation in the residual variation improved model R2 from 0.57 to 0.66 for number of all species, from 0.58 to 0.65 for number of tree species, from 0.26 to 0.41 for number of
shrub species and from 0.13 to 0.17 for species density of vines. The empirical models we developed can be used to predict
landscape-level species density in the Yucatan Peninsula, helping to guide and evaluate management and conservation strategies. |
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Keywords: | Akaike information criterion Plant diversity Remote sensing Universal Kriging Tropical forest |
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