Mapping species density of trees, shrubs and vines in a tropical forest, using field measurements, satellite multiespectral imagery and spatial interpolation
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.
| Main Authors: | , |
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| Format: | Texto biblioteca |
| Language: | eng |
| Subjects: | Plantas, Arbustos, Bosques tropicales, Fitogeografía, Artfrosur, |
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| Summary: | 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. |
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