Biodiversity Assessment at Multiple Scales: Linking Remotely Sensed Data with Field Information

We examine the efficacy of a scheme of multiscale assessment of biodiversity linking remote sensing on larger spatial scales with localized field sampling. A classification of ecological entities from biosphere to individual organisms in the form of a nested hierarchy is employed, such that entities at any level are differentiated in terms of their composition/configuration involving entities at the next lower level. We employ the following hierarchy: biosphere $(10^{14}m^2 )$, ecoregions $(10^{11}-10^{12}m^ 2 )$, ecomosaics $(10^8-10^{10}m^2 )$, ecotopes $(10^3-10^6 m^2 )$, and individual organisms $(10 {-4} -10^2 m^2 )$. Focusing on a case study of West Cost-Western Ghats ecoregion $(1.7 + 10^{11} m^2 )$ from India, we demonstrate that remotely sensed data permit discrimination of 205 patches of 11 types of sufficiently distinctive ecomosaics $(10^8 -10^10 m^2 )$ through unsupervised classification by using distribution parameters of the Normalized Difference Vegetation Index, with a pixel size of $(3.24 × 10^6 m^2 )$. At the ecomosaic scale, Indian Remote Sensing LISS-2 satellite data with a pixel size of $10^3 m^2$ permit discrimination of $\approx 30$ types of sufficiently distinctive ecotopes on the basis of supervised classification. Field investigations of angiosperm species distributions based on quadrats of $1-10^2 m^2$ in one particular landscape of $27.5 × 10^6 m^2$show that the seven ecotope types distinguished in that locality are significantly different from each other in terms of plant species composition. This suggests that we can effectively link localized field investigations of biodiversity with remotely sensed information to permit extrapolations at progressively higher scales