Biodiversity assessment at multiple scales: linking remotely sensed data with field information

Abstract

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 (10¹⁴ m²), ecoregions (10¹¹-10¹² m²), ecomosaics (10⁸-10¹⁰ m²), ecotopes (10³-10⁶ m²), and individual organisms (10^-4-10² m²). Focusing on a case study of West Cost-Western Ghats ecoregion (1.7 × 10¹¹ m²) from India, we demonstrate that remotely sensed data permit discrimination of 205 patches of 11 types of sufficiently distinctive ecomosaics (10⁸-10¹⁰ m²) through unsupervised classification by using distribution parameters of the Normalized Difference Vegetation Index, with a pixel size of (3.24 × 106 m2). At the ecomosaic scale, Indian Remote Sensing LISS-2 satellite data with a pixel size of 10³ m² permit discrimination of ≈30 types of sufficiently distinctive ecotopes on the basis of supervised classification. Field investigations of angiosperm species distributions based on quadrats of 1-10² m² in one particular landscape of 27.5 × 10⁶ m² 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.