Biodiversity assessment by remote sensing

Abstract

Measuring the complexity of species in (semi) natural environments is time consuming and expensive. In this paper we summarise remote sensing techniques developed for mapping and monitoring biodiversity of herbivores and vegetation. In particular, methods involving interannual variation of NDVI with respect to mammal and bird species richness in Kenya will be described. We show it is possible to predict species richness at a regional scale using NDVI derived from NOAA satellites, and that these relationships are unimodal. Further work relating species richness to climate parameters showed that these relationships are also unimodal. We also show that climate parameters are better predictors of species richness than NDVI alone.