Can remote sensing estimate fine-scale quality indicators of natural habitats?

Abstract

Efficient management and conservation of natural habitats requires a thorough knowledge and sustained monitoring of their ecological quality. In recent years, several methods have been developed to assess the local conservation status in the field. These typically combine estimates of coarse-scale indicators, such as tree and grass encroachments, with very fine-scale indicators that require precise fieldwork, such as the number of key species present. We first tested whether coarse-scale field characteristics can provide information on fine-scale indicators. Then, this idea was extended to remote sensing techniques to derive estimates of fine-scale properties that cannot be derived directly by the sensors. The method was elaborated for two Natura 2000 heathland areas, combining field conservation status assessments of over 650 locations with remote sensing information derived from an airborne hyperspectral scanner image. Boosted regression trees using field estimates of coarse-scale parameters as predictors were able to explain up to 43% of the variation in the fine-scale indicators. When using remote sensing data, models performed only slightly less. Up to 35% of the variation was explained using remote sensing estimates of coarse-scale parameters as predictors, and up to 39% was explained when additional remotely sensed land cover data were included in the models. Although these rates are not high in absolute terms, model predictions for certain parameters were more precise than field estimates, especially for criteria with a high between-observer variability. These results clearly illustrate the potential of remote sensing imagery to derive information on the conservation status of habitats, even for fine-scale elements that are too small to be derived directly.