Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing

Abstract

AbstractAim Conservation activities have increasingly focused on issues at the level of the landscape but are constrained by limited data and knowledge relating to biodiversity at this scale. Satellite remote sensing has considerable, but under‐exploited, potential as a source of information on biodiversity at the landscape level. Remote sensing has generally been used to assess biodiversity indirectly, using approaches that often fail to fully exploit the information content of the imagery and typically only with regard to the species richness component of biodiversity. The aim of this paper was to assess the potential of remote sensing as a source of information on the richness, evenness and composition of tree species in a tropical rain forest.Location The test site was a c. 225 km2 region centred on the Danum Valley Field Centre, Borneo. This test site contained regions of undisturbed and differentially logged rain forest.Methods Data on tree biodiversity had been acquired for fifty‐two sample plots by standard field survey methods and were used to derive summary indices of biodiversity for seedlings, saplings and mature trees. Differences between logged and unlogged sites were evaluated by comparison of the indices and species accumulation curves. A Landsat Thematic Mapper (TM) image of the site acquired close to the date of the field survey was obtained and rigorously pre‐processed. Feedforward neural networks were used to derive predictions of biodiversity indices from the imagery. A Kohonen self organizing map neural network was used to ordinate the field data to derive classes of forest defined by relative similarity in species composition. The separability of the defined classes in the Landsat TM image was evaluated with a discriminant analysis.Results Analyses of the field data revealed considerable variation in the biodiversity of seedlings, saplings and trees at the site, associated, in part, with differences in logging activities. This variation in biodiversity was manifest in the remotely sensed data. The analyses indicated an ability to (1) predict biodiversity indices, with the highest correlation between predicted and actual index observed for evenness described by Shannon entropy (r = 0.546), but especially to (2) classify nine forest classes defined on the basis of similarity in tree species composition (accuracy 95.8%).Main conclusions Logging activities impacted on biodiversity and the resulting variation in biodiversity was reflected in the remotely sensed imagery. Using methods that exploit more fully the information content of the imagery than those used in other previous studies, a richer representation of biodiversity may be derived. This representation includes estimates of key summary indices of biodiversity, notably richness and evenness, as well as information on species composition. The results indicate that remotely sensed data may be used as a source of information on biodiversity at the landscape scale that may be used to inform conservation science and management.