Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks

Foody, G.M and Cutler, M.E.J. (2006) Mapping the species richness and composition of tropical forests from remotely sensed data with neural networks. Ecological Modelling, 195, (1-2), 37-42.


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The understanding and management of biodiversity is often limited by a lack of data. Remote sensing has considerable potential as a source of data on biodiversity at spatial and temporal scales appropriate for biodiversity management. To-date, most remote sensing studies have focused on only one aspect of biodiversity, species richness, and have generally used conventional image analysis techniques that may not fully exploit the data's information content. Here, we report on a study that aimed to estimate biodiversity more fully from remotely sensed data with the aid of neural networks. Two neural network models, feedforward networks to estimate basic indices of biodiversity and Kohonen networks to provide information on species composition, were used. Biodiversity indices of species richness and evenness derived from the remotely sensed data were strongly correlated with those derived from field survey. For example, the predicted tree species richness was significantly correlated with that observed in the field (r=0.69, significant at the 95% level of confidence). In addition, there was a high degree of correspondence (?83%) between the partitioning of the outputs from Kohonen networks applied to tree species and remotely sensed data sets that indicated the potential to map species composition. Combining the outputs of the two sets of neural network based analyses enabled a map of biodiversity to be produced

Item Type: Article
ISSNs: 0304-3800 (print)
Related URLs:
Keywords: models, biodiversity, remote sensing, Neural network, tropical forest, ecological abundance, species richness
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GB Physical geography
Divisions : University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
ePrint ID: 57710
Accepted Date and Publication Date:
Date Deposited: 11 Aug 2008
Last Modified: 31 Mar 2016 12:38

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