Random Forest characterisation of upland vegetation and burning from aerial imagery
Chapman, Daniel S.; Bonn, Aletta; Kunin, William E.; Cornell, Stephen J.. 2010 Random Forest characterisation of upland vegetation and burning from aerial imagery. Journal of Biogeography, 37 (1). 37-46. 10.1111/j.1365-2699.2009.02186.xFull text not available from this repository.
Aim: The upland moorlands of Britain form distinctive landscapes of international conservation importance, comprising mosaics of heathland, acid grassland, blanket bog and bracken. Much of this landscape is managed by rotational burning to create suitable habitat for gamebirds and there is concern over whether this is driving long-term changes in upland vegetation communities. However, the inaccessibility and scale of uplands means that a practical way to monitor changes in vegetation and burning practices is through the use of remotely sensed data. We develop methods to classify aerial imagery into high-resolution vegetation maps, including the distribution of burns on managed grouse moors. Using the maps, we test for effects of environmental gradients on vegetation cover and its management. Location: Peak District National Park, UK. Methods: We classified colour and infra-red aerial photographs into eight dominant cover classes using the Random Forest ensemble machine learning algorithm. In addition, heather (Calluna vulgaris) was further differentiated into growth phases, including sites that were newly burnt. We then analysed the distributions of vegetation classes using detrended correspondence analysis and managed burning using generalised additive models. Results: Classification accuracy was ~95% and produced a 5 m resolution vegetation map for 514 km2 of moorland. Cover was highly aggregated and strong nonlinear effects of elevation and slope and weaker effects of aspect and bedrock type were evident in structuring moorland vegetation communities. The classification revealed the spatial distribution of managed burning and suggested that relatively steep areas may be disproportionately burnt. Main conclusions: Random Forest classification of aerial imagery is an efficient method for producing high-resolution maps of upland vegetation. These may be used to monitor long-term changes in vegetation and management burning and species-environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.
|Item Type:||Publication - Article|
|Digital Object Identifier (DOI):||10.1111/j.1365-2699.2009.02186.x|
|Programmes:||CEH Topics & Objectives 2009 onwards > Biodiversity > BD Topic 1 - Observations, Patterns, and Predictions for Biodiversity > BD - 1.3 - Long-term/large-scale monitoring and experiments ...|
|NORA Subject Terms:||Ecology and Environment|
|Date made live:||01 Apr 2010 10:13|
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