Random Forest characterization of upland vegetation and management burning from aerial imagery

Abstract

Aim The upland moorlands of Great 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 gamebird habitat 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 monitoring changes in vegetation and burning practices is difficult. We aim to overcome this problem by developing methods to classify aerial imagery into high-resolution maps of dominant vegetation cover, including the distribution of burns on managed grouse moors. Location Peak District National Park, England, UK. Methods Colour and infrared aerial photographs were classified into seven dominant land-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 the vegetation classes and managed burning using detrended correspondence analysis. Results Classification accuracy was c. 95% and produced a 5-m resolution map for 514 km2 of moorland. Cover classes were 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 infer species?environment relationships and can therefore provide an important tool for effective conservation at the landscape scale.