A tree-level analysis of baboon damage in commercial forest stands using deep learning techniques

Abstract

Commercial forest plantations in South Africa are homogeneous monocultures of highly bred exotic species grown to deliver timber products of the best potential quality. As such, these stands are susceptible to adverse effects of biotic and abiotic factors, and therefore require intense management to mitigate these risks. A sustainable forest monitoring system that can detect real-time changes in the physiological state of these plantations is needed for timeous management intervention to reduce losses. The use of machine learning algorithms has recently become popular, with acceptable levels of success. This study explores the application of deep learning neural networks for early detection of damage caused by baboons in evergreen plantations of Pinus species. Using PlanetScope imagery (spectral band 590–860 nm), which is captured by a constellation of Dove nanosatellites, with a high temporal resolution available daily at 3 m spatial resolution, the study achieved an overall accuracy of 81.54%, with a kappa value of 0.69, using a deep neural network. In comparison, using a random-forest classifier produced 74.04% accuracy and a kappa value of 0.62. The study successfully mapped different levels of baboon damage within commercial pine forests. We provide a repeatable method for daily monitoring initiatives, and attest to the utility of higher-resolution imagery such as PlanetScope for mapping health and damage severity at the tree level.