Remote sensing of forest health and vitality: a South African perspective
Commercial forestry plantations are an important and valuable segment of the South African economy and forest managers are required to maximise and sustain forest productivity. However, various factors such as the outbreak of damaging agents are constantly hampering forest health and thus decrease productivity. It is therefore important to detect the presence and spread of these agents within plantation forests, a task efficiently achieved using remote sensing technology. A wide assortment of sensors with varying resolutions are available and have been extensively used for this purpose. This paper reviews the current status of remote sensing of forest health in South Africa by providing insight on the latest developments on the use of the technology in forest plantations. A systematic search was executed on Google Scholar, ScienceDirect® and EBSCOhost® databases that identified 627 articles of which 29 made reference to remote sensing of forest health in South Africa. Four key results were found: (1) the latest technology is capable of detecting and monitoring forest health with great accuracy, especially with the adoption of machine learning methods; (2) studies employing remote sensing to characterise forest health have burgeoned since 2006 with even more applying hyperspectral data; (3) most studies were spatially concentrated in the KwaZulu-Natal Midlands region around Pietermaritzburg with only a few over the Western Cape; and (4) the remote detection of pest outbreaks and pathogens have received much attention followed by alien invasive plants and a few studies directed to fragmentation. Present and future partnerships may open up opportunities for exploiting remote sensing further; this should address growing expectations from government and industry for more detailed and accurate information concerning the health and condition of South Africa’s plantation forests.
Keywords: alien invasive plants, forest health, fragmentation, pest and pathogens, remote sensing