Modelling terrain roughness using LiDAR derived digital terrain model in eucalyptus plantation forests, in KwaZulu-Natal, South Africa.
South African commercial plantation forests are established primarily to meet both the local and global demands of industries that require direct raw materials such as pulpwood or timber. Consequently, the commercial forest industry in South Africa is held in high esteem as it makes up one of the largest economic forces within the country. For this reason, individuals responsible for implementing strategies pertaining to silvicultural and harvesting operations within commercial plantations require up to date and detailed multi-forest inventory datasets to ensure that optimal yields are guaranteed and that sites are well maintained. Despite this, various drawbacks within commercial plantations exist: steep slopes, high elevations, and other forms of topographic irregularities, can affect the productivity of the site and impact mechanical silvicultural and harvesting operations. In lieu of making more informed and efficient decision-making protocols, forest researchers are often tasked with implementing and utilising alternative technologies such as remote sensing to determine if specific methodologies can be used for gathering multi-forest inventory data that also incorporate terrain information. Light Detection and Ranging (LiDAR), a recent remote sensing technology, has demonstrated that it is highly robust and can lend itself towards providing highly accurate vertical forest structural attributes and horizontal topographic derivatives. This study employs the use of a LiDAR derived Digital Terrain Model (DTM) (1 m x 1 m spatial resolution) to create terrain indices that are representative of the horizontal features within the commercial forest sites of interest. In addition, a machine learning approach using a random forest (RF) ensemble classifier was adopted to determine how much of the variation in forest structural attributes: mean dominant height, mean height, pulpwood volumes and diameter at breast height can be attributed to terrain when using the LiDAR derived DTM terrain variables. The overall findings presented in this study are encouraging and show that a LiDAR derived DTM can be successfully used for creating highly accurate terrain indices and can be used for predicting variability within even-aged Eucalyptus forest structural attributes within commercial plantation forests in KwaZulu-Natal, South Africa, with an acceptable level of accuracy.