Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa.

Abstract

The planning and management of forest operations is a complex task. This complexity is attributed to the variability of forest plantations’ site and topographic conditions. Therefore, there is a need for an integrated approach towards forest management and decision-making which offer a continuous review of forest operation systems used to increase site productivity. Therefore, the aim of this study was to determine the impact of local terrain on forest operations and forest productivity using GIS and statistical modelling in a commercial forest plantation in KwaZulu-Natal, South Africa. The first objective of the study focused on determining the influence of terrain variation on the productivity of commercial forestry using LiDAR-derived topographic factors. A 1m LiDAR-derived Canopy Height Model (CHM) and 30m digital elevation model (DEM) were used in the study. Four model scenarios were generated using LiDAR data in conjunction with a stepwise multiple linear regression. The results showed that elevation, aspect, clay content and slope were significant variables in influencing tree productivity (R2 > 0.9). Furthermore, a strong correlation was observed between observed tree heights and predicted tree height values (R2 = 0.88). The results of the study suggest that topographic variables strongly influence commercial tree species productivity. The second objective of the study determined optimal terrain classes based on a national terrain classification system developed by Erasmus (1994) for South African forestry regions. The integration of logistic regression in a GIS environment proved to accurately map suitable terrain classes (AUC = 0.93). This was achieved using a cost-effective 30 m DEM and SOTER-based soil parameter estimates (SOTWIS) data which was used to derive site topographic variables. The study demonstrates the use of integrated approaches for providing efficient and feasible means to apply terrain classification for current forestry practices. The study provides an effective framework for classifying ideal terrain conditions for forest management applications and forest operations. Overall, the study establishes the significance of local topography on commercial forest production and contributes towards enhancing management decision-making during spatial planning initiatives and operations.