Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa

Gonipterus scutellatus Gyllenhal is a leaf feeding weevil that is a major defoliator of the genus Eucalyptus. Understanding the relationship between levels of weevil induced vegetation defoliation and the optimal spatial resolution of satellite images is essential for effective management of plantation resources. The objective of this study was to identify appropriate spatial resolutions for predicting levels of weevil induced defoliation. We resampled the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Enhanced Vegetation Index (EVI) images computed from a WorldView-2 pan-sharpened image, which is characterised with a 0.5 m spatial resolution and 8 spectral bands. Within each plantation compartment 30 × 30 m plots were established, representing different levels of defoliation. From the centre of each plot, the spatial resolution of the original image was progressively resampled from 1.5 to 8.5 m, with 1 m increments. The minimal variance for each level of defoliation was then established and used as an indicator for quantitatively selecting the optimal spatial resolution. Results indicate that an appropriate spatial resolution was established at 1.25, 1.25, 1.75 and 2.25 m for low, medium, high and severe levels of defoliation, respectively. In addition, an Artificial Neural Network was run to determine the relationship between the appropriate spatial resolution and levels of Gonipterus scutellatus induced defoliation. The model yielded an R2 of 0.80, with an RMSE of 1.28 (2.45% of the mean measured defoliation) based on an independent test dataset. We then compared this model to a model developed using the original 0.5 m image spatial resolution. Our results suggest that optimising the spatial resolution of remotely sensed imagery essentially improves the prediction of vegetation defoliation. In essence, this study provides the foundation for multi-scale defoliation mapping using high spatial resolution imagery.