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Gonipterus scutellatusoutbreaks may severely defoliateEucalyptusplantations growing in South Africa. Therefore, detecting and mapping the severity and extent ofG. scutellatusdefoliation is essential for the deployment of suppressive measures. In this study, we tested the utility of spatially optimized vegetation indices and an artificial neural network in detecting and mappingG. scutellatus-induced vegetation defoliation, using both visual estimates of percentage defoliation and optical leaf area index (LAI) measures. We tested both field methods to determine which of the two were more superior in detecting vegetation defoliation using optimized vegetation indices. These indices were computed from a WorldView-2 pan-sharpened image, which is characterized with a 0.5-m spatial resolution and eight spectral bands. The indices were resampled to spatial resolutions that best represented levels ofG. scutellatus-induced defoliation. The results showed that levels of defoliation, using visual percentage estimates, were detected with anR2of 0.83 and an RMSE of 1.55 (2.97% of the mean measured defoliation), based on an independent test data-set. Similarly, LAI subjected to defoliation was detected with anR2of 0.80 and an RMSE of 0.03 (0.06% of the mean measured LAI), based on an independent test data-set. Therefore, the results indicate that the cheaper less-complicated visual percentage estimates of defoliation was the more superior model of the two. A sensitivity analysis revealed that NDRE, MCARI2 and ARI ranked as the top three most influential indices in developing both percentage defoliation and LAI models. Furthermore, we compared the optimized model with a model developed using the original image spatial resolution. The results indicated that the optimized model performed better than the original 0.5-m spatial resolution model. Overall, the study showed that vegetation indices optimized to specific spatial resolutions can effectively detect and map levels ofG. scutellatus-induced defoliation and LAI subjected to defoliation. [ABSTRACT FROM AUTHOR]

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 R 2 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. [ABSTRACT FROM AUTHOR]

SPOT-5 multispectral and panchromatic image data were used to compute texture measures to estimate the road edge effect on adjacent Eucalyptus grandis forests. Employing a stepwise selection algorithm enabled the selection of optimal texture measures that were input into a backpropagation artificial neural network. The R2 of best models ranged from 0.67 to 0.89 for DBH, TH, BA, Volume and LAI on an independent test data set, with a root mean square error (RMSE) range of 0.01–5.36% for the respective variables. The result is critical for understanding and spatially predicting the road edge effect on adjacent vegetation. [ABSTRACT FROM AUTHOR]

• The majority of KwaZulu-Natal's SOC stocks were found to be distributed between 60–200 cm. • Forested areas in KwaZulu-Natal had the greatest SOC concentration per unit Area. • Rainfall, clay content, temperature, elevation, and RVI were found to be key determinants of SOC prediction at different depths. • DNN model performed well in topsoil (0–30 cm), but less accurate with deeper depth. The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model's prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change. [ABSTRACT FROM AUTHOR]