Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps have seen a proliferation of image classification techniques. Despite these efforts, derived LULC maps are still often judged to be of insufficient quality for operational applications, due to disagreement between generated maps and reference data. In this study we sought to pursue two objectives: first, to test the new-generation multispectral RapidEye imagery classification output using machine-learning random forest (RF) and support vector machines (SVM) classifiers in a heterogeneous coastal landscape; and second, to determine the importance of different RapidEye bands on classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers’ cover maps with respective independent validation data sets. An overall classification accuracy of 93.07% with a kappa value of 0.92, and 91.80 with a kappa value of 0.92 was achieved using RF and SVM, respectively. In this study, RF and SVM classifiers performed comparatively similarly as demonstrated by the results of McNemer’s test (Z = 1.15). An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types. Consequently, pursuit of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount.

Accurate estimates of papyrus (Cyperus papyrus) biomass are critical for an efficient papyrus swamp monitoring and management system. The objective of this study was to test the utility of random forest (RF) regression and two narrow-band vegetation indices in estimating above-ground biomass (AGB) for complex and densely vegetated swamp canopies. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were calculated from field spectrometry data and fresh AGB was measured in 82 quadrats at three different areas in the iSimangaliso Wetland Park, South Africa. NDVI was calculated from all possible band combinations of the electromagnetic spectrum (350 and 2500 nm), while EVI was calculated from possible band combinations in the blue, red, and near infrared of the spectrum. Backward feature elimination and RF regression were used as variable selection and modelling techniques to predict papyrus AGB. Results showed that the effective portions of electromagnetic spectrum for estimation AGB of papyrus swamp were located within the blue, red, red-edge, and near-infrared regions. The three best selected EVIs were computed from bands located at (i) 445, 682, and 829 nm, (ii) 497, 676, and 1091 nm, and (iii) 495, 678, and 1120 nm. These indices produced better predictive accuracies (R² = 0.90; root mean square error of prediction (RMSEP) = 0.289 kg m⁻²; 7.99% of the mean) than the best selected NDVIs (R² = 0.85; RMSEP = 0.343 kgm⁻²; 9.49% of the mean) that were calculated from bands located at (i) 739 and 829 nm, (ii) 739 and 814 nm, (iii) 744 and 789 nm, and (iv) 734 and 909 nm. The results of the present study demonstrate the utility of narrow-band vegetation indices (EVI and NDVI) and RF regression in estimating papyrus AGB at high density, a previously challenging task with broadband satellite sensors.

The cossid moth, Coryphodema tristis was first noted on Eucalyptus nitens trees in Mpumalanga province, South Africa during July 2004. Currently, the moth poses a major threat to commercial forestry in the country. In this study, selected climatic and topographical variables were used to model the susceptibility of E. nitens forests to cossid moth occurrence, thereby providing insight into the variables that may influence the occurrence and spread of the moth. A zigzag sampling technique was used to survey 5316 ha of E. nitens forests for the presence or absence of the moth. The random forest classification algorithm was then used to model the relationship between the climatic and topographical variables and the occurrence of the cossid moth. Results indicate that four variables that included elevation, maximum temperature for September, maximum temperature for April and the median rainfall for April best explained the presence or absence of C. tristis with an overall accuracy of 82% and a kappa value of 0.63. Partial dependence plots indicated that the areas that have a maximum temperature greater than 23°C in September and 22°C in April are likely to be infested by the cossid moth. The results from this study provide a robust and accurate spatial framework to assist forest managers in focussing their existing monitoring and control efforts to specific E. nitens forested areas that are highly susceptible to C. tristis infestations.