Detecting and mapping levels of Gonipterus scutellatus-induced vegetation defoliation and leaf area index using spatially optimized vegetation indices. Issue 3 (4th March 2018)

• Record Type: Journal Article
• Authors: Lottering, Romano; Mutanga, Onisimo; Peerbhay, Kabir
• Is Part Of: Geocarto international. Volume 33:Issue 3(2018)
• Page Start: 277
• Page End: 292
• Publication Date: 2018-03-04
• Subjects: Spatially optimized -- defoliation -- LAI -- vegetation indices -- artificial neural networks -- G. scutellatus; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2016.1250823
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Gonipterus scutellatus outbreaks may severely defoliate Eucalyptus plantations growing in South Africa. Therefore, detecting and mapping the severity and extent of G. scutellatus defoliation 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 mapping G. 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 of G. scutellatus -induced defoliation. The results showed that levels of defoliation, using visual percentage estimates, were detected with an R 2 of 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 an R 2 of 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 of G. scutellatus -induced defoliation and LAI subjected to defoliation.