The role of remote sensing in invasive alien plant species detection and the assessment of removal programs in two selected reserves in the eThekwini Municipality, KwaZulu-Natal Province.
One of the major current concerns by conservationists is alien invasive plants due to their rapid spread and threat to biodiversity. The detection of Invasive Alien Plant Species (IAPs) can aid in monitoring and managing their invasion on ecosystems. In South Africa approximately 10 million hectares of land have been invaded. To combat this invasion, the Working for Water program was initiated in 1995 aimed at manually removing them. Multispectral imagery can facilitate identification, assess removal initiatives and improve efficiency of IAP removal. The aim of this study is to determine the most appropriate sensor to detect three IAPs (Acacia podalyriifolia, Chromolaena odorata and Litsea glutinosa) and assess clearing programs of these species in two protected areas (Paradise Valley and Roosfontein Nature Reserves) within the eThekwini municipality, in KwaZulu-Natal province, South Africa using remote sensing. The three satellite sensors examined in this study included Landsat 7 ETM+, SPOT 5 and WorldView-2. The study also assessed four image classifiers (Parallelepiped, Maximum Likelihood, Spectral Angle Mapper and Iterative Self Organising Data Analysis Technique) in the detection of the selected IAPs. These sensors and techniques were compared based on their level of accuracy at detecting selected IAPs. The results of the study showed that WorldView-2 imagery and the Maximum Likelihood classifier had the highest overall accuracy (66.67%) , resulting in the successful classification of two (Acacia podalyriifolia and Chromolaena odorata) out of the three target species. This is due to the high spatial resolution of WorldView-2 imagery. This combination was then used to asses clearing of the selected IAPs by examining species distribution and density before and after clearing. Here the overall accuracies for the Paradise Valley and Roosfontein Nature Reserves were successful with accuracies above 85%. The density and distribution of all three IAPs decreased substantially in both sites except for the L. glutinosa species located in the Paradise Valley Nature Reserve which showed no significant decrease. These results show that geospatial data (especially remote sensing data) can be successfully used in both the detection of IAPs and the assessment of their removal.