Monitoring the invasion of Campuloclinium macrocephalum (less) DC plants using a novel MaxEnt and machine learning ensemble in the Cradle Nature Reserve, South Africa

Abstract

Abstract The proliferation of non-native plant species has caused significant changes in global ecosystems, leading to a surge in international interest in the use of remote sensing technologies for both local and global detection applications. The Greater Cradle Nature Reserve, a UNESCO World Heritage Site, is facing a decline in its global status due to the spread of pompom weeds, affecting its biodiversity. A significant reduction in grazing capacity leads to the displacement of game animals and the replacement of native vegetation. We used Sentinel-2A multispectral images to map the distribution of pompom weeds. At the nature reserve from 2019 to 2024, which allowed us to distinguish it from other land cover types and determine the appropriateness of the habitat. The SVM model provided 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively, whereas the RF model yielded 31.1% and 39.3%, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weeds at the nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. Classification of above 75%, indicating that they could distinguish pompom weeds from existing land cover types. For sustainable environmental management, this study suggests using predictive models to effectively eradicate the spatial distribution of invasive weeds in the present and future.

Keywords

• Campuloclinium macrocephalum (Less.) DC
• Sentinel-2A
• MaxEnt model
• Machine learning models
• Cradle Nature Reserve
• South Africa