Refining predictions of invasive species using remote sensing data

Study shows areas in South East Asia at risk of invasion by native species are of similar size as that of non-native invaders

GBIF-mediated data resources used : 2,011 species occurrences
Common lantana
Common lantana (Lantana camara), one of the invasive species modeled in the article. Photo by novembersyy via iNaturalist, licensed under CC BY-NC 4.0.

Given the potentially catastrophic consequences of invasive species, prediction, early detection and prevention are priorities in conservation policies. Relying primarily on climatic factors, species distribution models can aid in the prediction of invasiveness at a regional scale, but for finer scale models, biotic parameters and local abiotic conditions should be considered also.

In this study of invasive weeds in South East Asia, researchers used remote sensing (RS) data and climate data- separately and combined- to evaluate the potential distribution of 14 priority species based on occurrence data from While the climate data included temperature and precipitation metrics, the RS data provided information on soil, elevation, land cover and productivity.

Overall, the combined models predicted substantially smaller risk areas than models based on either dataset alone. While RS-only models didn't perform well, the combined models showed that adding RS-based biotic and abiotic parameters to climate data, clearly refined the spatial patterns of prediction distributions.

The study identified shrub species as having the highest potential invasion risk in South East Asia, and also revealed that native invaders may pose as serious a threat as alien invasive species.

Link to original article

Truong TTA, Hardy GESJ and Andrew ME (2017) Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions. Frontiers in Plant Science. Frontiers Media SA 8. Available at: