Bush encroachment monitoring using multi-temporal Landsat data and random forests
It is widely accepted that land degradation and desertification (LDD) are serious global threats to humans and the environment. Around a third of savannahs in Africa are affected by LDD processes that may lead to substantial declines in ecosystem functioning and services. Indirectly, LDD can be monitored using relevant indicators. The encroachment of woody plants into grasslands, and the subsequent conversion of savannahs and open woodlands into shrublands, has attracted a lot of attention over the last decades and has been identified as a potential indicator of LDD. Mapping bush encroachment over large areas can only effectively be done using Earth Observation (EO) data and techniques. However, the accurate assessment of large-scale savannah degradation through bush encroachment with satellite imagery remains a formidable task due to the fact that on the satellite data vegetation variability in response to highly variable rainfall patterns might obscure the underlying degradation processes.
Here, we present a methodological framework for the monitoring of bush encroachment-related land degradation in a savannah environment in the Northwest Province of South Africa. We utilise multi-temporal Landsat TM and ETM+ (SLC-on) data from 1989 until 2009, mostly from the dry-season, and ancillary data in a GIS environment. We then use the machine learning classification approach of random forests to identify the extent of encroachment over the 20-year period. The results show that in the area of study, bush encroachment is as alarming as permanent vegetation loss. The classification of the year 2009 is validated yielding low commission and omission errors and high k-statistic values for the grasses and woody vegetation classes. Our approach is a step towards a rigorous and effective savannah degradation assessment.