Mapping Savannah Land Cover
This research, mainly funded by the EU project LanDDApp and an MMU PhD Scholarship, investigates woody vegetation encroachment in the North West and Limpopo Provinces of South Africa.
Accurately mapping savannah land cover at the regional scale can provide useful input to policy decision making efforts regarding, for example, bush control or overgrazing, as well as to global carbon emissions models. Recent attempts have employed Earth observation data, either from optical or radar sensors, and most commonly from the dry season when the spectral difference between woody vegetation, crops and grasses is maximised. By far the most common practice has been the use of Landsat optical bands, but some studies have also used vegetation indices or SAR data. However, conflicting reports with regards to the effectiveness of the different approaches have emerged, leaving the respective land cover mapping community with unclear methodological pathways to follow.
We address this issue by employing Landsat and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data to assess the accuracy of mapping the main savannah land cover types of woody vegetation, grassland, cropland and non-vegetated land (Symeonakis et al. 2018). We test the performance of 15 different models comprised of combinations of optical and radar data from the dry and wet seasons. Our results show that a number of models perform well and very similarly. The highest overall accuracy is achieved by the model that incorporates both optical and synthetic-aperture radar (SAR) data from both dry and wet seasons with an overall accuracy of 91.1% (±1.7%): this is almost a 10% improvement from using only the dry season Landsat data (81.7 ± 2.3%).
The work on LanDDApp is ongoing with the aim to identify degrading areas where mitigation measures are required and thus to provide a management tool for the prioritization of such measures.
& the Northwest Province
The Northwest Province
& the Landsat scenes that correspond to its area
of the main land cover classes of the study area
a) woody vegetation; (b) grassland; (c) cropland, and (d) non-vegetated land.
Example land cover mapping results
A–C: the aerial imagery used for training and validation;
A1, B1, C1: the reference land cover map of the National Mapping Agency of South Africa (the NGI; Verhulp & Denner 2010);
A2–A6, B2–B6, C2–C6: classification model results of this study