Abstract
In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.
Highlights
Savannas cover over 40% of Africa’s land surface and support large populations of both wildlife and livestock [1]
The percentage cover of these vegetation types shifts with environmental gradients and large herbivore diversity, density, and activity [3]. These landscapes are generally poorly suited to agricultural cultivation, and so the livelihoods of people living in savannas are often dependent on livestock, and grazing resources [4]
This study successfully demonstrates the utility of using multi-season time-series imagery for classifying focal species with high classification accuracy (OA = 0.8217, Kappa = 0.7923)
Summary
Savannas cover over 40% of Africa’s land surface and support large populations of both wildlife and livestock [1]. The percentage cover of these vegetation types shifts with environmental gradients and large herbivore diversity, density, and activity [3]. These landscapes are generally poorly suited to agricultural cultivation, and so the livelihoods of people living in savannas are often dependent on livestock, and grazing resources [4]. The key resource concept suggests that wildlife and livestock productivity and resilience are controlled by access to heterogenous grazing resources that provide distinct functionality across space and time. The ability of these resources to meet the metabolic and nutritional requirements of both wildlife and livestock is dependent on spatially distinct species composition, which varies in terms of functionality [8]
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