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Open AccessArticle

Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

1
School of Geosciences, University of Edinburgh, Edinburgh EH9 9XP, UK
2
Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Oxford OX1 3SZ, UK
3
South Rift Association of Landowners, P.O. Box 15289, Nairobi 00509, Kenya
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 198; https://doi.org/10.3390/rs12010198
Received: 11 November 2019 / Revised: 24 December 2019 / Accepted: 3 January 2020 / Published: 6 January 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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. View Full-Text
Keywords: Grazing management; landscape monitoring; model comparison; remote sensing; ecosystem monitoring; Sentinel-2; supervised classification Grazing management; landscape monitoring; model comparison; remote sensing; ecosystem monitoring; Sentinel-2; supervised classification
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  • Supplementary File 1:

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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3538339 & 10.5281/zenodo.3563030
    Link: https://zenodo.org/record/3538339#.XcnVBTKwlhE & https://zenodo.org/record/3563030#.XfqDhZOwlhF
    Description: The csv file found at the first DOI link contains the training data (class and coordinates (latitude and longitude)) used in the classification models presented in the research article. The second DOI links the reader to further methodological details relating to R code and the protocol used for the collection of reference data.
MDPI and ACS Style

Hunter, F.D.; Mitchard, E.T.; Tyrrell, P.; Russell, S. Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem. Remote Sens. 2020, 12, 198.

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