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Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers

1
Department of Geography, University of Sussex, Brighton BN1 9QJ, UK
2
Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, UK
3
Regional Centre for Mapping of Resources for Development Technical Services, Nairobi 00618, Kenya
4
Development Seed, Washington, DC 20001, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Vitezslav Moudry
Remote Sens. 2021, 13(8), 1494; https://doi.org/10.3390/rs13081494
Received: 11 March 2021 / Revised: 2 April 2021 / Accepted: 4 April 2021 / Published: 13 April 2021
(This article belongs to the Section Ecological Remote Sensing)
Globally, grassland biomes form one of the largest terrestrial covers and present critical social–ecological benefits. In Kenya, Arid and Semi-arid Lands (ASAL) occupy 80% of the landscape and are critical for the livelihoods of millions of pastoralists. However, they have been invaded by Invasive Plant Species (IPS) thereby compromising their ecosystem functionality. Opuntia stricta, a well-known IPS, has invaded the ASAL in Kenya and poses a threat to pastoralism, leading to livestock mortality and land degradation. Thus, identification and detailed estimation of its cover is essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using ensemble machine learning classifiers. To illustrate the potential of Sentinel-2, the detection of Opuntia stricta was based on only the spectral bands as well as in combination with vegetation and topographic indices using Extreme Gradient Boost (XGBoost) and Random Forest (RF) classifiers to detect the abundance. Study results showed that the overall accuracies of Sentinel 2 spectral bands were 80% and 84.4%, while that of combined spectral bands, vegetation, and topographic indices was 89.2% and 92.4% for XGBoost and RF classifiers, respectively. The inclusion of topographic indices that enhance characterization of biological processes, and vegetation indices that minimize the influence of soil and the effects of atmosphere, contributed by improving the accuracy of the classification. Qualitatively, Opuntia stricta spatially was found along river banks, flood plains, and near settlements but limited in forested areas. Our results demonstrated the potential of Sentinel-2 multispectral sensors to effectively detect and map Opuntia stricta in a complex heterogeneous ASAL, which can support conservation and rangeland management policies that aim to map and list threatened areas, and conserve the biodiversity and productivity of rangeland ecosystems. View Full-Text
Keywords: invasive plant species; remote sensing; extreme gradient boost; random forest; spectral indices; topographic indices invasive plant species; remote sensing; extreme gradient boost; random forest; spectral indices; topographic indices
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MDPI and ACS Style

Muthoka, J.M.; Salakpi, E.E.; Ouko, E.; Yi, Z.-F.; Antonarakis, A.S.; Rowhani, P. Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers. Remote Sens. 2021, 13, 1494. https://doi.org/10.3390/rs13081494

AMA Style

Muthoka JM, Salakpi EE, Ouko E, Yi Z-F, Antonarakis AS, Rowhani P. Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers. Remote Sensing. 2021; 13(8):1494. https://doi.org/10.3390/rs13081494

Chicago/Turabian Style

Muthoka, James M., Edward E. Salakpi, Edward Ouko, Zhuang-Fang Yi, Alexander S. Antonarakis, and Pedram Rowhani. 2021. "Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers" Remote Sensing 13, no. 8: 1494. https://doi.org/10.3390/rs13081494

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