remote sensing Article Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers James M. Muthoka 1,* , Edward E. Salakpi 2, Edward Ouko 3, Zhuang-Fang Yi 4 , Alexander S. Antonarakis 1 and Pedram Rowhani 1 1 Department of Geography, University of Sussex, Brighton BN1 9QJ, UK;
[email protected] (A.S.A.);
[email protected] (P.R.) 2 Department of Physics and Astronomy, University of Sussex, Brighton BN1 9QH, UK;
[email protected] 3 Regional Centre for Mapping of Resources for Development Technical Services, Nairobi 00618, Kenya;
[email protected] 4 Development Seed, Washington, DC 20001, USA;
[email protected] * Correspondence:
[email protected] Abstract: 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 Citation: Muthoka, J.M.; Salakpi, E.E.; essential for drawing an effective management strategy. The study aimed at utilizing the Sentinel-2 Ouko, E.; Yi, Z.-F.; Antonarakis, A.S.; multispectral sensor to detect Opuntia stricta in a heterogeneous ASAL in Laikipia County, using Rowhani, P. Mapping Opuntia stricta ensemble machine learning classifiers.