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

James M Muthoka,Zhuang-Fang Yi,Alexander S Antonarakis,Pedram Rowhani,Edward Ouko,Edward E Salakpi

doi:10.3390/rs13081494

James M Muthoka, Zhuang-Fang Yi + Show 4 more

Open Access

https://doi.org/10.3390/rs13081494

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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 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.

Highlights

In the spectral separability (Figure 3) analysis we provided a qualitative assessment of the suitability of Sentinel-2 spectral bands to perform classification in these arid environments with abundant Opuntia stricta and Figure 6 indicates the spatial occurrence of Opuntia stricta
Our study sought to examine the ability of Sentinel-2 spectral and spatial properties to detect Opuntia stricta based on ensemble machine learning classifiers
This was the first time that Opuntia stricta mapping has been conducted based on satellite observations providing novel insights into its classification especially in heterogeneous ASALs

Summary

Introduction

Grassland biomes form one of the largest covers of terrestrial ecosystems and play a critical role ecologically, socially, and economically. Grasslands provide services for carbon sinks [1,2], biodiversity conservation [3], soil conservation [4,5], and forage biomass for herbivores [6,7]. They provide a livelihood to 800 million people [8,9] and their products contribute significantly to calories and protein consumed globally [10]. Tourism services are found in these biomes and coupled with the livestock market, directly and indirectly, earn governments billions of dollars

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