干旱区生态环境知识资源中心(Arid): Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers

Arid

DOI	10.3390/rs13081494
	Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers
	Muthoka, James M.; Salakpi, Edward E.; Ouko, Edward; Yi, Zhuang-Fang; Antonarakis, Alexander S.; Rowhani, Pedram
通讯作者	Muthoka, JM (corresponding author), Univ Sussex, Dept Geog, Brighton BN1 9QJ, E Sussex, England.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:8
英文摘要	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.
英文关键词	invasive plant species remote sensing extreme gradient boost random forest spectral indices topographic indices
类型	Article
语种	英语
开放获取类型	gold, Green Accepted
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000644679000001
WOS关键词	LAND-USE ; VEGETATION CLASSIFICATION ; COVER CLASSIFICATION ; RANDOM FORESTS ; MODIS NDVI ; ACCURACY ; TIME ; ALGORITHMS ; INVASION ; IMPACTS
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351507
作者单位	[Muthoka, James M.; Antonarakis, Alexander S.; Rowhani, Pedram] Univ Sussex, Dept Geog, Brighton BN1 9QJ, E Sussex, England; [Salakpi, Edward E.] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England; [Ouko, Edward] Reg Ctr Mapping Resources Dev Tech Serv, Nairobi 00618, Kenya; [Yi, Zhuang-Fang] Dev Seed, Washington, DC 20001 USA
推荐引用方式 GB/T 7714	Muthoka, James M.,Salakpi, Edward E.,Ouko, Edward,et al. Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers[J],2021,13(8).
APA	Muthoka, James M.,Salakpi, Edward E.,Ouko, Edward,Yi, Zhuang-Fang,Antonarakis, Alexander S.,&Rowhani, Pedram.(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(8).
MLA	Muthoka, James M.,et al."Mapping Opuntia stricta in the Arid and Semi-Arid Environment of Kenya Using Sentinel-2 Imagery and Ensemble Machine Learning Classifiers".REMOTE SENSING 13.8(2021).