干旱区生态环境知识资源中心(Arid): Modelling desert locust presences using 32-year soil moisture data on a large-scale

Arid

DOI	10.1016/j.ecolind.2020.106655
	Modelling desert locust presences using 32-year soil moisture data on a large-scale
	Gomez, Diego; Salvador, Pablo; Sanz, Julia; Luis Casanova, Jose
通讯作者	Gomez, D
来源期刊	ECOLOGICAL INDICATORS
ISSN	1470-160X
EISSN	1872-7034
出版年	2020
卷号	117
英文摘要	The desert locust is the world's most dangerous migratory pest according to the Food and Agriculture Organization of the United Nations (FAO), and its recession area extends over more than 30 countries. While promising assertions have been made to relate desert locust presences with remotely sensed soil moisture (SM), they have not yet been tested robustly in large-scale studies. The aim of this work was to evaluate the potential of soil moisture data to detect desert locust presences (solitarious nymphs) using the European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product from 1985 to 2017. Firstly, 5 machine learning algorithms were fitted using various pre-processing options and variable creation scenarios. Subsequently, the best performing models were fine-tuned using the k-fold cross validation technique and validate the results with an independent dataset taking random dates and locations. The best results were obtained by the weighted k-nearest neighbours (kknn) and random forest (rf) models. The kknn performance was ROC-AUC = 0.79, KAPPA = 0.61 and accuracy = 0.83; and the rf obtained a ROC-AUC = 0.91, KAPPA = 0.56 and accu-racy = 0.81. In general, both models agreed that SM values above 0.11 m(3)/m(3) led to increase the possibility to observe nymph of desert locust with a time delay between 35 and 79 days depending on the model. Furthermore, it was found that model performances increased when the time interval of the variables was smaller, so that we suggest the use of mean SM values over 4 days period to link presences and SM values. These results prove the validity of our methodology to identify favourable breeding areas by means of ESA CCI SM dataset using ma-chine learning approaches over the entire recession area of desert locust, and it could be used in any of the affected countries for this pest. Future improvements in ESA CCI SM product (e.g. higher spatial resolution) may lead to improve model accuracies with monitoring and preventive purposes.
英文关键词	ESA CCI SM Desert locust Machine learning Monitoring and preventive Soil moisture
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000555550000008
WOS关键词	PRESENCE-ONLY DATA ; SCHISTOCERCA-GREGARIA ; EARTH OBSERVATION ; SELECTION BIAS ; DISTRIBUTIONS ; VEGETATION ; PLAGUE ; HABITAT ; EAST
WOS类目	Biodiversity Conservation ; Environmental Sciences
WOS研究方向	Biodiversity & Conservation ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325384
作者单位	[Gomez, Diego; Salvador, Pablo; Sanz, Julia; Luis Casanova, Jose] Univ Valladolid, Remote Sensing Lab LATUV, Paseo Belen 11, Valladolid 47011, Spain
推荐引用方式 GB/T 7714	Gomez, Diego,Salvador, Pablo,Sanz, Julia,et al. Modelling desert locust presences using 32-year soil moisture data on a large-scale[J],2020,117.
APA	Gomez, Diego,Salvador, Pablo,Sanz, Julia,&Luis Casanova, Jose.(2020).Modelling desert locust presences using 32-year soil moisture data on a large-scale.ECOLOGICAL INDICATORS,117.
MLA	Gomez, Diego,et al."Modelling desert locust presences using 32-year soil moisture data on a large-scale".ECOLOGICAL INDICATORS 117(2020).