Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Diego Gómez; Pablo Salvador; Julia Sanz; Carlos Casanova; Daniel Taratiel; Jose Luis Casanova

doi:10.1117/1.JRS.12.036011

28 August 2018 Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture

Diego Gómez, Pablo Salvador, Julia Sanz, Carlos Casanova, Daniel Taratiel, Jose Luis Casanova

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Journal of Applied Remote Sensing, Vol. 12, Issue 3, 036011 (August 2018). https://doi.org/10.1117/1.JRS.12.036011

Abstract

Desert locusts have attacked crops since antiquity. To prevent or mitigate its effects on local communities, it is necessary to precisely locate its breeding areas. Previous works have relied on precipitation and vegetation index datasets obtained by satellite remote sensing. However, these products present some limitations in arid or semiarid environments. We have explored a parameter: soil moisture (SM); and examined its influence on the desert locust wingless juveniles. We have used two machine learning algorithms (generalized linear model and random forest) to evaluate the link between hopper presences and SM conditions under different time scenarios. RF obtained the best model performance with very good validation results according to the true skill statistic and receiver operating characteristic curve statistics. It was found that an area becomes suitable for breeding when the minimum SM values are over 0.07 m3 / m3 during 6 days or more. These results demonstrate the possibility to identify breeding areas in Mauritania by means of SM, and the suitability of ESA CCI SM product to complement or substitute current monitoring techniques based on precipitation datasets.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Diego Gómez, Pablo Salvador, Julia Sanz, Carlos Casanova, Daniel Taratiel, and Jose Luis Casanova "Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture," Journal of Applied Remote Sensing 12(3), 036011 (28 August 2018). https://doi.org/10.1117/1.JRS.12.036011

Received: 24 April 2018; Accepted: 7 August 2018; Published: 28 August 2018

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