Monitoring and Predicting Desert Locust Plague Severity in Asia–Africa Using Multisource Remote Sensing Time-Series Data

Abstract

The outbreak of large-scale desert locust plague in 2020 has attracted wide attention in the world and caused serious damage to food security and livelihood of African and Asian people. Remote sensing techniques can provide indirect feedback on locust plagues, facilitating quick, and real-time monitoring of the occurrence and development of locusts, which is of great significance for ensuring national and regional food security and stability. The hidden Markov model (HMM) is a classic machine learning model that has been widely applied in the fields of time-series data mining. In this study, we aim to predict the severity of locust plague in croplands using the time-series dynamic change features extracted from remote sensing data via HMM. In addition, we assess the damages on the croplands using change detection methods by comparing the crop spectrum before and after the locust plague from two-phase (Feburary 23 and March 7, 2020) hyperspectral images covering substudy area (northern Narok, Kenya). Evaluated by the ground truth data, the OA of predicted results of the plague severity in April, May, June, and July are 0.78, 0.71, 0.74, and 0.72, respectively. The land cover classification OA of the substudy area of the two-phase images are 97.45 and 96.14. Our study demonstrates the validity of the HMM-based method using the remote sensing time-series data to predict locust plague and evaluate its damage. The results of the cropland change detection suggest that the damage of locusts can be quantitatively evaluated using hyperspectral images.

Keywords

• Change detection
• Hidden Markov Model (HMM)
• hyperspectral imagery
• locust plague prediction
• moderate-resolution imaging spectroradiometer (MODIS)
• semisupervised classification