Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 7; p. 1276
• Main Authors: Ellenburg, W. Lee, Mishra, Vikalp, Roberts, Jason B., Limaye, Ashutosh S., Case, Jonathan L., Blankenship, Clay B., Cressman, Keith
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2021
• Subjects: Bands; Breeding grounds; Datasets; desert locust; Egg laying; Eggs; Environmental conditions; Estimates; Land information systems; modeling; Moisture content; Physical characteristics; Physical properties; Remote sensing; Soil moisture; Soil properties; Soil texture; Texture; Thresholds; Vegetation; Water content; Djibouti; Eritrea; Kenya; East Africa
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13071276

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.