Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Meteorological Observations
2.2.1. Precipitation Data from the GPCC
2.2.2. Land Surface Data from the ECMWF
2.3. Remotely Sensed Data
2.3.1. Soil Moisture Data from the Fengyun Satellite
2.3.2. MODIS NDVI
2.3.3. DEM Data
2.4. Analysis of the Climatic Anomalies’ Contributions to the Desert Locust Upsurge
2.5. Evaluation of Impacts and Growing Trends of Desert Locust Upsurge
3. Results
3.1. The Contribution of Climatic Conditions to the 2020 Desert Locust Upsurge
3.1.1. Precipitation and Its Anomalies
3.1.2. Air Temperature and Its Anomalies
3.1.3. Land Surface Soil Moisture
3.2. The Impact Assessment of Desert Locust Swarms on the Local Vegetation
3.2.1. NDVI Anomalies in East Africa
3.2.2. NDVI Anomalies in the Indo-Pakistan Border Area
3.3. Possibility Analysis of Desert Locust Swarms Migrating into the Indochina Peninsula and Southwest China
4. Discussion
4.1. The Importance of Long-Term Earth Observations
4.2. Implications of This Study on Desert Locust Control
4.3. Future Conditions for Desert Locust Outbreaks and Upsurges
4.4. Study Limitations and Uncertainty
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Middleton, N.J.; Sternberg, T. Climate hazards in drylands: A review. Earth-Sci. Rev. 2013, 126, 48–57. [Google Scholar] [CrossRef]
- Zhang, L.; Lecoq, M.; Latchininsky, A.; Hunter, D. Locust and Grasshopper Management. Ann. Rev. Entomol. 2019, 64, 15–34. [Google Scholar] [CrossRef] [PubMed]
- Arnold, v.H.; Cressman, K.; Magor, J.I. Preventing desert locust plagues: Optimizing management interventions. Entomol. Experiment. Appl. 2007, 122, 191–214. [Google Scholar] [CrossRef]
- Madeleine, S. A Plague of Locusts has Descendedon East Africa. Climate Change May Be to Blame. Available online: https://www.nationalgeographic.com/science/2020/02/locust-plague-climate-science-east-africa/ (accessed on 1 January 2021).
- Vallebona, C.; Genesio, L.; Crisci, A.; Pasqui, M.; Vecchia, A.D.; Maracchi, G. Large-scale climatic patterns forcing desert locust upsurges in West Africa. Clim. Res. 2008, 37, 35–41. [Google Scholar] [CrossRef]
- Wang, B.; Deveson, E.D.; Waters, C.; Spessa, A.; Lawton, D.; Feng, P.; Liu, D.L. Future climate change likely to reduce the Australian plague locust (Chortoicetes terminifera) seasonal outbreaks. Sci. Total. Environ. 2019, 668, 947–957. [Google Scholar] [CrossRef]
- Tian, H.; Stige, L.C.; Cazelles, B.; Kausrud, K.L.; Svarverud, R.; Stenseth, N.C.; Zhang, Z. Reconstruction of a 1,910-y-long locust series reveals consistent associations with climate fluctuations in China. Proc. Natl. Acad. Sci. USA 2011, 108, 14521–14526. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Meynard, C.N.; Lecoq, M.; Chapuis, M.P.; Piou, C. On the relative role of climate change and management in the current desert locust outbreak in East Africa. Glob. Chang. Biol. 2020, 26, 3753–3755. [Google Scholar] [CrossRef] [PubMed]
- Tratalos, J.A.; Cheke, R.A.; Healey, R.G.; Stenseth, N.C. Desert locust populations, rainfall and climate change: Insights from phenomenological models using gridded monthly data. Clim. Res. 2010, 43, 229–239. [Google Scholar] [CrossRef]
- Veran, S.; Simpson, S.J.; Sword, G.A.; Deveson, E.; Piry, S.; Hins, J.E.; Berthier, K. Modeling spatiotemporal dynamics of outbreaking species: Influence of environment and migration in a locust. Ecology 2015, 96, 737–748. [Google Scholar] [CrossRef] [Green Version]
- Dinku, T.; Ceccato, P.; Cressman, K.; Connor, S.J. Evaluating Detection Skills of Satellite Rainfall Estimates over Desert Locust Recession Regions. J. Appl. Meteorol. Clim. 2010, 49, 1322–1332. [Google Scholar] [CrossRef]
- Gómez, D.; Salvador, P.; Sanz, J.; Casanova, C.; Taratiel, D.; Casanova, J.L. Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture. J. Appl. Remote Sens. 2018, 12, 036011. [Google Scholar] [CrossRef] [Green Version]
- Escorihuela, M.J.; Merlin, O.; Stefan, V.; Moyano, G.; Eweys, O.A.; Zribi, M.; Kamara, S.; Benahi, A.S.; Ebbe, M.A.B.; Chihrane, J.; et al. SMOS based high resolution soil moisture estimates for desert locust preventive management. Remote Sens. Appl. Soc. Environ. 2018, 11, 140–150. [Google Scholar] [CrossRef]
- Piou, C.; Gay, P.-E.; Benahi, A.S.; Ebbe, M.A.O.B.; Chihrane, J.; Ghaout, S.; Cisse, S.; Diakite, F.; Lazar, M.; Cressman, K.; et al. Soil moisture from remote sensing to forecast desert locust presence. J. Appl. Ecol. 2018, 45, 966–975. [Google Scholar] [CrossRef]
- Latchininsky, A.V. Locusts and remote sensing: A review. J. Appl. Remote Sens. 2013, 7, 075099. [Google Scholar] [CrossRef] [Green Version]
- Hielkema, J.U.; Roffey, J.; Tucker, C.J. Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data. Int. J. Remote Sens. 1986, 7, 1609–1622. [Google Scholar] [CrossRef]
- Meynard, C.N.; Gay, P.E.; Lecoq, M.; Foucart, A.; Piou, C.; Chapuis, M.P. Climate-driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change. Glob. Chang. Biol. 2017, 23, 4739–4749. [Google Scholar] [CrossRef] [PubMed]
- Symmons, P.M.; Cressman, K. Desert Locust Guidelines; Food Agriculture Organization of the United Nations: Rome, Italy, 2001. [Google Scholar]
- Shroder, J.F.; Sivanpillai, R. Biological and Environmental Hazards, Risks, and Disasters; Elsevier: Amsterdam, The Netherlands, 2015; Chapter 4. [Google Scholar]
- Roffey, J.; Popov, G. Environmental and Behavioural Processes in a Desert Locust Outbreak. Nature 1968, 219, 446–450. [Google Scholar] [CrossRef]
- Pener, M.P.; Simpson, S.J. Locust Phase Polyphenism: An Update. Adv. Insect Physiol. 2009, 36, 1–272. [Google Scholar] [CrossRef]
- Kiage, L.M.; Liu, K. Palynological evidence of climate change and land degradation in the Lake Baringo area, Kenya, East Africa, since AD 1650. Palaeogeogr. Palaeoclim. Palaeoecol. 2009, 279, 60–72. [Google Scholar] [CrossRef]
- Schneider, U.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Ziese, M.; Rudolf, B. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 2014, 115, 15–40. [Google Scholar] [CrossRef] [Green Version]
- Becker, A.; Finger, P.; Meyer-Christoffer, A.; Rudolf, B. A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901 Cpresent. Earth Syst. Sci. Data Discuss. 2013, 5. [Google Scholar] [CrossRef] [Green Version]
- Schneider, U.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.25°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historical Data. 2018. Available online: https://www.dante-project.org/datasetPages/gpcc (accessed on 18 April 2019).
- Ziese, M.; Becker, A.; Finger, P.; Meyer-Christoffer, A.; Rudolf, B.; Schneider, U. GPCC First Guess Product at 1.0°: Near Real-Time First Guess monthly Land-Surface Precipitation from Rain-Gauges based on SYNOP Data. Available online: https://opendata.dwd.de/climate_environment/GPCC/html/download_gate.html (accessed on 18 April 2019).
- Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horanyi, A.; Munoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 1–51. [Google Scholar] [CrossRef]
- Sabaghya, S.; Walker, J.P.; Renzullo, L.J.; Jackson, T.J. Spatially enhanced passive microwave derived soil moisture: Capabilities and opportunities. Remote Sens. Environ. 2018, 209, 551–580. [Google Scholar] [CrossRef]
- Zhu, Y.; Li, X.; Pearson, S.; Wu, D.; Sun, R.; Johnson, S.; Wheeler, J.; Fang, S. Evaluation of Fengyun-3C soil moisture products using in-Situ data from the Chinese Automatic Soil moisture Observation Stations: A case study in Henan Province, China. Water 2019, 11, 248. [Google Scholar] [CrossRef] [Green Version]
- Wang, L.; Fang, S.; Pei, Z.; Zhu, Y.; Dao Nguyen, K.; Han, W. Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture. Remote Sens. 2020, 12, 1038. [Google Scholar] [CrossRef] [Green Version]
- Brown, J.F.; Howard, D.; Wylie, B.; Frieze, A.; Ji, L.; Gacke, C. Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition. Remote Sens. 2015, 7, 16226–16240. [Google Scholar] [CrossRef] [Green Version]
- Hayakawa, Y.S.; Oguchi, T.; Zhou, L. Comparison of new and existing global digital elevation models: ASTER G-DEM and SRTM-3. Geophys. Res. Lett. 2008, 35. [Google Scholar] [CrossRef]
- Danielson, J.; Gesch, D. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010); U.S. Geological Survey Open-File Report 2011-1073; US Department of the Interior, US Geological Survey: Washington, DC, USA, 2011; 26p.
- Easterling, W.E.; Aggarwal, P.K.; Batima, P.; Brander, K.M.; Erda, L.; Howden, S.M.; Kirilenko, A.; Morton, J.; Soussana, J.-F.; Schmidhuber, J.; et al. Food, fibre and forest products. In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linden, P.J., Hanson, C.E., Eds.; Cambridge University Press: Cambridge, UK, 2007; pp. 273–313. [Google Scholar]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wang, L.; Zhuo, W.; Pei, Z.; Tong, X.; Han, W.; Fang, S. Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss. Remote Sens. 2021, 13, 680. https://doi.org/10.3390/rs13040680
Wang L, Zhuo W, Pei Z, Tong X, Han W, Fang S. Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss. Remote Sensing. 2021; 13(4):680. https://doi.org/10.3390/rs13040680
Chicago/Turabian StyleWang, Lei, Wen Zhuo, Zhifang Pei, Xingyuan Tong, Wei Han, and Shibo Fang. 2021. "Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss" Remote Sensing 13, no. 4: 680. https://doi.org/10.3390/rs13040680
APA StyleWang, L., Zhuo, W., Pei, Z., Tong, X., Han, W., & Fang, S. (2021). Using Long-Term Earth Observation Data to Reveal the Factors Contributing to the Early 2020 Desert Locust Upsurge and the Resulting Vegetation Loss. Remote Sensing, 13(4), 680. https://doi.org/10.3390/rs13040680