Outbreak of Moroccan Locust in Sardinia (Italy): A Remote Sensing Perspective

The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats.

[1]  M. Villarreal,et al.  Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986-2020) , 2022, Remote. Sens..

[2]  Arianne J. Cease,et al.  What Have We Learned after Millennia of Locust Invasions? , 2022, Agronomy.

[3]  I. Georgieva,et al.  ESA WorldCover 10 m 2020 v100 , 2021 .

[4]  W. L. Ellenburg,et al.  Limitations of Remote Sensing in Assessing Vegetation Damage Due to the 2019–2021 Desert Locust Upsurge , 2021, Frontiers in Climate.

[5]  M. Herold,et al.  Global land use changes are four times greater than previously estimated , 2021, Nature Communications.

[6]  M. G. Sergeev.  Ups and Downs of the Italian Locust (Calliptamus italicus L.) Populations in the Siberian Steppes: On the Horns of Dilemmas , 2021, Agronomy.

[7]  N. Oppelt,et al.  Application of Remote Sensing Data for Locust Research and Management—A Review , 2021, Insects.

[8]  A. Showler,et al.  Incidence and Ramifications of Armed Conflict in Countries with Major Desert Locust Breeding Areas , 2021, Agronomy.

[9]  Wenjiang Huang,et al.  Land use/cover changes in the Oriental migratory locust area of China: Implications for ecological control and monitoring of locust area , 2020 .

[10]  Christopher Conrad,et al.  Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies , 2020, Remote. Sens..

[11]  Lukas W. Lehnert,et al.  Land Cover Classification using Google Earth Engine and Random Forest Classifier - The Role of Image Composition , 2020, Remote. Sens..

[12]  M. Hansen,et al.  Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series , 2020 .

[13]  Claudia Kuenzer,et al.  Automated Mapping of Antarctic Supraglacial Lakes Using a Machine Learning Approach , 2020, Remote. Sens..

[14]  D. Malakhov,et al.  An Ecological Niche Model for Dociostaurus maroсcanus, Thunberg, 1815 (Orthoptera, Acrididae): The Nesting Environment and Survival of Egg-Pods , 2020 .

[15]  Deren Li,et al.  A review of vegetation phenological metrics extraction using time-series, multispectral satellite data , 2020 .

[16]  Yingying Dong,et al.  Migratory Locust Habitat Analysis With PB-AHP Model Using Time-Series Satellite Images , 2020, IEEE Access.

[17]  Arianne J. Cease,et al.  A Global Review on Locusts (Orthoptera: Acrididae) and Their Interactions With Livestock Grazing Practices , 2019, Front. Ecol. Evol..

[18]  Olivier Merlin,et al.  Soil moisture from remote sensing to forecast desert locust presence , 2019, Journal of Applied Ecology.

[19]  Michel Lecoq,et al.  Locust and Grasshopper Management. , 2019, Annual review of entomology.

[20]  Carlos Casanova,et al.  Machine learning approach to locate desert locust breeding areas based on ESA CCI soil moisture , 2018, Journal of Applied Remote Sensing.

[21]  Olivier Merlin,et al.  Smos based High Resolution Soil Moisture Estimates for Desert Locust Preventive Management , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Wenjiang Huang,et al.  The influence of landscape's dynamics on the Oriental Migratory Locust habitat change based on the time-series satellite data. , 2018, Journal of environmental management.

[23]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[24]  R. Colditz,et al.  Timely monitoring of Asian Migratory locust habitats in the Amudarya delta, Uzbekistan using time series of satellite remote sensing vegetation index. , 2016, Journal of environmental management.

[25]  J. Weiss.  Do locusts seek greener pastures?: an evaluation of MODIS vegetation indices to predict presence, abundance and impact of the Australian plague locust in southeastern Australia , 2016 .

[26]  Pierre Defourny,et al.  Operational Monitoring of the Desert Locust Habitat with Earth Observation: An Assessment , 2015, ISPRS Int. J. Geo Inf..

[27]  A. Motroni,et al.  Bioclimate map of Sardinia (Italy) , 2015 .

[28]  Stefan Dech,et al.  Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X/TerraSAR-X data , 2015 .

[29]  P. Hostert,et al.  Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. , 2015 .

[30]  Pierre Defourny,et al.  A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS , 2015, Remote. Sens..

[31]  Alexey Terekhov,et al.  Long-term agricultural land-cover change and potential for cropland expansion in the former Virgin Lands area of Kazakhstan , 2015 .

[32]  Jeng-Tze Yang,et al.  Location and Characterization of Breeding Sites of Solitary Desert Locust Using Satellite Images Landsat 7 ETM+ and Terra MODIS , 2015 .

[33]  Robert A. Cheke,et al.  Soil moisture assessments for brown locust Locustana pardalina breeding potential using synthetic aperture radar , 2014 .

[34]  Cyril Piou,et al.  Coupling historical prospection data and a remotely-sensed vegetation index for the preventative control of Desert locusts , 2013 .

[35]  C. Piou,et al.  Effect of vegetation on density thresholds of adult desert locust gregarization from survey data in Mauritania , 2013 .

[36]  Stefan Dech,et al.  Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas — A novel product for the Mekong Basin , 2013 .

[37]  J. Lobo,et al.  Estimation of climatic favourable areas for locust outbreaks in Spain: integrating species' presence records and spatial information on outbreaks , 2013 .

[38]  Keith Cressman,et al.  Role of remote sensing in desert locust early warning , 2013 .

[39]  Alexandre V. Latchininsky,et al.  Locusts and remote sensing: a review , 2013 .

[40]  Edward Deveson,et al.  Satellite normalized difference vegetation index data used in managing Australian plague locusts , 2013 .

[41]  U. Gessner,et al.  Regional land cover mapping and change detection in Central Asia using MODIS time-series , 2012 .

[42]  V. Radeloff,et al.  Effects of institutional changes on land use: agricultural land abandonment during the transition from state-command to market-driven economies in post-Soviet Eastern Europe , 2012 .

[43]  Christelle Vancutsem,et al.  Development and Application of Multi-Temporal Colorimetric Transformation to Monitor Vegetation in the Desert Locust Habitat , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  G. Sword,et al.  Locusts and grasshoppers: behavior, ecology, and biogeography , 2011 .

[45]  Volker C. Radeloff,et al.  Determinants of agricultural land abandonment in post-Soviet European Russia , 2011 .

[46]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[47]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[48]  R. Sivanpillai,et al.  UTILITY OF THE IRS-AWiFS DATA TO MAP THE POTENTIAL ITALIAN LOCUST (Calliptamus italicus) HABITATS IN NORTHEAST KAZAKHSTAN , 2009 .

[49]  M. Lecoq,et al.  Preventive control and desert locust plagues , 2008 .

[50]  R. Sivanpillai,et al.  Can early season Landsat images improve locust habitat monitoring in the Amudarya River Delta of Uzbekistan , 2007 .

[51]  Ramesh Sivanpillai,et al.  Mapping Locust Habitats in the Amudarya River Delta, Uzbekistan with Multi-Temporal MODIS Imagery , 2007, Environmental management.

[52]  Pietro Ceccato,et al.  Use of Remote Sensing for Monitoring Climate Variability for Integrated Early Warning Systems: Applications for Human Diseases and Desert Locust Management , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[53]  A. Bouchard,et al.  Vegetation Composition and Succession of Abandoned Farmland: Effects of Ecological, Historical and Spatial Factors , 2005, Landscape Ecology.

[54]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[55]  D. Hunter.  Advances in the control of locusts (Orthoptera: Acrididae) in eastern Australia: from crop protection to preventive control , 2004 .

[56]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[57]  A. Latchininsky.  Moroccan locust Dociostaurus maroccanus (Thunberg, 1815): a faunistic rarity or an important economic pest? , 1998, Journal of Insect Conservation.

[58]  R. Pantaleoni,et al.  Some aspects of locust control in Sardinia in the first half of the twentieth century / Alcuni aspetti della lotta alle cavallette in Sardegna nella prima metà del XX secolo , 2004 .

[59]  E. Despland.  Fractal index captures the role of vegetation clumping in locust swarming , 2003 .

[60]  J. U. Hielkema,et al.  Operational use of environmental satellite remote sensing and satellite communications technology for global food security and locust control by FAO: The ARTEMIS and DIANA systems , 1994 .

[61]  M. Batistella,et al.  Static and dynamic cartographies of the biotopes of the grasshopper Rhammatocerus schistocercoides (Rehn, 1906) in the state of Mato Grosso, Brazil , 1994 .

[62]  P. Legendre.  Spatial Autocorrelation: Trouble or New Paradigm? , 1993 .

[63]  K. Bryceson,et al.  Use of Remotely-Sensed Data in the Australian Plague Locust Commission , 1993 .

[64]  K. Bryceson.  The use of Landsat MSS data to determine the locust eggbeds of locust eggbeds in the Riverina region of New South Wales, Australia , 1989 .

[65]  D. Hunter,et al.  Identification and monitoring of Australian plague locust habitats from landsat , 1983 .

[66]  D. Pedgley.  Erts Surveys a 500 KM 2 Locust Breeding Site in Saudi Arabia , 1974 .

[67]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .