Mapping of Oriental Migratory Locust Habitat Using Landsat OLI Images in Dongying City, China

Zheng, Xiaomei; Huang, Jingfeng; Li, Hongmei; Mansaray, Lamin R.; Song, Peilin; Dou, Yujie

doi:10.1109/agro-geoinformatics.2018.8476141

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Li et al [ 72 ] used 14 HJ-1 CCD images (30 m spatial resolution) to derive NDVI time series to produce a land cover classification map and convert it to potential habitats of Asian migratory locusts in Hebei Province. Zheng et al [ 73 ] applied decision tree-based classification for six Landsat Operational Land Imager (OLI) images in the Dongying region to derive Oriental migratory locust habitat in 2015. Shi et al [ 74 ] analyzed time series of MODIS and Landsat data between 2000 and 2016 to estimate annual changes in Oriental migratory locust habitat.…”

Section: Resultsmentioning

confidence: 99%

Application of Remote Sensing Data for Locust Research and Management—A Review

Klein

Oppelt

Kuenzer

2021

Insects

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Recently, locust outbreaks around the world have destroyed agricultural and natural vegetation and caused massive damage endangering food security. Unusual heavy rainfalls in habitats of the desert locust (Schistocerca gregaria) and lack of monitoring due to political conflicts or inaccessibility of those habitats lead to massive desert locust outbreaks and swarms migrating over the Arabian Peninsula, East Africa, India and Pakistan. At the same time, swarms of the Moroccan locust (Dociostaurus maroccanus) in some Central Asian countries and swarms of the Italian locust (Calliptamus italicus) in Russia and China destroyed crops despite developed and ongoing monitoring and control measurements. These recent events underline that the risk and damage caused by locust pests is as present as ever and affects 100 million of human lives despite technical progress in locust monitoring, prediction and control approaches. Remote sensing has become one of the most important data sources in locust management. Since the 1980s, remote sensing data and applications have accompanied many locust management activities and contributed to an improved and more effective control of locust outbreaks and plagues. Recently, open-access remote sensing data archives as well as progress in cloud computing provide unprecedented opportunity for remote sensing-based locust management and research. Additionally, unmanned aerial vehicle (UAV) systems bring up new prospects for a more effective and faster locust control. Nevertheless, the full capacity of available remote sensing applications and possibilities have not been exploited yet. This review paper provides a comprehensive and quantitative overview of international research articles focusing on remote sensing application for locust management and research. We reviewed 110 articles published over the last four decades, and categorized them into different aspects and main research topics to summarize achievements and gaps for further research and application development. The results reveal a strong focus on three species—the desert locust, the migratory locust (Locusta migratoria), and the Australian plague locust (Chortoicetes terminifera)—and corresponding regions of interest. There is still a lack of international studies for other pest species such as the Italian locust, the Moroccan locust, the Central American locust (Schistocerca piceifrons), the South American locust (Schistocerca cancellata), the brown locust (Locustana pardalina) and the red locust (Nomadacris septemfasciata). In terms of applied sensors, most studies utilized Advanced Very-High-Resolution Radiometer (AVHRR), Satellite Pour l’Observation de la Terre VEGETATION (SPOT-VGT), Moderate-Resolution Imaging Spectroradiometer (MODIS) as well as Landsat data focusing mainly on vegetation monitoring or land cover mapping. Application of geomorphological metrics as well as radar-based soil moisture data is comparably rare despite previous acknowledgement of their importance for locust outbreaks. Despite great advance and usage of available remote sensing resources, we identify several gaps and potential for future research to further improve the understanding and capacities of the use of remote sensing in supporting locust outbreak- research and management.

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Section: Resultsmentioning

confidence: 99%

Application of Remote Sensing Data for Locust Research and Management—A Review

Klein

Oppelt

Kuenzer

2021

Insects

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“…Previous studies have conducted considerable research on locust habitat monitoring and meteorological conditions needed for locust growth, respectively. For locust habitat monitoring, remote sensing has been widely used because of its advantages of real-time and large-scale observations [15][16][17][18]. The researchers mainly focused on mapping and dynamic change monitoring of locust habitat, detection of potential breeding areas, or dynamic changes of vegetation in locust habitat [13,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Study on the Forming Mechanism of the High-Density Spot of Locust Coupled with Habitat Dynamic Changes and Meteorological Conditions Based on Time-Series Remote Sensing Images

et al. 2022

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The outbreak of the Asian migratory locust (Locusta migratoria migratoria) (AML) can deal a great blow to agriculture and grassland farming. The emergence of high-density locusts facilitates the outbreak of locusts. Understanding the forming mechanism of the high-density spot of locust (HDSL) is very important for locust monitoring and control. To achieve this goal, this paper took Nong’an County, which used to form an HDSL in 2017, as the study area. Firstly, based on the habitat classification system, support vector machine (SVM), random forest (RF), and maximum likelihood (ML) methods were employed to explore the best classification method for locust habitats. Then, the optimal method was applied to monitor habitat dynamic changes from 2014 to 2017 in the HDSL in Nong’an. Finally, the HDSL forming mechanism was clarified coupled with habitat dynamic changes and meteorological data. The results showed that the SVM method was the optimal method, with an accuracy of 95.28%, which is higher than the RF and ML methods by 0.25% and 8.52%, respectively. The annual increased barren land and sufficient reeds provided adequate suitable habitats for the breeding of AML. From 2014 to 2016, the temperatures during the overwintering and hatching periods were higher than the 2010–2018 average, and the precipitation during the spawning period was lower than the 2010–2018 average. The precipitation during the growing period in 2017 was 30.8 mm less than the average from 2010 to 2018. All these characteristics were conducive to the reproduction of locusts. We concluded that the suitable habitat and meteorological conditions increased the locust quantity yearly, resulting in the formation of HDSL. These results are instrumental for monitoring potential high-risk outbreak areas, which is important to improve locust control and ensure food security.

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Mapping of Oriental Migratory Locust Habitat Using Landsat OLI Images in Dongying City, China

Cited by 2 publications

References 13 publications

Application of Remote Sensing Data for Locust Research and Management—A Review

Application of Remote Sensing Data for Locust Research and Management—A Review

Study on the Forming Mechanism of the High-Density Spot of Locust Coupled with Habitat Dynamic Changes and Meteorological Conditions Based on Time-Series Remote Sensing Images

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