Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors
The fall armyworm (FAW) (<i>Spodoptera frugiperda</i>) (J. E. Smith) is a migratory pest that lacks diapause and has raised widespread concern in recent years due to its global dispersal and infestation. Seasonal environmental changes lead to its large-scale seasonal activities, and quan...
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/17/4415 |
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| author | Yanru Huang Hua Lv Yingying Dong Wenjiang Huang Gao Hu Yang Liu Hui Chen Yun Geng Jie Bai Peng Guo Yifeng Cui |
| author_facet | Yanru Huang Hua Lv Yingying Dong Wenjiang Huang Gao Hu Yang Liu Hui Chen Yun Geng Jie Bai Peng Guo Yifeng Cui |
| author_sort | Yanru Huang |
| collection | DOAJ |
| description | The fall armyworm (FAW) (<i>Spodoptera frugiperda</i>) (J. E. Smith) is a migratory pest that lacks diapause and has raised widespread concern in recent years due to its global dispersal and infestation. Seasonal environmental changes lead to its large-scale seasonal activities, and quantitative simulations of its dispersal patterns and spatiotemporal distribution facilitate integrated pest management. Based on remote sensing data and meteorological assimilation products, we constructed a mechanistic model of the dynamic distribution of FAW (FAW-DDM) by integrating weather-driven flight of FAW with host plant phenology and environmental suitability. The potential distribution of FAW in China from February to August 2020 was simulated. The results showed a significant linear relationship between the dates of the first simulated invasion and the first observed invasion of FAW in 125 cities (<i>R</i><sup>2</sup> = 0.623; <i>p</i> < 0.001). From February to April, FAW was distributed in the Southwestern and Southern Mountain maize regions mainly due to environmental influences. From May to June, FAW spread rapidly, and reached the Huanghuaihai and North China maize regions between June to August. Our results can help in developing pest prevention and control strategies with data on specific times and locations, reducing the impact of FAW on food security. |
| first_indexed | 2024-03-10T01:16:59Z |
| format | Article |
| id | doaj.art-47b6b92ea4074271a600987c81f78728 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T01:16:59Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-47b6b92ea4074271a600987c81f787282023-11-23T14:06:18ZengMDPI AGRemote Sensing2072-42922022-09-011417441510.3390/rs14174415Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-FactorsYanru Huang0Hua Lv1Yingying Dong2Wenjiang Huang3Gao Hu4Yang Liu5Hui Chen6Yun Geng7Jie Bai8Peng Guo9Yifeng Cui10Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Plant Protection, Nanjing Agricultural University, Nanjing 210095, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCollege of Plant Protection, Nanjing Agricultural University, Nanjing 210095, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaCollege of Plant Protection, Nanjing Agricultural University, Nanjing 210095, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaInstitute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaThe fall armyworm (FAW) (<i>Spodoptera frugiperda</i>) (J. E. Smith) is a migratory pest that lacks diapause and has raised widespread concern in recent years due to its global dispersal and infestation. Seasonal environmental changes lead to its large-scale seasonal activities, and quantitative simulations of its dispersal patterns and spatiotemporal distribution facilitate integrated pest management. Based on remote sensing data and meteorological assimilation products, we constructed a mechanistic model of the dynamic distribution of FAW (FAW-DDM) by integrating weather-driven flight of FAW with host plant phenology and environmental suitability. The potential distribution of FAW in China from February to August 2020 was simulated. The results showed a significant linear relationship between the dates of the first simulated invasion and the first observed invasion of FAW in 125 cities (<i>R</i><sup>2</sup> = 0.623; <i>p</i> < 0.001). From February to April, FAW was distributed in the Southwestern and Southern Mountain maize regions mainly due to environmental influences. From May to June, FAW spread rapidly, and reached the Huanghuaihai and North China maize regions between June to August. Our results can help in developing pest prevention and control strategies with data on specific times and locations, reducing the impact of FAW on food security.https://www.mdpi.com/2072-4292/14/17/4415fall armywormdynamic distributionmigration simulationmaize phenologyenvironmental suitability |
| spellingShingle | Yanru Huang Hua Lv Yingying Dong Wenjiang Huang Gao Hu Yang Liu Hui Chen Yun Geng Jie Bai Peng Guo Yifeng Cui Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors Remote Sensing fall armyworm dynamic distribution migration simulation maize phenology environmental suitability |
| title | Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors |
| title_full | Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors |
| title_fullStr | Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors |
| title_full_unstemmed | Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors |
| title_short | Mapping the Spatio-Temporal Distribution of Fall Armyworm in China by Coupling Multi-Factors |
| title_sort | mapping the spatio temporal distribution of fall armyworm in china by coupling multi factors |
| topic | fall armyworm dynamic distribution migration simulation maize phenology environmental suitability |
| url | https://www.mdpi.com/2072-4292/14/17/4415 |
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