Identifying Corn Lodging in the Mature Period Using Chinese GF-1 PMS Images

Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to iden...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-02, Vol.15 (4), p.894
Main Authors: Huang, Xianda, Xuan, Fu, Dong, Yi, Su, Wei, Wang, Xinsheng, Huang, Jianxi, Li, Xuecao, Zeng, Yelu, Miao, Shuangxi, Li, Jiayu
Format: Article
Language:English
Subjects:
Claims adjustment (Insurance)
Classification
Corn
corn lodging
GF-1 PMS image
Identification
Lodging
Machine learning
Monitoring
Neural networks
random forest
Remote sensing
Robustness
Satellites
Software
Spectral bands
Support vector machines
texture feature
Typhoons
Vegetables
Vegetation
Vegetation index
Wheat
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Summary:Efficient, fast, and accurate crop lodging monitoring is urgent for farmers, agronomists, insurance loss adjusters, and policymakers. This study aims to explore the potential of Chinese GF-1 PMS high-spatial-resolution images for corn lodging monitoring and to find a robust and efficient way to identify corn lodging accurately and efficiently. Three groups of image features and five machine-learning approaches are used for classifying non-lodged, moderately lodged, and severely lodged areas. Our results reveal that (1) the combination of spectral bands, optimized vegetation indexes, and texture features classify corn lodging with an overall accuracy of 93.81% and a Kappa coefficient of 0.91. (2) The random forest is an efficient, robust, and easy classifier to identify corn lodging with the F1-score of 0.95, 0.92, and 0.95 for non-lodged, moderately lodged, and severely lodged areas, respectively. (3) The GF-1 PMS image has great potential for identifying corn lodging on a regional scale.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15040894