MONITORING MAIZE LODGING DISASTER VIA MULTI-TEMPORAL REMOTE SENSING IMAGES

被引：0
|
作者：
Gu, Xiaohe [1 ]
Sun, Qian [1 ]
Yang, Guijun [1 ]
Song, Xiaoyu [1 ]
Xu, Xingang [1 ]
机构：
[1] Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
来源：
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
关键词：
maize; lodging; vegetation index; change analysis; multi-temporal; YIELD;
D O I：
10.1109/igarss.2019.8900560
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
The study aimed to monitor maize lodging in a large scale by using multi-temporal HJ-1B CCD images. The variation of vegetation indexes before and after lodging was analyzed. The sensitive vegetation index of maize lodging was selected by correlation analysis method. The remote sensing monitoring model of maize lodging disaster was constructed, which used to map maize lodging distribution and disaster grade at large scale. The model was validated by field measured samples at last. Results showed that correlation between.RVI and lodging ratio was highest. The.RVI can be used as the best vegetation index for quantitative inversion of maize lodging by remote sensing. The overall accuracy of disaster grade classification was 87.5%, and Kappa coefficient was 0.817. It indicated that the model developed in the study could be used to map maize lodging coverage and spatial distribution of disaster grade.
引用
收藏
页码：7302 / 7305
页数：4
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