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
相关论文
共 50 条
  • [21] A comparative assessment of similarity measures for registration of multi-temporal remote sensing images
    Chen, HM
    Arora, MK
    Varshney, PK
    ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 2004, 3 : 3 - 11
  • [22] Protected Area Monitoring in the Niger Delta Using Multi-Temporal Remote Sensing
    Onojeghuo, Alex Okiemute
    Onojeghuo, Ajoke Ruth
    ENVIRONMENTS, 2015, 2 (04) : 500 - 520
  • [23] Infrastructure assessment for disaster management using multi-sensor and multi-temporal remote sensing imagery
    Butenuth, Matthias
    Frey, Daniel
    Nielsen, Allan Aasbjerg
    Skriver, Henning
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2011, 32 (23) : 8575 - 8594
  • [24] Monitoring maize lodging severity based on multi-temporal Sentinel-1 images using Time-weighted Dynamic time Warping
    Qu, Xuzhou
    Zhou, Jingping
    Gu, Xiaohe
    Wang, Yancang
    Sun, Qian
    Pan, Yuchun
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2023, 215
  • [25] Enhanced Dictionary based Sparse Representation Fusion for Multi-temporal Remote Sensing Images
    Lal, Anisha M.
    Anouncia, S. Margret
    EUROPEAN JOURNAL OF REMOTE SENSING, 2016, 49 : 317 - 336
  • [26] CHANGE DETECTION NETWORK OF NEARSHORE SHIPS FOR MULTI-TEMPORAL OPTICAL REMOTE SENSING IMAGES
    Cao, Jingyi
    You, Yanan
    Ning, Yuanyong
    Zhou, Wenli
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 2531 - 2534
  • [27] Information fusion techniques for change detection from multi-temporal remote sensing images
    Du, Peijun
    Liu, Sicong
    Xia, Junshi
    Zhao, Yindi
    INFORMATION FUSION, 2013, 14 (01) : 19 - 27
  • [28] Change detection of multi-temporal remote sensing images based on contourlet transform and ICA
    Wu Yi-Quan
    Cao Zhao-Qing
    Tao Fei-Xiang
    CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION, 2016, 59 (04): : 1284 - 1292
  • [29] A Multi-Temporal Convolutional Autoencoder Neural Network for Cloud Removal in Remote Sensing Images
    Sintarasirikulchai, Wassana
    Kasetkasem, Teerasit
    Isshiki, Tsuyoshi
    Chanwimaluang, Thitiporn
    Rakwatin, Preesan
    2018 15TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY (ECTI-CON), 2018, : 364 - 367
  • [30] Scene Distortion Detection on a Series of Multi-temporal Remote Sensing Images of the Same Territory
    Belov, A. M.
    Denisova, A. Y.
    ELEVENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2019), 2020, 11373
12345