| 协同多时相国产GF-1和GF-6卫星影像的艾草遥感识别 |
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| 引用本文： | 何真,胡洁,蔡志文,王文静,胡琼.协同多时相国产GF-1和GF-6卫星影像的艾草遥感识别[J].农业工程学报,2022,38(1):186-195. |
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| 作者姓名： | 何真 胡洁 蔡志文 王文静 胡琼 |
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| 作者单位： | 1.华中师范大学城市与环境科学学院/湖北省地理过程分析与模拟重点实验室，武汉 430079;;2. 华中农业大学植物科学技术学院，武汉 430070; 4. 华中农业大学宏观农业研究院，武汉 430070;;3. 华中农业大学资源与环境学院，武汉 430070; 4. 华中农业大学宏观农业研究院，武汉 430070; |
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| 基金项目： | 国家自然科学基金青年项目（41901380；41801371；42001303）；中央高校基本科研业务费专项基金（CCNU20QN032）；遥感科学国家重点实验室开放基金（OFSLRSS202022) |
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| 摘 要： | 艾叶具有巨大的食用和医用价值,近些年艾草种植面积在中国南方地区显著增加.掌握艾草空间分布信息对于区域作物种植结构调整、艾草产业布局优化具有重要现实意义.该研究以中国艾草主要生产地——湖北省蕲春县为例,探讨国产高分1号(GF-1)和高分6号(GF-6)卫星影像识别艾草的潜力.本文首先基于高分影像构建了20个光谱特征,然后...
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| 关 键 词： | 遥感 识别 GF-6 WFV GF-1 WFV 红边植被指数 随机森林 艾草 |
| 收稿时间： | 2021/8/24 0:00:00 |
| 修稿时间： | 2021/12/14 0:00:00 |
Remote sensing identification for Artemisia argyi integrating multi-temporal GF-1 and GF-6 images |
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| He Zhen,Hu Jie,Cai Zhiwen,Wang Wenjing,Hu Qiong.Remote sensing identification for Artemisia argyi integrating multi-temporal GF-1 and GF-6 images[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(1):186-195. |
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| Authors: | He Zhen Hu Jie Cai Zhiwen Wang Wenjing Hu Qiong |
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| Institution: | 2. College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China; 4. Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China;;3. College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China; 4. Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China;;1. School of Urban and Environmental Sciences, Central China Normal University/Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079, China;; 1.School of Resource Environment, Huazhong Agricultural University, Wuhan 430000, China; |
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| Abstract: | Artemisia argyi has been one of the typical Chinese herbs with a great an edible and medical value. The planting area has also increased significantly in southern China in recent years. It is of great practical significance to clarify the spatial distribution pattern of Artemisia argyi, particularly for the decision making on the regional crop planting structure and the optimization of industrial layout. In this study, the new identification of Artemisia argyi was made to integrate with the multi-temporal GF-1 and GF-6 satellite images. The study area was taken as the Qichun County, Hubei Province, the main production area of Artemisia argyi in China. A total of 20 spectral features were selected, including 8 single-band features, and 12 red-edge vegetation indices, according to the phenology of Artemisia argyi and the spectral bands of high-resolution images. A random forest classification was then performed to estimate the contributions of different red-edge vegetation indices to the Artemisia argyi identification. A systematic evaluation was also made to identify the potential of the integrated GF-1 and GF-6 images. The mapping accuracy was first assessed using the field samples, and then compared with four additional classification scenarios with different inputs of GF-1 and GF-6 images. In addition, the statistical data was used to verify the mapping areas extracted by remote sensing. The evaluation results showed that the integration of GF-1 and GF-6 images was generated the highest accuracy with the user''s and producer''s accuracy of 92.73% and 88.74%, respectively, indicating significantly higher than those of either a single GF-1 or GF-6 data only. Moreover, the fitting data of the mapping and statistical areas in each township showed that the determination coefficient R2 reached 0.70, indicating accurately matching the area and spatial distribution. The features importance was derived from random forest, where the number of red-edge bands and indices accounted for 54% of the top 50 features with the highest importance scores. The red-edge band I (B5) on June 23 (DOY204) of GF-6 data was contributed the most, which was considered as the best spectral feature to identify. The other newly added purple band (B7) of GF-6 was also valuable to distinguish rather than the traditional multiple bands. Furthermore, the optimal periods of identification were determined as early May and early September, when the first and second stubble of leaves were growing rapidly. Another optimal period was also found to extract the spatial distribution in late June and late September, when the plant was more distinguishable from others. Overall, the accuracy of crop identification was effectively improved under the newly added bands of GF-6 WFV images and the associated vegetation indices using the red-edge bands. The integration of GF-1 and GF-6 images can be widely expected to better capture the key phenological characteristics of crop types, where multiple temporal information was used to improve the classification accuracy. Consequently, the present crop identification was suitable for mapping Artemisia argyi. This finding can provide a typical application demonstration to fully realize the finer resolution of multi-source satellites. |
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| Keywords: | remote sensing identification GF-6 WFV GF-1 WFV red-edge vegetation index random forest Artemisia argyi |
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