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矿山恢复治理区植被物候与健康状况遥感监测
引用本文:帅爽,张志,吕新彪,陈思,马梓程,谢翠容.矿山恢复治理区植被物候与健康状况遥感监测[J].农业工程学报,2021,37(4):224-234.
作者姓名:帅爽  张志  吕新彪  陈思  马梓程  谢翠容
作者单位:1.中国地质大学(武汉)地质调查研究院,武汉 430074;3.湖北省国土测绘院,武汉 430010;2.中国地质大学(武汉)地球物理与空间信息学院,武汉 430074
基金项目:青海省青藏高原北部地质过程与矿产资源重点实验室专项基金(2019-kz-01),青海省科技厅创新平台建设专项项目"青海省自然资源要素与生态状况一体化遥感监测应用平台"(2019-ZJ-T04),中国地质调查局项目(020212000000180004;DD20190705;DD20190511)
摘    要:矿山恢复治理区植被物候与植被健康状况可以定量评价恢复治理工程实施效果,以往矿山恢复治理遥感监测多侧重于监测植被覆盖变化,忽略了对恢复治理区域植被物候特征和健康状况的评估.该研究使用时间序列哨兵2号影像,基于Savitzky-Golay滤波、动态阈值、曲率曲线等方法,以黑龙江省七台河市一玄武岩采石场及周边区域为例,分别提...

关 键 词:遥感  植被指数  矿山恢复治理  植被健康状况  哨兵2号  物候参数
收稿时间:2020/12/21 0:00:00
修稿时间:2021/2/26 0:00:00

Remote sensing monitoring of vegetation phenological characteristics and vegetation health status in mine restoration areas
Shuai Shuang,Zhang Zhi,Lyu Xinbiao,Chen Si,Ma Zicheng,Xie Cuirong.Remote sensing monitoring of vegetation phenological characteristics and vegetation health status in mine restoration areas[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(4):224-234.
Authors:Shuai Shuang  Zhang Zhi  Lyu Xinbiao  Chen Si  Ma Zicheng  Xie Cuirong
Institution:1.Institute of Geologic Survey, China University of Geosciences (Wuhan), Wuhan 430074, China; 3.Hubei Institute of Land Surveying and Mapping, Wuhan 430010, China;2.Institute of Geophysics & Geomatics, China University of Geoscience (Wuhan), Wuhan 430074, China;
Abstract:Abstract: Plant''s health status is one of the most important indicators to evaluate restoration projects and environmental evolution of degraded land, particularly in mine restoration areas. Most studies on vegetation cover changes did not consider the phenological characteristics and health status in remote sensing monitoring, although optical remote sensing technology has been available since the 1980s. Taking a basalt stone pit in Qitaihe City, Heilongjiang Province, China as the research area, this study aims to detect the health status of corn crops using remote sensing. Thirteen images were captured from the Sentinel-2 satellite from April to October 2020, and then reconstructed by the time-series of Normalized Difference Vegetation Index (NDVI). Different fittings were selected, including the Savitzky-Golay (S-G) filtering, double logistic (D-L) fitting, and asymmetric Gaussian (A-G) filtering. Dynamic thresholds and curvatures were used, where the phenological indicators of corn crops covered the periods of emergence, jointing, tasseling, and maturity, as well as the interval length between phenological periods. An analysis was made on the difference of corn phenological characteristics between the mine restored and normal cultivated areas. Some remote sensing spectral indices were extracted, including the Red-Edge Inflection Point (REIP), NDVI, green NDVI (GNDVI), Meris Terrestrial Chlorophyll Index (MTCI), pigment specific simple ratio (PSSRA), Inverted Red-Edge Chlorophyll Index (IRECI), Modified Chlorophyll Absorption Ratio Index (MCARI) and vegetation coverage (FVC). A systematic evaluation was made on the feasibility and sensitivity of indices to distinguish between healthy crops and sub-health crops in the restoration area. The results showed that the key phenological periods of corn was delayed in the restoration treatment area, where the emergence period was delayed by 5-12 days, while the jointing period was delayed by 9-12 days, and the maturity period was delayed by 21-22 days, compared with those in the normal cultivated areas. The phenological difference of crops gradually emerged as the crop grew. Remote sensing indicators, including the REIP, NDVI, GNDVI, PSSRA, IRECI, and FVC, can effectively distinguish the crops spectral features and the health status difference between the mine restoration and normal cultivation areas. The GNDVI received the lowest standard deviation for the restoration area (0.093 5 for area A, and 0.056 2 for area B), indicating the most stable indicator of the overall health status of crops. In PSSRA amplitude interval of pixels, the lower limit in the C area was 12.61% and 44.72% higher than the upper limit in the A and B areas, respectively, indicating the most sensitive among the indicators. The spectral features were related to the comprehensive influence of physiological difference between restoration and normal cultivation areas, including the leaf green content, leaf area index, and leaf water content of corn crops. This finding can provide an insightful understanding to rapidly evaluate the mine restoration using remote sensing technologies.
Keywords:remote sensing  vegetation index  mine restoration  vegetation health status  Sentinel-2  phenological parameters
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