[1]龚志远,李雪梅,李秋萍,等.兰州植物园植被春季物候无人机监测[J].江苏农业学报,2023,(08):1707-1712.[doi:doi:10.3969/j.issn.1000-4440.2023.08.010]
　GONG Zhi-yuan,LI Xue-mei,LI Qiu-ping,et al.Unmanned aerial vehicle (UAV) monitoring of spring vegetation phenology in Lanzhou Botanical Garden[J].,2023,(08):1707-1712.[doi:doi:10.3969/j.issn.1000-4440.2023.08.010]
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兰州植物园植被春季物候无人机监测()
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江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年08期
页码:
1707-1712
栏目:
农业信息工程
出版日期:
2023-12-31

文章信息/Info

Title:
Unmanned aerial vehicle (UAV) monitoring of spring vegetation phenology in Lanzhou Botanical Garden
作者:
龚志远; 李雪梅; 李秋萍; 赵俊卓; 李帆帆
(兰州交通大学测绘与地理信息学院/甘肃省地理国情监测工程实验室/地理国情监测技术应用国家地方联合工程研究中心,甘肃兰州730070)
Author(s):
GONG Zhi-yuan; LI Xue-mei; LI Qiu-ping; ZHAO Jun-zhuo; LI Fan-fan
(Faculty of Geomatics, Lanzhou Jiaotong University/Gansu Provincial Engineering Laboratory for National Geographic State Monitoring/National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China)
关键词:
植被物候; 无人机; 监测; 植被指数
Keywords:
vegetation phenology; unmanned aerial vehicle (UAV); monitoring; vegetation index
分类号:
Q948.15
DOI:
doi:10.3969/j.issn.1000-4440.2023.08.010
文献标志码:
A
摘要:
针对卫星遥感影像难以准确提取局部地区植被物候的问题,本研究以兰州植物园草坪草、连翘、牡丹、黄刺玫和香荚蒾等5种植被为研究对象,基于多时相无人机影像,提出了一种局部地区植被春季物候期估算方法。首先利用无人机获取的多时相兰州植物园植被影像,分析各植被超绿指数(ExG)、超绿超红差分指数(ExGR)、绿叶指数(GLI)和植被因子指数(VEG)等植被指数的时序变化特征,并进行一元三次多项式拟合,使用导数法提取各植被的发芽期、开花期、结果期等春季物候期,然后与人工观测物候资料进行比较,明确不同植被指数下物候期的估算精度。结果表明:除草坪草的发芽期和结果期以及牡丹的发芽期,4种植被指数估算得到的物候期基本一致,但其和实际物候期均存在不同程度的误差；发芽期估算误差最大的是黄刺玫,平均提前27 d,最小的是香荚蒾,平均推迟8 d；开花期估算误差较大的是草坪草和牡丹,平均误差均在20 d以上,最小的是连翘和香荚蒾；结果期估算误差最大的是香荚蒾,平均提前35 d,最小的是牡丹,平均提前5 d。基于ExG指数估算的开花期和结果期与实际观测期一致性最好,均方根差分别为14.01 d和17.28 d,而VEG指数估算发芽期效果最好,均方根差为13.81 d。本研究基于无人机遥感影像数据筛选出植被春季物候期监测适宜的植被指标,对局部地区植被物候监测研究具有一定意义。
Abstract:
Aiming at the problem that satellite remote sensing images are difficult to accurately extract vegetation phenology in local areas, this study took five vegetations such as lawn grasses, Forsythia suspensa, peony, Rosa xanthina and Viburnum sargentii in Lanzhou Botanical Garden as the research objects. Based on multi-temporal unmanned aerial vehicle (UAV) images, a method for estimating spring phenology of vegetation in local areas was proposed. Firstly, the multi-temporal vegetation images of Lanzhou Botanical Garden obtained by UAV were used to analyze the temporal variation characteristics of vegetation indices such as excess green index (ExG), excess-green minus excess red index (ExGR), green leaf index (GLI) and vegetative index (VEG), and one-dimensional cubic fitting was performed. The derivative method was used to extract the spring phenological periods such as germination period, flowering period and fruiting period of each vegetation, and then compared with the artificial observation phenological data to clarify the estimation accuracy of phenological periods under different vegetation indices. The results showed that except for the germination and fruiting period of turfgrass and the germination period of peony, the phenological periods estimated by the four vegetation indices were basically the same, but there were different degrees of errors between them and the actual phenological periods. The largest estimation error of germination period was 27 days in advance for Rosa xanthina, and the smallest was Viburnum insignis, which was delayed by eight days. The error of flowering period estimation was larger in turfgrass and peony, and the average error was more than 20 days, and the smallest was Forsythia suspensa and Viburnum sargentii. The largest estimation error of fruiting period was Viburnum sargentii, with an average of 35 days in advance, and the smallest was peony, with an average of five days in advance. The flowering period and fruiting period estimated based on the ExG index had the best consistency with the actual observation period, and the root mean square errors were 14.01 d and 17.28 d, respectively. The VEG index had the best effect on estimating the germination period, and the root mean square error was 13.81 d. This study is based on UAV remote sensing image data to screen out suitable vegetation indicators for monitoring vegetation phenology in spring, which is of certain significance to the study of vegetation phenology monitoring in local areas.

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备注/Memo

备注/Memo:
收稿日期:2022-09-01基金项目:国家自然科学基金项目(41761014、41971094、42161025、42104096)；兰州交通大学优秀科研平台(团队)科学研究资助计划项目(201806)作者简介:龚志远(1996-),男,安徽合肥人,硕士研究生,主要从事城市生态遥感研究。(E-mail)1043287311@qq.com通讯作者:李雪梅,(E-mail)lixuemei@lzjtu.edu.cn
更新日期/Last Update: 2024-01-15