测绘通报 ›› 2023, Vol. 0 ›› Issue (4): 106-110.doi: 10.13474/j.cnki.11-2246.2023.0112

• 无人机测绘技术应用推广 • 上一篇    下一篇

利用高光谱数据估算城市植被碳储量

李军吉1, 费佳宁2, 周婷2, 高广2, 赵珏晶2, 吴敦2   

  1. 1. 绍兴市勘察测绘院, 浙江 绍兴 312000;
    2. 宝略科技(浙江)有限公司, 浙江 宁波 315000
  • 收稿日期:2022-10-15 修回日期:2023-02-28 发布日期:2023-04-25
  • 通讯作者: 吴敦。E-mail:wudun@popsmart.cn
  • 作者简介:李军吉(1980—),男,高级工程师,主要从事实景三维及新型测绘技术研究及应用。E-mail:104053455@qq.com
  • 基金资助:
    宁波市重大科技攻关项目(2021Z050); 浙江省自然资源厅科技项目(2020-16)

Estimation of urban vegetation carbon storage using hyperspectral data

LI Junji1, FEI Jianing2, ZHOU Ting2, GAO Guang2, ZHAO Juejing2, WU Dun2   

  1. 1. Shaoxing Geotechnical Investigation & Surveying Insititute, Shaoxing 312000, China;
    2. Popsmart Technology (Zhejiang) Co., Ltd., Ningbo 315000, China
  • Received:2022-10-15 Revised:2023-02-28 Published:2023-04-25

摘要: 城市植被是低碳治理的重要因素之一,其固碳能力直接影响着城市碳排放。针对现有技术耗时费力、对环境造成一定的影响,本文提出了基于高光谱数据的城市植被碳储量估算研究,以绍兴市上虞区百官街道、曹娥街道、崧厦街道的中心城区为研究区域,将城市植被分为乔木、灌木、竹林和草地4种类型。利用提出的固碳指数对植被固碳能力进行定性分析,再通过生物量经验公式对碳储量进行定量计算,将两者结合得到高光谱植被碳储量估算模型,为大尺度碳储量快速估算提供一种新思路。结果表明:4种城市植被分类精度达88.10%,固碳能力排序为竹林>乔木>灌木>草地。

关键词: 碳储量, 高光谱, 固碳, 城市植被, 低碳

Abstract: Urban vegetation is one of the important factors in low-carbon governance, and its carbon sequestration capacity directly affects urban carbon emissions. In view of the time-consuming and labor-intensive existing technology, which has a certain impact on the environment, this paper proposes a study on the estimation of urban vegetation carbon reserves based on hyperspectral data. Taking the central urban area of Baiguan street, Cao'e street and Songxia street in Shangyu district, Shaoxing city as the study area, urban vegetation is divided into four types: arbor, shrub, bamboo forest and grassland. The proposed carbon sequestration index is used to qualitatively analyze the carbon sequestration capacity of vegetation, and the biomass empirical formula is used to quantitatively calculate the carbon storage. The hyperspectral vegetation carbon storage estimation model is obtained by combining the two, which provides a new idea for the rapid estimation of large-scale carbon storage. The results show that the classification accuracy of the four urban vegetation reaches 88.10%, and the carbon fixation capacity is ranked as bamboo forest>tree>shrub>grassland.

Key words: carbon storage, hyperspectral, carbon fixation, urban vegetation, low carbon

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