亚热带地区马尾松林碳储量的高光谱遥感估算——以长汀为例

黄绍霖; 王慧君; 何宁; 洪武扬

doi:10.13205/j.hjgc.202405019

亚热带地区马尾松林碳储量的高光谱遥感估算——以长汀为例

doi: 10.13205/j.hjgc.202405019

黄绍霖^1,2,
王慧君³,
何宁^4, ,,
洪武扬⁵

1. 深圳信息职业技术学院, 广东深圳 518172;
2. 福州大学环境与资源学院福州大学遥感信息工程研究所, 福州 350116;
3. 深圳市铁汉生态环境股份有限公司, 广东深圳 518040;
4. 宜春学院, 江西宜春 336000;
5. 深圳大学建筑与城市规划学院, 广东深圳 518000

基金项目:

江西省高校人文社科研究项目(JC21210)

国家自然科学基金项目(42101427)

深信息科研启动项目(SZIIT2022KJ046)

详细信息

作者简介:
黄绍霖(1986-),男,讲师,主要研究方向为环境与资源遥感。hsl515742109@foxmail.com

通讯作者:
何宁(1986-),男,副教授,主要研究方向为生态修复。hening2010@163.com

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- 文章访问数: 46
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- 被引次数: 0
出版历程
- 收稿日期: 2023-08-18
- 网络出版日期: 2024-07-11

HYPERSPECTRAL REMOTE-SENSING ESTIMATE OF CARBON STORAGE OF SUBTROPICAL PINUS MASSONIANA FOREST IN CHANGTING COUNTY, CHINA

1. Shenzhen Institute of Information Technology, Shenzhen 518172, China;
2. Institute of Remote Sensing Information Engineering, College of Environment and Resources, Fuzhou University, Fuzhou 350116, China;
3. Shenzhen Techland Ecology & Environment Company Limited, Shenzhen 518040, China;
4. Yichun University, Yichun 336000, China;
5. School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518000, China

摘要

摘要: 随着全球气候变暖的问题日趋严重,人们日益关注森林生态系统的碳储量变化。利用遥感技术估算森林碳储量是当前大面积森林碳储量估算的首选方法,而高光谱遥感技术的应用将有助于提高森林碳储量的估算精度。因此,进行了马尾松林碳储量的高光谱遥感估算研究,并将其结果与Landsat TM多光谱遥感估算的森林碳储量进行对比研究,分析了马尾松的光谱特征及其与碳储量之间的相关性,建立了马尾松林碳储量的高光谱、多光谱遥感模型。结果表明:基于高光谱遥感估算的马尾松林碳储量的精度高于多光谱遥感数据,基于高光谱的敏感波长构建的归一化植被指数建立的模型R²可接近0.8,均方根误差(root mean square error,RMSE)低至0.968 t/hm²。显然,高光谱遥感数据由于可以更准确地获取植被的微细光谱变化信息,更有利于植被生物量、碳储量等生化参数的反演,从而有效地提高马尾松林碳储量的估算精度。
- 高光谱 /
- 多光谱 /
- 马尾松林 /
- 碳储量
Abstract: The escalating concern over global warming has prompted a heightened focus on the dynamics of carbon storage within forest ecosystems. Remote sensing technology has emerged as the favored approach for estimating carbon storage across extensive forested regions. Leveraging hyperspectral remote sensing technology enhances the precision of forest carbon storage estimation. Consequently, this study undertook the task of hyperspectral remote sensing to estimate carbon storage within Pinus Massoniana forests and compared the results with estimates derived from Landsat TM multispectral remote sensing. The spectral attributes of Pinus Massoniana forests and their correlation with carbon storage were subjected to analysis. Subsequently, models for estimating carbon storage employing hyperspectral and multispectral remote sensing data were established. The findings indicated a significantly improved accuracy in carbon storage estimation through hyperspectral remote sensing when compared to multispectral remote sensing data. Specifically, the R-squared (R²) value for the model, based on the normalized difference vegetation index data derived from hyperspectral imagery at sensitive wavelengths, approached approximately 0.8, while the root mean square error (RMSE) was notably low at 0.968 t/hm². Hyperspectral remote sensing data excelled in capturing nuanced spectral variations within vegetation. This capability enhances the retrieval of vital parameters such as vegetation biomass and carbon storage, as well as other biochemical characteristics. Consequently, hyperspectral remote sensing contributes substantially to the enhancement of carbon storage estimation accuracy within Pinus Massoniana forests.
- hyperspectral /
- multispectral /
- Pinus Massoniana forest /
- carbon storage

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