首页|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

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

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

hyperspectralmultispectralPinus Massoniana forestcarbon storage

黄绍霖、王慧君、何宁、洪武扬

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深圳信息职业技术学院,广东深圳 518172

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

深圳市铁汉生态环境股份有限公司,广东深圳 518040

宜春学院,江西宜春 336000

深圳大学建筑与城市规划学院,广东深圳 518000

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高光谱 多光谱 马尾松林 碳储量

国家自然科学基金深信息科研启动基金江西省高等学校人文社会科学研究项目

42101427SZIIT2022KJ046JC21210

2024

环境工程
中冶建筑研究总院有限公司,中国环境科学学会环境工程分会

环境工程

CSTPCD
影响因子:0.958
ISSN:1000-8942
年,卷(期):2024.42(5)
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