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

HUANG Shaolin; WANG Huijun; HE Ning; HONG Wuyang

doi:10.13205/j.hjgc.202405019

Volume 42 Issue 5

May 2024

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2024 > 42(5): 147-153

CAO Lixia, LI Wenshuan, LIN Xin, LI Xiaojun, FU Wanlong. EFFECTS OF SELENIUM APPLICATION ON ARSENIC UPTAKE AND ACCUMULATION IN RICE[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(7): 271-276. doi: 10.13205/j.hjgc.202307036

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HUANG Shaolin, WANG Huijun, HE Ning, HONG Wuyang. HYPERSPECTRAL REMOTE-SENSING ESTIMATE OF CARBON STORAGE OF SUBTROPICAL PINUS MASSONIANA FOREST IN CHANGTING COUNTY, CHINA[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(5): 147-153. doi: 10.13205/j.hjgc.202405019

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

doi: 10.13205/j.hjgc.202405019

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

Received Date: 2023-08-18
Available Online: 2024-07-11

Abstract

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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References

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