Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data

Meixuan Song; Zihao Huang; Chao Chen; Xuejian Li; Fangjie Mao; Lei Huang; Yinyin Zhao; Lujin Lv; Jiacong Yu; Huaqiang Du

Ecological Indicators (Sep 2024)

Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data

Meixuan Song,
Zihao Huang,
Chao Chen,
Xuejian Li,
Fangjie Mao,
Lei Huang,
Yinyin Zhao,
Lujin Lv,
Jiacong Yu,
Huaqiang Du

Affiliations

Meixuan Song: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Zihao Huang: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Chao Chen: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Xuejian Li: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Fangjie Mao: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Lei Huang: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Yinyin Zhao: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Lujin Lv: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Jiacong Yu: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
Huaqiang Du: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China; School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China; Corresponding author at: State Key Laboratory of Subtropical Silviculture, Zhejiang A and F University, Hangzhou 311300, China. Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A and F University, Hangzhou 311300, China. School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China.

Journal volume & issue: Vol. 166
p. 112495

Abstract

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The Above-Ground Carbon (AGC) is an important indicator reflecting the carbon sink function of forest ecosystems. The area of coniferous forests in China accounts for about 50 % of the national forest area. Accurately estimating the AGC of coniferous forests plays an important role in evaluating forest carbon sink function. This study takes coniferous forests in Chun’an, Zhejiang Province as an example, using Landsat 8 OLI as the remote sensing data source, and innovatively proposes a multi-scale Geographically Weighted Regression model (MGWR) combined with Co-Kriging (COK), namely MGWR-COK and Ordinary Kriging (OK), namely MGWR-OK, for AGC estimation. This method first processes Landsat8 OLI remote sensing data, extracts and screens variables, and then constructs an MGWR model with AGC survey samples to estimate AGC and calculate residuals; Afterwards, two models, COK and OK, were used to spatially estimate the residuals; Finally, the AGC estimation results are overlaid with the residual estimation results to obtain the spatial distribution of AGC in coniferous forests. The study indicates that: (1) The selected remote sensing feature variables such as Optimized Soil-Adjusted Vegetation Index (OSAVI), Normalized Difference Vegetation Index (NDVI), Entropy (B3_ent_1 and B2_ent_2), Correlation (B4_corr and B5_corr_2) of texture information, B754 with band combination and elevation have a significant impact on AGC estimation. (2) The AGC accuracy R2 estimated by MGWR-OK and MGWR-COK are 0.837 and 0.857, respectively, which are 6 % and 8 % higher than the MGWR model, and the root mean square error is also reduced by 10 % and 12 %, respectively. This indicates that the combination of MGWR and Kriging interpolation can effectively reduce its spatial estimation error. (3) The range of AGC values estimated by the MGWR-COK model is 0.189–62.591 Mg ∙ hm−2, with a mean of 28.795 Mg ∙ hm−2. The spatial distribution shows a characteristic of high in the southeast and low in the northwest, which is consistent with the actual situation in the study area.

Published in Ecological Indicators

ISSN: 1470-160X (Print); 1872-7034 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General): Ecology
Website: https://www.sciencedirect.com/journal/ecological-indicators

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