Remote sensing monitoring of vegetation and analysis of carbon storage changes in Fengshan Park, Jiaozuo
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摘要:
全球气候变化使生态系统碳储量研究备受关注，矿山修复后植被碳储量核算是生态修复效果评价的关键内容之一，也是建设“双碳社会”的需要。传统植被碳储量估算方法存在局限，而遥感技术和地理信息系统等的发展为该研究提供了新方向。焦作市缝山公园曾因不规范矿山开采活动导致生态环境严重破坏，自2005年始，当地政府投入资金进行生态修复，采用挂网喷播复绿、鱼鳞坑复绿等修复工程模式，种植大量乔灌木，使公园植被覆盖度显著提升。研究利用2013年、2018年、2023年3期2 m分辨率的高分1号卫星数据，结合遥感 - 多元线性回归模型，通过样地调查与取样，结合遥感数据预处理，提取植被指数等特征因子，构建并检验植被碳储量估测模型。结果表明：（1）公园植被碳储量在2013— 2018年明显增加，从1.56×103 t增加到1.90×103 t，2018 —2023年轻微减少至1.78×103 t，10 a间总体呈上升趋势；（2）植被碳储量受坡度和人为因素影响，不同坡度区间植被碳密度有差异，如0.46°～ 8.32°缓坡区域植被碳密度较高，而30.45°～48.82°陡坡区域碳密度相对较低；（3）植被碳储量与植被覆盖度呈正相关，变化趋势一致。研究填补了废弃矿山转型为城市公园过程中植被碳储量研究的空白，为矿山生态修复效果评价及当地“双碳社会”建设提供了科学依据和数据支持。
Abstract:Global climate change has attracted much attention to the study of ecosystem carbon storage, with vegetation carbon estimation after mine restoration emerging as a critical component for evaluating ecological restoration effectiveness and supporting the development of a “dual-carbon society”. Traditional methods for estimating vegetation carbon storage have limitations, while advances in remote sensing technology offer promising alternatives. The fractured mountain park in Jiaozuo City has caused serious damage to the ecological environment due to non-standard mining activities. Since 2005, the local government has invested in ecological restoration, using engineering models such as hanging net spray seeding and fish-scale pits to plant a large number of trees and shrubs, significantly improving the vegetation cove of the park. Using the GF-1 satellite data with a resolution of 2 m in 2013, 2018 and 2023, combined with the remote sensing and multiple linear regression model, the vegetation index and other characteristic factors were extracted, and the estimation model of carbon storage was constructed and tested. The results showed that the vegetation carbon storage in the park increased significantly from 2013 to 2018, from 1.56×103 t to 1.90×103 t, and slightly decreased to 1.78×103 t from 2018 to 2023. Vegetation carbon storage is affected by slope and human activities. There are differences in vegetation carbon density in different slope ranges. The vegetation carbon density is higher in the 0.46°−8.32° gentle slope area, while the carbon density is relatively low in the 30.45°− 48.82°steep slope area. Vegetation carbon storage is positively correlated with vegetation coverage, and the change trend was consistent. The research fills the gap in the study of vegetation carbon storage in urban parks transformed from abandoned mines, and provides scientific basis and data support for the evaluation of mine ecological restoration effects and the construction of local “dual-carbon society”.
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Key words:
- GF-1 satellite image /
- RS-MLRM model /
- vegetation carbon storage /
- vegetation cover /
- Fengshan Park
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表 1 主要植被指数计算公式
Table 1. Calculation formula for major vegetation indices
植被指数 数学符号 计算公式 比值植被指数（ratio vegetation index，RVI） ${I_{{\mathrm{RV}}}} $ $ {I_{{\mathrm{RV}}}} = \dfrac{{{\rho _{{\mathrm{NIR}}}}}}{{{\rho _{{\mathrm{RED}}}}}} $ 插值植被指数（interpolated vegetation index，DVI） ${I_{{\mathrm{DV}}}} $ $ {I_{{\mathrm{DV}}}} = {\rho _{{\mathrm{NIR}}}} - {\rho _{{\mathrm{RED}}}} $ NDVI ${I_{{\mathrm{NDV}}}} $ $ {I_{{\mathrm{NDV}}}} = \dfrac{{{\rho _{{\mathrm{NIR}}}} - {\rho _{{\mathrm{RED}}}}}}{{{\rho _{{\mathrm{NIR}}}} + {\rho _{{\mathrm{RED}}}}}} $ 土壤调整植被指数（soil adjusted vegetation index，SAVI） $ {I_{{\mathrm{SAV}}}} $ $ {I_{{\mathrm{SAV}}}} = (1 + L)\dfrac{{\left( {{\rho _{{\mathrm{NIR}}}} - {\rho _{{\mathrm{RED}}}}} \right)}}{{\left( {{\rho _{{\mathrm{NIR}}}} + {\rho _{{\mathrm{RED}}}} + L} \right)}} $ 优化土壤调节植被指数（optimized soil adjusted vegetation index，OSAVI） ${I_{{\mathrm{OSAV}}}} $ $ {I_{{\mathrm{OSAV}}}} = (1 + 0.16)\dfrac{{\left( {{\rho _{{\mathrm{NIR}}}} - {\rho _{{\mathrm{RED}}}}} \right)}}{{\left( {{\rho _{{\mathrm{NIR}}}} + {\rho _{{\mathrm{RED}}}} + 0.16} \right)}} $ 重归一化植被指数（renormalized difference vegetation index，RDVI） ${I_{{\mathrm{RDV}}}} $ $ {I_{{\mathrm{RDV}}}} = \dfrac{{{\rho _{{\mathrm{NIR}}}} - {\rho _{{\mathrm{RED}}}}}}{{\sqrt {{\rho _{{\mathrm{NIR}}}} + {\rho _{{\mathrm{RED}}}}} }} $ 注： $ \rho_{\mathrm{NIR}} $ 和$ \rho_{\mathrm{RED}} $ 分别代表红外波段和红光波段的反射率，L为大气校正参数，L取0.5。
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表 2 乔灌木主要干生物量方程
Table 2. Equations for major stem biomass of trees and shrubs
树种 计算公式 侧柏[19] W干=125.31(D2H)0.733
W枝=137.403+12.887(D2H)
W叶=53.49+9.97(D2H)
W根=11.007(D2H)−160.386刺槐[20] lnW干=− 2.895531 +0.86764 ln(D2H)
lnW枝=−3.71916 +0.79079 ln(D2H)
lnW叶=−2.90872 +0.45739 ln(D2H)
lnW根=−2.16746 +0.63276 ln(D2H)火炬树[21] lgW= 2.3534 +0.6801l g(D2H)栾树[22] W=0.915+0.1D2H 雪松[22] W=1.26( 0.3721 D1.2928 +0.2805 D1.3313 )构树[22] W= 1.7519 (D2H)1.5784 阔叶树[23] W= 0.0396 (D2H)0.933针叶树[24] W= 0.0254 (D2H)0.948黄荆[25] W= 4052.84 −6982.50 D+3761.50 D2−518.76D3胡枝子[25] W=32.456+5.202(D2H) +24.479(D2H)2−3.751(D2H)3 其他灌木[26] W=100.71AC0.925 注： W为生物量，D为胸径，C为冠幅，H为株高，AC为冠幅面积，AC=πC1C2/4。
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表 3 植被碳储量与单波段和复合段之间的相关系数
Table 3. Correlation coefficients between sample carbon stocks and single and composite bands
单波段和复合段 B2 B3 B4 B(4-2) B(3-2) B(4/2) 相关系数 −0.396 −0.379 0.645** 0.825** −0.262 0.945** 注：**表示p<0.01；*表示p<0.05（双尾）；−表示负相关。
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表 4 植被碳储量与植被指数之间的相关系数
Table 4. Correlation coefficients between carbon stocks and vegetation indices in the sample plots
植被指数 RVI DVI NDVI SAVI OSAVI RDVI 相关系数 0.868** 0.862** 0.983** 0.983** 0.983** 0.984**
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表 5 植被碳储量与PCA和纹理特征之间的相关系数
Table 5. Correlation coefficients between sample site carbon stocks and PCA and texture characteristics
PC和纹理特征 PC Mean VA HOM CON DI EB SM COR 相关系数 −0.144 −0.306 0.193 −0.225 0.316 0.298 −0.032 −0.120 −0.098
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表 6 回归模型与参数
Table 6. Regression model and the parameters
回归模型 方程 R2 调整后R2 F Sig 多元逐步回归模型 y= 0.0812 x−0.001x1+0.0150.965 0.908 405.94 0.00 一元线性模型 y=0.077x+0.015 0.895 0.864 743.94 0.00 二次曲线模型 y=0.009+0.095x-0.011x2 0.842 0.785 673.39 0.00 指数模型 y=−0.339e−0.287x+0.347 0.883 0.859 535.84 0.00 幂次方模型 y=0.093x0.785 0.832 0.776 472.46 0.00 对数模型 y=0.095+0.054lnx 0.876 0.847 492.40 0.00
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表 7 缝山公园植被碳储量模型RE-RMSE评价表
Table 7. RE-RMSE evaluation of vegetation carbon stocks in Fengshan Park
样方 Bi/（g·m−2） $B_i' $ /（g·m−2）$I_{\mathrm{RE}} $ /%$I_{\mathrm{RMSE}} $ FSYF-02 5.34 5.58 4.46 10.68 FSYF-05 13.10 11.89 9.22 26.19 FSYF-16 14.53 15.85 9.11 29.06 FSYF-20 11.52 12.33 7.10 23.03 FSYF-22 9.05 8.42 6.92 18.09 FSYF-24 25.48 21.03 5.44 13.95
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表 8 不同年份缝山公园植被碳储量
Table 8. Vegetation carbon stocks in Fengshan Park in different years
指标 2013年 2018年 2023年 植被碳储量/（103 t） 1.56 1.90 1.78
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表 9 不同年份植被碳储量变化
Table 9. Changes of vegetation carbon storage in different years
年份 碳储量变化面积占比/% 碳储量年均变化量/t 增加 不变 减少 2013—2018年 60.52 2.24 37.23 68 2018—2023年 46.28 2.34 51.38 −24 2013—2023年 14.24 0.10 14.15 22
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表 10 不同年份各植被覆盖度下植被碳储量
Table 10. Vegetation carbon storage under different vegetation cover in different years
年份 项目 低植被
覆盖度较低植被
覆盖度中等植被
覆盖度较高植被
覆盖度高植被
覆盖度2013年 比例/% 0.42 0.60 3.95 46.09 48.95 面积/km2 0.05 0.07 0.46 5.38 5.71 植被碳
储量/t0.6 1.39 35.50 591.68 931.07 2018年 比例/% 0.52 0.51 2.24 21.24 75.48 面积/km2 0.06 0.06 0.26 2.48 8.81 植被碳
储量/t0.60 1.26 21.70 287.57 1594.44 2023年 比例/% 0.68 0.89 2.89 26.20 69.34 面积/km2 0.08 0.10 0.34 3.06 8.09 植被碳
储量/t0.80 1.86 26.65 369.97 1399.90
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表 11 不同坡度区间的植被碳密度
Table 11. Vegetation carbon density in different slope intervals
坡度/（°） 2013年植被
碳密度/（g·m−2）2018年植被
碳密度/（g·m−2）2023年植被
碳密度/（g·m−2）0.46～8.32 17.61 22.79 19.32 8.33～14.45 16.89 19.72 17.99 14.46～21.27 14.44 17.74 17.59 21.28～30.44 16.86 21.06 18.03 30.45～48.82 16.91 19.81 17.96
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