基于遥感估算的上海城市森林碳储量空间分布特征

doi:10.16258/j.cnki.1674-5906.2021.09.001

生态环境学报 ›› 2021, Vol. 30 ›› Issue (9): 1777-1786.DOI: 10.16258/j.cnki.1674-5906.2021.09.001

• 研究论文 • 下一篇

基于遥感估算的上海城市森林碳储量空间分布特征

张桂莲()

1.上海市园林科学规划研究院/城市困难立地生态园林国家林业和草原局重点实验室,上海 200232
2.国家林业和草原局城市困难立地绿化造林国家创新联盟,上海 200232
3.上海城市困难立地绿化工程技术研究中心,上海 200232

收稿日期:2020-10-28 出版日期:2021-09-18 发布日期:2021-12-08
作者简介:张桂莲（1976年生）,女,高级工程师,博士,研究方向为城市绿化碳汇计量与生态系统模型研究。E-mail: zgl@shsyky.com
基金资助:
国家重点研发计划(2017YFC0505706);上海市科委科研计划项目(19DZ1203301)

Spatial Distribution Characteristics of Carbon Storage of Urban Forests in Shanghai Based on Remote Sensing Estimation

ZHANG Guilian()

1. Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites/Shanghai Academy of Landscape Architecture Science and Planning, Shanghai 200232, China
2. National Innovation Alliance of National Forestry and Grassland Administration on Afforestation and Landscaping of Challenging Urban Sites, Shanghai 200232, China
3. Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai 200232, China

Received:2020-10-28 Online:2021-09-18 Published:2021-12-08

摘要/Abstract

摘要：

城市森林在固碳释氧、应对气候变化方面发挥着重要作用,对其碳储量的估算为中国城市做好碳达峰、碳中和工作提供重要数据基础。以上海城市森林为研究对象,采用样地调查数据与Landsat OLI遥感影像,分别计算样地碳储量和遥感参数变量（波段数据、植被指数数据和纹理数据）,构建基于多元逐步回归模型和普通克里格残差矫正相结合的估算模型,分析区域尺度城市森林碳储量和碳密度的空间分布特征。结果表明,（1）81个样地的碳储量范围为0.09—7.10 t,均值为2.14 t,数据有右偏分布和瘦尾特征,变异系数为0.75,样地类型多样;上海城市森林总碳储量为2.87 Mt,碳密度主要集中在13—40 t∙hm^-2之间,均值为25.09 t∙hm^-2,整体呈现中部较低,东西部较高的态势,与土地利用强度及城市森林分布有关。（2）主城区碳储量为0.37 Mt,碳密度为1—69 t∙hm^-2,均值为21.77 t∙hm^-2;非主城区碳储量为2.50 Mt,占上海城市森林碳储量的87.11%,碳密度范围为0—89 t∙hm^-2,均值为25.66 t∙hm^-2。（3）在选择波段反射率和遥感植被指数的同时,提取影像纹理特征并纳入模型,提高了影像的分类精度;采用多元逐步回归模型结合残差矫正的方法估算城市森林碳储量,使估算结果的均方根误差降低了10.29%,平均绝对误差降低了5.5%,提高了估算精度。

关键词: 城市森林, 碳储量, 逐步回归分析, 残差, 空间特征, 上海市

Abstract:

Urban forests play an important role in carbon sequestration, oxygen release and climate change response. The estimation of carbon storage of urban forests can provide important data basis for China to implement its carbon peak and carbon neutralization policy. The carbon storage and remote sensing parameters (band, vegetation indices and texture) were calculated based on sample plot survey and Landsat Operational Land Imager (OLI) data. A stepwise regression model combined with ordinary kriging of residual correction was established, and the spatial distribution characteristics of carbon storage and carbon density at regional scale were analyzed. The results showed that: (1) Carbon storage in 81 sample plots ranged from 0.09 t to 7.10 t, with an average of 2.14 t. The distribution of data was right-skewed, with a long tail toward higher carbon storage, and the coefficient of variation was 0.75, indicating that the spatial distribution of carbon storage varied greatly. The total carbon storage of urban forests in Shanghai was 2.87 Mt, and the carbon density ranged between 13 t∙hm^-2 and 40 t∙hm^-2, with an average of 25.09 t∙hm^-2. Overall, carbon storage was lower in the central part and higher in the eastern and western parts of Shanghai, which was closely related to the land use intensity and the urban forest distribution; (2) In the main urban areas, carbon storage was 0.37 Mt, and carbon density ranged from 1.00 to 69.00 t∙hm^-2, with an average of 21.77 t∙hm^-2. In the non-main urban areas, however, carbon storage was 2.50 Mt, accounting for 87.11% of the total carbon storage, and carbon density ranged from 0 to 89 t∙hm^-2, with an average of 25.66 t∙hm^-2. (3) While selecting band reflectance and remote sensing vegetation index, the texture features of images were extracted and analyzed to improve the classification accuracy. The combined use of stepwise regression and ordinary kriging of residual correction could reduce root-mean-square error (RMSE) and mean absolute (MAE) by 10.29% and 5.5%, respectively, and improved the accuracy of the model to estimate the carbon storage of urban forests.

Key words: urban forests, carbon storage, stepwise regression analysis, residual, spatial feature, Shanghai

中图分类号:

S718.5
X144

张桂莲. 基于遥感估算的上海城市森林碳储量空间分布特征[J]. 生态环境学报, 2021, 30(9): 1777-1786.

ZHANG Guilian. Spatial Distribution Characteristics of Carbon Storage of Urban Forests in Shanghai Based on Remote Sensing Estimation[J]. Ecology and Environment, 2021, 30(9): 1777-1786.

图/表 10

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区域 Area	林分类型 Forest type	样地数 Number of plots	样本量 Number of samples	最大胸径 Maximum D/cm	最小胸径 Minimum D/cm	平均胸径 Mean D/cm	最大树高 Maximum tree heigtht/m	最小树高 Minimum tree height/m	平均树高 Mean tree height/m
主城区 Main urban area	栾树 Koelreuteria paniculata	2	12	18.6—24.5	8.2—9.5	13.6—16.3	10.2—11.0	4.0—6.0	5.4—8.5
	旱柳 Salix matsudana	1	7	33.5	7.5	13.2	6.5	3.7	5.1
	雪松 Cedrus deodara	1	31	30.1	7.5	17.0	9.5	4.3	8.3
	水杉 Metasequoia glyptostroboides	1	20	27.6	12.9	19.8	16.5	6.0	14.6
	混交林 Mixed forest	2	54	38—40	5.2—6.0	14.9—16.7	8.8—16.0	1.1—3.0	6.4—8.1
	总计 Total	7	141	18.6—40.0	5.2—12.9	13.2—19.8	6.5—16.5	1.1—6.0	5.1—8.5
非主城区 Non-main urban area	龙柏 Sabina chinensis	1	15	19.1	9.8	12.7	6.5	4.0	5.5
	池杉 Taxodium ascendens	1	17	24.5	13.0	19.1	13.0	9.8	11.9
	杜英 Elaeocarpus decipiens	2	59	18.0—24.2	6.2—11.4	11.1—18.8	7.8—8.7	1.8—2.8	6.2—6.3
	枫香 Liquidambar formosana	1	59	17.2	6.0	11.3	12.0	1.2	8.9
	枫杨 Pterocarya stenoptera	1	50	32.4	10.5	20.3	20.5	4.2	14.9
	构树 Broussonetia papyrifera	1	10	20.5	9.6	14.4	7.0	4.5	5.9
	广玉兰 Magnolia grandiflora	1	84	20.5	7.2	13.8	9.5	3.6	7.0
	桂花 Osmanthus fragrans	1	29	11.5	5.2	7.2	3.5	2.7	3.0
	榉树 Zelkova serrata	1	42	16.0	5.0	11.3	8.8	4.3	7.1
	旱柳 Salix matsudana	1	85	18.2	5.0	9.9	10.5	1.7	8.0
	栾树 Koelreuteria paniculata	1	38	27.8	5.0	16.3	18.0	5.0	11.2
	落羽杉 Taxodium distichum	2	103	31.7—36.5	7.6—8.3	17.1—21	12.0—25.0	2.5—3.0	9.3—12.1
	马褂木 Liriodendron chinense	1	105	25.4	5.2	14.2	14.0	5.0	10.8
	女贞 Ligustrum lucidum	6	335	14.0—24.9	4.3—11.5	6.9—14.6	5.4—11.0	2.5—6.0	4.2—7.0
	水杉 Metasequoia glyptostroboides	8	349	11.3—39.2	1.9—16.7	8.4—26.7	6.6—29.5	2.3—8.3	5.6—19.4
	喜树 Camptotheca acuminata	2	71	21.2—24.3	6.3—8.2	14.0—17.5	12.5—16.5	2.8—7.0	11.0—11.9
	香樟 Cinnamomum camphora	16	1053	18.4—42.6	5.0—18.8	11.0—26.0	6.3—18.0	1.3—8.5	3.8—10.7
	毛白杨 Populus tomentosa	2	43	32.8—43.0	15.2—15.9	24.3—28.9	13.6—28.0	3.5—9.9	11.8—21.5
	银杏 Ginkgo biloba	4	200	8.0—14.5	5.0—8.0	7.2—9.7	6.0—9.0	0.5—4.5	5.4—6.1
	广玉兰 Magnolia grandiflora	1	69	53.0	6.3	12.1	14.6	4.0	9.6
	紫叶李 Prunus cerasifera	1	35	16.1	5.0	10.0	8.0	2.9	5.4
	棕榈 Trachycarpus fortunei	1	14	24.8	13.2	18.6	6.6	3.0	4.4
	混交林 Mixed forest	18	533	16.4—55	5.1—6.5	10.1—19.5	6.5—18	1.1—5.5	3.7—9.7
	总计 Total	74	3398	8.0—55.0	5.0—18.8	6.9—28.9	3.0—29.5	0.2—9.9	2.6—21.5
上海市 Shanghai	总计 Total	81	3539	8.0—55	5.0—18.8	6.9—28.9	3.0—29.5	0.2—9.9	2.6—21.5

区域 Area	林分类型 Forest type	样地数 Number of plots	样本量 Number of samples	最大胸径 Maximum D/cm	最小胸径 Minimum D/cm	平均胸径 Mean D/cm	最大树高 Maximum tree heigtht/m	最小树高 Minimum tree height/m	平均树高 Mean tree height/m
主城区 Main urban area	栾树 Koelreuteria paniculata	2	12	18.6—24.5	8.2—9.5	13.6—16.3	10.2—11.0	4.0—6.0	5.4—8.5
	旱柳 Salix matsudana	1	7	33.5	7.5	13.2	6.5	3.7	5.1
	雪松 Cedrus deodara	1	31	30.1	7.5	17.0	9.5	4.3	8.3
	水杉 Metasequoia glyptostroboides	1	20	27.6	12.9	19.8	16.5	6.0	14.6
	混交林 Mixed forest	2	54	38—40	5.2—6.0	14.9—16.7	8.8—16.0	1.1—3.0	6.4—8.1
	总计 Total	7	141	18.6—40.0	5.2—12.9	13.2—19.8	6.5—16.5	1.1—6.0	5.1—8.5
非主城区 Non-main urban area	龙柏 Sabina chinensis	1	15	19.1	9.8	12.7	6.5	4.0	5.5
	池杉 Taxodium ascendens	1	17	24.5	13.0	19.1	13.0	9.8	11.9
	杜英 Elaeocarpus decipiens	2	59	18.0—24.2	6.2—11.4	11.1—18.8	7.8—8.7	1.8—2.8	6.2—6.3
	枫香 Liquidambar formosana	1	59	17.2	6.0	11.3	12.0	1.2	8.9
	枫杨 Pterocarya stenoptera	1	50	32.4	10.5	20.3	20.5	4.2	14.9
	构树 Broussonetia papyrifera	1	10	20.5	9.6	14.4	7.0	4.5	5.9
	广玉兰 Magnolia grandiflora	1	84	20.5	7.2	13.8	9.5	3.6	7.0
	桂花 Osmanthus fragrans	1	29	11.5	5.2	7.2	3.5	2.7	3.0
	榉树 Zelkova serrata	1	42	16.0	5.0	11.3	8.8	4.3	7.1
	旱柳 Salix matsudana	1	85	18.2	5.0	9.9	10.5	1.7	8.0
	栾树 Koelreuteria paniculata	1	38	27.8	5.0	16.3	18.0	5.0	11.2
	落羽杉 Taxodium distichum	2	103	31.7—36.5	7.6—8.3	17.1—21	12.0—25.0	2.5—3.0	9.3—12.1
	马褂木 Liriodendron chinense	1	105	25.4	5.2	14.2	14.0	5.0	10.8
	女贞 Ligustrum lucidum	6	335	14.0—24.9	4.3—11.5	6.9—14.6	5.4—11.0	2.5—6.0	4.2—7.0
	水杉 Metasequoia glyptostroboides	8	349	11.3—39.2	1.9—16.7	8.4—26.7	6.6—29.5	2.3—8.3	5.6—19.4
	喜树 Camptotheca acuminata	2	71	21.2—24.3	6.3—8.2	14.0—17.5	12.5—16.5	2.8—7.0	11.0—11.9
	香樟 Cinnamomum camphora	16	1053	18.4—42.6	5.0—18.8	11.0—26.0	6.3—18.0	1.3—8.5	3.8—10.7
	毛白杨 Populus tomentosa	2	43	32.8—43.0	15.2—15.9	24.3—28.9	13.6—28.0	3.5—9.9	11.8—21.5
	银杏 Ginkgo biloba	4	200	8.0—14.5	5.0—8.0	7.2—9.7	6.0—9.0	0.5—4.5	5.4—6.1
	广玉兰 Magnolia grandiflora	1	69	53.0	6.3	12.1	14.6	4.0	9.6
	紫叶李 Prunus cerasifera	1	35	16.1	5.0	10.0	8.0	2.9	5.4
	棕榈 Trachycarpus fortunei	1	14	24.8	13.2	18.6	6.6	3.0	4.4
	混交林 Mixed forest	18	533	16.4—55	5.1—6.5	10.1—19.5	6.5—18	1.1—5.5	3.7—9.7
	总计 Total	74	3398	8.0—55.0	5.0—18.8	6.9—28.9	3.0—29.5	0.2—9.9	2.6—21.5
上海市 Shanghai	总计 Total	81	3539	8.0—55	5.0—18.8	6.9—28.9	3.0—29.5	0.2—9.9	2.6—21.5

类型 Types	遥感数据 Remote sensing data	计算公式 Formula
植被指数 Vegetation index	归一化植被指数 Normalized difference vegetation index (NDVI)	$Y=\frac{{{\rho }_{1}}-{{\rho }_{2}}}{{{\rho }_{1}}+{{\rho }_{2}}}$
	比值植被指数 Simple ratio index (SRI)	Y=ρ₁/ρ₂
	增强植被指数 Enhanced vegetation index (EVI)	$Y=2.5\left( \frac{{{\rho }_{1}}-{{\rho }_{2}}}{{{\rho }_{1}}+{{\rho }_{2}}-7.5{{\rho }_{2}}+1} \right)$
	大气阻抗植被指数 Atmospherically resistant vegetation index (ARVI)	$Y=\frac{{{\rho }_{1}}-(2{{\rho }_{2}}-{{\rho }_{3}})}{{{\rho }_{1}}+(2{{\rho }_{2}}-{{\rho }_{3}})}$
	结构不敏感色数指数 Structure-senstitive pigment index (SIPI)	$Y=\frac{{{\rho }_{4}}-{{\rho }_{5}}}{{{\rho }_{4}}+{{\rho }_{6}}}$
纹理特征 Textural features	平均值 Mean (ME)	$Y=\sum\limits_{i,j=0}^{N-1}{i}\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}$
	方差 Variance (VA)	$Y=\sum\limits_{i,j=0}^{N-1}{i}\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}\left( i-\sum\limits_{i,j=0}^{N-1}{i\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}} \right)$
	信息熵 Entropy (EN)	$Y=\sum\limits_{i,j=0}^{N-1}{\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}(-\ln {{P}_{i,j}})}$
	偏斜 Skewness (SK)	$Y=\frac{\left\| \sum\limits_{i,j=0}^{N-1}{{{\left( {{P}_{i,j}}-\sum\limits_{i,j=0}^{N-1}{i\times {{P}_{i,j}}} \right)}^{2}}} \right\|}{({{N}^{2}}-1){{(VA)}^{\frac{3}{2}}}}$
	数据范围 Datarange	—

类型 Types	遥感数据 Remote sensing data	计算公式 Formula
植被指数 Vegetation index	归一化植被指数 Normalized difference vegetation index (NDVI)	$Y=\frac{{{\rho }_{1}}-{{\rho }_{2}}}{{{\rho }_{1}}+{{\rho }_{2}}}$
	比值植被指数 Simple ratio index (SRI)	Y=ρ₁/ρ₂
	增强植被指数 Enhanced vegetation index (EVI)	$Y=2.5\left( \frac{{{\rho }_{1}}-{{\rho }_{2}}}{{{\rho }_{1}}+{{\rho }_{2}}-7.5{{\rho }_{2}}+1} \right)$
	大气阻抗植被指数 Atmospherically resistant vegetation index (ARVI)	$Y=\frac{{{\rho }_{1}}-(2{{\rho }_{2}}-{{\rho }_{3}})}{{{\rho }_{1}}+(2{{\rho }_{2}}-{{\rho }_{3}})}$
	结构不敏感色数指数 Structure-senstitive pigment index (SIPI)	$Y=\frac{{{\rho }_{4}}-{{\rho }_{5}}}{{{\rho }_{4}}+{{\rho }_{6}}}$
纹理特征 Textural features	平均值 Mean (ME)	$Y=\sum\limits_{i,j=0}^{N-1}{i}\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}$
	方差 Variance (VA)	$Y=\sum\limits_{i,j=0}^{N-1}{i}\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}\left( i-\sum\limits_{i,j=0}^{N-1}{i\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}} \right)$
	信息熵 Entropy (EN)	$Y=\sum\limits_{i,j=0}^{N-1}{\mathrm{ }\!\!\times\!\!\text{ }{{P}_{i,j}}(-\ln {{P}_{i,j}})}$
	偏斜 Skewness (SK)	$Y=\frac{\left\| \sum\limits_{i,j=0}^{N-1}{{{\left( {{P}_{i,j}}-\sum\limits_{i,j=0}^{N-1}{i\times {{P}_{i,j}}} \right)}^{2}}} \right\|}{({{N}^{2}}-1){{(VA)}^{\frac{3}{2}}}}$
	数据范围 Datarange	—

种类 Species	生物量方程 Biomass equation	碳质量分数 Mass fraction of carbon
香樟 Cinnamomum camphora	M_t=0.3455D^2.0333	0.4433
水杉 Metasequoia glyptostroboides	M_t=0.06291D^2.4841	0.4340
女贞 Ligustrum lucidum	M_t=0.139994D^1.90957	0.4289
银杏 Ginkgo biloba	M_t=0.133137D^2.3357	0.4437
杜英 Elaeocarpus sylvestri	M_t=0.18833D^2.14125	0.4402
马褂木 Liriodendron chinense	M_t=0.06393D^2.61147	0.4443
毛白杨 Populus tomentosa	M_t=0.01901D^3.10510	0.4347
广玉兰 Magnolia grandiflora	M_t=0.330788D^1.90957	0.4335
栾树 Koelreuteria bipinnata	M_t=0.10994D^2.48438	0.4283
无患子 Sapindus saponaria	M_a=0.14119D^2.35753	0.4359
雪松 Cedrus deodara	M_t=0.1606D^2.2134	0.4542
毛白杨 Populus simonii	M_t=0.01901D^3.10510	0.4347
其它软阔类 Other soft broad-leaved species	M_t=0.0275D^2.8203	0.4850*
其它硬阔类 Other hard broad-leaved species	M_t=0.2395D^2.1044	0.4970*
其他针叶类 Other conifers species	M_t=0.1606D^2.2134	0.4542

基于遥感估算的上海城市森林碳储量空间分布特征

Spatial Distribution Characteristics of Carbon Storage of Urban Forests in Shanghai Based on Remote Sensing Estimation

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模型 Regression model	方程 Equation	R²	Adjusted R²	F	Sig.
多元逐步回归模型 Stepwise regression model	Y=2219.23-605.05x₁+0.22x₂+151.83x₃+0.06x₄	0.464	0.40	6.92	0.000
线性模型 Linear model	Y=55.40+6152.52x₁	0.263	0.24	12.507	0.001
二次曲线模型 Second-order polynomial model	Y= -1007.41+14412.20x₁-13681.10$x_{1}^{2}$	0.286	0.24	6.809	0.006
三次曲线模型 Cubic curve model	Y=2717.23+136728.20x₁+179080.60$x_{1}^{2}$-214404$x_{1}^{3}$	0.365	0.31	6.332	0.003
复合模型 Composite model	Y=369.15×74.78x₁	0.297	0.28	14.817	0.002
幂 (次方) 模型 Power index model	Y=5902.48$x_{1}^{1.13}$	0.337	0.32	17.763	0.000
S模型 S-Model	Y=e^8.03-0.19/^x¹	0.315	0.30	16.092	0.000
成长模型 Growth model	Y=e^5.91+4.32^x¹	0.297	0.28	14.817	0.000
指数模型 Exponential model	Y=369.15e^4.32^x¹	0.297	0.30	14.817	0.000
对数模型 Logit model	Y=3980.33+1521.37lnx₁	0.264	0.24	12.537	0.001

项目 Content	统计值 Statistic						变异系数 Variable coefficient	偏度 Skewness	峰度 Kurtosis
项目 Content	样本量 Simples	最小值 Minimum value	最大值 Maximum value	均值 Mean value	中位数 Median value	标准偏差 Standard deviation	变异系数 Variable coefficient	偏度 Skewness	峰度 Kurtosis
样地碳储量 Carbon storage in samples	研究区 Study area	81	0.09	7.10	2.14	1.70	1.60	0.75	1.16
	非主城区 Non-main city area	75	0.09	7.10	2.20	1.75	1.64	0.75	1.07
	主城区 Main urban area	6	0.53	1.90	1.38	1.47	0.52	0.75	-0.86

	多元逐步回归模型 Multiple stepwise regression model	回归残差模型 Multiple stepwise linear regression+ordinary Kriging model
均方根误差 RMSE	21.37%	19.17%
平均绝对误差 MAE	15.87%	15.00%

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