基于CASA模型的上海城市森林碳汇估算

Estimation of Shanghai's Urban Forest Carbon Sink Based on CASA Model

摘要

【目的】快速、准确估算城市森林碳汇能力及其时空分布, 为城市森林“碳中和”评估提供科学数据支撑。【方法】以上海市闵行区为研究区域, 基于CASA模型估算2021年度闵行区城市森林碳汇量, 分析区域尺度城市森林碳汇量空间分布格局与季相变化特征, 定量评估不同城市森林类型碳汇能力。【结果】1) 闵行区碳汇呈规模聚集特征。2021年度闵行区城市森林碳汇总量为76243.84 t CO₂, 平均单位面积碳汇量为1352.35 g CO₂·m^-2, 整体呈现北部、南部较高, 中部较低的分布态势。2) 闵行区城市森林碳汇表现为典型的生长季单峰型模式, 受气候因子与植被物候节律主导。闵行区城市森林固碳主要集中在二、三季度, 约占全年固碳量的95%。3) 闵行区不同植被类型碳汇能力存在差异。常绿阔叶林年度碳汇量最高, 占全区碳汇总量的74.73%; 竹林碳汇能力最强, 单位面积碳汇量为1518.18 g CO₂·m^-2。4) 多方法交叉验证表明, CASA模型估算结果与清单法、样地实测法及同类模型研究偏差约15%, 收敛于同一量级。【结论】上海闵行区城市森林碳汇格局在时空分布、季节贡献、植被类型上存在分异特征。空间上, 植被斑块规模与空间联通性是调控城市区域碳汇格局的关键因子, 区域分布异质性突出; 时间上, 碳汇呈生长季高度集中的单峰型模式, 气候季节性差异是碳汇时序分异的核心驱动因素; 不同植被类型在碳汇功能上呈现总量贡献与效率水平的分异; CASA模型在城市化区域具有较好的适用性。未来可聚焦高分辨率遥感数据集构建、本地化参数库建立及多模型集成估算体系研发, 进一步提升估算精度, 为城市森林碳汇核算与“双碳”目标落地提供技术支撑。

Abstract

【Objective】The study aims to carry out rapid and accurate estimation of carbon sequestration capacity of urban forests and its spatiotemporal distribution, with the aim to provide scientific data for the assessment of urban forests' “carbon neutrality”. 【Method】Taking Minhang District in Shanghai as the study area, this paper estimates the carbon sink of its urban forests in 2021 based on the CASA model, analyzes the spatial distribution pattern and seasonal variation characteristics of urban forest carbon sink at the regional scale, and quantitatively evaluates the carbon sequestration capacity of different types of urban forest. 【Result】1) The urban forest carbon sink in Minhang District presents the characteristics of large-scale agglomeration. The total carbon sink of urban forests in the District in 2021 was at 76243.84 t CO₂, and the mean carbon sink per unit area at 1352.35 g CO₂·m^-2, which shows an overall distribution pattern of higher in the northern and southern parts and lower in the central part; 2) The urban forest carbon sink in the District follows a typical unimodal pattern during the growing season, driven by climatic factors and vegetation phenological rhythms. The urban forest carbon sink therein is mainly sequestered in the 2nd and 3rd quarters, accounting for about 95% of the annual carbon sequestration; 3) The carbon sequestration capacity varies across different vegetation types in Minhang District. Evergreen broad-leaved forests have the highest annual carbon sink, accounting for 74.73% of the total carbon sink in the District; bamboo forests have the strongest carbon sink capacity, while the carbon sink per unit area is 1518.18 g CO₂·m^-2; and 4) Multi-method cross-validation shows that the CASA model estimates deviate approximately 15% compared with those obtained using the inventory-based method, the plot measurement method and similar model studies, which converge to the same order of magnitude. 【Conclusion】The urban forest carbon sink patterns in Minhang District exhibit differentiated characteristics across spatiotemporal distribution, seasonal allocation and vegetation types. Spatially, vegetation patch size and spatial connectivity are the key factors regulating carbon sink patterns in urban areas. Temporally, carbon sink follows a unimodal pattern heavily concentrated in the growing season, while climate and seasonal variability is the core driving force behind the temporal heterogeneity of carbon sinks. Different types of vegetation show a divergence between total contribution and unit-area efficiency in carbon sink functions. The CASA model is confirmed to have good applicability in urbanized areas. Future research could further improve estimation accuracy by developing high-resolution remote sensing datasets, establishing localized parameter databases and developing integrated multi-model estimation systems, thereby providing technical support for urban forest carbon sink accounting and the implementation of “Dual Carbon” goals.

关键词

城市森林 / 碳汇估算 / CASA模型 / 上海市

Key words

urban forests / carbon sink estimation / CASA model / Shanghai

参考文献

[1] 朱念福, 郑晔施, 童冉, 等 . 长三角地区乔木林碳汇及其对“双碳”目标贡献预测[J]. 生态学杂志, 2024, 43(12): 3817-3827.

[2] 中共中央 国务院 关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见[EB/OL]. (2021—09—22)[2025—09—11]. https://www.gov.cn/gongbao/content/2021/content_5649728.htm, 2021—09—22.

[3] 国务院 关于印发2030年前碳达峰行动方案的通知[EB/OL]. (2021—10—24)[2025—09—11]. https://www.gov.cn/gongbao/content/2021/content_5649731.htm, 2021—10—24.

[4] 罗鑫玥, 陈明星. 城镇化对气候变化影响的研究进展[J]. 地球科学进展, 2019, 34(9): 984-997.

[5] LI H M, ZHANG G L, ZHONG Q C, et al. Prediction of urban forest aboveground carbon using machine learning based on Landsat 8 and Sentinel—2: a case study of Shanghai, China[J]. Remote Sensing, 2023, 15, 284.

[6] 张涛. “双碳”目标下智慧城市挺进“绿色”时代[J]. 中国建设信息化, 2022, 18: 52-53.

[7] 石晓冬, 杨悦, 张文雍, 等 . 超大城市落实“双碳”目标的策略: 以北京为例[J]. 科技导报, 2023, 41(22): 58-66.

[8] 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.

[9] 王修信, 高凤飞, 刘馨, 等 . 北京城市绿地三种常见林木冠层光合作用特征[J]. 科学技术与工程, 2019, 19(20): 104-109.

[10] 朴世龙, 何悦, 王旭辉, 等 . 中国陆地生态系统碳汇估算: 方法、进展、展望[J]. 中国科学(地球科学), 2022, 52(6): 1010-1020.

[11] 张桂莲, 邢璐琪, 张浪, 等 . 城市绿地碳汇计量监测方法研究进展[J]. 园林, 2022, 39(1): 4-9.

[12] POTTER C S, RANDERSON J T, FIELD C B, et al. Terrestrial ecosystem production: a process model based on global satellite and surface data[J]. Global Biogeochemical Cycles, 1993, 7(4): 811-841.

[13] FIELD C B, BEHRENFELD M J, RANDERSON J T, et al. Primary production of the biosphere: integrating terrestrial and oceanic components[J]. Science, 1998, 281(5374): 237-240.

[14] XIAO X M, ZHANG Q Y, BRASWELL B, et al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data[J]. Remote Sensing of Environment, 2004, 91(2): 256-270.

[15] YUAN W P, LIU S G, ZHOU G S, et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes[J]. Agricultural and Forest Meteorology, 2007, 143(3/4): 189-207.

[16] RUNNING S W, NEMANI R, GLASSY J M, et al. MODIS daily photosynthesis (PSN) and annual net primary production (NPP) product (MOD17) Algorithm Theoretical Basis Document: Version 3.0[R]. University of Montana, SCF At—Launch Algorithm ATBD Documents. 1999.

[17] 崔林丽, 史军, 唐娉, 等 . 中国陆地净初级生产力的季节变化研究[J]. 地理科学进展, 2005, 24(3): 8-16.

[18] 赵雪瑞, 韩玲, 刘明, 等 . 基于改进CASA模型的陕西省植被NPP遥感估算[J]. 水土保持研究, 2024, 31(3): 247-256.

[19] 朴世龙, 方精云, 郭庆华. 利用CASA模型估算我国植被净第一性生产力[J]. 植物生态学报, 2001, 25(5): 603—608, 644.

[20] 朱文泉, 潘耀忠, 张锦水. 中国陆地植被净初级生产力遥感估算[J]. 植物生态学报, 2007, 31(3): 413-424.

[21] 李传华, 曹红娟, 范也平, 等 . 基于校正的CASA模型NPP遥感估算及分析: 以河西走廊为例[J]. 生态学报, 2019, 39(5): 1616-1626.

[22] 刘凤, 曾永年. 2000—2015年青海高原植被碳源/汇时空格局及变化[J]. 生态学报, 2021, 41(14): 5792-5803.

[23] 马艺菲, 柯进恺, 张彬, 等 . 基于可持续视角的超大城市碳增汇分区与管理: 以北京市为例[J]. 环境科学, 2024, 45(12): 6836-6847.

[24] 温宥越, 孙强, 燕玉超, 等 . 粤港澳大湾区陆地生态系统演变对固碳释氧服务的影响[J]. 生态学报, 2020, 40(23): 8482-8493.

[25] XU F, WANG X R, LI L. NPP and vegetation carbon sink capacity estimation of urban green space using the optimized CASA model: a case study of five Chinese Cities[J]. Atmosphere, 2023, 14(7): 1161.

[26] 上海市闵行区人民政府办公室. 2023年政府工作报告[EB/OL]. (2023—01—04)[2025—09—11]. https://zwgk.shmh.gov.cn/mh—xxgk—cms/website/mh_xxgk/qfgzbg/content/87e90d61—8d92—47fe—8e38—d54cc4bcdc72.htm, 2023—1—4.

[27] 易扬, 张桂莲, 张浪, 等 . 基于高分二号遥感影像的城市植被三维绿量研究[J]. 园林, 2020, 37(11): 2-7.

[28] 朱文泉, 潘耀忠, 何浩, 等 . 中国典型植被最大光利用率模拟[J]. 科学通报, 2006, 51(6): 700-706.

[29] 苏胜涛, 曾源, 赵旦, 等 . 中国陆地植被净初级生产力估算模型优化与分析: 基于中国生态系统研究网络数据[J]. 生态学报, 2022, 42(4): 1276-1289.

[30] NI J. Net primary productivity in forests of China: scaling—up of national inventory data and comparison with model predictions[J]. Forest Ecology and Management, 2003, 176(1/2/3): 485-495.

[31] YI Y, ZHANG G L, WANG W, et al. Spatial—temporal dynamics and driving forces of urban vegetation carbon storage and carbon sink in Shanghai megacity[J]. Trees, Forests and People, 2025, 22: 100992.

[32] 徐佩. 上海市土地利用/土地覆盖变化对城市植被净初级生产力的影响[D]. 上海: 华东师范大学, 2015.

[33] 刘泽群. 基于多源数据融合的长三角沿海城市植被净初级生产力估算[D]. 南京: 南京师范大学, 2021.

[34] 彭思源, 付博, 赖雨亲, 等 . 基于光能利用率集成模型的GPP估算不确定性研究[J]. 北京大学学报(自然科学版), 2022, 58(2): 361-371.

[35] 朱安然, 孙睿, 王梦佳. 植被光能利用率遥感估算[J]. 遥感学报, 2021, 25(6): 1227-1243.

[36] 赵涔良, 朱文泉, 谢志英. 植被最大光能利用率的模拟方法对比评估[J]. 遥感学报, 2024, 28(3): 649-660.

[37] ZHANG L X, ZHOU D C, FAN J W, et al. Comparison of four light use efficiency models for estimating terrestrial gross primary production[J]. Ecological Modelling, 2015, 300: 30-39.

[38] 田定方, 范闻捷, 任华忠. 植被光合有效辐射吸收比率遥感研究进展[J]. 遥感学报, 2020, 24(11): 1307-1324.

基金资助

*国家重点研发计划项目(2022YFC3802605)

上海市绿化和市容管理局科研专项(GQ260202)

上海市青年科技英才杨帆计划(22YF1444000)