大型露天矿区生态修复植被碳储量研究——以重庆铜锣山为例  

Vegetation carbon storage in ecological restoration of large-scale open-pit mining areas:A case study of Tongluoshan,Chongqing

作　　者：刘江 严有龙 马磊 李成 应凌霄 朱冬雪 王琛 余西游 王科 司洪涛 钟小华 Liu Jiang;Yan Youlong;Ma Lei;Li Cheng;Ying Lingxiao;Zhu Dongxue;Wang Chen;Yu Xiyou;Wang Ke;Si Hongtao;Zhong Xiaohua(Observation and Research Station of Ecological Restoration for Chongqing Typical Mining Areas,Ministry of Natural Resources(Chongqing Institute of Geology andMineral Resources),Chongqing 401120;Wansheng Mining Area Ecological Environment Protection and Restoration of Chongqing Observation and Research Station(Chongqing Institute of Geology and Mineral Resources),Chongqing 401120;Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085;Chongqing Huadi Resources Environment Technology Co.,LTD,Chongqing 401120;Chongqing Dadukou Geological Environment Monitoring Station,Chongqing 400084;Zhongxian County Land Conservation and Construction Centre,Chongqing 404300)

机构地区：[1]自然资源部重庆典型矿区生态修复野外科学观测研究站(重庆地质矿产研究院),重庆401120 [2]重庆市万盛矿区生态环境保护修复野外科学观测研究站(重庆地质矿产研究院),重庆401120 [3]中国科学院生态环境研究中心,北京100085 [4]重庆华地资环科技有限公司,重庆401120 [5]重庆市大渡口地质环境监测站,重庆400084 [6]重庆市忠县耕地保护和建设用地事务中心,重庆404300

出　　处：《资源环境与工程》2024年第6期754-761,共8页Resources Environment & Engineering

基　　金：重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0280,CSTB2022NSCQ-MSX1529,CSTB2022NSCQ-MSX1121)。

摘　　要：大型露天矿区生态修复通过地貌重塑、土壤重构、植被重建与生态恢复等系列工程往往有助于重构区域碳库,特别是植被碳库,但现阶段针对不同生态修复模式的生态系统碳汇功能研究尚存在不足。以重庆市渝北区铜锣山国家矿山公园原损毁矿坑为例,基于GF-6 PMS卫星影像与地面实测数据,通过多种回归模型模拟构建大型露天矿区生态修复植被碳汇模型,对矿区植被生物量与碳储量进行估算,探讨不同修复模式与修复年限的植被碳储量空间分布特征。研究结果表明,基于GF-6 PMS卫星影像提取植被指数,结合野外实地采样数据可以完成大型露天矿区植被地上碳储量的估算,而且基于归一化绿波波段差值植被指数(GNDVI)构建的二次方程模型相关性较好;铜锣山矿区植被平均碳密度为1.84 t C/hm^(2),植被碳储量为408.68 t C,且不同修复模式与修复年限的植被碳储量存在差异,工程修复、自然恢复与关闭未治理矿区的植被平均碳密度分别为2.54、2.09、0.88 t C/hm^(2),工程修复完成1~3年的矿区植被平均碳密度分别为2.75、2.45、2.38 t C/hm^(2),说明工程修复可增加植被固碳效益;不同矿坑类型与修复模式对植被平均碳密度的影响显著,且矿坑类型、修复模式、修复年限均与植被平均碳密度存在显著相关性。研究建立的模型具有一定可靠性,可辅助大尺度植被碳平衡以及评价工作,对于推动中国自主高分卫星数据在生态修复领域应用具有重要意义。The ecological restoration of large-scale open-pit mining area is often helpful to reconstruct the regional carbon pool,especially the vegetation carbon pool,through a series of projects such as landform remodeling,soil reconstruction,vegetation reconstruction and ecological restoration.However,there are still some deficiencies in the study of ecosystem carbon sink function for different ecological restoration modes at this stage.Taking the original damaged mine pit of Tongluoshan National Mine Park in Yubei District of Chongqing as an example,this study uses GF-6 PMS satellite imagery and ground measurement data to simulate the construction of a large-scale open-pit ecological restoration vegetation carbon sink model through multiple regression models.The biomass and carbon storage of vegetation in the mining area are estimated,and the spatial distribution characteristics of vegetation carbon storage under different restoration modes and years are explored.The results show that the vegetation index extracted from GF-6 PMS satellite images combined with field sampling data can be used to estimate the above-ground carbon storage of vegetation in large open-pit mining areas,and the quadratic equation model based on GNDVI has a good correlation.The average carbon density of vegetation in the Tongluoshan mining area is 1.84 t C/hm^(2),and the carbon storage of vegetation is 408.68 t C,there are differences in the carbon storage of vegetation with different restoration engineering modes and years,the average carbon density of vegetation in engineering restoration,natural restoration,and untreated mining areas after closure are 2.54,2.09,and 0.88 t C/hm^(2),respectively.The average carbon density of vegetation in mining areas with 1-3 years of engineering restoration is 2.75,2.45,2.38 t C/hm^(2),respectively,indicating that engineering restoration can increase the carbon sequestration benefits of vegetation.Different pit types and restoration modes have significant effects on the average carbon density of vegetation,an

关 键 词：露天矿山生态修复 植被地上生物量 植被碳储量 西南山地丘陵区 

分 类 号：S154.1[农业科学—土壤学] X826[农业科学—农业基础科学]