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1949—2018年中国森林资源碳储量变化研究

张煜星 王雪军 蒲莹 张建波

张煜星, 王雪军, 蒲莹, 张建波. 1949—2018年中国森林资源碳储量变化研究[J]. 北京林业大学学报, 2021, 43(5): 1-14. doi: 10.12171/j.1000-1522.20200237
引用本文: 张煜星, 王雪军, 蒲莹, 张建波. 1949—2018年中国森林资源碳储量变化研究[J]. 北京林业大学学报, 2021, 43(5): 1-14. doi: 10.12171/j.1000-1522.20200237
Zhang Yuxing, Wang Xuejun, Pu Ying, Zhang Jianbo. Changes in forest resource carbon storage in China between 1949 and 2018[J]. Journal of Beijing Forestry University, 2021, 43(5): 1-14. doi: 10.12171/j.1000-1522.20200237
Citation: Zhang Yuxing, Wang Xuejun, Pu Ying, Zhang Jianbo. Changes in forest resource carbon storage in China between 1949 and 2018[J]. Journal of Beijing Forestry University, 2021, 43(5): 1-14. doi: 10.12171/j.1000-1522.20200237

1949—2018年中国森林资源碳储量变化研究

doi: 10.12171/j.1000-1522.20200237
基金项目: 国家重点研发项目(2017YFD0600903),全国森林资源清查生物量基础建模项目(LY2014-2018)
详细信息
    作者简介:

    张煜星,博士,教授级高级工程师。主要研究方向:森林资源监测和森林碳汇。Email:mrqzyx@sina.com 地址:100714北京市东城区朝阳门内大街130号国家林业和草原局林产工业规划设计院

    责任作者:

    王雪军,博士,教授级高级工程师。主要研究方向:森林资源监测与评价。Email:wangxuejun320@126.com 地址:100714北京市东城区和平里东街18号国家林业和草原局森林资源管理司

  • 中图分类号: S750

Changes in forest resource carbon storage in China between 1949 and 2018

  • 摘要:   目的  建立全国单木材积生物量模型非常重要且必要。研究中国的森林碳储量变化及其碳汇能力对于估算区域碳收支和制定应对气候变化的森林管理政策具有重要意义。  方法  本文结合森林资源清查抽样总体和气候区差异的实际,利用全国森林资源清查22种主要树种的生物量建模实测数据,分别建立区域性单木生物量与材积回归模型,利用1949—2018年间11个时间段的森林资源清查和统计数据,系统测算出了近70年全国及各个区域森林碳储量和碳源汇能力,以期揭示出70年来我国森林碳储量的变化规律和原因以及对碳汇的相关贡献。  结果  研究结果表明:在1949—2018年间,林分、疏林等森林资源碳储总量由4.509 × 1012 kg增加到8.601 × 1012 kg,增加了90.74%,其中2014—2018年期间年均增长3.09%;林分碳储量由4.38 × 1012 kg增加到7.97 × 1012 kg,增加了81.97%;林分碳密度在1973—1976年时段最低为31.64 t/hm2,2014—2018年时段增加到44.30 t/hm2,但是仍然没有达到1949年的46.48 t/hm2;各区域的森林资源碳储总量都有不同程度的增加,尤其是华北区和东南沿海区增长较快,分别增加了145.91%和116.63%;全国及各个区域的森林碳储量和碳密度的变化规律基本呈现出先下降后增加的趋势。全国碳源汇的变化规律也与此基本相同,1981年之前,年均碳积累是负值,以碳源为主,1981年之后碳积累是正值,年均生物量碳汇0.122 × 1012 kg/a,且积累能力不断提高。林分生物量碳库在森林总碳库中占据主要地位,各期的全国林分碳储量占碳储总量的比例均在80%以上;由于我国人工林面积大量增加,碳累积增长速度很快,自20世纪70年代以来年均生物量碳汇为0.04 × 1012 kg/a;天然林在1989年前虽然碳累积为负值,但自我国实行天然林保护工程以来,天然林碳汇能力持续增强,是我国森林碳逐年累积的主要贡献者。  结论  随着我国全面实施天然林保护、生态修复和保护政策持续加强,同时中国现阶段以中幼龄林为主的森林已进入快速增长期,因此未来中国的森林碳汇潜力巨大。

     

  • 图  1  不同区域生物量转换因子(BEF)与根茎比(RSR)比较

    Figure  1.  Comparison of BEF and RSR values in different regions

    图  2  两种测算方法地上生物量结果对比

    BJ为北京;TJ为天津;HB为河北;SX为山西;NM为内蒙古;LN为辽宁;JL为吉林;HLJ为黑龙江;SH为上海;JS为江苏;ZJ为浙江;AH为安徽;FJ为福建;JX为江西;SD为山东;HN为河南;HUB为湖北;HUN为湖南;GD为广东;GX为广西;HNN为海南;CQ为重庆;SC为四川;GZ为贵州;YN为云南;XZ为西藏;SHX为陕西;GS为甘肃;QH为青海;NX为宁夏;XJ为新疆。BJ, Beijing City; TJ, Tianjing City; HB, Hebei Province; SX, Shanxi Province; NM, Inner Mongolia Autonomous Region; LN, Liaoning Province; JL, Jilin Province; HLJ, Heilongjiang Province; SH, Shanghai City; JS, Jiangshu Province; ZJ, Zhejiang Province; AH, Anhui Province; FJ, Fujian Province; JX, Jiangxi Province; SD, Shandong Province; HN, Henan Province; HUB, Hubei Province; HUN, Hunan Province; GD, Guangdong Province; GX, Guangxi Zhuang Autonomous Region; HNN, Hainan Province; CQ, Chongqing City; SC, Sichuan Province; GZ, Guizhou Province; YN, Yunnan Province, XZ, Tibet; SHX, Shaanxi Province; GS, Gansu Province; QH, Qinhai Province; NX, Ningxia Hui Autonomous Region; XJ, Xinjiang Uygur Autonomous Region.

    Figure  2.  Comparison of aboveground biomass results by two calculating methods

    图  3  各时期森林碳储量和碳密度变化

    数字1 ~ 11代表研究时间段,依次为:1949、1950—1962、1973—1976、1977—1981、1984—1988、1989—1993、1994—1998、1999—2003、2004—2008、2009—2013、2014—2018年。下同。The No. 1−11 represent the periods of year 1949, 1950−1962, 1973−1976, 1977−1981, 1984−1988, 1989−1993, 1994−1998, 1999−2003, 2004−2008, 2009−2013, 2014−2018, respectively. The same below.

    Figure  3.  Changes of forest carbon storage and carbon density in different periods

    图  4  1949—2018年森林面积、蓄积量、碳储量、碳密度和碳储总量变化

    Figure  4.  Changes of forest area, volume stock, carbon storage, carbon density and total carbon storage from 1949 to 2018

    表  1  全国主要树种材积与生物量模型

    Table  1.   Regression model of volume to biomass of main tree species in China

    建模范围
    Modeling range
    建模树种(组)
    Modeling tree species (group)
    地上生物量与材积
    Aboveground biomass and volume
    地下与地上生物量
    Underground biomass and aboveground biomass
    模型表达式
    Model expression
    NR2模型表达式
    Model expression
    NR2
    CH 柳杉 Cryptomeria fortunei Y1 = 0.917 2X10.872 5 150 0.991 9 Y2 = 0.224 5X21.061 0 47 0.962 7
    马尾松 Pinus massoniana Y1 = 0.806 7X10.948 3 301 0.989 6 Y2 = 0.119 8X21.081 2 104 0.973 8
    杉木 Cunninghamia lanceolata Y1 = 0.684 6X10.908 9 301 0.987 8 Y2 = 0.240 8X20.983 9 102 0.953 3
    油松 Pinus tabuliformis Y1 = 0.840 0X10.958 4 149 0.983 9 Y2 = 0.189 2X21.041 2 49 0.965 2
    黄山松 Pinus taiwanensis Y1 = 0.686 1X10.966 0 150 0.988 6 Y2 = 0.189 1X20.981 1 51 0.955 6
    NE 落叶松 Larix dahurica Y1 = 0.762 9X10.947 7 150 0.994 2 Y2 = 0.199 9X21.091 1 50 0.979 1
    云杉 Picea asperata Y1 = 0.799 1X10.952 0 150 0.986 4 Y2 = 0.270 4X20.970 7 50 0.964 6
    冷杉 Abies fabri Y1 = 0.624 5X10.974 8 150 0.992 7 Y2 = 0.223 4X20.983 3 49 0.977 3
    栎树 Quercus spp. Y1 = 0.851 7X11.009 0 160 0.989 2 Y2 = 0.545 7X20.864 4 53 0.948 2
    白桦 Betula platyphylla Y1 = 0.668 2X11.005 9 150 0.994 9 Y2 = 0.362 2X20.956 1 53 0.966 8
    桦树 Betula spp. Y1 = 0.514 6X11.046 7 150 0.985 3 Y2 = 0.438 1X20.895 4 51 0.941 1
    山杨 Populus davidiana Y1 = 0.621 6X10.978 3 151 0.991 5 Y2 = 0.237 8X20.975 0 55 0.971 6
    人工杨 Populus simonii Y1 = 0.692 5X10.947 2 150 0.988 1 Y2 = 0.284 3X20.949 9 50 0.943 6
    樟子松 Pinus sylvestris var. mongolica Y1 = 0.578 3X10.956 1 151 0.981 7 Y2 = 0.335 3X21.121 9 49 0.939 6
    椴树 Tilia tuan Y1 = 0.463 9X11.024 5 150 0.983 3 Y2 = 0.871 6X21.006 0 50 0.934 0
    榆树 Ulmus pumila Y1 = 0.781 4X11.009 4 149 0.988 7 Y2 = 0.709 0X21.028 6 49 0.993 6
    NN、NW 落叶松 Larix dahurica Y1 = 0.733 8X10.935 1 300 0.983 5 Y2 = 0.285 9X20.963 3 100 0.957 0
    云杉 Picea asperata Y1 = 0.913 0X10.9128 300 0.978 6 Y2 = 0.300 5X20.958 7 100 0.940 7
    冷杉 Abies fabri Y1 = 0.696 8X10.957 0 150 0.987 5 Y2 = 0.206 6 X21.013 4 50 0.968 4
    柏木 Cupressus funebris Y1 = 1.133 7X10.9422 300 0.971 5 Y2 = 0.339 7X20.921 3 102 0.924 4
    栎树 Quercus spp. Y1 = 0.719 3X11.029 6 210 0.972 3 Y2 = 0.495 5X20.869 8 72 0.936 6
    桦树 Betula spp. Y1 = 0.855 1X10.979 1 240 0.969 1 Y2 = 0.335 9X20.941 4 79 0.961 1
    人工杨 Populus simonii Y1 = 0.611 6X11.001 6 150 0.990 4 Y2 = 0.304 2X20.931 4 48 0.990 1
    山杨 Populus davidiana Y1 = 0.631 0X10.998 7 150 0.988 5 Y2 = 0.281 6X20.951 6 55 0.974 9
    WS 云杉 Picea asperata Y1 = 0.818 1X10.927 3 302 0.967 0 Y2 = 0.211 5X21.009 8 98 0.961 5
    冷杉 Abies fabri Y1 = 0.690 1X10.939 9 301 0.985 5 Y2 = 0.282 5X20.948 3 101 0.950 8
    XZ 云杉 Picea asperata Y1 = 0.943 8X10.919 1 149 0.961 7 Y2 = 0.196 3X20.983 1 47 0.960 4
    冷杉 Abies fabri Y1 = 0.607 3X10.950 6 150 0.986 2 Y2 = 0.190 3X21.033 0 49 0.979 7
    WS、XZ 落叶松 Larix dahurica Y1 = 0.716 8X10.929 3 152 0.982 4 Y2 = 0.190 1X21.006 6 49 0.946 2
    栎树 Quercus spp. Y1 = 1.086 4X10.935 8 151 0.980 0 Y2 = 0.372 5X20.883 9 50 0.953 8
    桦树 Betula spp. Y1 = 0.692 6X10.987 1 150 0.977 6 Y2 = 0.261 7X20.978 3 53 0.944 6
    云南松 Pinus yunnanensis Y1 = 0.536 0X10.993 8 150 0.986 7 Y2 = 0.155 3X21.007 7 50 0.973 1
    山杨 Populus davidiana Y1 = 0.642 3X10.977 2 150 0.994 1 Y2 = 0.275 0X20.912 3 50 0.974 5
    思茅松 Pinus kesiya var. langbianensis Y1 = 0.391 8X11.060 3 150 0.994 7 Y2 = 0.170 1X20.984 2 49 0.986 4
    高山松 Pinus densata Y1 = 0.786 5X10.958 5 150 0.982 6 Y2 = 0.156 2X21.030 6 49 0.934 9
    柏木 Cupressus funebris Y1 = 1.020 3X10.931 2 150 0.991 7 Y2 = 0.232 6X20.936 4 51 0.973 0
    MS、ES 湿地松 Pinus elliottii Y1 = 0.677 4X10.980 1 154 0.991 9 Y2 = 0.388 9X20.917 3 50 0.937 2
    柏木 Cupressus funebris Y1 = 0.949 6X10.950 4 160 0.985 6 Y2 = 0.216 7X21.011 0 65 0.973 0
    栎树 Quercus spp. Y1 = 1.228 2X10.934 1 149 0.982 8 Y2 = 0.324 2X20.921 9 53 0.905 9
    木荷 Schima superba Y1 = 0.857 3X10.975 3 150 0.993 7 Y2 = 0.294 0X20.992 0 50 0.959 0
    枫香 Liquidambar formosana Y1 = 0.793 6X10.993 1 152 0.991 0 Y2 = 0.463 2X20.933 9 50 0.966 6
    人工杨 Populus simonii Y1 = 0.545 8X11.019 9 151 0.995 3 Y2 = 0.221 9X20.933 7 49 0.975 0
    注:CH为全国;NE为东北区;NN为华北区;NW为西北区;MS为中南区;ES为东南沿海区;WS为西南区;XZ为西藏区;N为原始数据样本数量,R2为决定系数;Y1为单木生物量(地上部分)(t),X1为单木材积(m3);Y2为单木地下生物量(t), X2为单木地上生物量(t)。下同。Notes: CH, whole country; NE, Northeast China; NN, North China; NW, Northwest China; MS, South Central China; ES, Southeast Coastal China; WS, Southwest China; XZ is Tibet; N, sample number of raw data; R2, coefficient of determination; Y1, biomass of a tree (aboveground) (t); X1, volume of single tree (m3); Y2, underground biomass of a tree (t); X2, aboveground biomass of a tree (t). The same below.
    下载: 导出CSV

    表  2  全国各区域按森林类型建立的材积与生物量回归模型

    Table  2.   Regression models of volume to biomass by forest types in different regions of China

    适用范围
    Scope of
    application
    建模树种(组)
    Modeling tree
    species (group)
    地上生物量与材积
    Aboveground biomass and volume
    地下与地上生物量
    Underground biomass and aboveground biomass
    模型表达式 Model expressionNR2模型表达式 Model expressionNR2
    NE A Y1 = 0.683 9X10.965 7 600 0.987 5 Y2 = 0.257 9X21.035 3 199 0.938 9
    B Y1 = 1.368 1X10.946 0 1 060 0.943 9 Y2 = 0.468 9X20.949 0 361 0.882 7
    C Y1 = 1.114 3X10.941 8 1 660 0.940 7 Y2 = 0.382 1X20.977 9 560 0.898 6
    NN A Y1 = 0.995 5X10.935 5 449 0.970 8 Y2 = 0.277 2X20.963 3 150 0.952 9
    B Y1 = 0.745 0X11.001 2 600 0.972 7 Y2 = 0.373 0X20.941 9 199 0.957 0
    C Y1 = 0.843 7X10.974 0 1 049 0.970 9 Y2 = 0.328 2X20.934 9 349 0.954 2
    NW A Y1 = 0.880 3X10.920 6 600 0.974 1 Y2 = 0.293 8X20.960 6 201 0.936 7
    B Y1 = 0.752 1X11.000 1 600 0.972 4 Y2 = 0.364 1X20.919 9 206 0.953 3
    C Y1 = 0.821 5X10.957 1 1 200 0.970 0 Y2 = 0.329 5X20.938 3 407 0.944 8
    MS A Y1 = 0.853 9X10.936 9 433 0.982 2 Y2 = 0.223 8X21.002 0 149 0.956 2
    B Y1 = 0.829 7X10.977 4 320 0.978 3 Y2 = 0.229 2X20.919 7 109 0.924 3
    C Y1 = 0.841 6X10.954 8 753 0.979 0 Y2 = 0.253 0X20.965 6 258 0.941 8
    ES A Y1 = 0.737 4X10.951 1 774 0.981 5 Y2 = 0.220 5X20.998 8 255 0.956 7
    B Y1 = 0.970 3X10.964 2 341 0.985 2 Y2 = 0.318 4X20.955 5 115 0.940 4
    C Y1 = 0.800 6X10.955 6 1 115 0.977 8 Y2 = 0.245 3X20.987 4 370 0.949 7
    WS A Y1 = 0.712 8X10.945 0 1 337 0.978 2 Y2 = 0.199 5X21.004 4 446 0.959 0
    B Y1 = 0.803 0X10.968 0 501 0.980 0 Y2 = 0.310 7X20.931 7 171 0.944 9
    C Y1 = 0.738 3X10.950 5 1 838 0.976 6 Y2 = 0.224 0X20.985 5 617 0.953 3
    XZ A Y1 = 0.759 8X10.946 6 569 0.971 8 Y2 = 0.185 4X20.990 4 186 0.950 6
    B Y1 = 0.642 3X10.977 2 150 0.994 1 Y2 = 0.275 0X20.912 3 50 0.974 5
    C Y1 = 0.734 0X10.953 0 719 0.976 4 Y2 = 0.204 0X20.971 1 236 0.953 9
    注:A为针叶树种;B为阔叶树种;C为针阔混交树种。下同。Notes: A, coniferous tree species; B, broadleaved tree species; C, coniferous and broadleaved mixed tree species. The same below.
    下载: 导出CSV

    表  3  各区域各森林类型的生物量转换因子(BEF)和根茎比(RSR)比较

    Table  3.   Comparison of biomass expansion factor (BEF) and root-to-shoot ratio (RSR) of different forest types in different regions

    区域
    Region
    类型
    Type
    变量
    Variable
    样本数
    Sample number
    平均值
    Average value
    最小值
    Min. value
    最大值
    Max. value
    标准差
    SD
    变动系数
    Coefficient of variation/%
    NE A BEF/(g·cm−3) 600 0.614 0.176 1.510 0.169 27.6
    RSR 199 0.354 0.079 2.581 0.289 81.8
    B BEF/(g·cm−3) 1 060 0.675 0.040 3.561 0.215 31.9
    RSR 361 0.574 0.074 3.587 0.471 82.1
    C BEF/(g·cm−3) 1 660 0.653 0.040 3.561 0.202 31.0
    RSR 560 0.496 0.074 3.587 0.429 86.5
    NN A BEF/(g·cm−3) 449 0.834 0.295 3.041 0.322 38.6
    RSR 150 0.267 0.059 0.960 0.126 47.2
    B BEF/(g·cm−3) 600 0.795 0.139 2.087 0.282 35.4
    RSR 199 0.303 0.036 1.200 0.162 53.5
    C BEF/(g·cm−3) 1 049 0.812 0.139 3.041 0.300 37.0
    RSR 349 0.288 0.036 1.200 0.149 51.6
    NW A BEF/(g·cm−3) 600 0.681 0.165 3.311 0.300 44.1
    RSR 201 0.287 0.052 1.351 0.154 53.6
    B BEF/(g·cm−3) 600 0.799 0.139 2.087 0.282 35.3
    RSR 206 0.302 0.036 1.200 0.163 53.9
    C BEF/(g·cm−3) 1 200 0.740 0.139 3.311 0.297 40.1
    RSR 407 0.295 0.036 1.351 0.158 53.8
    MS A BEF/(g·cm−3) 433 0.699 0.200 6.236 0.338 48.4
    RSR 149 0.254 0.073 1.347 0.164 64.5
    B BEF/(g·cm−3) 320 0.804 0.389 2.287 0.310 38.6
    RSR 109 0.430 0.033 1.301 0.247 57.4
    C BEF/(g·cm−3) 753 0.744 0.200 6.236 0.330 44.4
    RSR 258 0.329 0.033 1.347 0.221 67.2
    ES A BEF/(g·cm−3) 774 0.637 0.242 6.236 0.279 43.7
    RSR 255 0.243 0.065 0.838 0.120 49.1
    B BEF/(g·cm−3) 341 0.874 0.396 2.287 0.279 31.9
    RSR 115 0.308 0.033 1.200 0.181 58.6
    C BEF/(g·cm−3) 1 115 0.710 0.242 6.236 0.299 42.2
    RSR 370 0.264 0.033 1.200 0.144 54.7
    WS A BEF/(g·cm−3) 1 337 0.603 0.134 2.428 0.227 37.6
    RSR 446 0.225 0.059 0.912 0.115 51.1
    B BEF/(g·cm−3) 501 0.741 0.202 2.118 0.251 33.9
    RSR 171 0.273 0.020 0.954 0.141 51.6
    C BEF/(g·cm−3) 1838 0.641 0.134 2.428 0.242 37.8
    RSR 617 0.238 0.020 0.954 0.125 52.3
    XZ A BEF/(g·cm−3) 569 0.656 0.213 1.972 0.276 42.1
    RSR 186 0.199 0.045 0.559 0.093 47.0
    B BEF/(g·cm−3) 150 0.593 0.295 1.309 0.114 19.2
    RSR 50 0.217 0.113 0.527 0.099 45.6
    C BEF/(g·cm−3) 719 0.643 0.213 1.972 0.253 39.3
    RSR 236 0.203 0.045 0.559 0.095 46.8
    下载: 导出CSV

    表  4  1949—2018年不同时期森林面积、蓄积、碳储量及年均碳累积

    Table  4.   Forest area, volume, carbon storage and annual average carbon sequestration in different periods from 1949 to 2018

    调查期
    Investigating
    period
    林分 Stand 人工林 Plantation 天然林 Natural forest
    FA
    FV
    FC
    AC
    FA
    FV
    FC
    AC
    FC
    FV
    FC
    AC
    1949 94.23 92.23 4.38
    1950—1962 95.85 83.67 3.98 −0.033
    1973—1976 119.78 84.70 3.79 −0.014 17.81 1.64 0.07 101.97 83.06 3.72
    1977—1981 95.63 79.79 3.69 −0.020 12.73 2.73 0.13 0.011 82.90 77.06 3.56 −0.031
    1984—1988 102.19 80.91 3.71 0.003 18.74 5.30 0.24 0.017 83.45 75.61 3.47 −0.014
    1989—1993 108.64 90.87 4.08 0.074 21.37 7.12 0.32 0.015 87.27 83.75 3.76 0.059
    1994—1998 129.20 100.86 4.56 0.096 19.14 10.13 0.46 0.028 110.06 90.73 4.10 0.068
    1999—2003 142.79 120.98 5.43 0.174 32.29 15.05 0.68 0.044 110.50 105.93 4.75 0.130
    2004—2008 155.59 133.63 6.10 0.134 40.00 19.61 0.90 0.044 115.59 114.02 5.20 0.090
    2009—2013 164.60 147.79 6.81 0.142 47.07 24.83 1.14 0.050 117.53 122.96 5.67 0.092
    2014—2018 179.89 170.58 7.97 0.232 57.13 33.88 1.58 0.088 122.76 136.70 6.39 0.144
    注:1949年和1950—1962年两期资源数据不含西藏自治区的数据,1977—1981、1984—1988、1989—1993、1994—1998年4期数据统计不含西藏控制以外;FA为森林面积(106 hm2),FV为森林蓄积(108 m3),FC为碳储量(1012 kg),AC为年均碳累积(1012 kg/a);—表示没有调查统计数据。下同。Notes: The resource data for the periods of year 1949 and 1950−1962 do not include Tibet’s data, and data statistics for the periods of year 1977−1981, 1984−1988, 1989−1993 and 1994−1998 do not include data outside of Tibetan control; FA, forest area (106 ha); FV, forest volume (108 m3); FC, carbon storage (1012 kg); AC, annual average carbon sequestration (1012 kg/year); — indicates no survey statistic data. The same below.
    下载: 导出CSV

    表  5  全国各区域林分、疏林等碳储量占碳储总量的百分比

    Table  5.   Percentage of carbon storage in total carbon storage of forest and sparse forest in different regions of China %

    区域
    Region
    组分
    Component
    19491950—19621973—19761977—19811984—19881989—19931994—19981999—20032004—20082009—20132014—2018
    NE SF 0.97 5.91 6.20 7.48 9.49 10.59 10.55 10.20 6.51 6.15 6.93
    FL 99.03 94.09 93.80 92.52 90.51 89.41 89.45 89.80 93.49 93.85 93.07
    NN SF 0.66 10.87 15.20 13.83 15.84 22.07 21.39 17.14 13.87 10.45 8.27
    FL 99.34 89.13 84.80 86.17 84.16 77.93 78.61 82.86 86.13 89.55 91.73
    NW SF 28.78 9.27 14.11 11.92 14.94 16.87 13.04 12.22 9.11 9.70 9.37
    FL 71.22 90.73 85.89 88.08 85.06 83.13 86.96 87.78 90.89 90.30 90.63
    MS SF 3.95 11.46 28.04 28.94 31.47 34.44 32.01 22.02 15.75 16.35 12.56
    FL 96.05 88.54 71.96 71.06 68.53 65.56 67.99 77.98 84.25 83.65 87.44
    ES SF 0.00 11.41 21.59 21.78 22.60 18.44 14.85 12.47 9.38 9.39 7.51
    FL 100.00 88.59 78.41 78.22 77.40 81.56 85.15 87.53 90.62 90.61 92.49
    WS SF 0.00 7.78 13.46 15.21 15.58 29.34 10.93 11.54 8.70 8.84 6.77
    FL 100.00 92.22 86.54 84.79 84.42 70.66 89.07 88.46 91.30 91.16 93.23
    XZ SF 0.00 6.27 0.00 2.70 4.30 1.26 1.19 1.13 0.94
    FL 100.00 93.73 100.00 97.30 95.70 98.74 98.81 98.87 99.06
    CH SF 2.89 8.57 12.19 14.20 15.36 16.13 14.01 11.61 8.76 8.58 7.35
    FL 97.11 91.43 87.81 85.80 84.64 83.87 85.99 88.39 91.24 91.42 92.65
    注:SF为疏林等,FL为林分。Notes: SF, sparse forest, etc; FL, stand.
    下载: 导出CSV

    表  6  全国不同时期森林碳密度                  t/hm2

    Table  6.   Changes in forest carbon density in different periods of China t/ha

    区域
    Region
    19491950—19621973—19761977—19811984—19881989—19931994—19981999—20032004—20082009—20132014—2018
    CH 46.48 42.20 31.64 38.61 36.30 37.60 35.26 38.04 39.19 41.38 44.30
    NE 50.81 47.83 42.71 45.47 42.88 43.64 41.68 41.03 41.97 44.69 48.73
    NN 40.37 39.81 27.27 31.42 31.53 31.43 31.62 30.92 31.12 34.57 37.89
    NW 42.75 49.28 39.25 42.99 39.54 43.56 41.75 43.24 42.77 43.46 46.45
    MS 28.18 21.54 10.95 17.94 18.44 19.23 18.54 22.30 24.68 27.82 31.83
    ES 38.97 34.21 14.95 29.16 24.30 23.31 21.96 26.81 30.64 33.29 39.27
    WS 62.59 54.12 45.30 52.91 48.51 47.50 43.25 43.79 45.03 46.78 46.39
    XZ 76.05 58.89 58.89 96.75 95.97 90.88 92.10 91.87 88.65
    下载: 导出CSV

    表  7  本文碳储量与其他研究结果的对比

    Table  7.   Comparison of carbon storage results between this study and other research 1012 kg

    调查期
    Investigating
    period
    本文
    This study
    方精云等[1]
    Fang J Y, et al.[1]
    方精云等[12, 30]
    Fang J Y, et al.[12, 30]
    郭兆迪等[40]
    Guo Z D, et al.[40]
    Zhang C H, et al.[24] 朱永杰[39]
    Zhu Y J[39]
    李妍等[41]
    Li Y, et al.[41]
    林分
    Stand
    森林资源
    Forest
    resource
    林分
    Stand
    林分
    Stand
    林分
    Stand
    林分
    Stand
    森林资源
    Forest
    resource
    林分
    Stand
    林分
    Stand
    1949 4.38 4.51 5.06
    1950—1962 3.98 4.35 4.58
    1973—1976 3.79 4.32 4.44 4.11 4.38 3.85
    1977—1981 3.69 4.30 4.38 4.30 4.72 4.21 4.75 4.09 3.70
    1984—1988 3.71 4.38 4.45 4.46 4.89 4.18 4.82 4.04 3.76
    1989—1993 4.08 4.87 4.63 4.93 5.40 4.52 5.25 4.76 4.11
    1994—1998 4.56 5.30 4.75 5.01 5.39 4.50 5.12 5.25 4.66
    1999—2003 5.43 6.15 5.85 5.86 5.41 5.97 6.49 5.51
    2004—2008 6.10 6.68 6.43 6.24 6.83 7.895 6.09
    2009—2013 6.81 7.45
    2014—2018 7.97 8.60
    下载: 导出CSV
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  • 收稿日期:  2020-07-29
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