[1]崔立志,郭敬丽,巩建新,等.河北省木兰围场国有林场植被碳汇计量方法初探[J].浙江林业科技,2024,44(01):80-86.[doi:10.3969/j.issn.1001-3776.2024.01.011]
 CUI Lizhi,GUO Jingli,GONG Jianxin,et al.Preliminary Study on Forest Carbon Sink Estimation Method in Mulan Forest Farm[J].Journal of Zhejiang Forestry Science and Technology,2024,44(01):80-86.[doi:10.3969/j.issn.1001-3776.2024.01.011]
点击复制

河北省木兰围场国有林场植被碳汇计量方法初探()
分享到:

《浙江林业科技》[ISSN:1001-3776/CN:33-1112/S]

卷:
44
期数:
2024年01期
页码:
80-86
栏目:
研究简报
出版日期:
2024-01-20

文章信息/Info

Title:
Preliminary Study on Forest Carbon Sink Estimation Method in Mulan Forest Farm
文章编号:
1001-3776(2024)01-0080-07
作者:
崔立志郭敬丽巩建新李大勇李孝辉
(河北省木兰围场国有林场,河北 承德 068450)
Author(s):
CUI LizhiGUO JingliGONG JianxinLI DayongLI Xiaohui
(Mulanweichang State-owned Forest Farm of Hebei Province, Chengde 068450, China)
关键词:
森林碳汇碳汇计量碳汇技术木兰林场
Keywords:
forest carbon sink carbon sink estimation carbon sink technology Mulan Forest Farm
分类号:
S718.55
DOI:
10.3969/j.issn.1001-3776.2024.01.011
文献标志码:
A
摘要:
2022 年7—8 月,在河北省木兰围场国有林场(以下简称木兰林场))筛选出以白桦Betula platyphylla、落 叶松Larix gmelinii、油松Pinus tabuliformis 和蒙古栎Quercus mongolica 为优势树种的样地开展森林碳汇计量活动, 在该场二类连清数据的基础上建立30 个微样地,通过对这些微样地进行调查和统计,初步探讨森林碳汇量的计算 方法、调查方法、监测因素等。结果表明,2022 年,木兰林场的森林蓄积量为833.93×104 m3,生物量为733.21 ×104 t,碳储量为377.10×104 t,碳汇量为60.33×104 t,年均碳汇量为12.07×104 t·a-1;白桦、落叶松、油松和蒙 古栎4 个优势树种在不同龄组的蓄积量存在较大差异,其单位面积的生物量在不同龄组和树种之间存在一定的差 异;单位面积年均碳汇量,油松是其他树种的数倍之多,其次是蒙古栎,落叶松居第三位,表明落叶松、蒙古栎 和油松在固碳和碳汇方面具有较高的潜力。本文提出的县域陆表植被碳汇计测方法初步解决了传统植被调查中耗 时耗力的问题,实现了“国家—省域—县域—乡镇—村街—地块”逐级区划的低成本高效率计测。
Abstract:
In July and August 2022, 30 sample plots were established in Mulanweichang State-owned Forest Farm in Hebei province dominated by Betula platyphylla, Larix gmelinii, Pinus tabuliformis and Quercus mongolica, based on the forest management survey. Investigations were carried out on increment of DBH, growing stock, etc. of sample trees, and forest carbon sink were estimated. The results showed that in 2022, the forest growing stock in the Farm was 833.93×104 m3, biomass was 733.21×104 t, carbon stock was 377.10×104 t, and the carbon sink was 60.33×104 t, with an average annual carbon sink of 12.07×104t·a-1. The four dominant tree species in different age groups had large differences in forest growing stock, and their unit area biomass had differences among different age groups and tree species. The unit area average annual carbon sink was ordered by P. tabuliformis, followed by Qu. mongolica, and L. gmelinii, which indicated that they had high potentials of sequestering and sinking carbon.

参考文献/References:

[1] BONAN G B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests [J]. Science,2008,320(5882):1444-9.
[2] 崔海鸥,刘珉. 我国第九次森林资源清查中的资源动态研究[J]. 西部林业科学,2020,49(5):90-5.
[3] 张艳,李锋,李援. 碳中和背景下林业碳汇市场及海南发展林业碳汇交易研究[J]. 海南大学学报(人文社会科学版),2021,39(3):35-43.
[4] 习近平. 继往开来,开启全球应对气候变化新征程——在气候雄心峰会上的讲话[J]. 一带一路报道(中英文),2021(01):20-1.
[5] LU D,CHEN Q,WANG G,et al. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems [J].International Journal of Digital Earth,2014,9(1):63-105.
[6] BRIENEN R J W,CALDWELL L,DUCHESNE L,et al. Forest carbon sink neutralized by pervasive growth-lifespan trade-offs [J]. Nat Commun,2020,11(1):4241.
[7] YIN S,GONG Z,GU L,et al. Driving forces of the efficiency of forest carbon sequestration production:Spatial panel data from the national forest inventory in China [J]. Journal of Cleaner Production,2022,330.
[8] KE S F,ZHANG Z,WANG YM. China's forest carbon sinks and mitigation potential from carbon sequestration trading perspective [J].Ecological Indicators,2023,2:148.
[9] ZHAO M,YANG J,ZHAO N,et al. Estimation of China’s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013 [J]. Forest Ecology and Management,2019,448:528-34.
[10] 王兴昌,王传宽. 森林生态系统碳循环的基本概念和野外测定方法评述[J]. 生态学报,2015,35(13):4241-56.
[11] 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(06):10-20.
[12] EGGLESTON H,BUENDIA L,MIWA K,et al. 2006 IPCC guidelines for national greenhouse gas inventories [R]. 2006.
[13] 何潇,雷相东. 东北地区落叶松人工林生物量转换与扩展因子空间自回归模型 [J]. 林业科学,2021,57(10):49-58.
[14] 郑海妹,欧光龙,胥辉,等. 森林生物量扩展因子研究进展[J]. 广东农业科学,2014,41(20):145-9.
[15] 张佳音. 木兰围场北沟林场森林生态系统健康评价研究[D]. 北京:北京林业大学,2010.
[16] 赵自雨. 林场尺度空地一体化森林生物量估测方法研究[D]. 北京:北京林业大学,2021.
[17] 邱梓轩. 中国陆表森林植被碳汇测计方法与应用研究[D]. 北京:北京林业大学,2019.

相似文献/References:

[1]李灿,龙飞,祁慧博,等.森林碳汇替代配额减排的成本比较[J].浙江林业科技,2018,38(06):38.[doi:10.3969/j.issn.1001-3776.2018.06.007]
 LI Can,LONG Fei,Qi Hui-bo,et al.Cost Comparison onSubstitution of Carbon Emission Reduction Quotas with Forest Carbon Sink[J].Journal of Zhejiang Forestry Science and Technology,2018,38(01):38.[doi:10.3969/j.issn.1001-3776.2018.06.007]
[2]张勇,蒋仲龙,蒋科毅,等.浙江省林业应对气候变化成效及措施研究[J].浙江林业科技,2021,41(02):85.[doi:10.3969/j.issn.1001-3776.2021.02.015]
 ZHANG Yong,JIANG Zhong-long,JIANG Ke-yi,et al.Reviews of Forestry Response to Climate Change in Zhejiang Province[J].Journal of Zhejiang Forestry Science and Technology,2021,41(01):85.[doi:10.3969/j.issn.1001-3776.2021.02.015]

备注/Memo

备注/Memo:
收稿日期:2023-06-06;修回日期:2023-10-18
基金项目:2022 中央财政林业科技推广项目《木兰林场植被碳汇量计算及管理平台建设》(冀TG﹝2022﹞014)
作者简介:崔立志,正高级林业工程师,从事森林培育研究:E-mail: cuilzhi_2006@163.com。
更新日期/Last Update: 2024-02-20