新疆森林植被碳储量预测研究

doi:10.16258/j.cnki.1674-5906.2023.02.002

生态环境学报 ›› 2023, Vol. 32 ›› Issue (2): 226-234.DOI: 10.16258/j.cnki.1674-5906.2023.02.002

新疆森林植被碳储量预测研究

陈治中¹(), 昝梅¹^,²^,^*(), 杨雪峰¹^,², 董煜¹^,²

1.新疆师范大学地理科学与旅游学院，新疆乌鲁木齐 830054
2.新疆干旱区湖泊环境与资源重点实验室，新疆乌鲁木齐 830054

收稿日期:2022-10-31 出版日期:2023-02-18 发布日期:2023-05-11
通讯作者: *昝梅（1979年生），女，副教授，硕士研究生导师，主要从事干旱区森林碳储量与碳循环研究。E-mail: zanmei1102@163.com
作者简介:陈治中（1998年生），男，硕士研究生，主要从事干旱区森林碳储量研究。E-mail: 2192352335@qq.com
基金资助:
国家自然科学基金项目(42261013);新疆维吾尔自治区自然科学基金项目(2017D01A55);新疆维吾尔自治区重点实验室招标项目(XJDX0909-2021-01);新疆维吾尔自治区重点实验室招标项目(XJNUSYS2019A15);新疆师范大学博士科研启动基金项目(XJNUBS2003)

Prediction of Forest Vegetation Carbon Storage in Xinjiang

CHEN Zhizhong¹(), ZAN Mei¹^,²^,^*(), YANG Xuefeng¹^,², DONG Yu¹^,²

1. School of Geographical Sciences and Tourism, Xinjiang Normal University, Urumqi, Xinjiang 830054, P. R. China
2. Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi, Xinjiang 830054, P. R. China

Received:2022-10-31 Online:2023-02-18 Published:2023-05-11

摘要/Abstract

摘要：

森林在固碳与应对气候变化方面起着重要作用。在区域空间尺度上，如何准确预测森林碳储量仍是热点与难点。以新疆主要森林类型常绿针叶林、落叶针叶林、落叶阔叶林和针阔混交林为研究对象，综合激光雷达树高数据与森林调查数据，采用幂函数模型估算新疆森林植被生物量并转换为碳密度，基于森林植被生物量利用Logistic模型与Gompertz模型估算新疆森林年龄获得新疆森林年龄空间分布图。在构建适合新疆主要森林类型的年龄与碳密度模型的基础上，预测2030年和2060年新疆森林植被碳储量与碳汇速率。结果表明，（1）构建的适合新疆主要森林类型生物量和森林年龄的估算模型以及森林年龄与碳密度的生长模型拟合度和显著性水平都较高，通过验证确定了对应的最优模型及参数。其中新疆森林生长模型表现出随林龄增加碳密度逐渐增加，到达成熟林后碳密度逐渐趋于稳定的特征。（2）2019年新疆森林年龄与碳密度空间分布大致呈现西高东低，北高南低的格局，与不同森林类型的环境适应能力及生长速率有关。2019年新疆森林的平均生物量、平均碳密度和年龄分别为147.84 Mg·hm^-2、73.92 Mg·hm^-2和68 a，其中约占新疆森林面积56%的森林为中龄林。（3）在排除自然和人为干扰的情景下，2030年和2060年新疆森林植被碳储量的预测值分别为 (219.76±3.53) Tg和 (334.99±5.56) Tg。相比2019年，2030年和2060年的新疆森林植被碳储量呈增加趋势，常绿针叶林与针阔混交林碳汇速率呈减少趋势。总之，未来新疆森林具有巨大的碳汇潜力，但也存在一定的不确定性。以上研究结果可以为新疆森林碳储量现状及预测评估提供数据参考和理论依据。

关键词: 森林碳储量, 森林年龄, 森林碳密度, 激光雷达森林冠层高度, 新疆

Abstract:

Forests play an important role in sequestering carbon and affecting climate change. However, predicting forest carbon storage at the regional spatial scale accurately continues to be a research hotspot and difficulty. Taking the main forest types in Xinjiang, evergreen coniferous forest, deciduous coniferous forest, deciduous broad-leaved forest, and coniferous and broad-leaved mixed forest, as the research object, this study combined laser radar tree height data and forest survey data to estimate the forest vegetation in Xinjiang using the power function model. Based on the forest vegetation biomass, the logistic model and the Gompertz model were used to estimate forest age and obtain a spatial distribution map of forest age in Xinjiang. On the basis of constructing age and carbon density models suitable for the main forest types in Xinjiang, the carbon storage and carbon sink rate of forest vegetation in Xinjiang were predicted for 2030 and 2060. The study produced several interesting results: (1) The estimation model suitable for biomass and forest age of major forest types in Xinjiang and the growth model of forest age and carbon density have a high degree of fit and significance, and the corresponding optimal model and parameters have been determined through validation. The forest growth model in Xinjiang shows that the carbon density increases with the increase of forest age, and tends to be stable after reaching the mature forest. (2) The spatial distribution of forest age and carbon density in Xinjiang in 2019 generally showed a pattern of high in the west and low in the east and high in the north and low in the south. It is related to the environmental adaptability and growth rate of different forest types. The average biomass, average carbon density, and age of Xinjiang forests in 2019 were 147.84 Mg·hm^-2, 73.92 Mg·hm^-2, and 68 years, respectively. The forests accounting for 56% of the forest area in Xinjiang are medium aged forests (3) Under the scenario of excluding natural and human disturbances, the predicted values of forest vegetation carbon storage in Xinjiang in 2030 and 2060 are (219.76±3.53) Tg and (334.99±5.56) Tg, respectively. Compared with 2019, the carbon storage values of Xinjiang forest vegetation in 2030 and 2060 show an increasing trend, the carbon sink rate of evergreen coniferous forest and mixed coniferous and broad-leaved forest decreased. In a word, Xinjiang’s forests have huge carbon sink potential in the future, but there are also some uncertainties. The forests in Xinjiang have huge future carbon sink potential. The research results of this study provide a data reference and theoretical basis for the current situation and the prediction and evaluation of forest carbon storage in Xinjiang.

Key words: forest carbon storage, forest age, forest carbon density, Lidar forest canopy height, Xinjiang

中图分类号:

S718.5
X173

陈治中, 昝梅, 杨雪峰, 董煜. 新疆森林植被碳储量预测研究[J]. 生态环境学报, 2023, 32(2): 226-234.

CHEN Zhizhong, ZAN Mei, YANG Xuefeng, DONG Yu. Prediction of Forest Vegetation Carbon Storage in Xinjiang[J]. Ecology and Environment, 2023, 32(2): 226-234.

图/表 11

参考文献 47

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	ZHOU Z F, WANG Y, JIA G, et al., 2022. Carbon storage status and carbon sequestration potential prediction of arbor forests in Hebei Province[J]. Forestry Resource Management (2): 45-53.

森林类型	模型	参数		r²	n	σ_RMSE	P
森林类型	模型	a	b	r²	n	σ_RMSE	P
ENF	公式1	11.57	0.99	0.82	211	1.22	0.00
DNF		1.28	1.74	0.80	171	3.22	0.00
DBF		0.98	1.74	0.70	112	2.47	0.00
MF		3.80	1.36	0.79	109	3.25	0.00

森林类型	模型	参数		r²	n	σ_RMSE	P
森林类型	模型	a	b	r²	n	σ_RMSE	P
ENF	公式1	11.57	0.99	0.82	211	1.22	0.00
DNF		1.28	1.74	0.80	171	3.22	0.00
DBF		0.98	1.74	0.70	112	2.47	0.00
MF		3.80	1.36	0.79	109	3.25	0.00

森林类型	模型	参数			r²	n	σ_RMSE	P
森林类型	模型	α	β	γ	r²	n	σ_RMSE	P
ENF	公式3	615.16	1.79	0.02	0.77	211	18.16	0.00
DNF	公式2	529.91	4.16	0.03	0.76	171	19.00	0.00
DBF	公式2	389.78	4.21	0.02	0.72	112	12.38	0.00
MF	公式2	372.70	5.12	0.18	0.83	109	25.27	0.00

森林类型	模型	参数			r²	n	σ_RMSE	P
森林类型	模型	α	β	γ	r²	n	σ_RMSE	P
ENF	公式3	615.16	1.79	0.02	0.77	211	18.16	0.00
DNF	公式2	529.91	4.16	0.03	0.76	171	19.00	0.00
DBF	公式2	389.78	4.21	0.02	0.72	112	12.38	0.00
MF	公式2	372.70	5.12	0.18	0.83	109	25.27	0.00

森林类型	模型	参数				n	r²	P
森林类型	模型	δ	ε	μ	τ	n	r²	P
ENF	公式5	309.53± 3.19	1.88± 0.09	0.03± 0.00		209	0.93	0.00
DNF	公式4	279.51± 4.07	12.46± 1.64	125.80± 1.22	4.08± 0.13	171	0.99	0.00
DBF	公式4	204.88± 2.90	9.24± 1.21	185.00± 1.73	4.15± 0.13	112	0.99	0.00
MF	公式4	188.61± 1.68	10.72± 1.09	30.96± 0.15	5.44± 0.12	109	0.99	0.00

新疆森林植被碳储量预测研究

Prediction of Forest Vegetation Carbon Storage in Xinjiang

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森林类型	碳储量/Tg			碳汇速率/(Tg·a^-1)
森林类型	2019年	2030年	2060年	2030年	2060年
ENF	127.69±2.65	159.64±3.26	240.33±5.18	2.90±0.77	2.69±0.76
DNF	29.90±1.00	37.90±1.26	62.64±1.94	0.73±0.03	0.82±0.02
DBF	11.74±0.30	13.42±0.32	20.24±0.51	0.15±0.00	0.23±0.01
MF	4.87±0.19	8.80±0.27	11.78±0.29	0.36±0.01	0.10±0.00

森林类型	龄组	林龄/a		面积/m²	偏差/a
森林类型	龄组	估测值	文献值 (戴铭等, 2011)	面积/m²	偏差/a
ENF	幼龄	50	45	4.00×10⁹	5
	中龄	76	85	5.70×10⁹	-9
	近熟	106	110	1.60×10⁹	-4
	成熟	128	140	3.60×10⁸	-12
	过熟	184	170	1.10×10⁷	14
DNF	幼龄	22	35	9.60×10⁸	-13
	中龄	63	62.50	1.40×10⁹	0.50
	近熟	91	82.50	1.20×10⁹	8.50
	成熟	112	105	1.30×10⁹	7
	过熟	159	130	6.20×10⁷	29
DBF	幼龄	4	5	2.50×10⁹	-1
	中龄	13	12.50	8.90×10⁸	0.50
	近熟	18	18	7.40×10⁸	0
	成熟	23	25	1.10×10⁹	-2
	过熟	43	40	9.70×10⁸	3
MF	幼龄	27	32.50	2.70×10⁸	-5.50
	中龄	41	53.50	5.50×10⁸	-12.50
	近熟
	成熟
	过熟	124.50	104	7.60×10⁷	24.50

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