基于随机森林模型的东北三省乔木林生物质碳储量预测

doi:10.11707/j.1001-7488.20220405

林业科学 ›› 2022, Vol. 58 ›› Issue (4): 40-50.doi: 10.11707/j.1001-7488.20220405

基于随机森林模型的东北三省乔木林生物质碳储量预测

田惠玲¹,朱建华^1,^2,*,何潇³,陈新云⁴,简尊吉¹,李宸宇¹,郭学媛¹,黄国胜⁴,肖文发^1,²

1. 中国林业科学研究院森林生态环境与自然保护研究所国家林业和草原局森林生态环境重点实验室北京 100091
2. 南京林业大学南方现代林业协同创新中心南京 210037
3. 中国林业科学研究院资源信息研究所国家林业和草原局森林经营与生长模拟实验室北京 100091
4. 国家林业和草原局调查规划设计院北京 100714

收稿日期:2021-11-16 出版日期:2022-04-25 发布日期:2022-07-20
通讯作者: 朱建华
基金资助:
“十三五”国家重点研发计划课题“人工林生产力形成的关键生理生态与环境控制机制”(2016YFD0600201)

Projected Biomass Carbon Stock of Arbor Forest of Three Provinces in Northeastern China Based on Random Forest Model

Huiling Tian¹,Jianhua Zhu^1,^2,*,Xiao He³,Xinyun Chen⁴,Zunji Jian¹,Chenyu Li¹,Xueyuan Guo¹,Guosheng Huang⁴,Wenfa Xiao^1,²

1. Ecology and Nature Conservation Institute, CAF Key Laboratory of Forest Ecology and Environment, National Forestry and Grassland Administration Beijing 100091
2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University Nanjing 210037
3. Institute of Forest Resource Information Techniques, CAF Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration Beijing 100091
4. Academy of Forest Inventory and Planning, National Forestry and Grassland Administration Beijing 100714

Received:2021-11-16 Online:2022-04-25 Published:2022-07-20
Contact: Jianhua Zhu

摘要/Abstract

摘要：

目的: 利用全国森林资源清查固定样地连续监测数据，通过机器学习算法构建基于多因子的森林生长模型，提高森林生长和固碳量的模拟精度，预测东北三省乔木林未来碳汇潜力，探索乔木林碳汇的潜在分布，为准确定位我国东北森林在增汇减排中的作用以及科学制定国家“碳中和”行动路径和目标管理提供科学指导。方法: 利用1999—2018年4次全国森林资源连续清查固定样地监测数据，结合区域气候、土壤、林分和地形因子，采用随机森林模型构建区域主要优势树种(组)的生长-消耗模型，运用未来气候情景与未来乔木林面积扩增情景，预测东北三省2015—2060年间乔木林生物质碳储量变化与碳汇潜力。结果: 东北三省乔木林生物质碳储量2060年可达3 393.15 TgC，比2015年增加1 895.23 TgC，2015—2060年间年碳汇量为42.12 TgC ·a^-1，其中天然林是主体。辽宁省、吉林省和黑龙江省乔木林生物质碳储量分别由2015年的139.19、463.58和895.15 TgC增至2060年的328.95、915.83和2 148.37 TgC，乔木林平均生物质碳密度分别由2015年的32.71、59.75和45.11 MgC ·hm^-2增至2060年的75.20、109.32和85.24 MgC ·hm^-2。2015—2060年间辽宁省、吉林省和黑龙江省乔木林生物质年碳汇量分别为4.22、10.05和27.85 TgC ·a^-1。结论: 本研究构建的随机森林模型表现效果较好，能够用于东北三省未来乔木林碳储量预测。2015—2060年东北三省乔木林生物质碳储量将增加1 895.23 TgC，未来仍具有较大碳汇潜力。黑龙江省的碳汇潜力最大，年碳汇量达27.85 TgC ·a^-1，是未来重要的碳增汇区域；辽宁省的碳汇潜力较弱，年碳汇量仅为4.22 TgC ·a^-1。加强中、幼龄林经营管理，适度更新成、过熟林，有助于提升东北三省乔木林碳汇功能，发挥我国东北森林在增汇减排以及实现区域“碳中和”目标中的作用。

关键词: 生长-消耗模型, 随机森林模型, 森林资源清查, 多变量, 生物质碳储量

Abstract:

Objective: Based on the data of permanent monitoring plots of the successive national forest inventories, a multi-factor forest growth model was constructed through the machine learning algorithm, which improves the simulation accuracy of forest growth and carbon sequestration, predicts the future carbon sink potential of arbor forests of the three provinces in northeast China, and explore the potential distribution of arbor forest carbon sink. It provides a scientific guidance for accurately locating the role of forests in northeast China in increasing sinks and reducing emissions, and scientifically formulating the national "Carbon Neutral" action path and target management. Method: Based on the data of permanent monitoring plots of the successive national forest inventories from 1999 to 2018, and combined with multiple influence factors such as regional climate, soil, forest stand and topography, etc., we used the random forest algorithm to construct a growth-loss model of the main dominant tree species (groups) in the region. Combining the future climate and arbor forest area expansion scenario, the biomass carbon stock (BCS) and BCS changes of arbor forests of the three provinces in northeast China from 2015 to 2060 were predicted. Result: The BCS of arbor forests of three provinces in northeast China reached 3 393.15 TgC in 2060, an increase of 1 895.23 TgC compared with that in 2015, and the annual mean BCS changes was 42.12 TgC ·a^-1 during 2015-2060. Natural forests played the major role in the BCS and BCS changes. The BCS of arbor forests in Liaoning, Jilin, and Heilongjiang will be increased from 139.19, 463.58, and 895.15 TgC in 2015 to 328.95, 915.83, and 2 148.37 TgC in 2060, respectively. The average biomass Carbon Density (BCD) of arbor forests will be increased from 32.71, 59.75, and 45.11 t ·hm^-2 in 2015 to 75.20, 109.32, and 85.24 t ·hm^-2 in 2060, respectively, and the average annual biomass carbon sinks of arbor forests in Liaoning, Jilin, and Heilongjiang from 2015 to 2060 are 4.22, 10.05, and 27.85 TgC ·a^-1, respectively. Conclusion: The random forest model constructed in this study performs well and can be used to predict the future BCS of arbor forests of three provinces in northeast China. From 2015 to 2060, the BCS of arbor forests of three provinces in northeast China will be increased by 1 895.23 TgC, which will still have a large carbon sink potential in the future. Heilongjiang Province has the largest carbon sink potential, with an annual increase of carbon storage up to 27.85 TgC ·a^-1, which is an important carbon sink area in the future; while Liaoning Province is weaker, with an annual increase of carbon storage by only 4.22 TgC ·a^-1. Strengthening the management of the young and middle-aged forests, moderately regenerating the mature and over-mature forests, and enhancing the carbon sink function of arbor forests of the three provinces in northeast China will help to improve the role of forests in achieving the goal of "Carbon Neutral".

Key words: growth-loss model, random forest algorithm, national forest resources inventory, multivariate, biomass carbon stock

中图分类号:

S718.55

田惠玲,朱建华,何潇,陈新云,简尊吉,李宸宇,郭学媛,黄国胜,肖文发. 基于随机森林模型的东北三省乔木林生物质碳储量预测[J]. 林业科学, 2022, 58(4): 40-50.

Huiling Tian,Jianhua Zhu,Xiao He,Xinyun Chen,Zunji Jian,Chenyu Li,Xueyuan Guo,Guosheng Huang,Wenfa Xiao. Projected Biomass Carbon Stock of Arbor Forest of Three Provinces in Northeastern China Based on Random Forest Model[J]. Scientia Silvae Sinicae, 2022, 58(4): 40-50.

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年份Year	辽宁Liaoning		吉林Jilin		黑龙江Heilongjiang		合计Total
年份Year	人工 Planted	天然 Natural	人工 Planted	天然 Natural	人工 Planted	天然 Natural	人工 Planted	天然 Natural
2016—2020	1.42	0.71	7.41	3.70	64.20	32.10	73.03	36.52
2021—2025	1.32	0.66	6.89	3.45	59.74	29.87	67.95	33.98
2026—2030	1.25	0.63	6.52	3.26	56.50	28.25	64.27	32.14
2031—2035	1.20	0.60	6.24	3.12	54.05	27.03	61.48	30.74
2036—2040	1.16	0.58	6.02	3.01	52.19	26.10	59.37	29.69
2041—2045	1.13	0.56	5.86	2.93	50.79	25.39	57.77	28.89
2046—2050	0.44	0.22	2.29	1.15	19.89	9.94	22.62	11.31
2051—2055	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
2056—2060	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
2016—2060	7.92	3.96	41.23	20.62	357.36	178.68	406.51	203.25

起源 Origin	优势树种(组) Dominant tree species (group)	现有林面积 Existing forest area/ 10⁴ hm²	生物量-蓄积模型B_{j, p}=a_j×V_{j, p}^b_j×λ_j Biomass-volume model					生物量含碳率 Biomass carbon fraction
起源 Origin	优势树种(组) Dominant tree species (group)	现有林面积 Existing forest area/ 10⁴ hm²	样本数 Sample number	a	b	相关系数 Correlation coefficient (R²)	校正系数 Correction coefficient (λ)	生物量含碳率 Biomass carbon fraction
天然林 Natural forest	云杉Picea asperata	13.02	25	5.413	0.633	0.966	1.012	0.493
	落叶松Larix gmelinii	227.79	38	2.985	0.746	0.902	1.022	0.489
	油松Pinus tabulaeformis	6.35	72	2.475	0.752	0.951	1.013	0.517
	栎类Quercus	348.65	18	1.682	0.918	0.978	1.007	0.480
	白桦Betula platyphylla	319.48	12	8.329	0.467	0.627	1.050	0.487
	榆树Ulmus pumila	39.64	59	3.300	0.741	0.884	1.035	0.476
	杨树Populus	64.01	19	1.703	0.803	0.884	1.027	0.491
	针叶混交林Mixed coniferous forest	45.99	11	6.699	0.538	0.808	1.012	0.502
	阔叶混交林Mixed broadleaved forest	1 179.05	20	1.526	0.908	0.898	1.028	0.479
	针阔混交林Mixed coniferous and broadleaved forest	248.10	54	3.088	0.734	0.832	1.033	0.494
	小计Subtotal	2 568.28

人工林 Planted forest	云杉Picea asperata	13.15	25	5.413	0.633	0.966	1.012	0.493
	落叶松Larix gmelinii	208.17	38	2.985	0.746	0.902	1.022	0.489
	樟子松Pinus sylvestris var. mongolica	28.78	39	5.117	0.602	0.889	1.017	0.511
	油松Pinus tabulaeformis	41.40	72	2.475	0.752	0.951	1.013	0.517
	栎类Quercus	12.56	18	1.682	0.918	0.978	1.007	0.480
	杨树Populus	146.56	19	1.703	0.803	0.884	1.027	0.491
	针叶混交林Mixed coniferous forest	76.32	11	6.699	0.538	0.808	1.012	0.502
	阔叶混交林Mixed broadleaved forest	90.66	20	1.526	0.908	0.898	1.028	0.479
	小计Subtotal	617.59

起源Origin	优势树种(组)Dominant tree species (group)	样地数Sample plot number	最优模型mtry mtry of the optimal model	10折交叉验证评价指标10-fold cross-validation evaluation index
起源Origin	优势树种(组)Dominant tree species (group)	样地数Sample plot number	最优模型mtry mtry of the optimal model	R²	MAE/m³	RMSE/m³
天然林 Natural forest	云杉Picea asperata	43	8	0.740	0.677	1.050
	落叶松Larix gmelinii	660	29	0.653	0.457	0.606
	油松Pinus tabulaeformis	31	28	0.481	1.191	1.364
	栎类Quercus	1 657	29	0.661	0.506	0.726
	白桦Betula platyphylla	861	13	0.392	0.866	1.158
	榆树Ulmus pumila	24	20	0.829	0.977	1.042
	杨树Populus	73	14	0.670	1.112	1.488
	针叶混交林Mixed coniferous forest	169	16	0.684	1.350	1.913
	阔叶混交林Mixed broadleaved forest	4 376	5	0.308	1.237	1.643
	针阔混交林Mixed coniferous and broadleaved forest	474	5	0.396	1.422	1.820

人工林 Planted forest	云杉Picea asperata	29	29	0.828	2.573	2.908
	落叶松Larix gmelinii	939	19	0.409	1.902	2.519
	樟子松Pinus sylvestris var. mongolica	134	1	0.350	1.990	2.586
	油松Pinus tabulaeformis	220	28	0.570	0.375	0.451
	栎类Quercus	33	26	0.416	1.877	2.137
	杨树Populus	713	28	0.466	1.979	2.585
	针叶混交林Mixed coniferous forest	30	19	0.485	1.923	2.501
	阔叶混交林Mixed broadleaved forest	69	13	0.443	1.445	1.865

起源 Origin	实际样地数 Actual sample plot number	预测样地数Predictive sample plot number		误差率 Error rate (%)	评价指标Evaluation index
起源 Origin	实际样地数 Actual sample plot number	发生枯损 Mortality	未发生枯损 Non-mortality	误差率 Error rate (%)	R²	MAE	RMSE
天然林 Natural forest	发生枯损 Mortality	2 596	4	0.15	0.671	0.399	0.806
天然林 Natural forest	未发生枯损 Non-mortality	138	5 630	2.39	0.671	0.399	0.806
人工林 Planted forest	发生枯损 Mortality	1 188	12	1.00	0.731	0.598	1.126
人工林 Planted forest	未发生枯损 Non-mortality	2	965	0.21	0.731	0.598	1.126

项目Item	年份 Year	天然林Natural forest			人工林Planted forest			乔木林Arbor forest
项目Item	年份 Year	碳储量 Carbon storage/ TgC	年碳汇量 Annual carbon sink/ (TgC·a^-1)	碳密度 Carbon density/ (MgC·hm^-2)	碳储量 Carbon storage/ TgC	年碳汇量 Annual carbon sink/ (TgC·a^-1)	碳密度 Carbon density/ (MgC·hm^-2)	碳储量 Carbon storage/ TgC	年碳汇量 Annual carbon sink/ (TgC·a^-1)	碳密度 Carbon density/ (MgC·hm^-2)
现有林地 Existing woodland	2015	1 321.48	—	51.45	176.43	—	28.57	1 497.92	—	47.02
	2020	1 487.56	33.22	57.92	219.44	8.60	35.53	1 707.00	41.82	53.58
	2025	1 646.33	31.75	64.10	253.74	6.86	41.08	1 900.06	38.61	59.64
	2030	1 800.86	30.91	70.12	283.45	5.94	45.90	2 084.31	36.85	65.42
	2035	1 951.94	30.22	76.00	309.89	5.29	50.18	2 261.83	35.50	71.00
	2040	2 100.40	29.69	81.78	334.47	4.92	54.16	2 434.87	34.61	76.43
	2045	2 243.45	28.61	87.35	356.34	4.37	57.70	2 599.79	32.98	81.60
	2050	2 384.38	28.19	92.84	377.24	4.18	61.08	2 761.62	32.37	86.68
	2055	2 523.51	27.83	98.26	397.52	4.06	64.37	2 921.03	31.88	91.69
	2060	2 660.88	27.47	103.61	417.31	3.96	67.57	3 078.19	31.43	96.62

新增林地 New woodland	2015	0	—	0	0	—	0	0	—	0
	2020	3.04	0.61	8.32	11.64	2.33	15.93	14.68	2.94	13.40
	2025	7.92	0.98	11.23	31.18	3.91	22.12	39.10	4.89	18.49
	2030	14.42	1.30	14.05	56.94	5.15	27.74	71.36	6.45	23.18
	2035	22.36	1.59	16.76	87.36	6.08	32.75	109.72	7.67	27.42
	2040	31.65	1.86	19.41	121.41	6.81	37.23	153.06	8.67	31.29
	2045	42.15	2.10	21.96	158.10	7.34	41.18	200.26	9.44	34.78
	2050	52.45	2.06	25.80	191.58	6.70	47.13	244.03	8.75	40.02
	2055	62.04	1.92	30.52	219.60	5.60	54.02	281.63	7.52	46.19
	2060	71.27	1.85	35.06	243.69	4.82	59.95	314.95	6.66	51.65

基于随机森林模型的东北三省乔木林生物质碳储量预测

Projected Biomass Carbon Stock of Arbor Forest of Three Provinces in Northeastern China Based on Random Forest Model

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