基于立木胸径生长率模型的乔木林碳汇潜力评估

doi:10.13304/j.nykjdb.2022.0815

中国农业科技导报 ›› 2024, Vol. 26 ›› Issue (1): 99-109.DOI: 10.13304/j.nykjdb.2022.0815

基于立木胸径生长率模型的乔木林碳汇潜力评估

季文旭¹(), 冯仲科¹^,²(), 张瀚月³, 王媛¹

^1.北京林业大学林学院，精准林业北京市重点实验室，北京 100083
^2.海南大学农林学院，热带特色林木花卉遗传与种质创新教育部重点实验室，海口 570228
^3.上海市园林科学规划研究院，城市困难立地生态园林国家林业和草原局重点实验室，上海 200232

收稿日期:2022-09-23 接受日期:2022-12-08 出版日期:2024-01-15 发布日期:2024-01-08
通讯作者: 冯仲科
作者简介:季文旭 E-mail：wenxuji@126.com；
基金资助:
北京市自然科学基金项目(8232038);海南省重点研发计划项目(ZDYF2021SHFZ256);海南大学自然科学基金项目［KYQD（ZR）21115］

Assessment of Carbon Sink Potential of Arbor Forests Based on DBH Growth Rate Model for Standing Trees

Wenxu JI¹(), Zhongke FENG¹^,²(), Hanyue ZHANG³, Yuan WANG¹

^1.Precision Forestry Key Laboratory of Beijing，School of Forestry，Beijing Forestry University，Beijing 100083，China
^2.Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants，School of Agriculture and Forestry，Hainan University，Haikou 570228，China
^3.Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites，Shanghai Academy of Landscape Architecture Science and Planning，Shanghai 200232，China

Received:2022-09-23 Accepted:2022-12-08 Online:2024-01-15 Published:2024-01-08
Contact: Zhongke FENG

摘要/Abstract

摘要：

树木生长产生巨大碳汇，对于缓解碳排放带来的全球变暖等环境问题具有重要意义。为准确评估森林碳汇，基于第6至第9次国家森林资源连续清查数据建立北京市13个主要树种（组）4种形式的立木胸径年生长率模型，预测树木胸径变化的未来趋势，从而为生物量转换因子连续函数法计算碳储量提供计算依据，最终获得2050年北京市乔木林碳储量和碳密度。结果表明：8个树种（组）胸径的年生长率模型R²都大于0.900，椴树的R²最高为0.960；除柳树、水胡黄（水曲柳、胡桃楸、黄菠萝）外的11个树种（组）RMSE都小于0.5 cm；除杨树、其他硬阔类和榆树之外，Bias都小于1.0 cm。胸径预测精度验证中整体R²较高，刺槐最高（0.951），其他硬阔类最低（0.766）。预测2050年北京市乔木林碳储量为42.71 TgC，碳密度为43.35 MgC·hm^-2。基于胸径年生长率模型的树木生长模拟方法可以有效的提高未来北京市乔木林碳汇潜力评估的整体精度，能够为制定温室气体减排政策、实现2060碳中和目标提供基础。

关键词: 森林资源连续清查数据, 胸径生长率, 碳储量, 碳密度

Abstract:

The growth of trees generates a huge carbon sink， which is of great significance in alleviating environmental problems such as global warming caused by carbon emissions. To accurately assess the carbon sink of forests， a model of the annual growth rate of tree diameter at breast height （DBH） in 4 forms for 13 main tree species （groups） in Beijing was established based on the data from the 6th to 9th National Forest Inventory. This model predicted the future trend of DBH changes， thereby provided a computational basis for calculating carbon storage using the continuous function method of biomass conversion factors. Ultimately， it estimated the carbon storage and carbon density of deciduous forests in Beijing by 2050. The results showed that： 8 of the tree species （groups） had a DBH annual growth rate model R² greater than 0.900， with the highest R² of 0.960 for linden； except willow and Fraxinus mandshurica/Juglans mandshurica/Phellodendron amurense the RMSE of 11 tree species （groups） was less than 0.5 cm； except for poplars， other hard broad-leaved forests and elms， Bias was less than 1.0 cm. The overall R² in the validation of DBH prediction accuracy was high， with the highest for black locusts （0.951） and the lowest for other hard broad-leaved forests （0.766）. It was predicted the carbon stock of arbor forests in Beijing in 2050 was 42.71 TgC， and the carbon density is 43.35 MgC·hm^-2. The study found that the tree growth simulation method based on the annual growth rate at breast height model could effectively improve the overall accuracy of future carbon sink potential assessment of tree forests in Beijing， which could provide a theoretical basis for formulating greenhouse gas reduction policies and achieving the 2060 carbon neutrality target.

Key words: National Forest Inventory data, DBH growth rate, carbon stock, carbon density

中图分类号:

S758

季文旭, 冯仲科, 张瀚月, 王媛. 基于立木胸径生长率模型的乔木林碳汇潜力评估[J]. 中国农业科技导报, 2024, 26(1): 99-109.

Wenxu JI, Zhongke FENG, Hanyue ZHANG, Yuan WANG. Assessment of Carbon Sink Potential of Arbor Forests Based on DBH Growth Rate Model for Standing Trees[J]. Journal of Agricultural Science and Technology, 2024, 26(1): 99-109.

图/表 7

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树种（组） Tree species （group）	样木数量 Number of sample trees	建模样本数量 Number of modeling samples	胸径范围 DBH range/cm
柏木Cupressus L.	1 631	120	5.0~21.9
刺槐Robinia pseudoacacia L.	186	139	5.0~36.9
椴树Tilia	1 113	181	5.0~34.6
桦木Betula spp.	617	171	5.0~40.7
栎类Quercus L.	3 514	266	5.0~45.3
柳树Salix	374	158	5.0~40.2
落叶松Larix gmelinii	148	72	5.2~22.2
其他软阔类Other soft broad-leaved forest	332	130	5.0~28.4
其他硬阔类Other hard broad-leaved forest	1 072	184	5.0~31.3
水胡黄Fraxinus manchuria/Juglans mandshurica/Phellodendron amurense	288	133	5.0~43.0
杨树Populus L.	671	259	5.1~62.5
油松Pinus tabuliformis	2 847	229	5.0~33.7
榆树Ulmus pumila	388	139	5.0~34.1

树种（组） Tree species （group）	样木数量 Number of sample trees	建模样本数量 Number of modeling samples	胸径范围 DBH range/cm
柏木Cupressus L.	1 631	120	5.0~21.9
刺槐Robinia pseudoacacia L.	186	139	5.0~36.9
椴树Tilia	1 113	181	5.0~34.6
桦木Betula spp.	617	171	5.0~40.7
栎类Quercus L.	3 514	266	5.0~45.3
柳树Salix	374	158	5.0~40.2
落叶松Larix gmelinii	148	72	5.2~22.2
其他软阔类Other soft broad-leaved forest	332	130	5.0~28.4
其他硬阔类Other hard broad-leaved forest	1 072	184	5.0~31.3
水胡黄Fraxinus manchuria/Juglans mandshurica/Phellodendron amurense	288	133	5.0~43.0
杨树Populus L.	671	259	5.1~62.5
油松Pinus tabuliformis	2 847	229	5.0~33.7
榆树Ulmus pumila	388	139	5.0~34.1

树种（组） Tree species （group）	模型Model	参数估计值Parameter estimate		评价指标 Evaluation indicator
树种（组） Tree species （group）	模型Model	a	b	R²	S/cm	RMSE/cm	rRMSE/%	Bias/cm	rBias/%
柏木Cupressus Linn.	4	-0.029	25.378	0.940	0.25	0.07	2.64	0.03	0.26
刺槐Robinia pseudoacacia L.	1	25.705	-1.044	0.937	0.25	0.17	9.01	0.06	0.36
椴树Tilia	2	4.52	-0.084	0.955	0.15	0.06	4.33	0.06	0.49
桦木Betula spp	3	-0.213	0.073	0.924	0.36	0.19	11.80	-0.01	-0.07
栎类Quercus L.	1	11.828	-0.763	0.938	0.17	0.05	3.05	0.01	0.08
柳树Salix	4	-1.401	85.645	0.924	0.98	0.71	14.60	0.05	0.29
落叶松Larix gmelinii	3	-0.104	0.041	0.868	0.76	0.44	11.61	-0.02	-0.13
其他软阔类Other soft broad-leaved forest	3	-0.169	0.076	0.824	0.40	0.24	14.40	0.78	6.20
其他硬阔类Other hard broad-leaved forest	4	0.161	15.902	0.756	0.39	0.14	4.41	1.85	13.89
水胡黄Fraxinus mandshurica/Juglans mandshurica/Phellodendron amurense	2	7.715	-0.071	0.911	0.66	0.55	18.71	0.75	5.62
杨树Populus L.	3	0.046	0.018	0.843	0.32	0.16	11.70	2.93	13.63
油松Pinus tabuliformis	1	15.74	-0.817	0.917	0.23	0.07	3.57	0.12	0.83
榆树Ulmus pumila	3	-0.099	0.054	0.869	0.43	0.28	13.29	1.18	9.25

树种（组） Tree species （group）	模型Model	参数估计值Parameter estimate		评价指标 Evaluation indicator
树种（组） Tree species （group）	模型Model	a	b	R²	S/cm	RMSE/cm	rRMSE/%	Bias/cm	rBias/%
柏木Cupressus Linn.	4	-0.029	25.378	0.940	0.25	0.07	2.64	0.03	0.26
刺槐Robinia pseudoacacia L.	1	25.705	-1.044	0.937	0.25	0.17	9.01	0.06	0.36
椴树Tilia	2	4.52	-0.084	0.955	0.15	0.06	4.33	0.06	0.49
桦木Betula spp	3	-0.213	0.073	0.924	0.36	0.19	11.80	-0.01	-0.07
栎类Quercus L.	1	11.828	-0.763	0.938	0.17	0.05	3.05	0.01	0.08
柳树Salix	4	-1.401	85.645	0.924	0.98	0.71	14.60	0.05	0.29
落叶松Larix gmelinii	3	-0.104	0.041	0.868	0.76	0.44	11.61	-0.02	-0.13
其他软阔类Other soft broad-leaved forest	3	-0.169	0.076	0.824	0.40	0.24	14.40	0.78	6.20
其他硬阔类Other hard broad-leaved forest	4	0.161	15.902	0.756	0.39	0.14	4.41	1.85	13.89
水胡黄Fraxinus mandshurica/Juglans mandshurica/Phellodendron amurense	2	7.715	-0.071	0.911	0.66	0.55	18.71	0.75	5.62
杨树Populus L.	3	0.046	0.018	0.843	0.32	0.16	11.70	2.93	13.63
油松Pinus tabuliformis	1	15.74	-0.817	0.917	0.23	0.07	3.57	0.12	0.83
榆树Ulmus pumila	3	-0.099	0.054	0.869	0.43	0.28	13.29	1.18	9.25

基于立木胸径生长率模型的乔木林碳汇潜力评估

Assessment of Carbon Sink Potential of Arbor Forests Based on DBH Growth Rate Model for Standing Trees

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