北京不同林龄人工林生物量空间格局及其影响因素

doi:10.17521/cjpe.2023.0325

植物生态学报 ›› 2025, Vol. 49 ›› Issue (6): 939-951.DOI: 10.17521/cjpe.2023.0325 cstr: 32100.14.cjpe.2023.0325

北京不同林龄人工林生物量空间格局及其影响因素

刘新月¹, 王立平², 刘春和², 孙艳丽³, 刘鹏¹, 田赟¹, 贾昕¹, 查天山¹^,^*(), 钱多⁴

¹北京林业大学水土保持学院, 北京林业大学水土保持国家林业和草原局重点实验室, 北京 100083
²北京市八达岭林场管理处, 北京 102112
³北京市园林绿化规划和资源监测中心, 北京 100193
⁴北京农业职业学院, 北京 102442

收稿日期:2023-11-07 接受日期:2024-05-06 出版日期:2025-06-20 发布日期:2024-05-07
通讯作者: *查天山(tianshanzha@bjfu.edu.cn)
基金资助:
中央高校基本科研业务费专项资金(2021ZY49)

Spatial pattern of biomass and its influencing factors for plantations with different stand ages in Beijing

LIU Xin-Yue¹, WANG Li-Ping², LIU Chun-He², SUN Yan-Li³, LIU Peng¹, TIAN Yun¹, JIA Xin¹, ZHA Tian-Shan¹^,^*(), QIAN Duo⁴

¹School of Soil and Water Conservation, Key Laboratory of National Forestry and Grassland Administration on Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China
²Beijing Badaling Forest Farm, Beijing 102112, China
³Planning and Monitoring Center of Beijing Forestry and Landscape, Beijing 100193, China
⁴Beijing Vocational College of Agriculture, Beijing 102442, China

Received:2023-11-07 Accepted:2024-05-06 Online:2025-06-20 Published:2024-05-07
Contact: *ZHA Tian-Shan(tianshanzha@bjfu.edu.cn)
Supported by:
Supported by the Fundamental Research Funds for the Central Universities(2021ZY49)

摘要/Abstract

摘要：

为揭示城市人工林生物量空间变化及其空间异质性的驱动因子, 阐明不同驱动因子对生物量空间分异影响的适宜范围。以北京市不同林龄人工林为研究对象, 基于森林清查数据, 利用转换因子连续函数法得到小班尺度人工林生物量; 选取气候(气温、降水量、日照时数)、植被(归一化植被指数, NDVI)、地形(高程、坡度、坡向)和人为(夜间灯光指数、国内生产总值、人口密度)因子, 运用线性回归分析、多项式拟合等方法分析生物量对各影响因子的响应; 利用随机森林预测不同林龄生物量的空间分布, 基于地理探测器量化各因子及其交互作用对生物量空间异质性的解释力度, 确定各因子对生物量影响的适宜范围。主要结果: (1)生物量随林龄的增加而增大, 幼龄林、中龄林、近熟林、成熟林和过熟林生物量分别为35.22、75.42、88.71、96.17和148.59 Mg·hm^-2。(2)相较于地形因子, 气候因子、植被因子和人为因子对生物量的影响更大。幼龄林生物量对各因子的响应与其他林龄存在差别, 可能与幼龄林存在较多的抚育有关。(3)对生物量空间异质性解释最强的因子分别为夜间灯光指数(52.91%)、日照时数(51.28%)和降水量(45.75%)。(4)影响因子之间的交互作用均为非线性增强或双因子增强, 林龄自身对生物量空间异质性的影响较小, 但其与降水量、夜间灯光指数、国内生产总值和人口密度交互后解释力度达到70%以上。(5)气温在12.3-13.0 ℃、降水量在526.8-542.6 mm、日照时数在2 543.1-2 602.8 h、NDVI在0.46-0.59范围内, 中度人为干扰的平坡上具有较高的生物量。因此, 生物量的空间分异是由多种因子共同作用的, 想要获取更高的生物量需要综合考虑研究区的特点。结果有助于更好地理解人工林生物量与影响因子之间的关系, 为后续造林工程的实施和人工林抚育等提供科学合理的理论依据。

关键词: 人工林, 生物量, 空间格局, 地理探测器, 城市森林

Abstract:

Aims Elucidating the spatial pattern of biomass and its influencing factors is crucial for the management of plantations, yet such study in urban forests with different stand ages is lacking.

Methods We collected the inventory data of forests in Beijing and used the method of biomass expansion factor to estimate plantation biomass with different stand ages. Linear regression and polynomial fitting method were used to identify the factors that affect on the biomass of plantations. The spatial patterns of biomass were simulated by the way of random forest. Finally, the spatial correlations of biomass with the factors of climate, vegetation, and topography as well as the anthropogenic effects in different age stands were conducted by the technique of Geodetector.

Important findings The results showed that the biomass increased with increasing stand ages, from 35.22 Mg·hm^-2 for young stand increased to 148.59 Mg·hm^-2 for over-mature forests. The climatic, vegetation, and anthropogenic factors had greater influence on biomass compared to topographical factors. In addition, the factors that influenced young forest biomass differed from the stands with other ages, due to its frequently management. Moreover, contrasting with other factors, the nighttime lights, sunshine duration and precipitation were likely to be important factors influencing the spatial pattern of biomass, with explaining power of 52.91%, 51.28% and 45.75%, respectively. Furthermore, there was also a strong interaction among these factors on spatial pattern of biomass in these urban forests, with over 70% of explaining power induced by the interactive of precipitation, nighttime lights, gross domestic product and population density. In present study, higher biomass was associated within the ranges of 12.3-13.0 °C in temperature, 526.8-542.6 mm in precipitation, 2 543.1-2 602.8 h in sunshine duration, and 0.46-0.59 in vegetation index of normalized difference vegetation index (NDVI). Our study highlights that the spatial pattern of biomass in urban forests was influenced by multiple factors. Identifying such influences is an important matter for better management of urban forests in practice.

Key words: plantation, biomass, spatial pattern, Geodetector, urban forests

刘新月, 王立平, 刘春和, 孙艳丽, 刘鹏, 田赟, 贾昕, 查天山, 钱多. 北京不同林龄人工林生物量空间格局及其影响因素. 植物生态学报, 2025, 49(6): 939-951. DOI: 10.17521/cjpe.2023.0325

LIU Xin-Yue, WANG Li-Ping, LIU Chun-He, SUN Yan-Li, LIU Peng, TIAN Yun, JIA Xin, ZHA Tian-Shan, QIAN Duo. Spatial pattern of biomass and its influencing factors for plantations with different stand ages in Beijing. Chinese Journal of Plant Ecology, 2025, 49(6): 939-951. DOI: 10.17521/cjpe.2023.0325

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2023.0325

https://www.plant-ecology.com/CN/Y2025/V49/I6/939

图/表 11

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数据类别 Data type	数据名称 Data	数据来源 Data source
森林清查数据 Forest inventory data	林分蓄积 Stand volume	2014年北京市森林清查数据 Forest inventory data of Beijing in 2014
	林分面积 Stand area
	林龄 Stand age
	优势树种 Dominate tree species
气候因子 Climatic factor	气温 Air temperature	国家科技基础条件平台-国家地球系统科学数据中心 National Earth System Science Data Center (http://www.geodata.cn)
	降水 Precipitation
	日照时数 Sunshine duration
植被因子 Vegetation factor	归一化植被指数 Normalized difference vegetation index (NDVI)	NASA MODIS归一化植被指数产品 The NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset (MOD13Q1 V006) (https://ladsweb.modaps.eosdis.nasa.gov/search)
地形因子 Topographical factor	高程 Elevation	空间分辨率12.5 m的ALOS高程数据 Elevation of ALOS with 12.5 m spatial resolution (http://urs.earthdata.nasa.gov)
	坡度 Slope	基于数字高程模型 Based on digital elevation model (DEM)
	坡向 Aspect	基于数字高程模型 Based on digital elevation model (DEM)
人为因子 Anthropogenic factor	夜间灯光指数 Nighttime lights index	地球观测组年度夜间灯光指数 Earth Observation Group annual nighttime lights index (https://eogdata.mines.edu/products/vnl/)
	国内生产总值 Gross domestic product	国家科技基础条件平台-国家地球系统科学数据中心 National Earth System Science Data Center (http://www.geodata.cn)
	人口密度 Population density

数据类别 Data type	数据名称 Data	数据来源 Data source
森林清查数据 Forest inventory data	林分蓄积 Stand volume	2014年北京市森林清查数据 Forest inventory data of Beijing in 2014
	林分面积 Stand area
	林龄 Stand age
	优势树种 Dominate tree species
气候因子 Climatic factor	气温 Air temperature	国家科技基础条件平台-国家地球系统科学数据中心 National Earth System Science Data Center (http://www.geodata.cn)
	降水 Precipitation
	日照时数 Sunshine duration
植被因子 Vegetation factor	归一化植被指数 Normalized difference vegetation index (NDVI)	NASA MODIS归一化植被指数产品 The NASA Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset (MOD13Q1 V006) (https://ladsweb.modaps.eosdis.nasa.gov/search)
地形因子 Topographical factor	高程 Elevation	空间分辨率12.5 m的ALOS高程数据 Elevation of ALOS with 12.5 m spatial resolution (http://urs.earthdata.nasa.gov)
	坡度 Slope	基于数字高程模型 Based on digital elevation model (DEM)
	坡向 Aspect	基于数字高程模型 Based on digital elevation model (DEM)
人为因子 Anthropogenic factor	夜间灯光指数 Nighttime lights index	地球观测组年度夜间灯光指数 Earth Observation Group annual nighttime lights index (https://eogdata.mines.edu/products/vnl/)
	国内生产总值 Gross domestic product	国家科技基础条件平台-国家地球系统科学数据中心 National Earth System Science Data Center (http://www.geodata.cn)
	人口密度 Population density

树种 Species	a	b	参考文献 Reference
白桦 Betula platyphylla	1.069	10.237	Yang et al., 2022
白皮松 Pinus bungeana	0.529	25.087	Yang et al., 2022
侧柏 Platycladus orientalis	0.490	30.427	Zhang et al., 2013
胡桃楸 Juglans mandshurica	1.039	2.373	Zhang et al., 2013
华山松 Pinus armandi	0.458	32.666	Yang et al., 2022
栎属 Quercus	0.785	16.715	Zhang et al., 2013
垂柳 Salix babylonica	0.892	28.441	Zhang et al., 2013
栾树 Koelreuteria paniculata	0.892	28.441	Zhang et al., 2013
落叶松 Larix gmelinii	0.610	33.806	Yang et al., 2022
五角槭 Acer pictum subsp. mono	0.892	28.441	Zhang et al., 2013
杨属 Populus	0.497	26.973	Yang et al., 2022
银杏 Ginkgo biloba	0.892	28.441	Zhang et al., 2013
油松 Pinus tabuliformis	0.869	9.121	Yang et al., 2022
硬木类、软木类 Hardwoods, softwoods	0.892	28.441	Zhang et al., 2013
阔叶混交林 Mixed broadleaf forest	0.739	43.210	Zhang et al., 2013

树种 Species	a	b	参考文献 Reference
白桦 Betula platyphylla	1.069	10.237	Yang et al., 2022
白皮松 Pinus bungeana	0.529	25.087	Yang et al., 2022
侧柏 Platycladus orientalis	0.490	30.427	Zhang et al., 2013
胡桃楸 Juglans mandshurica	1.039	2.373	Zhang et al., 2013
华山松 Pinus armandi	0.458	32.666	Yang et al., 2022
栎属 Quercus	0.785	16.715	Zhang et al., 2013
垂柳 Salix babylonica	0.892	28.441	Zhang et al., 2013
栾树 Koelreuteria paniculata	0.892	28.441	Zhang et al., 2013
落叶松 Larix gmelinii	0.610	33.806	Yang et al., 2022
五角槭 Acer pictum subsp. mono	0.892	28.441	Zhang et al., 2013
杨属 Populus	0.497	26.973	Yang et al., 2022
银杏 Ginkgo biloba	0.892	28.441	Zhang et al., 2013
油松 Pinus tabuliformis	0.869	9.121	Yang et al., 2022
硬木类、软木类 Hardwoods, softwoods	0.892	28.441	Zhang et al., 2013
阔叶混交林 Mixed broadleaf forest	0.739	43.210	Zhang et al., 2013

判断依据 Criterion	交互结果 Interaction result
q(X1∩X2) < min[q(X1), q(X2)]	非线性减弱 Weaken, nonlinear
min[q(X1), q(X2)] < q(X1∩X2) < max[q(X1), q(X2)]	单因子非线性减弱 Weaken, univariate
q(X1∩X2) > max(q(X1), q(X2))	双因子增强 Enhance, bivariate
q(X1∩X2) = q(X1) + q(X2)	独立 Independent
q(X1∩X2) > q(X1) + q(X2)	非线性增强 Enhance, nonlinear

北京不同林龄人工林生物量空间格局及其影响因素

Spatial pattern of biomass and its influencing factors for plantations with different stand ages in Beijing

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