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

doi:10.17521/cjpe.2023.0325

Abstract

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

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[J]. Chin J Plant Ecol, 2025, 49(6): 939-951.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2023.0325

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

Figures/Tables 11

References 47

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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