基于数字影像的北京松山森林物候模拟及其与气象因子的关系

doi:10.11707/j.1001-7488.20220110

Abstract

Abstract:

Objective: This study was carried out to understand the phenological changes of temperate deciduous broad-leaved forest and its relationships with meteorological factors in order to improve the accuracy of vegetation carbon sequestration model and regional carbon sequestration simulation. Method: The relative green chromatic coordinate (Gcc) was calculated from the continuous images produced by digital cameras in a deciduous broad-leaved ecosystem in Songshan, Beijing in 2019. The relationships between Gcc and environmental factors were analyzed using Pearson correlation analysis. Furthermore, phenology stage was estimated using Gcc, normalized difference vegetation index(NDVI) and gross primary productivity (GPP) by eddy covariance, then their differences were compared. Result: The correlation coefficients between Gcc and air temperature (T_a), soil temperature (T_s), soil volume water content (SWC), photosynthetically active radiation (PAR), vapor pressure deficit (VPD) and precipitation (P) were 0.88, 0.86, 0.29, 0.45, 0.65 and 0.25, respectively. The starting day of year of growing season (SOS) dericed from Gcc, NDVI and GPP was 129, 116 and 110, respectively. The ending day of year of growing season (EOS) was 277, 277 and 283, respectively. The length of growing season (LOS) was 148, 162 and 173 days, respectively. Conclusion: During the study period, vegetation growth was mainly affected by temperature, PAR and VPD. Precipitation had a pulse effect on Gcc. The digital camera and SRS-NDVI could sensitively capture the dynamics in vegetation growth. The accuracy of digital camera for late growth might be higher than that for early growth.

Key words: phenology, digital image, relative green chromatic coordinate(Gcc), normalized difference vegetation index(NDVI), gross primary productivity(GPP)

CLC Number:

Rundong Li,Wendong Tian,Haiqun Yu,Xinhao Li,Chuan Jin,Peng Liu,Tianshan Zha,Yun Tian. Forest Phenology Estimation and Its Relationships with Corresponding Meteorological Factors Based on Digital Images in Songshan, Beijing, China[J]. Scientia Silvae Sinicae, 2022, 58(1): 89-97.

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相关指标 Relevant indicators	相对绿度指数 Relative green chromatic coordinate (Gcc)	空气温度 Air temperature(T_a)	土壤温度 Soil temperature(T_s)	饱和水气压差 Vapor pressure deficit(VPD)	光合有效辐射 Photosynthetically active radiation(PAR)	降雨 Precipitation(P)	土壤体积含水量 Soil volume water content(SWC)
相对绿度指数 Relative green chromatic coordinate(Gcc)	1.00
空气温度 Air temperature(T_a)	0.88^**	1.00
土壤温度 Soil temperature(T_s)	0.86^**	0.96^**	1.00
饱和水气压差 Vapor pressure deficit(VPD)	0.65^**	0.76^**	0.66^**	1.00
光合有效辐射 Photosynthetically active radiation(PAR)	0.45^**	0.61^**	0.55^**	0.76^**	1.00
降雨 Precipitation(P)	0.25^**	0.20^**	0.26^**	-0.06	-0.07	1.00
土壤体积含水量 Soil volume water content(SWC)	0.29^**	0.41^**	0.45^**	0.30^**	0.33^**	0.19^**	1.00

环境因子 Environmental factors	空气温度 Air temperature(T_a)	土壤温度 Soil temperature(T_s)	饱和水气压差 Vapor pressure deficit(VPD)	光合有效辐射 Photosynthetically active radiation(PAR)	土壤体积含水量 Soil volume water content(SWC)	降雨量 Precipitation(P)
第1主成分中的载荷 Load in the first principal component	0.52	0.49	0.45	0.45	0.27	0.06
第2主成分中的载荷 Load in the second principal component	0.06	0.16	-0.29	-0.33	0.82	0.29

自变量数 Number of independent variables	最优变量组合 Optimal combination of variables	调整决定系数 Adjust coefficient of determination	贝叶斯信息准则 Bayesian information criterion
2	T_a、VPD	0.56	-290
3	T_a、VPD、PAR	0.57	-290
4	T_a、T_s、VPD、PAR	0.58	-280
5	T_a、T_s、VPD、PAR、P	0.57	-280
6	T_a、T_s、VPD、PAR、P、SWC	0.52	-280

	生长季开始时间 Start of growing season	生长季结束时间 End of growing season	生长季长度 Length of growing season	峰值位置 Position of peak value (maximum)	生长季平均值 Mean growing season value	春季生长速率 Rate of spring green up	秋季衰老速率 Rate of autumn senescence	峰值 Peak value (maximum)	春季平均值 Mean spring value	秋季平均值 Mean autumn value
GPP	110	283	173	195	3.503	0.002	-0.002	7.812	2.909	3.306
Gcc	129	277	148	160	0.365	0.027	-0.002	0.368	0.343	0.338
相对差异 Relative difference	0.17	0.021	0.144	0.179	0.896	12.5	0	0.953	0.882	0.898

GPP	110	283	173	195	3.503	0.002	-0.002	7.812	2.909	3.306
NDVI	116	277	162	152	0.713	0.05	0.11	0.806	0.211	0.214
相对差异 Relative difference	0.05	0.021	0.064	0.221	0.796	24	56	0.897	0.927	0.935

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Forest Phenology Estimation and Its Relationships with Corresponding Meteorological Factors Based on Digital Images in Songshan, Beijing, China

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