Spatio-temporal Pattern of Aboveground Biomass in Daqing Grassland and Its Relationship with Climatic Factors

doi:10.7525/j.issn.1673-5102.2025.01.005

Abstract

Abstract:

Grassland is one of the most important land types in Daqing， and it is of great significance to grasp the spatial and temporal dynamics of grassland biomass to understand the carbon sink potential of Daqing. The remote sensing inversion model of aboveground grass biomass in Daqing was constructed by using MODIS-NDVI remote sensing data and aboveground grass biomass measured data and regression analysis. Trend analysis and correlation analysis were used to clarify the spatial and temporal distribution pattern of aboveground biomass and its relationship with major climate factors（precipitation and temperature） in Daqing over the past 20 years. The results showed that the exponential function inversion model constructed by using normalized difference vegetation index（NDVI） had the best interpretation of the aboveground biomass of grassland in Daqing， and the coefficient of determination R² was 0.77 and the root mean square error （RMSE） was 38 g⋅m^-2. The grassland biomass in Daqing urban areas showed a trend of fluctuating increase from 2000 to 2023， and reached a maximum value of 314 g⋅m^-2 in 2019； the aboveground biomass of grassland in most areas showed a significant increasing trend， with a maximum value of 423 g⋅m^-2 and an average value of 280 g⋅m^-2， and its spatial characteristics showed a gradually increasing distribution pattern from southeast to northwest， with concentration in the north and dispersion in the south. Precipitation had a significant effect on biomass（r=0.584， P<0.05）， but the monthly mean temperature had no significant effect on biomass. The above results might provide a strong theoretical basis and data support for the scientific setting of livestock loading and oil extraction area and the optimization of grassland resource utilization strategy in Daqing.

Key words: Daqing urban area, grassland, remote sensing inversion model, aboveground biomass of grassland, normalized difference vegetation index

CLC Number:

S812.6

Zhaoxin HE, Lianfeng WU, Yongzhe WANG, Yunjie GU, Fengwei ZHAO, Xingchang WANG, Xiaochun WANG. Spatio-temporal Pattern of Aboveground Biomass in Daqing Grassland and Its Relationship with Climatic Factors[J]. Bulletin of Botanical Research, 2025, 45(1): 34-44.

Figures/Tables 8

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植被指数 Vegetation index	回归模型 Model	拟合方程 Formula（N=25）	P	R²	均方根误差 RMSE
MODIS-NDVI	线性 Linear	y=486.49x-2.17	<0.001	0.598	40.91
	对数 Logarithmic	y=188.33ln（x）+391.64	<0.001	0.467	47.72
	多项式 Polynomial	y=718.10x²-262.34x+167.60	<0.001	0.651	39.68
	乘幂 Power	y=410.21x^{0.763 3}	<0.001	0.702	42.25
	指数 Exponential	y=97.749e^{1.653 5}^x	<0.001	0.772	37.96
MODIS-EVI	线性 Linear	y=635.18x+30.28	<0.001	0.598	39.43
	对数 Logarithmic	y=154.17ln（x）+433.90	<0.001	0.445	46.94
	多项式 Polynomial	y=1 304.22x²-293.23x+168.60	<0.001	0.670	47.75
	乘幂 Power	y=491.55x^{0.637 1}	<0.001	0.682	40.89
	指数 Exponential	y=103.08e^{2.339 0}^x	<0.001	0.711	40.82

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