Net primary productivity simulation and environmental response analysis of the Jianghe River basin in western Hubei Province based on the BEPS-TerrainLabV2.0 model
CHEN Peipei(), ZHANG Lihua(), CUI Yue, CHEN Junhong
School of Geographic and Information Engineering, China University of Geosciences(Wuhan), Wuhan 430074, China
The simulation-based estimation and spatio-temporal variations of the net primary productivity (NPP) of regional vegetation hold critical significance for analyzing regional vegetation quality and carbon balance. This study investigated the Jianghe River basin in western Hubei Province. First, it pre-processed the input data, including land cover, topography, soil, meteorology, and vegetation indices. Based on this, it estimated the NPP of vegetation in the Jianghe River basin from 1986 to 2017 using the BEPS-TerrainlabV2.0 model, with the model's simulation accuracy evaluated. Moreover, this study explored the spatio-temporal variations of the NPP and its response to environmental changes. The results are as follows: ① The NPP of vegetation in the Jianghe River basin exhibited a unimodal distribution, with higher values in summer and lower values in winter, on an intra-annual scale, and a fluctuating rising trend on an inter-annual scale; ② The spatial distribution of the NPP manifested higher values in the north and lower values in the south; ③ The NPP values of different land cover types followed the sequence below: broad-leaved forests > mixed forests > coniferous forests > farmland > urban areas. The NPP rose with an increase in elevation. The NPP values of different soil textures rank below: sandy soil > sandy loam > loamy sand > silty loam; ④ Radiation and temperature manifested the strongest impact on NPP on a daily basis, and the leaf area index (LAI) exhibited the most significant influence on NPP on an annual basis, both passing the 0.01 significance test.
Peipei CHEN,Lihua ZHANG,Yue CUI, et al. Net primary productivity simulation and environmental response analysis of the Jianghe River basin in western Hubei Province based on the BEPS-TerrainLabV2.0 model[J]. Remote Sensing for Natural Resources,
2023, 35(4): 273-281.
Fig.1 Geographical location of the Jianghe River basin
Fig.2 Comparison of simulated NPP data in the Jianghe River basin and measured NPP data in Dajiuhu
Fig.3 MOD17A3 data spatial distribution
覆盖类型
BEPS- Terrainlab V2.0
刘世荣 等[16]
朱文泉 等[17]
刘明亮 等[18]
阔叶林
1 336
250~1 300
114~1 669
945
针叶林
1 307
150~680
179~824
580
农田
993
—
—
752
混交
1 326
250~1 000
257~717
870
Tab.1 Comparison of BEPS-Terrain labV2.0 model simulated land cover type NPP data with other simulated or measured values
Fig.4 Multi-year average change of NPP in the Jiang River Basin
Fig.5 The NPP of the Jiang River changes over the years
Fig.6 NPP mutation analysis results
Fig.7 Spatial analysis of multi-year annual total NPP in the Jianghe River basin
Fig.8 Distribution of NPP for different environmental factors
日尺度 NPP
回归系数 (Beta)
标准化回 归系数
显著性 (P值)
偏相关 系数
温度
0.135
0.496
<0.001
0.637
降水
-0.004
-0.011
0.035
-0.020
辐射
<0.001
0.522
<0.001
0.647
风速
-0.009
-0.003
0.589
-0.005
常量
-0.269
—
<0.001
—
Tab.2 Summary of daily-scale NPP linear regression equations
年尺度 GPP
回归系 数(Beta)
标准化回 归系数
显著性 (P值)
偏相关 系数
温度
26.587
0.094
0.515
0.134
降水
36.627
0.131
0.130
0.305
辐射
0.014
0.094
0.382
0.179
风速
14.530
0.041
0.720
0.074
LAI
224.511
0.814
<0.001
0.874
常量
-136.813
—
0.728
—
Tab.3 Summary of annual-scale NPP linear regression equations
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