The Spatiotemporal Variation Patterns of Vegetation Net Primary Productivity and Its Influencing Factors in the Mu Us Sandy Land

doi:10.16258/j.cnki.1674-5906.2025.09.004

Ecology and Environmental Sciences ›› 2025, Vol. 34 ›› Issue (9): 1361-1372.DOI: 10.16258/j.cnki.1674-5906.2025.09.004

• Papers on Carbon Cycling and Carbon Emission Reduction • Previous Articles Next Articles

The Spatiotemporal Variation Patterns of Vegetation Net Primary Productivity and Its Influencing Factors in the Mu Us Sandy Land

XU Da¹^,²(), GONG Chengcheng³, ZHANG Zaiyong¹^,²^,^*(), RAN Bin¹^,², HU Yue¹^,², WANG Hanbing⁴^,⁵, CHEN Chen⁴^,⁵

1. Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education/Chang’an University, Xi’an 710054, P. R. China
2. School of Water and Environment, Chang’an University, Xi’an 710054, P. R. China
3. State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, P. R. China
4. Yantai Coastal Zone Geological Survey Center, China Geological Survey, Yantai 264000, P. R. China
5. Field Scientific Observatory for Comprehensive Utilization of Natural Resources, Ordos, Inner Mongolia 017000, P. R. China

Received:2024-09-23 Online:2025-09-18 Published:2025-09-05

毛乌素沙地植被净初级生产力的时空变化规律及影响因素

许达¹^,²(), 宫程程³, 张在勇¹^,²^,^*(), 冉彬¹^,², 胡月¹^,², 王寒冰⁴^,⁵, 陈晨⁴^,⁵

1.旱区地下水文与生态效应教育部重点实验室，陕西西安 710054
2.长安大学水利与环境学院，陕西西安 710054
3.中国科学院西北生态环境资源研究院/干旱区生态安全与可持续发展全国重点实验室，甘肃兰州 730000
4.中国地质调查局烟台海岸带地质调查中心，山东烟台 264000
5.内蒙古鄂尔多斯自然资源综合利用野外科学观测研究站，内蒙古鄂尔多斯 017000

通讯作者: *E-mail: zaiyongzhang@126.com
作者简介:许达（1999年生），男，硕士研究生，研究方向为旱区水文生态环境演化与调控技术。E-mail: 305137025@qq.com
基金资助:
国家自然科学基金项目(42372287);国家资助博士后研究人员计划项目(GZC20232955);陕西省留学人员科技活动择优资助项目(2020006);中国博士后科学基金项目(2024M753472);中国博士后科学基金项目(2024MD763937);中央高校基本科研业务费资助项目(300102292201);旱区地下水文与生态效应教育部重点实验室开放基金项目(2024SHEEAR002);银肯塔拉生物多样性调查及限制因子分析研究(2023ZL01)

Abstract

Abstract:

Vegetation Net Primary Productivity (NPP) is crucial for evaluating ecosystem productivity, carbon sequestration capacity, climate regulation, and ecological health. This study explored the spatiotemporal variations in NPP and its driving mechanisms in Mu Us Sandy Land. The analysis was based on MOD17A3HGF v061 NPP data, meteorological data, and 1 km human activity intensity data. This study used a range of statistical methodologies to analyze the spatiotemporal characteristics of the vegetation NPP. We analyzed the spatiotemporal characteristics of vegetation NPP using multiple statistical methods between 2001 and 2023. This investigation focused on elucidating the primary driving factors that led to changes in the vegetation NPP between 2001 and 2020. Additionally, we quantified the contributions of diverse influencing factors to the vegetation NPP. The results show that the vegetation NPP exhibits a significant growth trend, with a growth rate of C 0.311 Tg·a⁻¹ on a time scale (2001-2023). On a spatial scale, the eastern region has a high growth rate of vegetation NPP due to frequent human activities and abundant precipitation, whereas the western region is constrained by factors such as precipitation, resulting in lower vegetation NPP values. The continuous increase in human activities was a key factor driving vegetation NPP growth. Owing to human activities, the areas of grassland and cultivated land continue to increase, with growth rates of 233 km²·a⁻¹ and 73.5 km²·a⁻¹, respectively. In contrast, the wasteland area decreased at a rate of 325 km²·a⁻¹. Precipitation was identified as the most significant meteorological factor affecting changes in vegetation NPP, with a relative contribution rate of 19.8%. The findings of this study provide a scientific basis for a deeper understanding of the carbon cycle in the region, evaluation of the effectiveness of ecological engineering, and development of ecological protection strategies.

Key words: vegetation net primary productivity, climate change, human activity, Mu Us Sandy Land

摘要：

植被净初级生产力（Net Primary Productivity，NPP）对于评估生态系统生产力、碳汇能力、气候调节及生态健康至关重要。为探究毛乌素沙地植被NPP的时空变化特征及其驱动机制，基于MOD17A3HGF.061版本NPP数据、气象数据及黄土高原1 km人类活动强度数据集，采用多种统计方法分析了2001-2023年毛乌素沙地植被NPP时空变化特征，研究了2001-2020年毛乌素沙地植被NPP变化的主要影响因素，并量化了不同影响因素对植被NPP贡献的大小。研究结果显示：在时间尺度上（2001-2023年），毛乌素沙地植被NPP呈现显著的增长趋势，增长率为C 0.311 Tg·a⁻¹；在空间尺度上，东部地区由于人类活动频繁且降水充沛，植被NPP增长速率高，而西部受制于降水等因素，植被NPP较低。研究期内，人类活动强度持续增强，是推动植被NPP增长的关键影响因素。受人类活动的影响，草地和耕地面积不断增加，增长速率分别为233 km²·a⁻¹和73.5 km²·a⁻¹；荒地面积不断减少，减少速率为325 km²·a⁻¹。降水是影响植被NPP变化最重要的气象影响因素，其贡献率为19.8%。上述研究结果揭示了毛乌素沙地植被NPP变化的驱动机制，可为深入理解该地区碳循环过程、评估生态工程效果以及制定生态保护策略提供科学依据。

关键词: 植被净初级生产力, 气候变化, 人类活动, 毛乌素沙地

CLC Number:

Q948
X173

XU Da, GONG Chengcheng, ZHANG Zaiyong, RAN Bin, HU Yue, WANG Hanbing, CHEN Chen. The Spatiotemporal Variation Patterns of Vegetation Net Primary Productivity and Its Influencing Factors in the Mu Us Sandy Land[J]. Ecology and Environmental Sciences, 2025, 34(9): 1361-1372.

许达, 宫程程, 张在勇, 冉彬, 胡月, 王寒冰, 陈晨. 毛乌素沙地植被净初级生产力的时空变化规律及影响因素[J]. 生态环境学报, 2025, 34(9): 1361-1372.

Figures/Tables 12

Figure 1 Geographical Overview of the Mu Us Sandy Land

Table 1 Criteria for judging interaction effects

判据	交互作用
q(X₁∩X₂)<Min[q(X₁), q(X₂)]	非线性减弱
Min[q(X₁), q(X₂)]<q(X₁∩X₂)<Max[q(X₁), q(X₂)]	单因子非线性减弱
q(X₁∩X₂)>Max[q(X₁), q(X₂)]	双因子增强
q(X₁∩X₂)=q(X₁)+q(X₂)	独立
q(X₁∩X₂)>q(X₁)+q(X₂)	非线性增强

Figure 2 Interannual variation of vegetation NPP in the Mu Us Sandy Land from 2001 to 2023

Figure 3 Changes in area of different land use types and total vegetation NPP in the Mu Us Sandy Land from 2001 to 2023

Figure 4 Spatial variation patterns of vegetation multi-year averages NPP, change rates, and coefficients of variation in the Mu Us Sandy Land from 2001 to 2023

Table 2 Classification of vegetation NPP coefficient of variation in the Mu Us Sandy Land

波动等级	变异系数	面积占比/%
最低波动	0-0.08	0.02
较低波动	0.08-0.14	5.50
适中波动	0.14-0.20	46.6
较高波动	0.20-0.26	41.2
最高波动	0.26-1.10	6.68

Figure 5 Spatial distribution of multi-year mean values of influencing factors in the Mu Us Sandy Land from 2001 to 2020

Figure 6 Temporal variation trends of influencing factors in the Mu Us Sandy Land from 2001 to 2020

Figure 7 Interaction detection results of influencing factors from 2001 to 2020

Figure 8 Spatial distribution of the relative contributions of influencing factors to vegetation NPP in the Mu Us Sandy Land from 2001 to 2020

Figure 9 Spatial distribution of absolute contributions of influencing factors to vegetation NPP in the Mu Us Sandy Land from 2001 to 2020

Table 3 Statistical analysis of the absolute contributions of different factors with different interval areas from 2001 to 2020

影响因素	影响因素的绝对贡献在不同区间的面积占比/%
影响因素	−1.00-0	0-0.01	0.01-0.02	0.02-0.04	0.04-1.00
人类活动	4.10	13.6	24.5	53.5	4.30
降水	1.16	77.0	20.2	1.48	0.16
气温	27.7	67.8	3.90	0.51	0.09
实际日照时数	11.9	83.5	4.47	0.11	0.02
空气相对湿度	46.7	45.6	6.30	1.26	0.14

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The Spatiotemporal Variation Patterns of Vegetation Net Primary Productivity and Its Influencing Factors in the Mu Us Sandy Land

毛乌素沙地植被净初级生产力的时空变化规律及影响因素

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