生态环境学报 ›› 2025, Vol. 34 ›› Issue (9): 1361-1372.DOI: 10.16258/j.cnki.1674-5906.2025.09.004
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
收稿日期:
2024-09-23
出版日期:
2025-09-18
发布日期:
2025-09-05
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:
XU Da1,2(), GONG Chengcheng3, ZHANG Zaiyong1,2,*(
), RAN Bin1,2, HU Yue1,2, WANG Hanbing4,5, CHEN Chen4,5
Received:
2024-09-23
Online:
2025-09-18
Published:
2025-09-05
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。
中图分类号:
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.
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.
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 |
表1 交互作用判断依据
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 |
图3 2001-2023年毛乌素沙地不同土地利用类型的面积和植被NPP总量时间变化
Figure 3 Changes in area of different land use types and total vegetation NPP in the Mu Us Sandy Land from 2001 to 2023
图4 2001-2023年毛乌素沙地植被NPP多年均值、变化速率及变异系数的空间变化格局
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
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 |
表2 毛乌素沙地植被NPP变异系数分类表
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 |
图5 2001-2020年毛乌素沙地影响因素多年均值空间分布
Figure 5 Spatial distribution of multi-year mean values of influencing factors in the Mu Us Sandy Land from 2001 to 2020
图8 2001-2020年各影响因素对毛乌素沙地植被NPP的相对贡献空间分布
Figure 8 Spatial distribution of the relative contributions of influencing factors to vegetation NPP in the Mu Us Sandy Land from 2001 to 2020
图9 2001-2020年各影响因素对毛乌素沙地植被NPP的绝对贡献空间分布
Figure 9 Spatial distribution of absolute contributions of influencing factors to vegetation NPP in the Mu Us Sandy Land 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/% | ||||
---|---|---|---|---|---|
−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 |
表3 2001—2020年各影响因素的绝对贡献不同区间面积统计
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/% | ||||
---|---|---|---|---|---|
−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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