1. 中国科学院水利部水土保持研究所, 黄土高原土壤侵蚀与旱地农业国家重点室, 陕西 杨凌,712100
2. 中国科学院大学,北京,100049
3. 西北农林科技大学水土保持研究所, 黄土高原土壤侵蚀与旱地农业国家重点室, 陕西 杨凌,712100
4. 西北农林科技大学, 陕西 杨凌,712100
5. 云南大学国际河流与生态安全研究院,昆明,650091
[ "刘逸滨(1997-),女,博士研究生,主要从事土壤侵蚀与水土保持研究。E-mail:liuyibin19@mails.ucas.ac.cn" ]
纸质出版:2022
移动端阅览
刘逸滨, 刘宝元, 成城, 等. 退耕还林草20年来榆林市植被覆盖度时空变化及影响因素分析[J]. 水土保持学报, 2022,36(2):197-208.
LIU Yibin, LIU Baoyuan, CHENG Cheng, et al. Spatio-temporal Changes and Influencing Factors of Vegetation Coverage in Yulin City During the Past 20 Years Since the Implementation of the “Grain for Green” Program[J]. 2022, 36(2): 197-208.
刘逸滨, 刘宝元, 成城, 等. 退耕还林草20年来榆林市植被覆盖度时空变化及影响因素分析[J]. 水土保持学报, 2022,36(2):197-208. DOI: 10.13870/j.cnki.stbcxb.2022.02.026.
LIU Yibin, LIU Baoyuan, CHENG Cheng, et al. Spatio-temporal Changes and Influencing Factors of Vegetation Coverage in Yulin City During the Past 20 Years Since the Implementation of the “Grain for Green” Program[J]. 2022, 36(2): 197-208. DOI: 10.13870/j.cnki.stbcxb.2022.02.026.
为了解退耕还林草工程实施20年来黄土高原生态脆弱区生态环境的变化及其主要驱动力
选取榆林市作为黄土高原生态环境脆弱区的代表
基于像元二分模型估算榆林市2001-2020年的植被覆盖度
利用一元线性回归法分析其20年来植被覆盖度的时空变化特征
并结合偏相关分析和地理探测器法筛选退耕还林草不同时期影响榆林市植被覆盖度年际变化和空间分异的主导因素。结果表明:(1)2001-2020年榆林市植被覆盖度随时间序列呈显著增加趋势(S=0.011
p<0.01)。建设期(2001-2010年)榆林市整体植被覆盖度显著增加(S=0.013
p<0.01)
而巩固期(2011-2020年)增加趋势不显著(S=0.005
p>0.05)。(2)2001-2020年榆林市植被覆盖度空间分布呈由东向西递减的格局
榆林市各县(区)植被覆盖度总体呈现增长趋势
其中榆林市东部地区增长趋势较为显著。建设期榆林市植被覆盖度呈增加趋势的面积占比(82.1%)多于巩固期(58.0%)。(3)2001-2020年影响植被覆盖度年际变化的主导因素为累积退耕还林草面积和降雨量
累积退耕还林草面积和降雨量分别是建设期和巩固期的首要影响因素。(4)2001-2020年影响榆林市植被覆盖度空间分异的主导因素为土壤质地、降雨量和土地利用方式。在建设期和巩固期土壤质地的空间差异都与植被覆盖度空间分异密切相关。植被分布的空间异质性和主要影响因素之间的交互作用可为生态恢复措施的精准实施提供科学依据。
To understand changes in the ecological environment and its main driving forces in the ecologically fragile areas of the Loess Plateau in the past 20 years since the implementation of the "Grain for Green" Program
taking Yulin City as a typical example of the fragile ecological environment on the Loess Plateau
The vegetation coverage of Yulin City from 2001 to 2020 was estimated based on the pixel dichotomy model. This study analyzed the spatial and temporal variations of the vegetation coverage in Yulin City during the past 20 years by using univariate linear regression methods. The dominant factors affecting the interannual changes and spatial distribution of vegetation coverage were analyzed by using the partial correlation analysis and geographic detector methods during the different periods of "Grain for Green" Program. The results showed that:(1) The vegetation coverage in Yulin City significantly increased with time series (S=0.011
p<0.01) from 2001 to 2020. The vegetation coverage during the construction period (2001-2010) increased significantly (S=0.013
p<0.01)
while its increasing trend in the consolidation period (2011-2020) was not significant (S=0.005
p>0.05). (2) The spatial distribution of vegetation coverage in Yulin City decreased from east to west from 2001 to 2020. The vegetation coverage of all counties (districts) in Yulin City showed an overall increasing trend
among which the eastern part of Yulin City had a more significant growth trend. However
the proportion of area with increasing vegetation coverage in Yulin City in the construction period (82.1%) was larger than that in the consolidation period (58.0%). (3) From 2001 to 2020
the dominant factors affecting inter annual change of vegetation coverage were the cumulative area of the Grain for Green Program and rainfall. The cumulative area of the Grain for Green Program was the primary factor in the construction periods
while rainfall was the primary factor in the consolidation period. (4) Soil texture
rainfall and land use type mainly affected the spatial distribution of vegetation coverage from 2001 to 2020. During construction and consolidation periods
the spatial differences of soil texture were closely related to spatial stratified heterogeneity in the vegetation coverage. The spatial heterogeneity of vegetation distribution and the interaction between the main influencing factors could provide a scientific basis for the precise implementation of ecological restoration measures.
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