银川市湿地景观演变及其驱动因素
收稿日期: 2020-10-10
修回日期: 2020-12-15
网络出版日期: 2021-06-17
基金资助
国家重点研发计划“两屏三带”生态系统服务格局优化(2018YFC0507300);第二次青藏高原综合科学考察研究项目“生态安全屏障功能与优化体系”(2019QZKK0405)
Wetland landscape evolution and its driving factors in Yinchuan
Received date: 2020-10-10
Revised date: 2020-12-15
Online published: 2021-06-17
基于1990—2019年Landsat TM/OLI遥感影像,采用面向对象分类方法提取银川市湿地景观信息,通过景观指数、冗余分析方法定量分析研究区湿地景观演变特征及其驱动因素。结果表明：（1） 银川市湿地面积由1990年的264.86 km2减少到2019年的241.32 km2,减少了23.54 km2。与1990年相比,2019年的自然湿地面积减少了33.57 km2,人工湿地面积增加了10.03 km2。（2） 1990—2019年间,银川市湿地景观的破碎化程度下降、聚集程度降低、形状逐渐不规则化、多样性与异质性增加。（3） 选取12个驱动因素指标进行冗余分析,社会经济因素是湿地景观演变的主导因素,非农业人口数、水产品产量、第二产业产值与建成区面积对湿地景观变化的影响最为显著,降水量、气温等自然因素作用相对较弱。研究结果可为银川市的湿地资源的合理利用与保护提供重要参考。
王晓峰,延雨,李月皓,张兴,符鑫鑫 . 银川市湿地景观演变及其驱动因素[J]. 干旱区研究, 2021 , 38(3) : 855 -866 . DOI: 10.13866/j.azr.2021.03.27
Wetland is the transitional zone of aquatic and terrestrial ecosystems, and it is an ecosystem with the highest biodiversity and productivity on Earth. Recently, under the influence of climate change and human activities, the wetland ecosystem has rapidly degraded. Spatio-temporal dynamic monitoring of wetland plays a vital role in the sustainable development of wetland landscape. The object-oriented classification method was used to extract the wetland landscape information in Yinchuan based on the Landsat TM/OLI remote sensing images from 1990 to 2019. The landscape index and redundancy analysis methods were used to analyze the dynamic changes and their driving factors in wetlands. The results showed that Yinchuan’s wetland area decreased from 264.86 km2 in 1990 to 241.32 km2in 2019, a decrease of 23.54 km2. The area of natural wetlands in 2019 decreased by 33.57 km2, while artificial wetlands increased by 10.03 km2. In the past 30 years, PD and AI of the landscape patterns have decreased, while the LSI and SHDI increased. This indicated that the wetland landscape patterns has undergone significant changes, fragmentation and aggregation degree gradually decreased, patch shape tended to be more complicated, and landscape diversity and heterogeneity increased. Twelve driving factors were selected for redundancy analysis, and socio-economic factors played a dominant role in the wetland landscape change, including nonagricultural population and aquatic products yield, the output value of various industries, and developed areas of cities. Altogether, natural factors such as precipitation, temperature, and hours of sunshine change could also impact the wetland landscape change, which is relatively weak. The research results provide important references to protect and rationally use wetlands resources in Yinchuan.
Key words： wetland; landscape pattern; evolution; driving factors; Yinchuan
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