摘要
以广东省海岸线为研究对象,采集2000、2005、2010和2015共4期Landsat系列卫星遥感影像,通过修复归一化差值水体指数(MNDWI)和人工目视解译相结合的方法,提取了广东省4个时期的海岸线,借助ArcGIS的空间叠加分析和统计工具,研究其15 a以来的动态演变特征,并分析海岸线变化原因。结果表明:①2000—2015年,广东省海岸线总体呈增长趋势,长度增加158.57 km,年均增速为10.57 km/a。海岸线变化最显著的时段为2005—2010年,其次为2010—2015年,变化最缓慢的为2000—2005年,其年均变化率依次为0.14%、0.40%和0.24%。②从空间上看,珠海、汕头、揭阳、东莞和中山在广东省沿海14市中海岸线增长幅度最为显著。③广东省海岸线变迁趋势以向海延伸为主,驱动因素主要表现为填海造陆、沿海工程建设和围垦养殖等人为活动。
It is very important to carry out monitoring changes in the erosion and growth of coastlines.Taking the coastline of Guangdong Province as the research object,the Landsat series satellite remote sensing images of 2000,2005,2010 and 2015 years were used to extract the coastline of Guangdong Province by combination of Modified Normalized Difference Water Index(MNDWI)and manual visual interpretation.The dynamic evolution characteristics of coastline in the past 15 years were studied using ArcGIS's spatial overlay analysis and statistical tools and the reasons for coastline changes were analyzed.The analysis results show that:①From 2000 to 2015,the coastline length of Guangdong Province showes an overall growth trend,increasing 158.57km,with an average annual growth rate of 10.57 km/a.The most significant period of coastline change is 2005—2010,followed by 2010—2015,and the slowest period of change is 2000—2005.The average annual rates of change are 0.14%,0.40%and 0.24%,respectively.②In terms of space,Zhuhai,Shantou,Jieyang,Dongguan and Zhongshan have the most significant growth of the coastline in the 14 cities along the coast of Guangdong Province.③The trend of coastline change in Guangdong Province is mainly extended to the sea.The driving factors are mainly human activities such as land reclamation,coastal engineering construction and cofferdam breeding.
作者
刘朱婷
LIU Zhuting(Guangdong Hydropower Planning&Design Institute Co.,Ltd.,Guangzhou 510635,China)
出处
《广东水利水电》
2024年第4期104-111,117,共9页
Guangdong Water Resources and Hydropower
基金
广东省水利电力勘测设计研究院有限公司科研课题(编号:Y2023-06)。
关键词
广东省
海岸线变迁
遥感提取
驱动因素
Guangdong province
coastline change
remote sensing extraction
driving factors