摘要
以攸县黄丰桥林场为研究区,选择2001,2009和2013年3期Landsat影像,应用影像代数变化检测、掩膜处理变化检测、写功能存储插入法变化检测和分类后比较变化检测4种算法开展森林变化检测对比研究。结果表明:分类后比较变化检测方法在总体精度上有较明显的优越性,其2013年与2009年、2009年与2001年总体精度均为96.67%;其次是写功能存储插入法变化检测,其2013年与2009年、及2009年与2001年总体精度分别为94%和95.67%;掩膜处理变化检测精度排名第三,其2013年与2009年、2009年与2001年总体精度分别为93.67%和95.67%;效果最差的是影像代数变化检测方法,其2013年与2009年、2009年与2001年总体精度分别为93.33%和94.33%。通过对上述4种方法优缺点及精度的综合分析,得出分类后比较变化检测算法是最适合黄丰桥林场的变化检测方法。
Remote sensing technologies have been widely used to map forest change detection. In this study, four kinds of change detection methods, including image algebra, mask processing, writing function memory insertion and post-classification comparison change detection, were carried out in Huangfengqiao forest farm in Youxian county using Landsat satellite data with three periods of 2001, 2009 and 2013. The results indicated that post-classification comparison change detection method had the highest accuracy in three periods, and its overall accuracy were 96.67% from 2001 to 2009 and from 2009 to 2013; Followed by writing function memory insertion method, the accuracy from 2001 to 2009 and from 2009 to 2013 were 95.67% and 94%, respectively. The Accuracy of mask processing method was inferior to writing function memory insertion method, which from 2001 to 2009 and from 2009 to 2013 respectively were 95.67% and 93.67%. The results of change detection using image algebra had the lowest accuracy, which were 94.33% from 2001 to 2009 and 93.33% from 2009 to 2013. Companion with other three methods, post-classification comparison change detection method provided greater potential of accurately predictions of forest change detection in this forest farm.
出处
《中南林业科技大学学报》
CAS
CSCD
北大核心
2017年第12期65-71,共7页
Journal of Central South University of Forestry & Technology
基金
湖南省创新平台与人才计划(科技人才)项目(2015RS4048)
国家级大学生研究性学习和创新性实验计划项目(201410538008)
湖南省大学生研究性学习和创新性实验计划项目(湘教通[2014]248号)
关键词
遥感变化检测
影像代数
掩膜处理
写功能存储插入法
分类后处理
remote sensing change detection
image algebra
mask processing
writing function memory insertion method
post-classification comparison
