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王超军,吴锋,赵红蕊,陆胜寒.时间信息熵及其在植被覆盖时空变化遥感检测中的应用.生态学报,2017,37(21):7359~7367 本文二维码信息
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时间信息熵及其在植被覆盖时空变化遥感检测中的应用
Temporal information entropy and its application in the detection of spatio-temporal changes in vegetation coverage based on remote sensing images
投稿时间:2016-08-12  
DOI: 10.5846/stxb201608121653
关键词遥感  变化检测  时序数据  时间信息熵  植被覆盖
Key Wordsremote sensing  change detection  time series data  temporal information entropy  vegetation coverage
基金项目国家自然科学基金项目(41571414)
作者单位E-mail
王超军 清华大学3S中心, 清华大学土木工程系地球空间信息研究所, 北京 100084  
吴锋 中国科学院地理科学与资源研究所, 北京 100101;中国科学院农业政策研究中心, 北京 100101  
赵红蕊 清华大学3S中心, 清华大学土木工程系地球空间信息研究所, 北京 100084 zhr@tsinghua.edu.cn 
陆胜寒 清华大学3S中心, 清华大学土木工程系地球空间信息研究所, 北京 100084  
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摘要:
基于遥感影像的变化检测是当前的研究热点,可为区域生态环境保育、资源管理与发展规划等提供决策支撑。目前遥感影像的变化检测多基于两个时相,不能充分地反映植被在时间维的连续变化特征。通过引入信息论,提出了利用时间信息熵来综合表征植被长时间序列的变化特征。研究以延河流域为试验区,基于MODIS/NDVI数据,应用时间信息熵方法来计算了2000-2010年该区域的植被覆盖变化信息,厘清了时空变化特征。研究结果表明,近10年延河流域的植被覆盖的变化以增加为主,占流域面积的80.7%;植被覆盖明显增加的区域占流域面积13.9%,主要分布在流域的东北部和东南部;植被覆盖减少的区域占比2.4%,主要分布在流域的西部和西北部;严重减少的区域占比1.1%,主要分布在流域的中部和西南部,是需要重点的生态恢复与治理区域。时间信息熵方法与回归分析法相比,能够更为客观地表征长时间序列植被覆盖的连续变化强度和变化趋势,可为区域生态环境的保护和管理提供更为科学的理论依据。
Abstract:
Change detection based on remote sensing images is a hot research topic, which can provide decision support for regional ecological conservation, resource management and development planning, etc. However, the current studies mostly focus on bio-temporal change detection, which cannot adequately reflect the continuous change characteristics in time dimension. In this paper, through the introduction of information theory, we propose a novel method of temporal information entropy to characterize the long time change features of vegetation comprehensively. Taking Yanhe watershed as the study area and based on Normalized Difference Vegetation Index (NDVI) data, we used this method to calculate the change information of vegetation coverage in Yanhe watershed from 2000 to 2010 and identified its spatio-temporal change characteristics. The results showed that the change level of vegetation coverage in Yanhe watershed was mostly increased in this period, accounted for 80.7% of the total area. Obviously increased area accounted for 13.9% and mainly distributed in northeast and southeast of the watershed. Vegetation coverage decreased area accounted for 2.4%, mainly distributed in the western and northwestern; Severely decreased area accounted for 1.1%, mainly distributed in the central and southwest of the study area, where needs strict environmental management. Comparing with the regression analysis method, temporal information entropy can be more objectively in reflecting the continuous change intensity and trend of vegetation coverage in long time series, which can provide scientific theoretical references for the protection and management of regional ecological environment.
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