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应用生态学报 ›› 2019, Vol. 30 ›› Issue (1): 285-291.doi: 10.13287/j.1001-9332.201901.016

• 研究论文 • 上一篇    下一篇

基于植被信息季节变换的植被覆盖度变化——以福建省连江县为例

杨绘婷1,2,徐涵秋1,2*,施婷婷1,2,陈善沐3   

  1. 1福州大学环境与资源学院, 空间数据挖掘与信息共享教育部重点实验室, 福州 350116;
    2福州大学遥感信息工程研究所, 福建省水土流失遥感监测评价重点实验室, 福州 350116;
    3福建省水土保持监测站, 福州 350001
  • 收稿日期:2018-06-08 修回日期:2018-10-21 出版日期:2019-01-20 发布日期:2019-01-20
  • 通讯作者: hxu@fzu.edu.cn
  • 作者简介:杨绘婷, 女, 1994年生, 硕士研究生. 主要从事环境资源遥感应用研究. E-mail: htyfzu@163.com
  • 基金资助:

    本文由国家重点研发计划专项(2016YFA0600302)、国家自然科学基金项目(41501469)和福建省水利科技项目(MSK201704)资助

Fractional vegetation cover change based on vegetation seasonal variation correction: A case in Lianjiang County, Fujian Province, China

YANG Hui-ting1,2, XU Han-qiu1,2*, SHI Ting-ting1,2, CHEN Shan-mu3   

  1. 1Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing, College of Environment and Resources, Fuzhou University, Fuzhou 350116, China;
    2Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China;
    3Fujian Monitoring Station of Water and Soil Reservation, Fuzhou 350001, China
  • Received:2018-06-08 Revised:2018-10-21 Online:2019-01-20 Published:2019-01-20
  • Supported by:

    This work was supported by the National Key Research and Development Project (2016YFA0600302), the National Natural Science Foundation of China (41501469), and the Water Conservancy Science and Technology Project of Fujian Province, China (MSK201704).

摘要: 基于植被覆盖度的植被信息遥感变化检测已成为研究植被及其相关生态系统变化的主要途径,但由于云覆盖等天气条件的影响,很难获得不同年份同一季节覆盖整个研究区的光学遥感影像来进行植被变化检测,而采用季节差异的影像必然会影响植被变化检测的结果.为此,本研究利用中高分辨率遥感数据的空间分辨率优势和MODIS遥感数据的时间分辨率优势,基于二者关系的拟合,提出一种植被信息季节变换的方法,将不同季节影像的植被覆盖度变换到研究所需的季节上.结果表明: 将该方法应用到福建敖江流域连江片区发现,植被信息变换的效果较好,经过将覆盖研究区的2007年冬季和2013年春季的中高分辨率影像的植被信息统一变换到夏季后,2007年的植被覆盖度由66.5%上升到79.7%,2013年由58.6%上升到77.9%,有效消除了因季节差异而对植被覆盖度估算产生的误差,提高了结果的准确性.

Abstract: Remote sensing change detection based on fractional vegetation cover (FVC) has become an important way in the research of vegetation and related ecosystems. It is difficult to meet the requirement for optical remote sensing in subtropical areas because of cloudy/rainy weather conditions. Using images from different seasons in the vegetation change detection will inevitably lead to errors in the change detection results due to the seasonal difference. To overcome this problem, we proposed a method for correcting vegetation seasonal variations by taking advantage of high temporal resolution advantage of MODIS remote sensing data and the high spatial resolution of remote sensing data. Based on the relationship between MODIS vegetation data in different seasons via regression analysis, we transformed the vegetation information of the high resolution images of corresponding years to the required season of the years. The method was applied in the Aojiang basin area of Lianjiang County in Fujian Province, China, with good results of vegetation information transformation. The results showed that after transforming vegetation information of the 2007 winter scene and 2013 spring scene of high resolution images to those of summer season, the FVC was enhanced from 66.5% to 79.7% for 2007, and from 58.6% to 77.9% for 2013. Our method effectively removed the seasonal difference of FVC and improved the accuracy of the FVC-based change detection results.