测绘通报 ›› 2019, Vol. 0 ›› Issue (10): 12-16,113.doi: 10.13474/j.cnki.11-2246.2019.0310

• 自然资源监测 • 上一篇    下一篇

NDVI时间变换一致性的贵州茂兰植被覆盖变化分析

陈炫炽1, 田鹏举2, 陈蓉1, 吴愈锋1, 王跃跃1   

  1. 1. 贵州大学矿业学院, 贵州 贵阳 550025;
    2. 贵州省生态气象和卫星遥感中心, 贵州 贵阳 550025
  • 收稿日期:2019-04-28 出版日期:2019-10-25 发布日期:2019-10-26
  • 作者简介:陈炫炽(1996-),男,硕士生,主要从事遥感应用方面的研究。E-mail:942283115@qq.com
  • 基金资助:
    国家自然科学基金(41463009);贵州省教育厅重大创新群体项目(黔教合KY[2016]字024);贵州省生态学一流学科建设项目(GNYL[2017]007)

Study on vegetation cover change of Maolan in Guizhou based on time-varying consistency of NDVI

CHEN Xuanchi1, TIAN Pengju2, CHEN Rong1, WU Yufeng1, WANG Yueyue1   

  1. 1. College of Mining, Guizhou University, Guiyang 550025, China;
    2. Guizhou Ecological Meteorology and Satellite Remote Sensing Center, Guiyang 550025, China
  • Received:2019-04-28 Online:2019-10-25 Published:2019-10-26

摘要: 利用近18年贵州茂兰自然保护区的Landsat TM/ETM+/OLI数据,针对云覆盖对影像质量的影响,提出并使用了一种基于NDVI时间变换一致性的方法,构建出较为完整的研究区植被指数时间序列,实现了小区域尺度下长时间序列的植被覆盖变化研究,并采用一元线性回归模型和相关分析法探讨研究区植被覆盖变化趋势及其对气象因子的响应关系。得出结论:NDVI时间变换一致性处理方法可以有效地消除云覆盖的影响;研究区近18年植被覆盖状况良好且正呈缓慢上升趋势,气候因子与植被覆盖变化呈显著正相关关系,其中平均温度的影响在当月最强,而降水量和平均相对湿度的影响则存在滞后性。

关键词: 植被覆盖, 时间变换, 归一化植被指数, 云覆盖

Abstract: Aiming at the effects of cloud cover on image quality, put forward and use a method based on time-varying consistency of NDVI, The Landsat TM/ETM+/OLI data of Guizhou Maolan nature reserve in recent 18 years are used to build a relatively complete vegetation index time series in the study area, small regional scale for the long time series of vegetation cover change research, and adopts univariate linear regression model and correlation analysis to explore the vegetation coverage change trend in the study area and its response to meteorological factors relationship.The conclusion is that the time-varying consistency of NDVI processing method can effectively eliminate the influence of cloud cover.Recently 18 a vegetation cover in the study area is in good condition and is on a slow rising trend. There is a significant positive correlation between climate factors and vegetation cover change, in which the influence of average temperature is the strongest in that month, while the influence of precipitation and average relative humidity is lagging behind.

Key words: vegetation coverage, time variation, NDVI, cloud cover

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