地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (8): 1110-1122.doi: 10.3724/SP.J.1047.2016.01110

• 遥感科学与应用技术 • 上一篇    下一篇

基于SSA-Mann Kendall的草原露天矿区NDVI时间序列分析

贾铎(), 牟守国*(), 赵华   

  1. 1. 中国矿业大学(徐州)环境与测绘学院,徐州 221116;2. 中国矿业大学(徐州) 江苏省资源环境信息工程重点实验室,徐州 221116
  • 收稿日期:2016-02-28 修回日期:2016-05-08 出版日期:2016-08-10 发布日期:2016-08-10
  • 通讯作者: 牟守国 E-mail:jiaduo_geo@163.com;mushouguo@163.com
  • 作者简介:

    作者简介:贾 铎(1993-),男,硕士生,研究方向为遥感应用。E-mail:jiaduo_geo@163.com

  • 基金资助:
    国家科技基础性工作专项(2014FY110800)

Analysis of NDVI Time Series in Grassland Open-cast Coal Mines Based on SSA-Mann Kendall

JIA Duo(), MU Shouguo(), ZHAO Hua   

  1. 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;2. Institute of Land Resources, China University of Mining and Technology, Xuzhou 221116, China
  • Received:2016-02-28 Revised:2016-05-08 Online:2016-08-10 Published:2016-08-10

摘要:

针对煤矿区植被指数时间序列研究中,存在年际尺度上对植被动态规律刻画不全面、月际尺度上因物候性周期波动导致变化趋势和周期振荡信号微弱难以提取、基于变换的变化检测物理意义不够明确的问题,本文以胜利露天矿区为例,在月际尺度,基于SSA-Mann Kendall重建草原露天矿区的采矿扰动区和伪不变特征区MODIS NDVI时间序列的趋势和周期振荡信号,从显著程度和突变时间2方面对趋势成分进行定量化分析,并结合各特征区周期振荡演变特征揭示采矿扰动下草原露天矿区植被生长的动态规律。结果表明:SSA-Mann Kendall能将NDVI时间序列的微弱信号充分放大,便于提取,并可对趋势成分进行定量化表达,结合周期振荡与趋势成分的演变特征有助于辅助识别矿区植被生长的动态特点;伪不变特征区植被无显著下降趋势,采矿扰动区下降趋势显著,且露天采场较排土场的趋势更为明显,草原露天矿区地表植被损伤具有突发性,突变点多发于矿井开工建设时;扰动形式差异导致部分矿井露天采场和排土场周期振荡演变特征存在差异,露天采场植被消失殆尽,排土场因植被恢复措施而具有更复杂的动态特点。

关键词: NDVI时间序列, 奇异谱分析, Mann Kendall, 变化趋势, 周期振荡

Abstract:

Vegetation index time series have difficulty to depict the detailed response of vegetation dynamics to coal mining and the signals of change trend and periodic oscillation at an inter-annual scale. While at the monthly scale, the signals are so weak that they are hard to be extracted due to the disturbance of vegetation phenology. In addition, the physical significance of the transformation detection algorithm is still unclear. In order to solve these problems and to reveal the change trend and periodic oscillation of vegetation growth in the disturbance area of grassland open-cast mine, this paper selected Shengli open-cast mine area as an study area to extract, amplify and quantitatively compute the signals of the monthly change trend and periodic oscillation based on the MODIS NDVI time series from January 2001 to December 2013 in the mining disturbance area and the unchanged feature region. SSA-Man Kendall was adopted to extract the change trend and periodic oscillation. Moreover, we quantitatively analyzed the significant degree and jump time. For the change trend, the Sen slope of the change components were calculated, which could indicates the change direction. Then the trend significance was measured based on the results of Mann Kendall trend analysis. Combined with the moving-t test for some fuzzy catastrophe points, the time points of abrupt changes were also detected. For the periodic oscillation, it was estimated based on the utilization of power spectrum analysis. The evolution characteristics of NDVI time series′ periodic oscillation in different feature regions were also studied. The results show that SSA-Mann Kendall can effectively extract the signals of change trend and be competent in depicting the periodic oscillation at different time scales, as well as quantitatively express the change trend signals. A downward trend of NDVI time series in the stopes is significant, and it is more significant than the waste dumps within the same mine area, while the trend in the unchanged feature region is relative stable. In addition, damage of vegetation is a sudden event in the grassland opencast mine area, and the catastrophe points of NDVI time series usually occur at the beginning of the mine construction. In the partial open-cast mines, NDVI time series′ periodic oscillation in the stopes and waste dumps are different, which are related to the different disturbance forms within these areas. In particular, the vegetation almost vanishes in the stopes with coal mining, however, the dynamics of vegetation growth are more complex in the waste dumps due to the effective vegetation restoration.

Key words: NDVI time series, singular spectrum analysis, Mann Kendall, trend of change, periodic oscillation