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干旱区地理 ›› 2017, Vol. 40 ›› Issue (2): 405-414.

• 地球信息科学 • 上一篇    下一篇

2001-2015年喜马拉雅南麓地区植被变化遥感监测

马磊, 闫浩文, 何毅, 张乾, 刘波   

  1. 兰州交通大学测绘与地理信息学院/甘肃省地理国情监测工程实验室, 甘肃 兰州 730070
  • 收稿日期:2016-10-27 修回日期:2017-01-10 出版日期:2017-03-25
  • 通讯作者: 闫浩文.Email:haowen2010@gmail.com
  • 作者简介:马磊(1989-),男,硕士研究生,研究方向为GIS应用.Email:malei2013@hotmail.com
  • 基金资助:

    国家自然科学基金(41671447、71563025、41371435)资助

Vegetation changes in south Himalayas areas based on remote sensing monitoring during 2001-2015

MA Lei, YAN Hao-wen, HE Yi, ZHANG Qian, LIU Bo   

  1. Faculty of Geomatics, Lanzhou Jiaotong University/Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, Gansu, China
  • Received:2016-10-27 Revised:2017-01-10 Online:2017-03-25

摘要: 本文应用喜马拉雅南麓地区MODIS NDVI 植被遥感数据和格点数据,采用趋势线分析、多元回归等方法分析了该研究区2001-2015 年植被 NDVImax 时空变化特征,同时利用Person 相关分析探讨了植被 NDVImax 时空变化特征与气候因子的响应关系。结果表明:(1)2001-2015 年,喜马拉雅南麓地区年内平均 NDVImax 1~3 月份呈下降趋势,4~6 月份开始缓慢生长,6~9 月份进入植被生长高峰期,10 月份开始逐渐降低;植被 NDVImax 平均值为0.59,植被覆盖度较高;空间上植被覆盖度总体呈东南高西北低,由东南向西北递减;平均 NDVImax 随海拔变化表现出明显规律性,80%的植被主要分布在较低海拔区(<4 050 m)。(2)15 a 间,喜马拉雅南麓地区植被 NDVImax 变化具有阶段性特征,年均 NDVImax 呈三个变化阶段:2001-2006 年和2010-2015 年分别以0.003 9·a-1、0.005 3·a-1 的速率增长,而2006-2010 年以-0.007 0·a-1 的速率减少。植被生长季 NDVImax 呈4 个阶段:2001-2004 和2007-2010 年分别以-0.001 8·a-1、-0.010 6·a-1 的速率逐年减少,但2005、2006 两年(0.014 8·a-1)快速增长至最大值,2010-2015 年(0.006 3 a-1)波动增长。空间上大部分地区表现出不显著退化,但少部分地区表现出不显著改善(0.05< p<0.01),而西段低海拔区表现出极显著改善。(3)喜马拉雅南麓地区植被的变化主要由温度和降水量共同影响,此外,高海拔区气温上升引起的冰川融水对植被生长起到一定的作用,中部低海拔区可能还受到人类活动的影响。

关键词: 喜马拉雅南麓, 植被变化, 遥感, 气候变化

Abstract: Vegetation plays an important role in the global cycle of geobiochemistry, significantly affects the climate and sensitively responds to the climate change on the contrary. Consequently, studying the spatio-temporal features of the vegetation is vital for climate monitoring. The ecosystem of vegetation in the Eurasia is severely affected by climate change. As the border of the Indian subcontinent and Tibet Plateau, Himalayas alike makes intensivelyresponse to the climate change. It is an all-but uninhabited part in the world, holds abundant biodiversity, and has the most integrated vertical distribution of the vegetation.The vegetation in Himalayas Mountain has been well studied on the climate patterns, vegetation forms, land use and climate factors. However, the vegetation on the whole south mountain and the conditions of growth rate haven't been well studied. Besides, the primary climate factors affecting the vegetation change are still unknown. Based on remote sensing and grid data from MODIS NDVI in the mountainous areas of south Himalayas during 2001-2015, trend curvetheory and multiple regression method were applied toanalyze the spatio-temporal features of south Himalayas, and Pearson correlation analysis was utilized to study the correlations between climate factors and the NDVImax values in this area during the past 15 years. The results show as follows:(1)during 2001-2015, the average value of NDVImax decreased from January to March and slowly increased from April to June, then reached the peak period from June to September and began to decrease gradually from October to December. On the whole, the average value of NDVImax was high(0.59), decreased from South-East to North-West, and furthermore, varied over elevation changes regularly, and 80% of the vegetation was below 4 050 meters; (2)the NDVImax value in this area changed by timephase, of which the annual average varied in 3 phases, the change rates were 0.003 9·a-1 and 0.005 3·a-1 in 2001-2006 and 2010-2015, respectively, while-0.007 0·a-1 in 2006-2010; during the growing season, the NDVImax varied in 4 phases, the rates of change were-0.001 8·a-1 and-0.010 6·a-1 in 2001-2004 and 2007-2010, respectively, 0.014 8·a-1 in 2005-2006 when the NDVImax value reached the maximum and 0.006 3·a-1 in 2010-2015 when the increase of NDVImax value fluctuated. The vegetation was degrading insignificantly in most areas, while improving insignificantly(0.05< p<0.01)in a few areas and improving most significantly at the low altitudes in the west; (3)the vegetation in the area was affected by temperature and precipitation at the same time. Meanwhile, the vegetation could be improved by glacial melt run-off caused by temperature rising at high altitudes and affected by human activities at low altitudes in the middle of south Himalayas to some extent.

Key words: south Himalayas Mountain, vegetation change, remote sensing, climate change

中图分类号: 

  • Q948.15