摘要
[目的]利用遥感技术估算西藏地区多年植被覆盖度变化情况。[方法]基于MODIS-NDVI数据,利用像元二分模型,估算了2005~2014年西藏地区生长季期间的植被覆盖度,并对其时空变化特征进行了分析;借助趋势分析法分析了10年间西藏地区植被覆盖度的趋势斜率;通过划分气候区,在控制气候要素不变的情况下,讨论人类活动对西藏植被覆盖度变化的影响。[结果]2005~2014年西藏地区生长季期间平均植被覆盖度为33.00%~36.00%,整体表现为稳定的上升趋势;东南地区植被覆盖明显好于西北地区;森林、灌丛、农作物等类型植被覆盖度较高,而草原、草甸、高山植被以及荒漠等植被类型相对较低;2005~2014年西藏地区趋势斜率总体保持稳定,植被覆盖明显减少的部分集中分布于拉萨市与林芝市的交界地带、山南地区的东南部及林芝市的中部地区,主要是人类活动影响所致。[结论]利用像元二分模型估算西藏地区多年生长季植被覆盖度是可行的。
[Objective]To estimate changes in vegetation coverage in Tibet over the past ten years based on remote sensing. [Method]Based on MODIS-NDVI data,the vegetation coverage during the growing season in Tibet from 2005 to 2014 was calculated by using the dimidiate pixel model,and spatial and temporal variation characteristics of the vegetation coverage were analyzed,and the changing trends of vegetation coverage in Tibet in recent ten years were also analyzed. Through the division of climate zones in Tibet,the influences of human activities on the changes of vegetation coverage in Tibet were discussed when climates factors were constant. [Result] In the growing season from 2005 to2014,the average vegetation coverage in Tibet changed from 33. 00% to 36. 00%,showing an increasing trend on the whole. Vegetation coverage in the southeast of Tibet was higher than that in the northwest of Tibet. The vegetation coverage of forest,shrubs and crops was relatively higher,whereas it was relatively lower in grassland,meadow,alpine and desert. During 2005- 2014,the trend slope of vegetation coverage in Tibet kept stable,and vegetation coverage decreased significantly in the border between Lhasa City and Nyingchi City,the southeast of Lhoka City,and the center of Nyingchi City,which was caused by human activities. [Conclusion] It is feasible to calculate vegetation coverage during the growing season in Tibet over the past years by using the dimidiate pixel model.
出处
《安徽农业科学》
CAS
2016年第19期33-37,52,共6页
Journal of Anhui Agricultural Sciences
基金
国家973基金项目(2013CB956000)
关键词
植被覆盖度
像元二分模型
趋势变化分析
聚类分析
Vegetation coverage
Dimidiate pixel model
Analysis of changing trend
Clustering analysis
