基于遥感的黄土高原植被物候监测及其对气候变化的响应

为了分析黄土高原地区植被物候特征,该文基于AVHRR传感器获取的陆地长期数据记录（land long term data record,LTDR）V4 NDVI数据,对黄土高原1982-2011年间植被物候的时空变化进行分析,并借助偏相关分析方法对物候与气温和降雨的关系进行量化分析。结果表明：黄土高原近30 a间春季物候提前显著（0.54 d/a,P〈0.001）,主要集中在北部草地和灌木植被;秋季物候推迟显著（0.74 d/a,P〈0.001）,主要分布在甘肃、陕北、内蒙古和山西北部等地。不同植被的春秋物候稍有差异,稀疏灌木林春季物候提前趋势最多（1.31 d/a）,常绿针叶林最小（0.19...

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Bibliographic Details
Published in农业工程学报 Vol. 31; no. 15; pp. 153 - 160
Main Author 谢宝妮 秦占飞 王洋 常庆瑞
Format Journal Article
LanguageChinese
Published 西北农林科技大学资源环境学院,杨凌,712100 2015
Subjects
植被
气候变化
物候
遥感
陆地长期数据记录
黄土高原
LTDR(land long term data record)
植被
黄土高原
phenology
Chinese Loess Plateau
vegetation
remote sensing
遥感
气候变化
物候
陆地长期数据记录
climate change
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2015.15.021

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Summary:为了分析黄土高原地区植被物候特征,该文基于AVHRR传感器获取的陆地长期数据记录（land long term data record,LTDR）V4 NDVI数据,对黄土高原1982-2011年间植被物候的时空变化进行分析,并借助偏相关分析方法对物候与气温和降雨的关系进行量化分析。结果表明：黄土高原近30 a间春季物候提前显著（0.54 d/a,P〈0.001）,主要集中在北部草地和灌木植被;秋季物候推迟显著（0.74 d/a,P〈0.001）,主要分布在甘肃、陕北、内蒙古和山西北部等地。不同植被的春秋物候稍有差异,稀疏灌木林春季物候提前趋势最多（1.31 d/a）,常绿针叶林最小（0.19 d/a）;秋季物候推迟最多的为乔木园地（1.18 d/a）,最少的是水田（0.17 d/a）。黄土高原植被物候主要受气温影响,降雨的变化也会对物候产生一定影响。冬季和前年秋季气温上升是春季物候提前的主要驱动因子;夏季和秋季降雨则对秋季物候休眠期延迟起着重要作用。该研究可为黄土高原生态环境评价及气候变化预测模型提供一定依据。
Bibliography:11-2047/S
vegetation; remote sensing; climate change; phenology; LTDR（land long term data record）; Chinese Loess Plateau
Xie Baoni, Qin Zhanfei, Wang Yang, Chang Qingrui （College of Natural Resources and Environment, Northwest A ＆F University, Yangling 712100, China）
It is crucial to understand vegetation phenology changes and their relationship with climate change at biome-level when projecting regional ecosystem carbon exchange and climate-biosphere interactions. To further understand the relationship between vegetation growth and climatic factors, in this study, we investigated the variation in vegetation phenology and its linkage with climate change on the Chinese Loess Plateau through analyzing the Land Long Term Data Record（LTDR） NOAA/AVHRR Normalized Difference Vegetation Index（NDVI） and concurrent temperature and precipitation during 1982-2011. Firstly, the maximum value composite（MVC） method was used to composite the 10 d LTDR NDVI dataset in order to reduce effects of atmospheric and cloud noise. The H
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2015.15.021