干旱半干旱草地区域植被对降水响应的多月滞后效应——以内蒙古呼伦贝尔为例

IF 1.8 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES Natural Resource Modeling Pub Date : 2022-03-21 DOI:10.1111/nrm.12342
Taosuo Wu, Hongmei Bai, Feng Feng, Qian Lin
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引用次数: 2

摘要

利用16 a归一化植被指数(NDVI)和降水资料,分析了区域尺度上生长季NDVI对降水响应的时滞效应。本研究以呼伦贝尔多年生植物为主的干旱半干旱草原为研究对象。使用简单的统计方法对多月滞后效应进行了检验,该方法可以检测出具有四种主要土地覆盖类型的四个次区域的两种不同的滞后。生长季NDVI对降水的响应在1个月(当年5月)和13个月(前一年5月)的滞后时间中存在“正”滞后效应,而在9个月(前一年9月)的滞后时间中存在“负”滞后效应。此外,基于降水的NDVI预测结果表明,考虑月降水滞后的NDVI预测模型具有较好的性能。因此,揭示时间滞后效应对于准确预测生长季NDVI和评价植被动态具有重要意义。
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Multi‐month time‐lag effects of regional vegetation responses to precipitation in arid and semi‐arid grassland: A case study of Hulunbuir, Inner Mongolia
The 16 years of normalized difference vegetation index (NDVI) and precipitation data are used to analyze the time‐lag effects of the growing‐season NDVI response to precipitation at regional scales. This study focuses on the arid and semi‐arid Hulunbuir grassland dominated by perennials in northeast China. The multi‐month time‐lag effects are examined using simple statistical approaches, which can detect the two distinct time‐lags for four subregions with four major land‐cover types. A “positive” time‐lag effect of the growing‐season NDVI response to precipitation is observed at 1‐month (May in the current year) time‐lag and 13‐month (May in the previous year) time‐lag while a “negative” time‐lag effect is observed at 9‐month (September in the previous year) time‐lag. In addition, the prediction results of NDVI based on precipitation indicate that the NDVI prediction model considered the lagged monthly precipitation has good performance. Therefore, revealing the time‐lag effects is very important for accurately predicting the growing‐season NDVI and evaluating the vegetation dynamics.
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来源期刊
Natural Resource Modeling
Natural Resource Modeling 环境科学-环境科学
CiteScore
3.50
自引率
6.20%
发文量
28
审稿时长
>36 weeks
期刊介绍: Natural Resource Modeling is an international journal devoted to mathematical modeling of natural resource systems. It reflects the conceptual and methodological core that is common to model building throughout disciplines including such fields as forestry, fisheries, economics and ecology. This core draws upon the analytical and methodological apparatus of mathematics, statistics, and scientific computing.
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