地理研究 ›› 2017, Vol. 36 ›› Issue (1): 37-48.doi: 10.11821/dlyj201701003

• 研究论文 • 上一篇    下一篇

基于SPOT-VGT数据的锡林郭勒盟草原返青期遥感监测

郭剑(), 陈实, 徐斌, 申格, 金云翔, 张玉静, 杨秀春()   

  1. 中国农业科学院农业资源与农业区划研究所,北京 100081
  • 收稿日期:2016-07-11 修回日期:2016-11-27 出版日期:2017-01-20 发布日期:2017-01-20
  • 作者简介:

    作者简介:郭剑(1990- ),男,山西省临汾人,硕士,主要从事草原物候遥感研究。E-mail: guojianxiaobai@163.com

  • 基金资助:
    国家自然科学基金项目(41571105,31372354)

Remote sensing monitoring of grassland vegetation greenupbased on SPOT-VGT in Xilingol League

Jian GUO(), Shi CHEN, Bin XU, Ge SHEN, Yunxiang JIN, Yujing ZHANG, Xiuchun YANG()   

  1. Institute of Agricultural Resources and Regional Planning, Chinese Academy ofAgricultural Sciences, Beijing 100081, China
  • Received:2016-07-11 Revised:2016-11-27 Online:2017-01-20 Published:2017-01-20

摘要:

使用1999-2012年SPOT-VGT NDVI数据,采用D-L滤波方法对NDVI时间序列进行重建,基于动态阈值法提取锡林郭勒盟草原返青期,结合地面实测返青期数据对结果进行验证。研究表明:① 提出的草原植被遥感返青期监测和地面验证相结合的新方法,采用25%动态阈值系数提取了锡林郭勒盟草原的返青期,其精度可达68%。② 锡林郭勒盟大部分地区的遥感返青期发生在4月上旬-5月中旬,呈现由南向北推迟的空间格局。1999-2012年锡盟草原返青期整体上呈现提前的趋势,为-1.5 d/10a。论文成果为增强中国对气候变化的应对能力、指导当地的农牧业生产、保护脆弱的草原生态系统具有重要的理论和实际价值。

关键词: 返青期, 遥感, 草原, NDVI

Abstract:

This paper extracts grasslands greenup from SPOT-VGT NDVI filtered by D-L during 1999 to 2012, and uses ground greenup date to verify the result. The main conclusions are as follows: (1) A new method, which combine grassland vegetation remote sensing regreening stage monitoring and ground validation, is presented. Greenup date of Xilingol grassland is extracted by using dynamic threshold coefficients of 25% and its precision can reach 68%. (2) The mean greenup date in Xilingol league is mainly observed from early April to late May. The greenup dates are delayed from south to north, with the average change being -1.5 d/10a. Exploring spatiotemporal characteristics of grasslands phenology in Xilingol is of theoretical and practical significance in China's combat to climate change, grassland protection and management in the presence of specific problems, decision making in local agricultural and animal husbandry, and vulnerable grassland ecosystems rehabilitation.

Key words: greenup date, remote sensing, grassland, NDVI