img

官方微信

  • CN 62-1070/P
  • ISSN 1000-694X
  • 双月刊 创刊于1981年
高级检索
生物与土壤

基于Whittaker滤波的陕西省植被物候特征

  • 张晗 ,
  • 任志远
展开
  • 陕西师范大学 旅游与环境学院/西北国土资源研究中心/西北历史环境与经济社会发展研究院, 陕西 西安 710062
张晗(1990-),男,陕西咸阳人,硕士研究生,主要从事国土资源与GIS研究。Email: zhhan1990@126.com 通迅作者:任志远(Email: renzhy@snnu.edu.cn)

收稿日期: 2014-04-01

  修回日期: 2014-06-03

  网络出版日期: 2015-07-20

基金资助

教育部人文社会科学重点研究基地项目(14JJD840004);国家自然科学基金项目(41371523)

Remote Sensing Analysis of Vegetation Phenology Characteristics in Shaanxi Province Based on Whittaker Smoother Method

  • Zhang Han ,
  • Ren Zhiyuan
Expand
  • College of tourism and environment sciences/Center for Land Resources Research in Northwest China/Center for Historical Environment and Socio-Economic Development in Northwest China, Shaanxi Normal University, Xi'an 710062, China

Received date: 2014-04-01

  Revised date: 2014-06-03

  Online published: 2015-07-20

摘要

运用Whittaker滤波重构MODIS NDVI时序数列,利用地理探测器对比滤波前后影像信噪比,采用动态阈值法获取2000-2012年陕西省植被的3个关键物候参数(返青期、枯黄期和生长周期),在此基础上分析了该区植被物候参数空间分布特征。结果表明:(1)Whittaker滤波能够平滑原始NDVI曲线,有效减少原始影像的噪声,提高影像辨识度,并且参数设置简单;(2)陕西省植被物候地区分异明显,不同气候区划类植被物候表现出中温带半干旱区-暖温带半干旱区-暖温带半湿润区-北亚热带湿润区的递变规律:返青期逐步提前,枯黄期逐步推迟;(3)植被物候受高程和纬度影响,并且纬度影响更显著。海拔每升高200 m,返青期推迟1.3 d,枯黄期提前0.6 d;纬度每升高0.5°,返青期推迟3.6 d,枯黄期提前1.2 d。

本文引用格式

张晗 , 任志远 . 基于Whittaker滤波的陕西省植被物候特征[J]. 中国沙漠, 2015 , 35(4) : 901 -906 . DOI: 10.7522/j.issn.1000-694X.2014.00073

Abstract

This paper used Whittaker smoother to reconstruct MODIS NDVI time series and geographical detection method to compare the recognizable of images before and after filtering. Using dynamic threshold to get three phenological parameters (the start of season-SOS, end of season-EOS, and length of season-LOS) from 2000 to 2012, and then analyzed the spatial distribution characteristics of vegetation phenology in Shaanxi Province. The result shows that: (1) the Whittaker smoother with simple parameter setting can smooth the NDVI time series, reduce the noise of the original image effectively, and enhance the recognizable of images; (2) the vegetation phenology of Shaanxi Province has significant regional differentiation, and considering for different climate zones, three phenological parameters all performed the order of temperate semiarid region, warm temperate semiarid region, warm temperate semi-humid zones, north subtropical zone: SOS gradually advanced and EOS gradually postponed; (3) vegetation phenology is affected by the altitude and latitude, and the latter was more significant. SOS postponed 1.3 d and EOS advanced 0.6 d for every 200 m higher in elevation. SOS postponed 3.6 d and EOS advanced 1.2 d for every 0.5 of latitude northward.

参考文献

[1] 赵艳芬,师玮,潘伯荣,等.沙拐枣属(Calligonum L.)植物物候对长期气温变化的响应[J].中国沙漠,2014,34(3):732-739.
[2] 徐昔保,杨桂山.太湖流域1995-2010年耕地复种指数时空变化遥感分析[J].农业工程学报,2013,29(3):148-155,297.
[3] 丁明军,张镱锂,孙晓敏,等.近10年青藏高原高寒草地物候时空变化特征分析[J].科学通报,2012,57(33):3185-3194.
[4] 方修琦,余卫红.物候对全球变暖响应的研究综述[J].地球科学进展,2002,17(5):714-719.
[5] White M A,Hoffman F,Hargrove W W,et al.A global framework for monitoring phenological responses to climate change[J].Geophysical Research,2005,32:L04705.
[6] Jeong S J,Ho C H,Gim H J,et al.Phenology shifts at start vs.end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008[J].Global Change Biology,2011,17(7):2385-2399.
[7] Zhang X Y,Friedl M A,Schaaf C B,et al.Monitoring vegetation phenology using MODIS[J].Remote Sensing of Environment,2003,84(3):471-475.
[8] Beck P S A,Atzberger C,Hogda K A,et al.Improved monitoring of vegetation dynamics at very high latitudes:a new method using MODIS NDVI[J].Remote Sensing of Environment,2006,100(3):321-334.
[9] 包刚,覃志豪,包玉海,等.1982-2006年蒙古高原植被覆盖时空变化分析[J].中国沙漠,2013,33(3):918-927.
[10] Atzberger C,Eilers P H C.A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America[J].International Journal of Digital Earth,2011,4(5):365-386.
[11] 夏传福,李静,柳钦火.植被物候遥感监测研究进展[J].遥感学报,2013,17(1):1-16.
[12] Peter A M,Jeganathan C,et al.Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology[J].Remote Sensing of Environment,2012,123:400-417.
[13] Vilela M,Borges C C H,Vinga S,et al.Automated smoother for the numerical decoupling of dynamics models[J].BMC Bioinformatics,2007,8:305.
[14] 梁嘉如,陈华舟,秦强.FT-NIR光谱法与Whittaker平滑应用于土壤有机质和总氮的定量检测[J].分析试验室,2013,32(9):11-15.
[15] 贺映娜.秦岭植被物候期及遥感生长季的变化研究[D].西安:西北大学,2012.
[16] 何磊,王超,别强,等.利用MOD13Q1产品监测肯尼亚2001-2010年荒漠化动态[J].中国沙漠,2013,33(1):46-52.
[17] 郑景云,尹云鹤,李炳元.中国气候区划新方案[J].地理学报,2010,65(1):3-12.
[18] Atzberger C,Rembold F.Estimation of inter-annual winter crop area variation and spatial distribution with low resolution NDVI data by using neural nets trained on high resolution images[C].Proceedings of SPIE7472,2009.
[19] Wang J F,Li X H,Christakos G,et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the He shun Region,China[J].International Journal of Geographical Information Science,2010,24(1):107-127.
[20] 刘彦随,杨忍.中国县域城镇化的空间特征与形成机理[J].地理学报,2012,67(8):1011-1020.
[21] 梁守真,施平,邢前国.MODIS NDVI时间序列数据的去云算法比较[J].国土资源遥感,2011(1):33-36.
[22] 朱博,王新鸿,唐伶俐,等.光学遥感图像信噪比评估方法研究进展[J].遥感技术与应用,2010,25(2):303-309.
[23] 夏传福,李静,柳钦火.植被物候遥感监测研究进展[J].遥感学报,2013,17(1):1-16.
[24] Jönsson P,Eklundh L.Seasonality extraction by function fitting to time-series of satellite sensor data[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(8):1824-1932.
[25] 李登科,郭铌.陕西MODIS/NDVI的区域分布和季节变化[J].中国沙漠,2008,28(1):108-112,196.
文章导航

/