多云雾地区高时空分辨率植被覆盖度构建方法研究

doi:10.11873/j.issn.1004-0323.2016.3.0518

遥感技术与应用

2016, Vol. 31

Issue (3): 518-529 DOI: 10.11873/j.issn.1004-0323.2016.3.0518

数据与图像处理

多云雾地区高时空分辨率植被覆盖度构建方法研究

陈阳^１,２,范建容^３,张云^１,２,李胜^１,２,甘泉^１,２,应国伟^１,２,曹伟超^１,２

(１．四川省地理国情监测工程技术研究中心,四川成都　６１０５００;
２．四川省第三测绘工程院,四川成都　６１０５００;
３．中国科学院水利部成都山地灾害与环境研究所,四川成都　６１００４１)

Research on Constructing Vegetation Fractional Coverage with Higher Spatial and Temporal Resolution in Cloudy and Foggy Region

Chen Yang^1,2,Fan Jianrong³,Zhang Yun^1,2,Li Sheng^1,2,Gan Quan^1,2,Ying Guowei^1,2,Cao Weichao^1,2

(1.Geographic National Condition Monitoring Engineering Research Center of
Sichuan Province,Chengdu 610500, China;
2.The Third Surveying and Mapping Engineering Institute of Sichuan,Chengdu 610500,China;
3.Institute of Mountain Hazards and Environment,Chinese Academy of Sciences &Ministry
of Water Conservancy,Chengdu 610041,China)

全文: PDF(18580 KB)

摘要：

针对多云雾地区高时空分辨率数据缺乏现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法.首先,通过时空适应反射率融合模型(STARFM)有效地将TM 的较高空间分辨率与MODIS的高时间分辨率融合在一起,构建了研究区植被生长峰值阶段的NDVI数据;然后,以植被生长峰值阶段的NDVI为输入,基于地表覆被类型,综合应用等密度和非密度亚像元模型对研究区的植被覆盖度进行估算.结果表明:①即使数据源存在大量的云雾,且存在一定的时相差异,研究区植被覆盖度的估算结果过渡自然,不存在明显的不接边效应;②以植被生长峰值阶段的NDVI数据为输入进行植被覆盖度估算,有效拉开了同一地表覆被类型不同覆盖度像元的NDVI梯度,提高了亚像元估算模型对输入数据的抗扰动性;③基于地表覆被类型,应用亚像元混合模型,能够提高植被覆盖度的估算精度.经野外实测数据验证,总体约８５％的估算精度表明,针对高时空分辨率遥感数据缺乏的多云雾区域,本研究提出的方法能够实现区域尺度植被覆盖度数据的构建.

关键词： 高时空分辨率; 区域尺度; STARFM; 亚像元模型; 植被覆盖度

Abstract:

Focusing on the cloudy and foggy region lacked of remote sensing data with highspatial and temporal resolution,a method of constructing vegetationfractional coveragewith high spatial and temporal resolution,on region scale has been proposed,in this paper.First,normalized difference vegetation index (NDVI) data with higher spatial andtemporal resolution was constructed by combining advantages both of TM and MODIS usingspatial and temporal adaptive reflectance fusion model (STARFM).Then,based on the land coverage typedata and NDVI data in the peak stage of vegetation growth,the vegetation fractional coverageof study area was estimated by density sub\|pixel model andnon\|density sub\|pixel model.The result shows that:①Even the data sources with a lot of snow and cloud or shoot at different times,in the estimated vegetation fractional coverage image,the color of the area covered by cloud or it’s shade is consistent with the color of area uncontaminated;②The normalizeddifference vegetation index (NDVI) data,in the peak stage of vegetation growth,as input dataimproved the anti\|disturbance to input data of sub\|pixel mixed model estimates of vegetationcoverage by maximum the NDVI difference of same vegetation with differentvegetation fractional coverage;③Based on land coverage using sub\|pixel model can improve the accuracy ofestimating vegetation fractional coverage.Validated by the data measured in the field,the accuracy of estimated vegetation fractional coverage isabout 85%,which suggest that it is viable to estimate vegetation fractional coverage in large regions,especially lacking of remote sensing data with high spatial and temporal resolution.

Key words: High spatial and temporal resolution;Region scale;STARFM;Sub\ pixel model;Vegetation fractional coverage

收稿日期: 2015-04-13 出版日期: 2016-07-19

TP７９　

基金资助:

四川省测绘地理信息局科技支撑项目“基于解译知识库的面向对象信息提取技术在地里国(省)情地表覆盖解译中的应用研究”(J２０１３ZC０３),四川省测绘地理信息局科技支撑项目“地理国情监测支持下的山区公路沿线生态地质环境承载力研究”(J２０１４ZC０３).

通讯作者: 范建容(１９６９－),女,四川井研人,研究员,主要从事生态与灾害遥感研究、山地土壤侵蚀研究.Email:fjrong＠imde．ac．cn.

作者简介: 陈阳(１９８７－),男,四川仪陇人,助理工程师,主要从事GIS与生态遥感应用研究.Email:sunshine１９８７６１＠１６３．com.

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引用本文:

陈阳,范建容,张云,李胜,甘泉,应国伟,曹伟超. 多云雾地区高时空分辨率植被覆盖度构建方法研究[J]. 遥感技术与应用, 2016, 31(3): 518-529.

Chen Yang,Fan Jianrong,Zhang Yun,Li Sheng,Gan Quan,Ying Guowei,Cao Weichao. Research on Constructing Vegetation Fractional Coverage with Higher Spatial and Temporal Resolution in Cloudy and Foggy Region. Remote Sensing Technology and Application, 2016, 31(3): 518-529.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2016.3.0518 或 http://www.rsta.ac.cn/CN/Y2016/V31/I3/518

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