Extraction and Inversion of deciduous broad-leaved forest Based on HJ-CCD Remote Sensing Data

被引：0

作者：

Wang Yan ^{[1
]}

Tian Qingjiu ^{[1
]}

Huang Yan ^{[1
]}

机构：

[1] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210008, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF THE 2013 THE INTERNATIONAL CONFERENCE ON REMOTE SENSING, ENVIRONMENT AND TRANSPORTATION ENGINEERING (RSETE 2013) | 2013年 / 31卷

关键词：

HJ-CCD; deciduous broad-leaved forest; NDVI; poplar forest; LAI;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, Chuzhou area in Anhui province was taken as the study area, and deciduous broad-leaved forest as the study object. The NDVI was obtained based on HJ-CCD remote sensing images acquired on April 1,2012 and May 4,2012 when broad-leaved forest was respectively in leaf expansion and flowering. Next, combined with the position information collected in field, the recognition model for deciduous broad-leaved forest was proposed with NDVI difference rate between leaf expansion and flowering. And then deciduous broad-leaved forest in the study area was extracted effectively and the result was verified. Finally, poplar forest was taken as an example, and then the LAI inversion was carried out and the result was verified by using the LAI data obtained in field combined with poplar forest data information collected at the plot. The results show the validity of NDVI difference rate recognition method proposed in this paper and also verify the advantages of LAI inversion for poplar forest using HJ-CCD data.

引用

页码：195 / 199

页数：5

共 27 条

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