Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2009, Vol. 21 Issue (3) : 19-23     DOI: 10.6046/gtzyyg.2009.03.04
Technology and Methodology |
RESEARCHES ON THE DIGITAL ELEVATION MODEL
EXTRACTION METHOD BASED ON ALOS PALSAR DATA
NI Wen-jian 1,2, GUO Zhi-feng 1, SUN Guo-qing 3
1.Institute of Remote Sensing Application,Chinese Academy of Science,Beijing 100101,China;
2.Graduate School of Chinese Acadermy of Science,Beijing 100049,China;
3.Department of Geography,University of Maryland,College Park,MD 20742, USA
Download: PDF(5498 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

In the past decades, researchers have successfully rebuilt the digital elevation model (DEM) using such Interferometric synthetic aperture radar data (InSAR) as SIR-C/X SAR and ERS1/2. As a new generation of synthetic aperture radar, Phased Array type L-band Synthetic Aperture Radar (PALSAR) , which is onboard Advanced Land Observing Satellite (ALOS), works at a longer wave length-L band. Its penetrating depth is deeper than the radars that work at C band. Thus it has advantages in the construction of DEM. However, there have been few reports about the DEM extraction from this technology. The open source program-package ROI_PAC version3.0 provided by NASA/JPL can be used to rebuild DEM from PALSAR Level 1.0 data that is not calibrated. Therefore, ROI_PAC version 3.0 was modified in this study to make it rebuild DEM from PALSAR Level 1.1 data. The workflow of ROI_PAC was described. The method introduced in this paper was validated by a set of PALSAR Level 1.1 data. A comparison between InSAR DEM and reference DEM was made. The difference between them is 0.27 m, with a standard deviation of 9.24 m. There are more than 80% pixels having height errors within 10 m. The results show that the method proposed in this study is useful.

Keywords TM image      Nanxiang basin      Gas and oil forecast     
: 

TP 722.6

 
Issue Date: 04 September 2009
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cite this article:   
NI Wen-Jian, GUO Zhi-Feng, SUN Guo-Qing. RESEARCHES ON THE DIGITAL ELEVATION MODEL
EXTRACTION METHOD BASED ON ALOS PALSAR DATA[J]. REMOTE SENSING FOR LAND & RESOURCES,2009, 21(3): 19-23.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2009.03.04     OR     https://www.gtzyyg.com/EN/Y2009/V21/I3/19
[1] TIAN Lei, FU Wenxue, SUN Yanwu, JING Linhai, QIU Yubao, LI Xinwu. Research on spatial change of the boreal forest cover in Siberia over the past 30 years based on TM images[J]. Remote Sensing for Land & Resources, 2021, 33(1): 214-220.
[2] GUO Qiaozhen, NING Xiaoping, WANG Zhiheng, JIANG Weiguo. Impact analysis of landform for land use dynamic change of the partly mountainous area: A case study of Jixian County in Tianjin City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 153-159.
[3] XU Xu, REN Feipeng, HAN Nianlong. Remote sensing monitoring of spatio-temporal changes of ecosystem service values in Hebei Province, 2000—2009[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 187-193.
[4] LIU Juan, CAI Yanjun, WANG Jin. Soil classification of Qinghai Lake basin based on remote sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(1): 57-62.
[5] XU Chao, ZHAN Jinrui, PAN Yaozhong, ZHU Wenquan. Extraction of cropland information based on multi-temporal TM images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(4): 166-173.
[6] QIN Yan, DENG Ru-ru, HE Ying-qing, CHEN Lei, CHEN Qi-dong, XIONG Shou-ping. Algorithm for Removing Thick Clouds in TM Image Based on Spectral and Geometric Information[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 55-61.
[7] SHEN Jin-xiang, YANG Liao, CHEN Xi, LI Jun-li, PENG Qing-qing, HU Ju. A Method for Object-oriented Automatic Extraction of Lakes in the Mountain Area from Remote Sensing Image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 84-91.
[8] CHEN Lei, DENG Ru-ru, CHEN Qi-dong, HE Ying-qing, QIN Yan, LOU Quan-sheng. The Extraction of Water Body Information from TM Imagery Based on Water Quality Types[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(1): 90-94.
[9] ZHANG Zhi-xin, DENG Ru-ru, LI Hao, CHEN Lei, CHEN Qi-dong, HE Ying-qing. Remote Sensing Monitoring of Vegetation Coverage in Southern China Based on Pixel Unmixing: A Case Study of Guangzhou City[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(3): 88-94.
[10] LIU Yan, DING Tao- , RUAN Hui-Hua, LIN Na. The Monitoring of Land Desertification in the Manasi River Basin Based
on Multi-source Remotely Sensed Data
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(1): 81-84.
[11] SUN Yong-Jun, TONG Qing-Xi, QIN Qi-Ming. THE OBJECT-ORIENTED METHOD FOR WETLAND INFORMATION EXTRACTION[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(1): 79-82.
[12] ZHANG Li-Su, WU Jia-Ping. REGIONAL LAND USE/COVER CLASSIFICATION WITH
A STRATIFIED AND REGIONALIZED APPROACH:
A CASE STUDY IN QIANTANG RIVER WATERSHED, ZHEJIANG PROVINCE
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2007, 19(3): 74-77.
[13] QI Zhi-Xin, DENG Ru-Ru. THE ATMOSPHERIC CORRECTION METHOD FOR NONHOMOGENEOUS
ATMOSPHERE BASED ON MANY DARK OBJECTS
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2007, 19(2): 16-19.
[14] GAO Zhong-Ling, WANG Xiao-Qin, ZHOU Xiao-Cheng. THE EXTRACTING OF FIRE SCARS FROM TM IMAGE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2005, 17(4): 38-41.
[15] DONG Yan-Fang, SUN Guo-Qing, PANG Yong, FU An-Min. A COMPARITIVE STUDY OF SOME ATMOSPHERIC CORRECTION METHODS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2005, 17(2): 16-19.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech