Viability Statistics of GLAS/ICESat Data Acquired Over Tropical Forests

被引:8
作者
Baghdadi, Nicolas N. [1 ]
El Hajj, Mohamad [1 ]
Bailly, Jean-Stephane [2 ]
Fabre, Frederic [3 ]
机构
[1] IRSTEA, UMR TETIS, Montpellier, France
[2] AgroParisTech, UMR LISAH, F-34060 Montpellier, France
[3] EADS Astrium, F-31402 Toulouse, France
关键词
GLAS/ICESAT; tropical forests; HEIGHT; LIDAR; ICESAT;
D O I
10.1109/JSTARS.2013.2273563
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The Geoscience Laser Altimeter System (GLAS) on-board the Ice, Cloud, and land Elevation satellite (ICESat) provides a useful dataset for characterizing tropical forests. However, some GLAS data are not viable for science processing. This work aims at quantifying GLAS data viability at a global scale over all tropical forests and determining the parameters that affect this viability. The percentage of nonviable data was analyzed according to several parameters: latitude, longitude, transmitted energy, GLAS mission, local hour of acquisition, and cloud parameters. Results show that only 79.9% of all GLAS data acquired between 2003 and 2009 is viable for tropical forests characterization. By applying additional filters used by scientists in the GLAS data processing for forestry applications, only 32.8% of GLAS data acquired over tropical forests becomes exploitable. The percentage of nonviable data seems higher over the equator, for low transmitted energy, for acquisition time between 10 and 13 local hour, for high cloud humidity, and for some geographical areas. Finally, in a multi-factor approach, the Random Forest regression method demonstrated that the parameters that most significantly influence the returned LiDAR signal are transmitted energy and cloud presence index.
引用
收藏
页码:1658 / 1664
页数:7
相关论文
共 24 条
[1]   Geoscience Laser Altimeter System (GLAS) on the ICESat mission: On-orbit measurement performance [J].
Abshire, JB ;
Sun, XL ;
Riris, H ;
Sirota, JM ;
McGarry, JF ;
Palm, S ;
Yi, DH ;
Liiva, P .
GEOPHYSICAL RESEARCH LETTERS, 2005, 32 (21) :1-4
[2]   Cloud detection with MODIS. Part II: Validation [J].
Ackerman, S. A. ;
Holz, R. E. ;
Frey, R. ;
Eloranta, E. W. ;
Maddux, B. C. ;
McGill, M. .
JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2008, 25 (07) :1073-1086
[3]  
[Anonymous], DERIVATION RANGE RAN
[4]   Structure based chemical shift prediction using random forests non-linear regression [J].
Arun, K ;
Langmead, CJ .
PROCEEDINGS OF THE 4TH ASIA-PACIFIC BIOINFORMATICS CONFERENCE, 2006, 3 :317-326
[5]  
Bae S., 2002, GEOSCIENCE LASER ANT
[6]   Testing Different Methods of Forest Height and Aboveground Biomass Estimations From ICESat/GLAS Data in Eucalyptus Plantations in Brazil [J].
Baghdadi, Nicolas ;
le Maire, Guerric ;
Fayad, Ibrahim ;
Bailly, Jean Stephane ;
Nouvellon, Yann ;
Lemos, Cristiane ;
Hakamada, Rodrigo .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (01) :290-299
[7]   Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec [J].
Boudreau, Jonathan ;
Nelson, Ross F. ;
Margolis, Hank A. ;
Beaudoin, Andre ;
Guindon, Luc ;
Kimes, Daniel S. .
REMOTE SENSING OF ENVIRONMENT, 2008, 112 (10) :3876-3890
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]   ICESat validation of SRTM C-band digital elevation models [J].
Carabajal, CC ;
Harding, DJ .
GEOPHYSICAL RESEARCH LETTERS, 2005, 32 (22) :1-5
[10]   Retrieving vegetation height of forests and woodlands over mountainous areas in the Pacific Coast region using satellite laser altimetry [J].
Chen, Qi .
REMOTE SENSING OF ENVIRONMENT, 2010, 114 (07) :1610-1627