Alan B. NASA Map Sees Earth’s Trees in a New Light. 2012. Available: http://www.nasa.gov/topics/earth/features/forest20120217.html
[2]
Bartholomé E, Belward A S. 2005. GLC2000: A new approach to global land covers mapping from Earth observation data. Int J Remote Sens, 26: 1959-1977
[3]
Brown S, Schroeder P E. 1999. Spatial patterns of aboveground production and mortality of woody biomass for eastern US forests. Ecol Appl, 9: 968-980
[4]
Clapk M L, CLark D B, Roberts D A. 2004. Small footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sens Environ, 91: 68-89
[5]
Fang H, Liang S. 2003. Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Trans Geosci Remote Sens, 41: 2052-2062
[6]
Friedl M A, McIver D K, Hodges J C F, et al. 2002. Global land cover mapping from MODIS: Algorithms and early results. Remote Sens Environ, 83: 287-302
[7]
Gao F, Schaaf C B, Strahler A H, et al. 2003. Detecting vegetation structure using a Kernel-based BRDF Model. Remote Sens Environ, 86: 198-205
[8]
Hansen M C, DeFries R S, Townshend J R G, et al. 2000. Global land and cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens, 21: 1331-1364
[9]
Heiskanen J. 2006. Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data. Remote Sens Environ, 103: 503-511
[10]
Hyde P, Nelson R, Kimes D, et al. 2007. Exploring LIDAR-Radar synergy predicting above ground biomass in a south western Ponderosa pine forest using LIDAR, SAR and InSAR. Remote Sens Environ, 105: 28-38
[11]
Jiao Z, Li X, Wang J, et al. 2011. Assessment of MODIS BRDF shape indicators. J Remote Sens, 15: 432-443
[12]
Kimes D S, Ranson K J, Sun G Q, et al. 2006. Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sens Environ, 100: 503-511
[13]
Lee J. 2006. ICESat (GLAS) Science Processing Software Document Series-GSAS User′s Guide (V6.0). Available: http://nsidc.org/data/docs/ daac/glas_ancillary_products.html
[14]
Wang Z S, Crystal B S, Philip L, et al. 2011. Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data. Remote Sens Environ, 115: 1595-1601
[15]
Wang C, Glenn F N. 2008. A linear regression method for tree canopy height estimation using airborne LiDAR data. Can J Remote Sens, 34: 217-227
[16]
Xing Y Q, Alfred D Gr, Zhang J J, et al. 2010. An improved method for estimating forest canopy height using ICESat-GLAS full waveform data over sloping terrain: A case study in Changbai Mountains, China. Int J Appl Earth Obs, 12: 385-392
[17]
Zhang J, Rivard B, Sánchez-Azofeifa A, et al. 2006. Intra-and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery. Remote Sens Environ, 105: 129-141
Abshire J B, Sun X, Riris H, et al. 2005. Geoscience Laser Altimeter System (GLAS) on the ICESat mission: On-orbit measurement performance. Geophys Res Lett, 32: 1534-1536
[21]
Cloude S R, Papathanassiou K P. 1998. Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens, 36: 1551-1565
[22]
De Jong S M, Pebesma E J, Lacaze B. 2003. Above-ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: The DAIS Peyne experiment. Int J Remote Sens, 24: 1505-1520
[23]
Deng F, Chen J.M, Plununer S, et al. 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans Geosci Remote Sens, 44: 2219-2229
[24]
Lefsky M A, Harding D J, Keller M, et al. 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys Res Lett, 32, doi: 10.1029/2005GL023971
[25]
Lefsky M A, Keller M, Pang Y, et al. 2007. Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms. J Appl Remote Sens, 1, doi: 10.1117/1.2795724
[26]
Loveland T R, Reed B C, Brown J F, et al. 2000. Development of a global land covers characteristics database and IGBP DISCover from 1 km AVH RR data. Int J Remote Sens, 21: 1303-1330
[27]
Neumann M, Ferro F L, Reigber A. 2009. Improvement of Vegetation Parameter Retrieval from Polarimetric SAR Interferometry using a Simple Polarimetric Scattering Model. 4th POLinSAR Workshop
[28]
Nilsson M. 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ, 56: 1-7
[29]
Pang Y, Lefsky M A, Hans-Erik A, et al. 2008b. Validation of the ICEsat vegetation product using crown-area-weighted mean height derived using crown delineation with discrete return lidar data. Can J Remote Sens, 34: 471-484
[30]
Ran Y H, Li X, Lu L, et al. 2012. Large-scale land cover mapping with the integration of multi-source information based on the Dempster-Shafer theory. Int J Geogr Inf Sci, 26: 169-191
[31]
Ran Y H, Li X, Lu L. 2010. Evaluation of four remote sensing based land cover products over China. Int J Remote Sens, 31: 391-401
[32]
Salomonson V V, Barnes W, Xiong J, et al. 2002. An overview of the Earth Observing System MODIS instrument and associated data systems performance. In: Geoscience and Remote Sensing Symposium. IGARSS''02. IEEE International, 2: 1174-1176
[33]
Sun G Q, Ranson K J, Zhang Z J, 2006. Forest vertical parameters from lidar and multi-angle imaging spectrometer data. J Remote Sens, 10: 523-530
[34]
Tighe M L, Chamberlain D. 2009. Accuracy Comparison of the SRTM, ASTER, NED, NEXTMAP？ USA Digital Terrain Model over Several USA Study Sites. In: Proceedings of the ASPRS/MAPPS 2009 Fall Conference
[35]
Wang C, Tang F X, Li L W, et al. 2013. Wavelet analysis for ICESat/GLAS waveform decomposition and its application in average tree height estimation. IEEE Geosci Remote Sens Lett, 10: 115-119
