Forest canopy height mapping over China using GLAS and MODIS data

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• 作者：Ting Yang (1) (2)
  Cheng Wang (1)
  GuiCai Li (3)
  SheZhou Luo (1)
  XiaoHuan Xi (1)
  Shuai Gao (4)
  HongCheng Zeng (5)
  
  1. Key Laboratory of Digital Earth Science ; Institute of Remote Sensing and Digital Earth ; Chinese Academy of Sciences ; Beijing ; 100094 ; China
  2. University of Chinese Academy of Sciences ; Beijing ; 100049 ; China
  3. National Satellite Meteorological Center ; China Meteorological Administration ; Beijing ; 100081 ; China
  4. National Key Laboratory of Remote Sensing ; Institute of Remote Sensing and Digital Earth ; Chinese Academy of Sciences ; Beijing ; 100101 ; China
  5. Faculty of Foretry ; University of Toronto ; Ontario ; M5S3B3 ; Canada
  
• 关键词：GLAS ; waveform decomposition ; terrain index ; canopy height model
• 刊名：Science China Earth Sciences
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：58
• 期：1
• 页码：96-105
• 全文大小：2,098 KB
• 参考文献：1. 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鈥?536 CrossRef
  2. Bartholom茅 E, Belward A S. 2005. GLC2000: A new approach to global land covers mapping from Earth observation data. Int J Remote Sens, 26: 1959鈥?977 CrossRef
  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鈥?80
  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鈥?9 CrossRef
  5. Cloude S R, Papathanassiou K P. 1998. Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sensing, 36: 1551鈥?565 CrossRef
  6. 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鈥?520 CrossRef
  7. Deng F, Chen J M, Plununer S, et al. 2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans Geosci Remote Sensing, 44: 2219鈥?229 CrossRef
  8. Dong L X, Li G C, Tang S H. 2011. Estimation of forest canopy height by integrating GLAS and FY3A-MERSI data in Jiangxi Mountain (in Chinese). J Remote Sens, 15: 1308鈥?314
  9. Fang H, Liang S. 2003. Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Trans Geosci Remote Sensing, 41: 2052鈥?062 CrossRef
  10. 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鈥?02 CrossRef
  11. 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鈥?05 CrossRef
  12. 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鈥?364 CrossRef
  13. Heiskanen J. 2006. Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data. Remote Sens Environ, 103: 503鈥?11 CrossRef
  14. 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鈥?8 CrossRef
  15. Jiao Z, Li X, Wang J, et al. 2011. Assessment of MODIS BRDF shape indicators. J Remote Sens, 15: 432鈥?43
  16. 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鈥?11 CrossRef
  17. 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: 1鈥? CrossRef
  18. 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: 1鈥?8
  19. 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鈥?330 CrossRef
  20. 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
  21. Nilsson M. 1996. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ, 56: 1鈥? CrossRef
  22. Pang Y, Zhao F, Li Z Y, et al. 2008a. Forest height inversion using airborne Lidar technology (in Chinese). J Remote Sens, 12: 152鈥?58
  23. 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鈥?84 CrossRef
  24. 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鈥?91 CrossRef
  25. 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鈥?01 CrossRef
  26. 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鈥?02. IEEE International, 2: 1174鈥?176 CrossRef
  27. 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鈥?30
  28. Tighe M L, Chamberlain D. 2009. Accuracy comparison of the SRTM, ASTER, NED, NEXTMAP庐 USA digital terrain model over several USA study sites. Procof the ASPRS/MAPPS 2009 Fall Conference
  29. 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鈥?19 CrossRef
  30. 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鈥?601 CrossRef
  31. 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鈥?27 CrossRef
  32. 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鈥?92 CrossRef
  33. Zhang J, Rivard B, S谩nchez-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鈥?41 CrossRef
  
• 刊物主题：Earth Sciences, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：1869-1897

文摘

The Geoscience Laser Altimeter System (GLAS) accurately detects the vertical structural information of a target within its laser spot and is a promising system for the inversion of structural features and other biophysical parameters of forest ecosystems. Since the GLAS footprints are discontinuously distributed with a relativity low density, continuous vegetation height distributions cannot be mapped with a high accuracy using GLAS data alone. The MODIS BRDF product provides more forest structural information than other optical remote sensing data. This study aimed to map forest canopy heights over China from the GLAS and MODIS BRDF data. Firstly, the waveform characteristic parameters were extracted from the GLAS data by the method of wavelet analysis, and the terrain index was calculated using the ASTER GDEM data. Secondly, the model reducing the topographic influence was constructed from the waveform characteristic parameters and terrain index. Thirdly, the final canopy height estimation model was constructed from the neural network combining the canopy height estimated with the GLAS point and the MODIS BRDF data, and applied to get the continuous canopy height map over China. Finally, the map was validated by the measured data and the airborne LiDAR data, and the validation results indicated that forest canopy heights can be estimated with high accuracy from combined GLAS and MODIS data.