王梦佳等：Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

作者:Wang, MJ (Wang, Mengjia)[ 1,2,3 ] ; Sun, R (Sun, Rui)[ 1,2,3 ] ; Xiao, ZQ (Xiao, Zhiqiang)[ 1,2,3 ]

REMOTE SENSING

卷: 10  期: 2

文献号: 344

DOI: 10.3390/rs10020344

出版年: FEB 2018

文献类型:Article

摘要

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R-2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.

通讯作者地址: Sun, R (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Sun, R (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China.

通讯作者地址: Sun, R (通讯作者)

Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China