Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing

作者:Lin, SR (Lin, Shangrong)[ 1,2 ] ; Li, J (Li, Jing)[ 1 ] ; Liu, QH (Liu, Qinhuo)[ 1,2,3 ] ; Huete, A (Huete, Alfredo)[ 4 ] ; Li, LH (Li, Longhui)[ 5,6,7 ]

REMOTE SENSING

卷: 10  期: 9

文献号: 1329

DOI: 10.3390/rs10091329

出版年: SEP 2018

摘要

Gross primary production (GPP) in forests is the most important carbon flux in terrestrial ecosystems. Forest ecosystems with high leaf area index (LAI) values have diverse species or complex forest structures with vertical stratifications that influence the carbon-water-energy cycles. In this study, we used three light use efficiency (LUE) GPP models and site-level experiment data to analyze the effects of the vertical stratification of dense forest vegetation on the estimates of remotely sensed GPP during the growing season of two forest sites in East Asia: Dinghushan (DHS) and Tomakomai (TMK). The results showed that different controlling environmental factors of the vertical layers, such as temperature and vapor pressure deficit (VPD), produce different responses for the same LUE value in the different sub-ecosystems (defined as the tree, shrub, and grass layers), which influences the GPP estimation. Air temperature and VPD play important roles in the effects of vertical stratification on the GPP estimates in dense forests, which led to differences in GPP uncertainties from -50% to 30% because of the distinct temperature responses in TMK. The unequal vertical LAI distributions in the different sub-ecosystems led to GPP variations of 1-2 gC/m(2) /day with uncertainties of approximately -30% to 20% because sub-ecosystems have unique absorbed fractions of photosynthetically active radiation (APAR) and LUE. A comparison with the flux tower-based GPP data indicated that the GPP estimations from the LUE and APAR values from separate vertical layers exhibited better model performance than those calculated using the single-layer method, with 10% less bias in DHS and more than 70% less bias in TMK. The precision of the estimated GPP in regions with thick understory vegetation could be effectively improved by considering the vertical variations in environmental parameters and the LAI values of different sub-ecosystems as separate factors when calculating the GPP of different components. Our results provide useful insight that can be used to improve the accuracy of remote sensing GPP estimations by considering vertical stratification parameters along with the LAI of sub-ecosystems in dense forests.

通讯作者地址: Li, J; Liu, QH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

通讯作者地址: Liu, QH (通讯作者)

Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.

通讯作者地址: Liu, QH (通讯作者)

JCGCS, Beijing 100875, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China

[ 3 ] JCGCS, Beijing 100875, Peoples R China

[ 4 ] Univ Technol Sydney, Plant Funct Biol & Climate Change Cluster, Ultimo, NSW 2007, Australia

[ 5 ] Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China

[ 6 ] Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China

[ 7 ] Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China