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

1,2, 1,*, 1,2,3,*, 4 and 5,6,7
1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Joint Center for Global Change Studies (JCGCS), Beijing 100875, China
4
Plant Functional Biology and Climate Change Cluster, University of Technology Sydney, Ultimo, NSW 2007, Australia
5
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China
6
Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China
7
School of Geographical Sciences, Nanjing Normal University, Nanjing 210023, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(9), 1329; https://doi.org/10.3390/rs10091329
Received: 21 June 2018 / Revised: 30 July 2018 / Accepted: 17 August 2018 / Published: 21 August 2018
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Abstract

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/m2/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. View Full-Text
Keywords: vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles vertical vegetation stratification; gross primary production (GPP); light use efficiency; dense forest; MODIS; VPM; temperature profiles; humidity profiles
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Lin, S.; Li, J.; Liu, Q.; Huete, A.; Li, L. Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing. Remote Sens. 2018, 10, 1329.

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