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Forests 2017, 8(8), 288; doi:10.3390/f8080288

Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship

1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA
4
School of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China
5
College of Applied Sciences and Humanities of Beijing Union University, Beijing 100083, China
6
Haihe basin Soil and Water Conservation Monitor Centre, Tianjing 300171, China
7
Survey Planning and Design Institute, State Forest Administration of China, Beijing 100714, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 30 June 2017 / Revised: 31 July 2017 / Accepted: 1 August 2017 / Published: 8 August 2017
View Full-Text   |   Download PDF [2467 KB, uploaded 8 August 2017]   |  

Abstract

This study develops a modeling framework for utilizing the large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and Land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-Radiometer (MODIS) imagery, meteorological data, and forest measurements for monitoring stocks of total biomass (including aboveground biomass and root biomass). The forest tree height models were separately used according to the artificial neural network (ANN) and the allometric scaling and resource limitation (ASRL) tree height models which can both combine the climate data and satellite data to predict forest tree heights. Based on the allometric approach, the forest aboveground biomass model was developed from the field measured aboveground biomass data and the tree heights derived from two tree height models. Then, the root biomass should scale with the aboveground biomass. To investigate whether this approach is efficient for estimating forest total biomass, we used Northeast China as the object of study. Our results generally proved that the method proposed in this study could be meaningful for forest total biomass estimation (R2 = 0.699, RMSE = 55.86). View Full-Text
Keywords: forest aboveground biomass; root biomass; tree heights; GLAS; artificial neural network; allometric scaling and resource limitation forest aboveground biomass; root biomass; tree heights; GLAS; artificial neural network; allometric scaling and resource limitation
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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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MDPI and ACS Style

Ni, X.; Cao, C.; Zhou, Y.; Ding, L.; Choi, S.; Shi, Y.; Park, T.; Fu, X.; Hu, H.; Wang, X. Estimation of Forest Biomass Patterns across Northeast China Based on Allometric Scale Relationship. Forests 2017, 8, 288.

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