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Article

Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data

by 1,2, 1,2,*, 3 and 1,2
1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing 100875, China
3
Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lalit Kumar, Onisimo Mutanga, Nicolas Baghdadi and Prasad S. Thenkabail
Remote Sens. 2017, 9(4), 341; https://doi.org/10.3390/rs9040341
Received: 21 December 2016 / Revised: 29 March 2017 / Accepted: 31 March 2017 / Published: 2 April 2017
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing’anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model. View Full-Text
Keywords: forest above ground biomass (AGB); random forest; mapping forest above ground biomass (AGB); random forest; mapping
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MDPI and ACS Style

Liu, K.; Wang, J.; Zeng, W.; Song, J. Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data. Remote Sens. 2017, 9, 341. https://doi.org/10.3390/rs9040341

AMA Style

Liu K, Wang J, Zeng W, Song J. Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data. Remote Sensing. 2017; 9(4):341. https://doi.org/10.3390/rs9040341

Chicago/Turabian Style

Liu, Kaili; Wang, Jindi; Zeng, Weisheng; Song, Jinling. 2017. "Comparison and Evaluation of Three Methods for Estimating Forest above Ground Biomass Using TM and GLAS Data" Remote Sens. 9, no. 4: 341. https://doi.org/10.3390/rs9040341

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