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Remote Sens. 2016, 8(3), 230; doi:10.3390/rs8030230

Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China

1
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
2
Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Academic Editors: Cheng Wang, Peter Krzystek, Wei Yao, Yong Pang, Marco Heurich, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 31 October 2015 / Revised: 6 February 2016 / Accepted: 7 March 2016 / Published: 11 March 2016
View Full-Text   |   Download PDF [3909 KB, uploaded 11 March 2016]   |  

Abstract

Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical forests, which tend to underestimate AGB predictions. This study was applied to the temperate mixed forest of the Liangshui National Nature Reserve in Northeastern China and demonstrated the capability of FOTO texture indices to obtain a higher prediction quality of forest AGB. Based on high spatial resolution aerial photos (1.0 m spatial resolution) acquired in September 2009, the relationship between FOTO texture indices and field-derived biomass measurements was calibrated using a support vector regression (SVR) algorithm. Ten-fold cross-validation was used to construct a robust prediction model, which avoided the over-fitting problem. By further comparison the performance of the model estimates for greater coverage, the predicted results were compared with a reference biomass map derived from LiDAR metrics. This study showed that the FOTO indices accounted for 88.3% of the variance in ground-based AGB; the root mean square error (RMSE) was 34.35 t/ha, and RMSE normalized by the mean value of the estimates was 22.31%. This novel texture-based method has great potential for forest AGB estimation in other temperate regions. View Full-Text
Keywords: AGB; high spatial resolution image; aerial photos; FOTO indices; SVR; temperate forest; LiDAR AGB; high spatial resolution image; aerial photos; FOTO indices; SVR; temperate forest; LiDAR
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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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Meng, S.; Pang, Y.; Zhang, Z.; Jia, W.; Li, Z. Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China. Remote Sens. 2016, 8, 230.

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