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Remote Sens. 2015, 7(5), 5057-5076; doi:10.3390/rs70505057

Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth

1
Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK
2
École française d’Extrême-Orient, Siem Reap, Cambodia
3
Department of Geography, National University of Singapore, 1 Arts Link, 117570 Singapore
4
APSARA National Authority, Angkor International Research and Documentation Centre, Siem Reap, Cambodia
5
APSARA National Authority, Department of Forestry Management, Cultural Landscape and Environment, Siem Reap, Cambodia
*
Author to whom correspondence should be addressed.
Academic Editors: Josef Kellndorfer and Prasad S. Thenkabail
Received: 17 December 2014 / Revised: 8 April 2015 / Accepted: 13 April 2015 / Published: 23 April 2015
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Abstract

This study develops a modelling framework for utilizing very high-resolution (VHR) aerial imagery for monitoring stocks of above-ground biomass (AGB) in a tropical forest in Southeast Asia. Three different texture-based methods (grey level co-occurrence metric (GLCM), Gabor wavelets and Fourier-based textural ordination (FOTO)) were used in conjunction with two different machine learning (ML)-based regression techniques (support vector regression (SVR) and random forest (RF) regression). These methods were implemented on both 50-cm resolution Digital Globe data extracted from Google Earth™ (GE) and 8-cm commercially obtained VHR imagery. This study further examines the role of forest biophysical parameters, such as ground-measured canopy cover and vertical canopy height, in explaining AGB distribution. Three models were developed using: (i) horizontal canopy variables (i.e., canopy cover and texture variables) plus vertical canopy height; (ii) horizontal variables only; and (iii) texture variables only. AGB was variable across the site, ranging from 51.02 Mg/ha to 356.34 Mg/ha. GE-based AGB estimates were comparable to those derived from commercial aerial imagery. The findings demonstrate that novel use of this array of texture-based techniques with GE imagery can help promote the wider use of freely available imagery for low-cost, fine-resolution monitoring of forests parameters at the landscape scale. View Full-Text
Keywords: above-ground biomass; Angkor Thom; Google Earth; Fourier-based textural ordination; machine learning; support vector regression; LiDAR above-ground biomass; Angkor Thom; Google Earth; Fourier-based textural ordination; machine learning; support vector regression; 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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MDPI and ACS Style

Singh, M.; Evans, D.; Friess, D.A.; Tan, B.S.; Nin, C.S. Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth. Remote Sens. 2015, 7, 5057-5076.

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