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

Remote Sensing

Minerva Singh²  Damian Evans¹  Daniel A. Friess³  Boun Suy Tan⁴  Chan Samean Nin⁵  Josef Kellndorfer⁶ 

[1] École française d’Extrême-Orient, Siem Reap, Cambodia; E-Mail:;Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, UK;Department of Geography, National University of Singapore, 1 Arts Link, 117570 Singapore; E-Mail:;APSARA National Authority, Angkor International Research and Documentation Centre, Siem Reap, Cambodia; E-Mail:;APSARA National Authority, Department of Forestry Management, Cultural Landscape and Environment, Siem Reap, Cambodia; E-Mail:;id="af1-remotesensing-07-05057">1Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA,

关键词: above-ground biomass;    Angkor Thom;    Google Earth;    Fourier-based textural ordination;    machine learning;    support vector regression;    LiDAR;   

DOI  :  10.3390/rs70505057

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.