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

Minerva Singh,Chan Nin,Damian Evans,Boun Tan,Daniel Friess

doi:10.3390/rs70505057

Minerva Singh, Chan Nin + Show 3 more

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https://doi.org/10.3390/rs70505057

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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.

Highlights

Tropical forests cover only 6% of the Earth’s surface, they are a crucial reservoir of biodiversity, providing a multitude of ecosystem services from the local to the global scale, such as carbon storage and the provision of timber and non-timber forest products to local communities
This study examined the utility of three-band remote sensing imagery obtained from two different sources—50-cm resolution Digital Globe imagery and 8-cm very high-resolution (VHR)
The grey level co-occurrence metrics (GLCMs) method achieved strong to very strong positive correlations between support vector regression (SVR)-predicted above-ground biomass (AGB)

Summary

Introduction

Tropical forests cover only 6% of the Earth’s surface, they are a crucial reservoir of biodiversity, providing a multitude of ecosystem services from the local to the global scale, such as carbon storage and the provision of timber and non-timber forest products to local communities. Tropical forest loss has been especially severe in Southeast Asia [2], especially in countries with turbulent political histories that experienced several decades of war (e.g., Cambodia, Laos and Vietnam). Payments for ecosystem service (PES) instruments, such as Reduced Emissions from Degradation and Deforestation (REDD+), provide mechanisms to incentivise tropical forest conservation and avoid deforestation through payments that protect forest carbon stocks. Remote sensing data play an important role in landscapescale AGB estimation and the subsequent evaluation of land use change impacts on AGB stocks [6]. Google EarthTM (GE) has offered free public access to VHR Digital

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