Applications of the Google Earth Engine and Phenology-Based Threshold Classification Method for Mapping Forest Cover and Carbon Stock Changes in Siem Reap Province, Cambodia

Manjunatha Venkatappa,Sutee Anantsuksomsri,Nophea Sasaki,Benjamin Smith

doi:10.3390/rs12183110

Manjunatha Venkatappa, Sutee Anantsuksomsri + Show 2 more

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https://doi.org/10.3390/rs12183110

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Abstract

Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115 million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.

Highlights

The rapid loss of forest cover and acceleration of forest degradation in the tropics have been caused by land clearing, burning, and overexploitation [1,2]
The details of the forest land cover category’s overall accuracies (OA), Kappa coefficients (K), producer’s accuracies (PA) and user accuracies (UA) of the maps are presented in Tables 5 and 6
Our study found that the phenology-based threshold classification method (PBTC) mapping accuracy (Tables A1–A7) is higher than that of previous studies in the region (FREL, 2016) and our results show a significant decrease in forested land in Siem Reap province from 1990 to 2018 (Table A10)

Summary

Introduction

The rapid loss of forest cover and acceleration of forest degradation in the tropics have been caused by land clearing, burning, and overexploitation [1,2]. A recent study using remote sensing technology revealed that tropical forests have been degraded drastically since 2000 [3]. Annual losses of tropical forests were 7.8 million ha in 1990–2000, 5.2 million ha in 2000–2010, and 4.7 million ha in 2010–2020 [6]. Such losses and forest degradation account for about 20% of total global emissions, and represent the second largest source of global emissions [7]

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