remote sensing Article 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 1,2,3,* , Nophea Sasaki 4 , Sutee Anantsuksomsri 1,2 and Benjamin Smith 5,6 1 Regional Urban and Built Environmental Analytics, Faculty of Architecture, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand;
[email protected] 2 Department of Urban and Regional Planning, Faculty of Architecture, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand 3 LEET intelligence Co., Ltd., Perfect Park, Suan Prikthai, Muang Pathum Thani, Pathum Thani 12000, Thailand 4 Natural Resources Management, School of Environment, Resources and Development, Asian Institute of Technology. P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand;
[email protected] 5 Hawkesbury Institute for the Environment, Western Sydney University, Bourke St, Richmond, Sydney, NSW 2753, Australia;
[email protected] 6 Department of Physical Geography and Ecosystem Science, Sölvegatan 12, Lund University, S-223 62 Lund, Sweden * Correspondence:
[email protected] Received: 25 July 2020; Accepted: 15 September 2020; Published: 22 September 2020 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.