The Thai government’s project called “Eastern Economic Corridor (EEC)” was announced in 2016 to stimulate economic development and help the country escape from the middle-income trap. The project provides investment incentives for the private sector and the infrastructure development of land, rail, water, and air transportation. The EEC project encompasses three provinces in the eastern region of Thailand because of their strategic locations near deep seaports and natural resources in the Gulf of Thailand. Clearly, this policy will lead to dramatic changes in land uses and the livelihoods of the people in these three provinces. However, the extent to which land use changes will occur because of this project remains unclear. This study aims to analyze land use changes in the eastern region of Thailand using a Cellular Automata–Markov model. The results show that land uses of the coastal areas have become more urbanized than inland areas, which are primarily agricultural lands. The predicted land uses suggest shrinking agricultural lands of paddy fields, field crops, and horticulture lands but expanding perennial lands. These changes in land uses highlight challenges in urban administration and management as well as threats to Thailand’s agricultural cultures in the future.

This study examines the impacts of the new mass transit systems on the land values of residential development in the Bangkok Metropolitan Region, Thailand, using geographic information systems and spatial econometrics. The study finds that the proximity to mass transit stations spatially correlates with an increase in the prices of residential land. The benefits of the new mass transit stations, however, may not be equally distributed to the residents of Bangkok due to the lack of value-capture mechanisms such as a capital gains tax or a property tax. Policy implications regarding property taxation are also discussed.

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.

Although vegetation phenology thresholds have been developed for a wide range of mapping applications, their use for assessing the distribution of natural bamboo and the related carbon stocks is still limited, especially in Southeast Asia. Here, we used Google Earth Engine (GEE) to collect time-series of Landsat 8 Operational Land Imager (OLI) and Sentinel-2 images and employed a phenology-based threshold classification method (PBTC) to map the natural bamboo distribution and estimate carbon stocks in Siem Reap Province, Cambodia. We processed 337 collections of Landsat 8 OLI for phenological assessment and generated 121 phenological profiles of the average vegetation index for three vegetation land cover categories from 2015 to 2018. After determining the minimum and maximum threshold values for bamboo during the leaf-shedding phenology stage, the PBTC method was applied to produce a seasonal composite enhanced vegetation index (EVI) for Landsat collections and assess the bamboo distributions in 2015 and 2018. Bamboo distributions in 2019 were then mapped by applying the EVI phenological threshold values for 10 m resolution Sentinel-2 satellite imagery by accessing 442 tiles. The overall Landsat 8 OLI bamboo maps for 2015 and 2018 had user’s accuracies (UAs) of 86.6% and 87.9% and producer’s accuracies (PAs) of 95.7% and 97.8%, respectively, and a UA of 86.5% and PA of 91.7% were obtained from Sentinel-2 imagery for 2019. Accordingly, carbon stocks of natural bamboo by district in Siem Reap at the province level were estimated. Emission reductions from the protection of natural bamboo can be used to offset 6% of the carbon emissions from tourists who visit this tourism-destination province. It is concluded that a combination of GEE and PBTC and the increasing availability of remote sensing data make it possible to map the natural distribution of bamboo and carbon stocks.