Park, J; Lee, PSH (2020). Relationship between Remotely Sensed Ambient PM10 and PM2.5 and Urban Forest in Seoul, South Korea. FORESTS, 11(10), 1060.

Currently particulate matter (PM) is one of the major threats to public health and safety in urban areas such as Seoul, South Korea. The limited amount of air-quality monitoring systems may not provide sufficient data or coverage, in particular on the spots of urban forest. Considering urban forest as a possible contributor to mitigate PM in an urban area, this study investigated the relationship between the size and topography of urban forests near the air-quality monitoring stations and PM measurements from those stations. The average of PM measurements during the study period of August 2017 to July 2019 was computed into three different domains by using three concentric buffers from 25 monitoring stations distributed across Seoul. To estimate PM concentrations, multiple linear regression models were developed by using satellite-borne multi-spectral band data retrieved from Moderate Resolution Imaging Spectroradiometer onboard Terra (MODIS) and Landsat 8 in conjunction with meteorological data sets. Overall, PM10 and PM2.5 measurements significantly varied with season and tended to be lower with large urban forests than small ones by 5.3% for PM10 and 4.8% for PM2.5. Overall, PM10 and PM2.5 measurements were lower at the domains encompassing high urban forests in elevation than those of relatively flattened forests by 9.1% for PM10 and 3.9% for PM2.5. According to the findings from this study, the topographical difference among urban forests could exert a more significant influence on PM mitigation. The result from correlation analysis between the PM estimates from Landsat 8-based models and ground-based PM measurements was considered reliable based on Pearson's coefficients of 0.21 to 0.74 for PM10 and -0.33 to 0.74 for PM2.5. It was considered that using a satellite imagery-derived PM model could be effective to manage urban forest over a large area which in general implies the limitation of data collection.