ENVIRONMENT QUALITY IDENTIFICATION USING LANDSAT-8 IN THE PERIOD OF COVID-19 LOCKDOWN IN JAKARTA

Abstract

The quality of the urban environment during the Covid-19 lockdown became a concern because it was reported that it had improved but the spatial studies were still limited. Spatial information at regional scale can be extracted from Landsat-8 imagery. This study aims to spatially and temporally analyze environmental quality variables from Landsat-8 Imagery and compare environmental quality indices before, during and after the Covid-19 lockdown in Jakarta. Environmental quality variables extracted from Landsat-8 imagery are PM10, LST, NDVI, NDWI, NDMI. Radiometric correction and masking were applied to obtain Landsat-8 reflectance and radian values. PM10 concentrations were estimated using linear regression between station data and visible-near infrared (VNIR) reflectance band values. The variable land surface temperature (LST) is obtained from the brightness temperature band 10 extraction. NDVI, NDWI, and NDMI are extracted from the transformation of the reflectance band index. The environmental quality index is extracted from a weighted linear combination method where each variable has a weighted value of 50% PM10, 31% LST, 11% NDVI, 5% NDWI, and 3% NDMI. The results of the distribution of the environmental quality index before, during and after the Covid-19 lockdown show changes. Before the lockdown, some areas in Jakarta had a poor environmental quality index, while during the lockdown, only a few areas were still of poor quality, including the reclamation island and the Cilincing industrial area, North Jakarta. After the lockdown, the environmental quality index decreased again i.e. good, medium and bad categories but the distribution was not as wide as before the lockdown.