In recent years there is a drastic increase in the events of forest fire in the North-Eastern states of India mainly Mizoram. In spite of this, not much temporal study related to forest fire has been carried out in this part of the country. Forest fire events could be related to Normalized Difference Vegetation Index (NDVI); Normalized Difference Moisture Index (NDMI);Global Vegetation Moisture Index (GVMI); Aerosol Free Vegetation Index (AFRI 1600) and Land Surface Temperature (LST) which could be analyzed using Landsat 8 satellite data at every 16 days. Processing, estimating and analyzing such high temporal resolution satellite data could be easily carried out using Google Earth Engine (GEE) platform. Hence in this study we used GEE platform to carry out the temporal variation study for several indiceson surface reflectance data of Landsat 8 satellite for part of Mizoram region, India. The analysis was carried out from January 2016 to June 2021 on roughly bimonthly basis. The time series analysis of these indices showed that, in general all the indices increased during the monsoon season and reached the peak value around September and further started decreasing and attained a minimum value in April. Moreover, all indices showed deviation from the general behaviour at very few instances which could related to few forest fire events. Further random samples were collected over seven different land cover types namely settlement, farm land, dense forest, moderate forest, sparse forest, barren land and fallow land. It was observed that the forest fires were more frequent in sparse forest in comparison to moderate and dense forest. Also most fire incidents were found to happen in the month of March with certain exceptions for events in winters. The general behaviour and anomalies were appropriately explained and all the results were in consonance with the real scenarios and reported incidents. The results of this study demonstrate that an automated time series analysis using GEE can be successfully applied across diverse land cover types that will provide vital information for future monitoring of such hazards.

GEE; NDVI; NDMI; GVMI; AFRI; LST; Forest Fire