Utilizing MODIS Fire Mask for Predicting Forest Fires Using Landsat-9/8 and Meteorological Data

Abstract

Recent years have seen some of the largest forest fires ever, including the 2020 California megafires and the Australian bushfires, causing billions of dollars in property damage and destroying millions of acres of green reserves. The subject of forest fires becomes even more alarming when viewed in conjunction with the increasingly concerning problems of climate change and global warming. The planning regarding prevention and mitigation of forest fires and management of nearby areas can greatly benefit from an accurate prediction model. The objective of this study is to develop deep learning models which use satellite images and meteorological data to pinpoint potential fires at a pixel granularity. Data from the recently launched Landsat-8 and Landsat-9 satellite systems have been used to predict forest fires at a spatial resolution of 30m. The proposed solution uses the comprehensive geographical, meteorological, and MODIS-based fire history of the region, integrated from different data sources with pixel-level reprojection, as a multivariate time series (MVTS) to model the prediction problem as a binary classification problem. We adopt an encoder-classifier architecture: the BiLSTM-attention-based encoder is trained with supervised contrastive learning, while the fully-connected classifier is optimized against a weighted loss for increased recall. Our experiments demonstrate that the proposed model is robust to spatial and temporal variations in occurrence of fires, thereby making its deployment possible in any region of the world. With a mean AUC of 0.99, our proposed model outperforms the existing forest fire prediction models.