Vegetation Change Detection and Recovery Assessment on Post-fire Satellite Imagery using Deep Learning

Abstract

Wildfire are uncontrolled fires fueled by dry conditions, high winds and flammable materials that tends to have a profound impact on vegetation due to the intense heat generated by it which can cause the destruction of trees, small plants and other vegetation leading to significant consequences including noteworthy changes to ecosystems. Due to the periodic wildfires, vegetation communities in forest systems have changed adaptively to deal with ecological rebuilding. In this study we provide a novel methodology, to understand and evaluate post-fire effects on vegetation. In regions which are affected by wildfire, earth-observation data provided by various satellite sources can be very vital in monitoring vegetation and assessing the effect a wildfire tends to have on it. These effects can be understood by detecting the change of vegetation over years using an unsupervised method termed Deep Embedded Clustering (DEC), which enables us to classify regions on whether there has been a change in vegetation after fire. Appropriate vegetation indices can be used to evaluate evolution of vegetation pattern over the years, for this study we utilized Enhanced Vegetation Index (EVI) based trend analysis. Vegetation recovery maps can be created to assess re-vegetation in regions affected by fire which is performed via a deep learning based unsupervised method, Adaptive Generative Adversarial Neural Network Model (AdaptiGAN) on postfire data collected from various regions affected by wildfire. Through the results obtained from the study we can arrive at a conclusion that our approach tends to have notable merits when compared to pre-existing works.