Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area

Abstract

Bushfires pose a severe risk, among others, to humans, wildlife, and infrastructures. Rapid detection of fires is crucial for fire-extinguishing activities and rescue missions. Besides, mapping burned areas also supports evacuation and accessibility to emergency facilities. In this study, we propose a generic approach for detecting fires and burned areas based on machine learning (ML) approaches and remote sensing data. While most studies investigated either the detection of fires or mapping burned areas, we addressed and evaluated, in particular, the combined detection on three selected case study regions. Multispectral Sentinel-2 images represent the input data for the supervised ML models. First, we generated the reference data for the three target classes, burned, unburned, and fire, since no reference data were available. Second, the three regional fire datasets were preprocessed and divided into training, validation, and test subsets according to a defined schema. Furthermore, an undersampling approach ensured the balancing of the datasets. Third, seven selected supervised classification approaches were used and evaluated, including tree-based models, a self-organizing map, an artificial neural network, and a one-dimensional convolutional neural network (1D-CNN). All selected ML approaches achieved satisfying classification results. Moreover, they performed a highly accurate fire detection, while separating burned and unburned areas was slightly more challenging. The 1D-CNN and extremely randomized tree were the best-performing models with an overall accuracy score of 98 % on the test subsets. Even on an unknown test dataset, the 1D-CNN achieved high classification accuracies. This generalization is even more valuable for any use-case scenario, including the organization of fire-fighting activities or civil protection. The proposed combined detection could be extended and enhanced with crowdsourced data in further studies.

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

“…In particular, a number of methods take as input only post-event imagery and perform semantic segmentation where the goal is to classify each pixel in the image into one of the predefined classes of burnt/unburnt (e.g. [32], [33], [34], [35], [36], [37]). The most common models in this category employ the U-Net architecture and variations.…”

Object Tracking Based on Satellite Videos: A Literature Review

“…The proposed CNN model combined the residual convolution and separable convolution blocks to enable deeper features of the tracking target. A review of remote sensing-based fire detection is given in [140] in 2020, and more recent published works can be found in [141][142][143]. As detection is different from tracking and is out of our scope, we focus here on tracking only and do not provide the details on fire detection.…”

A GIS Plugin for Susceptibility Modeling: Case Study of Wildfires in Vila Nova de Foz Côa

“…Adab (2013) [27] also suggests moisture indexes and distance to roads and urbanizations. The normalized difference vegetation index (NDVI) is mostly used for burned area detection but can also be used to access susceptibility (as it expresses fuel availability) recurrently used to study fire risk [28,[31][32][33][34] and is explored in this work considering both winter and summer values. As this is a work intended to be of wider application that can be used in several and different contexts, it does not include variables, such as elevation, topographic roughness index, annual average and maximum temperature, demographic variables, fuel connectivity, fire recurrence patterns and daily climate data, mentioned in other works [2,25,29].…”