لخّصلي

Early detection of active forest fires is critical to determine the starting point of the fire for effective emergency responses.Future work will be based on using geostationary satellite imagery for rapid monitoring of active fires to provides high temporal resolution for fast monitoring in a larger scale.A deep CNN (Fire-Net) was proposed in this work to detect active forest fires in various regions.

Early detection of active forest fires is critical to determine the starting point of the fire for effective emergency responses. In this study, active fire detection was performed on a medium spatial resolution dataset (Landsat-8 imagery) where the extent of active fires was very low. A deep CNN (Fire-Net) was proposed in this work to detect active forest fires in various regions. Specifically, the USA and Australia regions were used to train the network whereas testing was done for Africa, Brazil, Ukraine, Australia (the parts was not involved in training) regions. The results depicted a high transferability of the proposed method. Then, the Fire-Net was compared with another state-of-the-art deep network, i.e., MSR-U-Net and other common machine learning algorithms. The results for active fire detection were qualitatively and quantitatively assessed. The performance evaluations showed there was a trade-off between active fire and non-active fires detection. Due to the extent of active fires in small areas, most of the machine learning algorithms could not detect active fires. The high accuracies measured by OA index for these algorithms, were mostly for non-fire zones. The other indices namely precision, KC, and F1-score for these models were low for the detection of active fires. In contrast, the proposed Fire-Net method showed high efficacy for both active and non-active fire detection. Here, small active fires were detected with high accuracy and low miss detection rates. The efficiency of Fire-Net originated in its architecture and convolution layers structure enabling high level and informative features extraction. Experimental results indicate that Fire-Net: (1) has higher accuracy, (2) obtains higher sensitivity to small active fires, (3) the proposed method can be applied as real time processing due to high transferability. Future work will be based on using geostationary satellite imagery for rapid monitoring of active fires to provides high temporal resolution for fast monitoring in a larger scale.