  Since frequent severe droughts are lengthening the dry season in the Amazon
Rainforest, it is important to detect wildfires promptly and forecast possible
spread for effective suppression response. Current wildfire detection models
are not versatile enough for the low-technology conditions of South American
hot spots. This deep learning study first trains a Fully Convolutional Neural
Network on Landsat 8 images of Ecuador and the Galapagos, using Green and
Short-wave Infrared bands to predict pixel-level binary fire masks. This model
achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from
Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus
band using K-Means Clustering to simplify continuous pixel values into three
discrete classes representing differing degrees of cirrus cloud contamination.
Three additional Convolutional Neural Networks are trained to conduct a
sensitivity analysis measuring the effect of simplified features on model
accuracy and train time. The Experimental model trained on the segmented cirrus
images provides a statistically significant decrease in train time compared to
the Control model trained on raw cirrus images, without compromising binary
accuracy. This proof of concept reveals that feature engineering can improve
the performance of wildfire detection models by lowering computational expense.
