The deep learning model demonstrated a notable level of accuracy in its ability to map wildfire susceptibility, as seen by the significantly high average accuracy scores observed on both the training and validation datasets. The model’s mean AUC on the test dataset provided strong support for thes...
The Histogram inset shows the distribution of values shown on the map (x-axis limits were restricted, 0.9% of data not shown). To more directly compare the average observed and modeled wildfire probability, see Figs. 4 and 5 Full size image Model fitting We fit a logistic regression model ...
To understand how regional climate influences fire patterns, we used the R package (R Core Team2017)ggplot2(Wickham2009) to generate a hexagon heatmap of two-dimensional bin counts (pattern metric by year) and graphed the results using a color gradient of scPDSI values. We specified 20 bins ...
We explore and map the different wildfire regime components, as well as their regulating environmental factors, and model wildfire exposure patterns in the GNP landscape. In other words, the analysis of this research is about seeking answers to two important questions: (1) how are wildfire regime...
[Google Scholar] [CrossRef] [Green Version] USFS. The Rising Cost of Fire Operations: Effects on the Forest Service’s Non-Fire Work; United States Department Agriculture Forest Service: Washington, DC, USA, 2015; pp. 1–16. Knapp, P. Spatio-temporal patterns of large grassland fires in ...