Search Results (3)

Wildfires are occurring throughout the world, causing more damage to plant and animal species, humans, and the environment. Fire danger indices are useful for forecasting fire danger, and these indices involve the integration of both static and dynamic indices. The static indicators, such as vegetation, topographic characteristics, etc., are constant over the study area and are variables that promote the ignition of fires and, therefore, are useful for understanding fire patterns and distribution in the study area. In this study, the Static Fire Danger Index (SFDI) is generated using the MODIS Land cover type (MCD12Q1), the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Open Street Map datasets by applying a Random Forest (RF) algorithm. Random Forest (RF) is a machine learning algorithm that can automatically select important variables and flexibly evaluate the complex interactions between variables. The MODIS, TERRA, and AQUA active fire points (MCD14) during 2011–2017 have been used to train the RF algorithm, and fire probability maps are generated for the years 2018 and 2019. The fire probability maps are categorized into five fire danger classes, i.e., very low, low, medium, high, and very high, on the basis of the RF prediction probability values. The active fire points (MCD14) have been used to validate the SFDI, and accuracy is found to be 85.74% and 87.91% for the years 2018 and 2019, respectively. Thus, the machine learning algorithm is successfully applied for generating the wildfire susceptibility maps. Full article

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events. Full article

Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human- and lightning-caused fires during the period 1961–2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009–2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact. Full article