Processing of Near Real Time Land Surface Temperature and Its Application in Forecasting Forest Fire Danger Conditions

Abstract

Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Space-based instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near real-time Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60-125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDF-EOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a method/algorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed method/algorithm could be applied on any swath data product available in LANCE for any location in the world.

Examining Post-Fire Perceptions of Selected Mitigation Strategies after the 2016 Horse River Wildland Fire in Alberta, Canada

“…We used the wildland fire-induced risk map for the Fort McMurray communities, the primary basis of this study, that was prepared after the HRF event (the fire entered from the southwest on 1 May 2016) primarily using a WorldView-2 satellite image acquired on 6 June 2016 [1]. Additionally, we collected remote sensing-based forecasting models to understand wildland fire danger conditions in the area, which were primarily based on the meteorological and biophysical variables of vegetation, such as land surface temperature, precipitable water, normalized difference vegetation index, normalized difference water index; and historical ignition cause-specific static fire danger (SFD) maps [44][45][46]. We considered all the recommendations of these models for preparing the survey questionnaire in this study.…”

Mapping the Recovery Process of Vegetation Growth in the Copper Basin, Tennessee Using Remote Sensing Technology

“…The Normalized Difference Vegetation Index [21,22] is one of the most widely accepted and utilized vegetation indices [23,24]. It can be used as an indicator of relative biomass and greenness [25][26][27].…”

Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the southwest region of Canada

“…These models and algorithms utilize graphics and animation to visually depict the advancement of forest fires, delivering invaluable insights to fire management teams (Martinez-de Dios et al 2008, Vásquez et al 2021). By incorporating key factors like forest flammability, weather variations, and terrain characteristics, these simulations enable managers to devise efficient fire suppression strategies, mitigating the disastrous consequences of forest fires on the environment and human lives (Rui et al 2018, Qiao et al 2018, Ahmed et al 2020, Ahmed and Hassan 2023.…”