Remotely sensed dry matter productivity and soil moisture content as potential predictors of arid rangeland wildfires: A case study of Kgalagadi District, Botswana
Department of Agricultural and Biosystems Engineering, Botswana University of Agriculture and Natural Resources. Private Bag 0027, Gaborone.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2022, 07(02), 143–156.
Article DOI: 10.30574/wjaets.2022.7.2.0143
Publication history:
Received on 27 October 2022; revised on 06 December 2022; accepted on 08 December 2022
Abstract:
Fire is a critical tool for managing rangeland ecosystems; however, the increasing wildfire occurrence poses a considerable danger to rangeland ecosystem continuity. Predicting fire occurrence and mapping wildfire danger is critical in managing highly flammable rangelands. To identify potential remotely sensed variables for wildfire prediction, this study employed a Random Forest (RF) classifier using selected environmental variables to assess their possible use for wildfire prediction in Kgalagadi District, Botswana. The study used 107,883 active fire points from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor from 2015 to 2021. Datasets of remotely sensed Dry Matter Productivity (DMP), Soil Moisture (SM), Land Surface Temperature (LST), Live Fuel Moisture Content (LFMC), and Dead Fuel Moisture Content (DMFC) were analysed in ArcMap 10.7 Esri©. The RF model developed gave an Out of Bag (OOB) error of 9.91% and an overall accuracy of 90.15% for classifying fires and non-fire points using the test dataset. The results also showed a Kappa coefficient of 0.803, with 88.25% and 91.76% producer and user accuracies, respectively, for classifying fire points. The DMP was the most significant variable with Mean Decrease Accuracy (MDA)= 1,055.20 and Mean Decrease Gini (MDG)= 9.328.62), followed by SM (MDA= 828.39 and MDG= 15,745). The LFMC and DMFC were found to be weak in detecting fires. It is recommended that field studies be carried out in the study area to calibrate these to improve their contribution to accurate fire prediction, as most literature shows that they are significant in fire prediction.
Keywords:
Wildfire prediction; Rangelands; Random Forest; Soil moisture; Dry Matter Productivity; Remote sensing
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