Development of fire potential index over golden gate highlands national park using remote sensing

Abstract

Fire is a natural phenomenon in many ecosystems. The positive and negative impacts of fire on biodiversity and natural resources has been a centre of attention across the world particularly within protected areas. Fire risk assessment systems provide an integrated approach for managing resources at stake and reducing the negative impact of fire. Fire Risk Index is of great assistance in which estimates the probability of fire occurrence and areas are quantitatively divided into different zone classified based on similar characteristics, which influence fire behaviour. Fire risk have traditionally been measured from point data collected at sparse weather stations and field survey. The accuracy of assessment may be limited by density of point data and spatial interpolation method errors. Remote Sensing techniques provide a cost-effective way of assessing required parameters such as fuel characteristics (moisture & biomass) and weather conditions in near-real time. Moreover, RS techniques have the ability to reveal spatial pattern of fire risk in recurrent, consistent way over large, remotely inaccessible mountainous area. This study focused on development of Fire Potential Index for mountainous Golden Gate Highlands National Park, Free State Province, South Africa using Geospatial techniques. MODIS products MOD11A1, MODO9GA for fire seasons of 2011 -2014; and 30m Advanced Spaceborne Thermal Emission and Reflection Radiometer -Digital Elevation Model (ASTER-DEM) were used for data retrieval. Land Surface Temperature (LST); Normalized Difference Water Index derived Relatively Greenness Index (RGIndwi); Normalized Multi-Drought Index (NMDI) and Elevation were selected based on their significance in fire risk assessment. Variables were used to estimate two critical parameters, Fuel Moisture Content (RGIndwi & NMDI) and Potential Surface Temperature (LST & Elevation). GIS was used during index calculation, data processing and analysis among other processes. Conversion of parameter’s values into common danger scale was conducted using Normalization Tool. Reclass Tool for classification each data layer into five classes using manual classification method based on its impact on increasing the fire potential. Pairwise comparison of Analytic Hierarchy Process for assigning weightages for the parameters. Weighted Overlay tool for integration these parameters into construction of FPI. The final FPI Map was categorized into five classes as insignificant, low, medium, high and extreme high based on the FPI values. Fire points were used to validate the FPI map applying Extract Values to Points Tool. Geographical Weighted Regression (GWR) analysis was used to measure FPI performance. The results revealed that about 12% of the park area was identified as high to extreme high danger zone,13%- medium danger zone and 42% - low danger zone towards fire. Largest area coverage of high to extreme fire danger classes was observed during 2013 (17%), 2014 (16%), 2012 (8%), and 2011(6%). The area was observed during September (17%), August (11%) and July (6%). The model revealed an overall accuracy of 89% ranging from 33%-100% indicating that maximum of fires fell under low to extreme high fire danger classes. GWR analysis show a sound agreement between FPI and the fire danger with overall R2 of 0,69 ranging from 0,17 to 0,98. Therefore, the results suggest that the constructed FPI can be useful for monitoring spatiotemporal distribution of susceptibility of vegetation to fire. The use of image fusion techniques to improve spatial and temporal resolutions of sensors as they are many freely available sensors that are sufficient in spectral resolution but have poor spatial and temporal resolutions should be encouraged. Plans to prevent and control fire in GGHNP should be more orientated to high and extreme fire danger areas. It was recommended the prediction of the index may be increased by incorporating more parameters such as Land-Cover-Land-Use (LULC), fuel type map and meteorological variables (wind speed and direction & insolation).