Development of post-fire vegetation response-ability model in grassland mountainous ecosystem using GIS and remote sensing

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 164; pp. 173 - 183
• Main Authors: Adagbasa, Efosa. G., Adelabu, Samuel. A., Okello, Tom. W.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2020
• Subjects: Adaptive strategy; Environmental variables; Fire severity; GIS; Post-fire recovery; Remote sensing; GIS; Adaptive strategy; Fire severity; Remote sensing; Post-fire recovery; Environmental variables
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2020.04.006

The mountainous grassland ecosystem in Golden Gate National Park (South Africa) has post-fire ecological resilience. However, vegetation species composition and structure can alter when the ecosystem continually has uncontrolled fires. This study developed a vegetation response-ability model by integrating environmental factors (elevation, aspect, rainfall, land surface temperature, soil, and fire severity), adaptive strategies (flowering months, water requirements, resprouter/seeders) and ecological status (increaser or decreaser) for the park. Vegetation recovery index derived from pre- and post-fire normalised difference vegetation index (NDVI) was used with correlation and regression analysis to validate the model. The results showed that amongst the environmental variables, elevation and fire were the most important factors influencing vegetation response-ability followed by soil, aspect, rainfall, and land surface temperature. Almost half (48%) of the park had a high vegetation response-ability, 43% medium, and 9% low. On the other hand, the vegetation recovery index showed 34% of the park fully recovered to pre-fire conditions, while 61% and 5% largely and slightly recovered, respectively. There was a strong correlation between vegetation response-ability and vegetation recovery index. The regression analysis showed a good relationship between vegetation response-ability and vegetation recovery index (R = 0.91) with 83.34% coefficient of determination.