Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica

Abstract

This study develops a site specific burn severity modelling using remote sensing techniques to develop severity patterns on vegetation and soil in the fire prone region of the Palo Verde National Park in Guanacaste, Costa Rica. Terrain physical features, soil cover, and scorched vegetation characteristics were examined to develop a fire risk model and to quantify probable burned areas. Spectral signatures of affected areas were captured through multi-spectral analysis; i.e., Normalized Burn Ratio (NBR), Landsat derived differenced Normalized Burn Ratio (dNBR) and relativized dNBR (RdNBR). A partial unmixing algorithm, Mixture Tuned Matched Filtering (MTMF) was used to isolate endmembers for scorched vegetation and soil. The performance of dNBR and RdNBR for predicting ground cover components was acceptable with an overall accuracy of 84.4% and Cohen’s Kappa 0.82 for dNBR and an overall accuracy of 89.4% and Cohen’s Kappa 0.82 for RdNBR. Landsat derived RdNBR showed a strong correlation with scorched vegetation (r2 = 0.76) and moderate correlation with soil cover (r2 = 0.53), which outperformed dNBR. The ecologically diverse and unique park area is threatened by wetland fires, which pose a potential threat to various species. Human induced fires by poachers are a common occurrence in such areas to gain access to these species. This paper aims to prioritize areas that are at a higher risk from fire and model spatial adaptations in relation to the direction of fire within the affected wetlands. This assessment will help wildlife personnel in managing disturbed wetland ecosystems.