Enhancing Burned Area Mapping Accuracy: Integrating Multi-temporal PCA with NDVI Analysis

Abstract

Forested lands in the west coast of Turkey, with their similarity to Mediterranean forests, are often found to be highly susceptible to wildfires, necessitating the development of a forest management program to refine and quantify forest fires and their impacts on the environment. In light of this fact, a multi-temporal approach combining Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) analysis derived from Sentinel-2 imagery is suggested in the current study. Through PCA of carefully selected bands of Sentinel-2, both recent and historic fire impacts are attempted to be captured. It was found that the first two principal components (PC1 and PC2) predominantly describe landscape characteristics, while the third and fourth components (PC3 and PC4) have high abilities in detecting burn scars. It is worth noting that an increase in the ability to detect burn scars was observed with the inclusion of NDVI and its difference in time (dNDVI) within the PCA process. A high effectiveness level in distinguishing burnt areas from unburnt landscapes was presented by the multi-temporal PCA approach, particularly with dNDVI integration. PC2 and PC3, especially with dNDVI integration, are found to be strong indicative factors of burnt areas. In the classification result, accuracies of different years of fire events differed, and a high accuracy of 98.76% was found in the last fire event year of 2019. However, slight underestimation and overestimation were also observed in older fire scars. Mean accuracy, on average, for the PCA-dNDVI method was found to be higher than that of the MLC method. Furthermore, significant vegetation losses by fire, particularly by the 2019 fire incident, were realized through NDVI assessment. Although it worked well in recent fire scars, overestimating the extent in the case of burned areas from previous years was observed. The potential of multi-temporal PCA integration with NDVI for analysis in mapping burned areas at different scales in fire-prone ecosystems in western Turkey is underlined by the results of this work. Much more successful forest management and assessment strategies after fires have occurred in these ecosystems are helped to be created by this approach. Moreover, the approach is suggested to be one of the strong tools for monitoring fire induced damages across many time scales toward better understanding and management of long-term impacts caused by forest fires in the region.