Enhancing Burned Area Mapping Accuracy: Integrating Multi-temporal PCA with NDVI Analysis
Souad Ghouzlane, Okan FıstıkoğluForested lands on the west coast of Türkiye, 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, we attempt to capture both recent and historic fire impacts. 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 (Δ𝑁𝐷𝑉𝐼) 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 Δ𝑁𝐷𝑉𝐼 integration. PC2 and PC3, especially with Δ𝑁𝐷𝑉𝐼 integration, were 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 Δ𝑁𝐷𝑉𝐼 method was found to be higher than that of the Maximum Likelihood Classification (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 results of this work highlight the potential of integrating multi-temporal PCA with NDVI for mapping burned areas at various scales in fire-prone ecosystems in western Türkiye. This approach contributes to the development of more effective forest management and assessment strategies following fires in these ecosystems. 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.
