Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images

Abstract

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing

“…In fact, validation clearly showed that RG provides the highest accuracy by reducing commission errors. Although a full comparison with published values is difficult due to the differences in input data and algorithms, our results are more than satisfactory and comparable to published reference values for accuracy metrics of burned area maps [64][65][66].…”

Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

“…Therefore, a land cover mask was applied to minimize the negative effect, and it was worth considering the accuracy of the land cover mask as it could directly affect our detection result. In addition, the combination of multiple spectral indices can also be an alternative to cope with the confusion between burned and unburned areas [33,51]. Moreover, some isolated false detections appeared as salt-and-pepper noises, and a spatial filter with a moving window proved to be useful for reducing the noises and refining the results by following the majority vote rule [52].…”

Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest

“…Considering the literature, the main remote sensing techniques for BA mapping include supervised and unsupervised classification methods, spectral indices, spectral mixture analysis, logistic regression modeling, principal component analysis, and recently, machine learning [7,13,15,[17][18][19][20]. The datasets used in these methods are generally bitemporal images or single post-fire images.…”