Wildland fires are among the main factors affecting the surrounding territory in terms of ecological and socioeconomic changes at different temporal and spatial scales. In the Mediterranean environment, although fire can positively influence some bio-physical dynamics of habitats, it acts as a pressing disturbance on ecosystems when the severity, spatial scale, and/or frequency are high, thereby determining their degradation. Therefore, knowing and mapping the accurate quantitative spatial distribution of all areas affected by fire during an entire high-frequency fire season and on a relatively large scale (regional/national scale) is an essential step to initialize the numerous subsequent effect monitoring analyses that can be carried out. This work proposes a reliable and open-access workflow to map burned areas on regional and national scales during the entire fire season. To achieve this, we integrated optical (Sentinel-2, S2) and Synthetic Aperture Radar (SAR; Sentinel-1, S1) free high spatial and temporal resolution data into a multitemporal composite criterion. Open-source software and Python-based libraries were used to develop the workflow. In particular, the second-lowest near infra-red (NIR) image composite (secMinNIR) criterion, based on the retrieval of the second minimum values that the NIR values reached in each pixel during the entire time frame considered, was applied to cloud-free S2 imagery to optimize the separability between burned and unburned areas. Subsequently, a second temporal composite criterion was developed and applied to the S1 time series, relying on the SAR capacity to detect vegetation fire-induced structural and humidity changes. It was based on retrieving the S1 pixel value of the first next (or the same) date to the corresponding date of the pixel value previously found by secMinNIR. The burned area map was created using an object-based geographic analysis (GEOBIA) process, using two optical and SAR composite images as input layers. The large-scale mean-shift (LSMS) algorithm was employed to segment the image, while the random forest (RF) classifier was the machine-learning model used to perform supervised classification. GEOBIA-based burned area classification was also performed using only the optical composite. The resulting accuracy values were compared using the precision (p), recall (r), and F-score accuracy metrics. The classification achieved high accuracy levels (F-score value greater than 0.9) in both cases (S1+ S2, 0.956; S2, 0.914), highlighting the increased effectiveness of this approach in detecting burned areas, heterogeneous in terms of amplitude, and affected site-specific characteristics that occurred during the fire season. Although the use of only optical data is sufficient to map the fire-affected areas early, some commission errors, represented by small regions scattered over the entire study area, remain, proving that the integration of SAR data improves the quality of the obtained results.

Regional-scale burned area mapping in Mediterranean regions based on the multitemporal composite integration of Sentinel-1 and Sentinel-2 data

De Luca, G;Modica, G
2022

Abstract

Wildland fires are among the main factors affecting the surrounding territory in terms of ecological and socioeconomic changes at different temporal and spatial scales. In the Mediterranean environment, although fire can positively influence some bio-physical dynamics of habitats, it acts as a pressing disturbance on ecosystems when the severity, spatial scale, and/or frequency are high, thereby determining their degradation. Therefore, knowing and mapping the accurate quantitative spatial distribution of all areas affected by fire during an entire high-frequency fire season and on a relatively large scale (regional/national scale) is an essential step to initialize the numerous subsequent effect monitoring analyses that can be carried out. This work proposes a reliable and open-access workflow to map burned areas on regional and national scales during the entire fire season. To achieve this, we integrated optical (Sentinel-2, S2) and Synthetic Aperture Radar (SAR; Sentinel-1, S1) free high spatial and temporal resolution data into a multitemporal composite criterion. Open-source software and Python-based libraries were used to develop the workflow. In particular, the second-lowest near infra-red (NIR) image composite (secMinNIR) criterion, based on the retrieval of the second minimum values that the NIR values reached in each pixel during the entire time frame considered, was applied to cloud-free S2 imagery to optimize the separability between burned and unburned areas. Subsequently, a second temporal composite criterion was developed and applied to the S1 time series, relying on the SAR capacity to detect vegetation fire-induced structural and humidity changes. It was based on retrieving the S1 pixel value of the first next (or the same) date to the corresponding date of the pixel value previously found by secMinNIR. The burned area map was created using an object-based geographic analysis (GEOBIA) process, using two optical and SAR composite images as input layers. The large-scale mean-shift (LSMS) algorithm was employed to segment the image, while the random forest (RF) classifier was the machine-learning model used to perform supervised classification. GEOBIA-based burned area classification was also performed using only the optical composite. The resulting accuracy values were compared using the precision (p), recall (r), and F-score accuracy metrics. The classification achieved high accuracy levels (F-score value greater than 0.9) in both cases (S1+ S2, 0.956; S2, 0.914), highlighting the increased effectiveness of this approach in detecting burned areas, heterogeneous in terms of amplitude, and affected site-specific characteristics that occurred during the fire season. Although the use of only optical data is sufficient to map the fire-affected areas early, some commission errors, represented by small regions scattered over the entire study area, remain, proving that the integration of SAR data improves the quality of the obtained results.
Wildland fires
geographic object-based image analysis (GEOBIA)
Orfeo toolbox (OTB)
random forest (RF) classifier
multitemporal composite integration
sentinel-1 and sentinel-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/130546
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