A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems

Luca, Giandomenico De; Silva, João M. N.; Modica, Giuseppe

doi:10.1080/15481603.2021.1907896

Cited by 38 publications

(21 citation statements)

References 109 publications

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“…Among eight indicators, a few indicators demonstrating the most distinct difference between burned and unburned areas were chosen by visual interpretation: dissimilarity, entropy, homogeneity and GLCM variance. In addition, some studies have shown that those four indicators are suitable for separating burned from unburned locations [54][55][56][57].…”

Section: Additional Features-ndvi Glcm Texture and Land-cover Mapmentioning

confidence: 99%

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Cho

Park

Kim

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The risk and damage of wildfire has been increasing due to various reasons including climate change, and Republic of Korea is no exception to this situation. Burned Area Mapping is crucial not only to prevent further damage but also to manage burned areas. Burned area mapping using satellite data, however, has been limited by spatial and temporal resolution of satellite data, and classification accuracy. This paper presents a new burned area mapping method, by which damaged areas can be mapped using semantic segmentation. For this research, PlanetScope imagery that has high-resolution images with very short revisit time were used, and the proposed method is based on U-Net that requires a uni-temporal PlanetScope image. The network was trained using 17 satellite images for 12 forest fires and corresponding label images which were obtained semiautomatically by setting threshold values. Band combination tests were conducted to produce an optimal burned area mapping model. The results demonstrated that the optimal and most stable band combination is Red, Green, Blue, and NIR of PlanetScope. To improve classification accuracy, NDVI (Normalized Difference Vegetation Index), dissimilarity extracted from GLCM (Grey-Level Co-occurrence Matrix), and Land Cover Maps were used as additional datasets. In addition, topographic normalization was conducted to improve model performance and classification accuracy by reducing shadow effects. The F1 scores and overall accuracies of the final image segmentation models are ranged from 0.883 to 0.939, and from 0.990 to 0.997, respectively. These results highlight the potential of detecting burned areas using the deep learning based approach.

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Section: Additional Features-ndvi Glcm Texture and Land-cover Mapmentioning

confidence: 99%

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Cho

Park

Kim

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…[37][38][39] Recently, many studies have combined machine learning techniques with Sentinel-1 images to detect burned areas. An example of this is de Luca et al, 40 which proposes an unsupervised scheme for detecting burned areas. According to such schemes, vegetation indices adapted for SAR sensors are calculated, followed by the extraction of the gray level co-occurrence matrix (GLCM) and application of PCA and K-means clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Except for Negri et al 34 . and De Luca et al., 40 the references herein cited are dependent on supervised classification methods, which rely on accurate and extensive labeled training sets. However, acquiring labeled training data may be challenging or even impossible in certain circumstances.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised burned areas detection using multitemporal synthetic aperture radar data

Simões,

Negri,

Souza

et al. 2024

J. Appl. Rem. Sens.

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Climate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing images, statistical modeling, and unsupervised classification for mapping fire-damaged areas. To validate the proposed methodology, multiple remote sensing images acquired by the Sentinel-1 satellite between August and October 2021 were collected and analyzed in two case studies comprising Brazilian biomes affected by burns. Our results demonstrate that the proposed approach outperforms another method evaluated in terms of precision metrics and visual adherence. Our methodology achieves the highest overall accuracy of 58.15% and the highest F1 score of 0.72, both of which are higher than the other method. These findings suggest that our approach is more effective in detecting burned areas and may have practical applications in other environmental issues such as landslides, flooding, and deforestation.

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“…However, as with any spectral index, finding optimal thresholds is often a difficult task, since they are usually scene-dependent [34,35]. Additionally, the characteristics and spectral reflectance of burned areas may also be highly variable, depending on fire severity or the density of pre-fire vegetation [29,36].…”

Section: Introductionmentioning

confidence: 99%

A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

2021

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This work presents a change detection method (MINDED-BA) for determining burned extents from multispectral remote sensing imagery. It consists of a development of a previous model (MINDED), originally created to estimate flood extents, combining a multi-index image-differencing approach and the analysis of magnitudes of the image-differencing statistics. The method was implemented, using Landsat and Sentinel-2 data, to estimate yearly burn extents within a study area located in northwest central Portugal, from 2000–2019. The modelling workflow includes several innovations, such as preprocessing steps to address some of the most important sources of error mentioned in the literature, and an optimal bin number selection procedure, the latter being the basis for the threshold selection for the classification of burn-related changes. The results of the model have been compared to an official yearly-burn-extent database and allow verifying the significant improvements introduced by both the pre-processing procedures and the multi-index approach. The high overall accuracies of the model (ca. 97%) and its levels of automatization (through open-source software) indicate potential for being a reliable method for systematic unsupervised classification of burned areas.

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A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems

Cited by 38 publications

References 109 publications

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep Learning Based Approach

Unsupervised burned areas detection using multitemporal synthetic aperture radar data

A New Method (MINDED-BA) for Automatic Detection of Burned Areas Using Remote Sensing

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