Spatio-Temporal Assessment of Fire Severity and Vegetation Recovery Utilising Sentinel-2 Imagery in New South Wales, Australia

Rahman, Shahriar; Chang, Hsing-Chung; Magill, Christina; Tomkins, Kerrie M.; Hehir, Warwick

doi:10.1109/igarss.2019.8899242

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…For Sentinel 2 data, bands 11 and 12 with 20m resolution were resampled to 10m resolution in SNAP. After Layer stacking, bands of the selected Landsat-8 and Sentinel-2A images were standardized using the following formula (Rahman, et al, 2019):…”

Section: Preprocessingmentioning

confidence: 99%

“…Additionally, studies have shown the suitability and even superiority of Sentinel-2 data in natural resources applications (van der Werff and van der Meer 2016; Shoko and Mutanga 2017). The effectiveness of Sentinel-2 twin satellites to detect burned areas and reforestation with accuracy improvement have been demonstrated in several studies (Huang et al 2016;Navarro et al 2017;Rahman et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Determination of Forest Burn Scar and Burn Severity from Free Satellite Images: a Comparative Evaluation of Spectral Indices and Machine Learning Classifiers

Mashhadi

Algancı

2021

International Journal of Environment and Geoinformatics

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Remote sensing data indicates a considerable ability to map post-forest fire destructed areas and burned severity. In this research, the ability of spectral indices, which are difference Normalized Burned Ratio (dNBR), relative differenced Normalized Burn Ratio (RdNBR), Relativized Burn Ratio (RBR), and difference Normalized Vegetation Index (dNDVI), in mapping burn severity was investigated. The research was conducted with free access moderate to high-resolution Landsat 8 and Sentinel 2 satellite images for two forest fires cases that occurred in Izmir and Antalya provinces of Turkey. Performance of the burn severity maps from different indices were validated by use of NASA Firms active fires dataset. The results confirmed that, RdNBR showed more precise results than the other indices with an accuracy of (89%, 93%) and (84%, 79%) for Landsat 8 and Sentinel 2 satellites over Izmir and Antalya respectively. Moreover, in this research, the ability of machine learning classifiers, which are Support Vector Machine (SVM) and Random Forest (RF), in mapping burned areas were evaluated. According to the accuracy metrics that are user’s accuracy, producer's accuracy and Kappa coefficient, we concluded that both classifiers indicate reliable and accurate detection for both regions.

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Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of Forest Burn Scar and Burn Severity from Free Satellite Images: a Comparative Evaluation of Spectral Indices and Machine Learning Classifiers

Mashhadi

Algancı

2021

International Journal of Environment and Geoinformatics

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“…The study found that the soil-adjusted vegetation index (SAVI) outperformed the NDVI in areas with a single type of vegetation, NDVI outperformed the SAVI in areas with heterogeneous vegetation cover and a single soil type, and overall, the NDVI was the most robust VI for assessing vegetation recovery. Several studies of post-fire vegetation responses are based on the discrimination of spectral bands and vegetation indices (mostly NDVI, dNBR and EVI) by MODIS, Landsat, SPOT and Sentinel multi-temporal imagery in different regions and forest ecosystems of the world [241][242][243][244][245].…”

Section: Post-fire Vegetation Recovery Monitoringmentioning

confidence: 99%

Remote Sensing and GIS Applications in Wildfires

Zagalikis

2023

Geographic Information Systems - Data Science Approach

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Wildfires are closely associated with human activities and global climate change, but they also affect human health, safety, and the eco-environment. The ability of understanding wildfire dynamics is important for managing the effects of wildfires on infrastructures and natural environments. Geospatial technologies (remote sensing and GIS) provide a means to study wildfires at multiple temporal and spatial scales using an efficient and quantitative method. This chapter presents an overview of the applications of geospatial technologies in wildfire management. Applications related to pre-fire conditions management (fire hazard mapping, fire risk mapping, fuel mapping), monitoring fire conditions (fire detection, detection of hot-spots, fire thermal parameters, etc.) and post-fire condition management (burnt area mapping, burn severity, soil erosion assessments, post-fire vegetation recovery assessments and monitoring) are discussed. Emphasis is given to the roles of multispectral sensors, lidar and evolving UAV/drone technologies in mapping, processing, combining and monitoring various environmental characteristics related to wildfires. Current and previous researches are presented, and future research trends are discussed. It is wildly accepted that geospatial technologies provide a low-cost, multi-temporal means for conducting local, regional and global-scale wildfire research, and assessments.

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Deep Learning Technology for Automatic Burned Area Extraction Using Satellite High Spatial Resolution Images

Kashtan

Hnatushenko

2022

Lecture Notes on Data Engineering and Communications Technologies

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Spatio-Temporal Assessment of Fire Severity and Vegetation Recovery Utilising Sentinel-2 Imagery in New South Wales, Australia

Cited by 6 publications

References 19 publications

Determination of Forest Burn Scar and Burn Severity from Free Satellite Images: a Comparative Evaluation of Spectral Indices and Machine Learning Classifiers

Determination of Forest Burn Scar and Burn Severity from Free Satellite Images: a Comparative Evaluation of Spectral Indices and Machine Learning Classifiers

Remote Sensing and GIS Applications in Wildfires

Deep Learning Technology for Automatic Burned Area Extraction Using Satellite High Spatial Resolution Images

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