Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery DOI Creative Commons
José Manuel Fernández‐Guisuraga, Leonor Calvo,

Luis A. Pérez-Rodríguez

et al.

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 304 - 304

Published: Aug. 27, 2024

We propose a novel mono-temporal framework with physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, mimicked methodology for CBI assessment remote sensing framework. strata identified individual tree segmentation geographic object-based image analysis (GEOBIA). In each stratum, wildfire effects estimated following methods: (i) vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as proxy biomass consumption; (ii) biophysical variables retrieved multispectral data by inversion PROSAIL radiative transfer model, direct link remaining after canopy scorch torch. The scores predicted UAV ecologically related metrics level featured fit respect field-measured (R2 > 0.81 RMSE < 0.26). Conversely, conventional retrieval using battery spectral predictors (point height distribution indices) plot provided much worse performance = 0.677 0.349).

Language: Английский

Integrated wildfire danger models and factors: A review DOI
Ioannis Zacharakis, Vassiliοs A. Tsihrintzis

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 899, P. 165704 - 165704

Published: July 23, 2023

Language: Английский

Citations

25
Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification DOI Creative Commons
Pengkun Liu,

Shaoxiang Ni,

S Makhanov Stanislav

et al.

Developments in the Built Environment, Journal Year: 2025, Volume and Issue: unknown, P. 100612 - 100612

Published: Jan. 1, 2025

Language: Английский

Citations

1
Impact of fire severity on forest structure and biomass stocks using NASA GEDI data. Insights from the 2020 and 2021 wildfire season in Spain and Portugal DOI Creative Commons
Juan Guerra-Hernández, José M. C. Pereira, Atticus Stovall

et al.

Science of Remote Sensing, Journal Year: 2024, Volume and Issue: 9, P. 100134 - 100134

Published: May 16, 2024

Wildfires have been progressively shrinking the C sink capacity of forest accelerating climate change effects on biodiversity, especially where megafires are recurrent and increased frequency such as in Mediterranean. Data from The Global Ecosystem Dynamics Investigation (GEDI) mission can inform changes structure to fire impacts vegetation. In this study, we assessed performance GEDI at measuring biomass structural wildfires using 2020/21 summer seasons Spain Portugal. hybrid-inference method was used calculate mean total pre- post-fire stages, while footprint data further explain severity classes derived optical data. Our results showed increasing impact stocks ecological metrics by severity. More than over stocks, severe fires substantially altered trends plant area volume density. integration had an accuracy 52% considering five 69% when three main classes: unburned, moderate high. Structural be improve optical-based estimates globally evaluate potential based time-series tracks showcased but also measure recovery between seasons. extension is a major support for wildfire mapping efforts, integrated approaches capture biodiversity monitoring carbon stocks.

Language: Английский

Citations

5
Wildland-urban interface typologies prone to high severity fires in Spain DOI
David Beltrán-Marcos, Leonor Calvo, José Manuel Fernández‐Guisuraga

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 894, P. 165000 - 165000

Published: June 20, 2023

Language: Английский

Citations

10
Estimation of Prometheus fuel types using physically based remote sensing techniques DOI
José Manuel Fernández‐Guisuraga,

Andrea Monzón-González,

Víctor Fernández-García

et al.

Fire Ecology, Journal Year: 2025, Volume and Issue: 21(1)

Published: May 11, 2025

Language: Английский

Citations

0
Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms DOI Creative Commons
Kangsan Lee, Willem van Leeuwen, Jeffrey K. Gillan

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1803 - 1803

Published: May 19, 2024

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest on burn severity as result 2020 Bighorn Fire Santa Catalina Mountains Arizona. Using stepwise regression model and remotely sensed data from Landsat 8 LiDAR, we analyzed effects structural functional vegetation traits factors analysis revealed that difference normalized ratio (dNBR) was more reliable indicator compared to relative dNBR (RdNBR). Stepwise identified index (NDVI), canopy cover, tree density significant variables across all land cover types explained severity, suggesting denser areas with higher greenness experienced severe burns. Interestingly, residuals between actual estimated were lower herbaceous zones forested at similar elevations, potentially predictable open areas. Spatial using Geary’s C statistics further strong negative autocorrelation: high tended be clustered, interspersed. Overall, this demonstrates potential readily available remote sensing predict values before fire event, providing valuable information for managers develop strategies mitigating future damage.

Language: Английский

Citations

2
The Assessment of Burn Severity and Monitoring of Recovery Process of Wildfire in Mongolia DOI Open Access
Battsengel Vandansambuu, Byambakhuu Gantumur, Falin Wu

et al.

Published: Sept. 1, 2023

Due to the intensification of climate change in world, incidence natural disasters is increasing year by year, and monitoring, forecasting, detecting evolution using satellite imaging technology an important guide for remote sensing. This study aims monitor occurrence fire Sentinel-2 technology, determine burned severity area with its classification recovery process determining extraordinary phenomena. The was sampled southeastern part Mongolia, where have most wildfires each near Shiliin Bogd mountain steppe zone Bayan-Uul soum forest-steppe zone. For methods, NBR used map ​​the site into 5 categories: unburned, low, moderate-low, moderate-high, high, which are process-defined works. NDVI index a timely series summer from April October. In result, areas were mapped images, total 1164.27 km2, 757.34 km2 (65.00 percent) 404.57 (34.70 remaining 2.36 (0.30 588,35 158.75 (26.90 297.75 (50.61 131.25 (22.31 moderate-high 0.60 (0.10 high-medium. Finally, we believe that this research helpful emergency workers, researchers, environmental specialists.

Language: Английский

Citations

2
Assessment of Burn Severity and Monitoring of the Wildfire Recovery Process in Mongolia DOI Creative Commons
Battsengel Vandansambuu, Byambakhuu Gantumur, Falin Wu

et al.

Fire, Journal Year: 2023, Volume and Issue: 6(10), P. 373 - 373

Published: Sept. 26, 2023

Due to the intensification of climate change around world, incidence natural disasters is increasing year by year, and monitoring, forecasting, detecting evolution using satellite imaging technology are important methods for remote sensing. This study aimed monitor occurrence fire Sentinel-2 determine burned-severity area via classification recovery process observe extraordinary phenomena. The that was sampled in southeastern part Mongolia, where most wildfires occur each near Shiliin Bogd Mountain steppe zone Bayan-Uul sub-province forest-steppe zone. normalized burn ratio (NBR) method used map site burned area. Normalized Difference Vegetation Index (NDVI) a timely series summer from April October. results severity were demonstrated distribution maps images, it can be seen total 1164.27 km2, which 757.34 km2 (65.00 percent) classified as low, 404.57 (34.70 moderate-low, remaining 2.36 (0.30 moderate-high, 588.35 158.75 (26.98 297.75 (50.61 131.25 (22.31 0.60 (0.10 high. Finally, we believe this research helpful emergency workers, researchers, environmental specialists.

Language: Английский

Citations

1
Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery DOI Creative Commons
José Manuel Fernández‐Guisuraga, Leonor Calvo,

Luis A. Pérez-Rodríguez

et al.

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 304 - 304

Published: Aug. 27, 2024

We propose a novel mono-temporal framework with physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, mimicked methodology for CBI assessment remote sensing framework. strata identified individual tree segmentation geographic object-based image analysis (GEOBIA). In each stratum, wildfire effects estimated following methods: (i) vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as proxy biomass consumption; (ii) biophysical variables retrieved multispectral data by inversion PROSAIL radiative transfer model, direct link remaining after canopy scorch torch. The scores predicted UAV ecologically related metrics level featured fit respect field-measured (R2 > 0.81 RMSE < 0.26). Conversely, conventional retrieval using battery spectral predictors (point height distribution indices) plot provided much worse performance = 0.677 0.349).

Language: Английский

Citations

0