scholarly journals Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal’s fires of 2017

2019 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Konstantinos P. Ferentinos
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Burn Severity ◽  
Support Vector ◽  
Burnt Area ◽  
Vector Machines ◽  
European Forest ◽  
Burnt Areas ◽  
Sentinel 2

The recent launch of Sentinel missions offers a unique opportunity to assess the impacts of wildfires at higher spatial and spectral resolution provided by those Earth Observing systems. Herein, an assessment of the Sentinel-1 & 2 to map burnt areas has been conducted. Initially the use of Sentinel-2 solely was explored, and then in combination with Sentinel-1 and derived radiometric indices. As a case study, the large wildfire occurred in Pedrógão Grande, Portugal in 2017 was used. Burnt area estimates from the European Forest Fires Information System (EFFIS) were used as reference. Burnt area was delineated using the Maximum Likelihood (ML) and Support Vector Machines (SVMs) classifiers, and two multi-index methods. Following this, burn severity was assessed using SVMs and Artificial Neural Networks (ANNs), again for both standalone Sentinel-2 data and in combination with Sentinel-1 and spectral indices. Soil erosion predictions were evaluated using the Revised Universal Soil Loss Equation (RUSLE) model. The effect of the land cover derived from CORINE operational product was also evaluated across the burnt area and severity maps. SVMs produced the most accurate burnt area map, resulting a 94.8% overall accuracy and a Kappa coefficient of 0.90. SVMs also achieved the highest accuracy in burn severity levels estimation, with an overall accuracy of 77.9% and a kappa of 0.710. From an algorithmic perspective, implementation of the techniques applied herein, is based on EO imagery analysis provided nowadays globally at no cost. It is also robust and adaptable, being potentially integrated with other high EO data available. All in all, our study contributes to the understanding of Mediterranean landscape dynamics and corroborates the usefulness of Sentinels data in wildfire studies.

scholarly journals Appraisal of the Sentinel-1 & 2 use in a large-scale wildfire assessment: A case study from Portugal's fires of 2017

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Konstantinos P. Ferentinos
Keyword(s):  
Large Scale ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Spectral Indices ◽  
Burnt Area ◽  
Vector Machines ◽  
European Forest ◽  
Burnt Areas ◽  
Sentinel 2

The recent launch of Sentinel missions offers a unique opportunity to assess the impacts of wildfires at higherspatial and spectral resolution provided by those Earth Observing (EO) systems. Herein, an assessment of theSentinel-1 & 2 to map burnt areas has been conducted. Initially the use of Sentinel-2 solely was explored, andthen in combination with Sentinel-1 and derived radiometric indices. As a case study, the large wildfire occurredin Pedrógão Grande, Portugal in 2017 was used. Burnt area estimates from the European Forest FiresInformation System (EFFIS) were used as reference. Burnt area was delineated using the Maximum Likelihood(ML) and Support Vector Machines (SVMs) classifiers, and two multi-index methods. Following this, burn severitywas assessed using SVMs and Artificial Neural Networks (ANNs), again for both standalone Sentinel-2 dataand in combination with Sentinel-1 and spectral indices. Soil erosion predictions were evaluated using theRevised Universal Soil Loss Equation (RUSLE) model. The effect of the land cover derived from CORINE operationalproduct was also evaluated across the burnt area and severity maps. SVMs produced the most accurateburnt area map, resulting a 94.8% overall accuracy and a Kappa coefficient of 0.90. SVMs also achieved thehighest accuracy in burn severity levels estimation, with an overall accuracy of 77.9% and a kappa of 0.710.From an algorithmic perspective, implementation of the techniques applied herein, is based on EO imageryanalysis provided nowadays globally at no cost. It is also robust and adaptable, being potentially integrated withother high EO data available. All in all, our study contributes to the understanding of Mediterranean landscapedynamics and corroborates the usefulness of Sentinels data in wildfire studies.

scholarly journals On the Use of Satellite Sentinel 2 Data for Automatic Mapping of Burnt Areas and Burn Severity

2018 ◽  
Vol 10 (11) ◽  
pp. 3889 ◽  
Author(s):  
Rosa Lasaponara ◽  
Biagio Tucci ◽  
Luciana Ghermandi
Keyword(s):  
Model Development ◽  
Fire Effects ◽  
Mitigation Strategies ◽  
Decision Makers ◽  
Burn Severity ◽  
Burnt Area ◽  
Local Index ◽  
Information Translation ◽  
Burnt Areas ◽  
Sentinel 2

In this paper, we present and discuss the preliminary tools we devised for the automatic recognition of burnt areas and burn severity developed in the framework of the EU-funded SERV_FORFIRE project. The project is focused on the set up of operational services for fire monitoring and mitigation specifically devised for decision-makers and planning authorities. The main objectives of SERV_FORFIRE are: (i) to create a bridge between observations, model development, operational products, information translation and user uptake; and (ii) to contribute to creating an international collaborative community made up of researchers and decision-makers and planning authorities. For the purpose of this study, investigations into a fire burnt area were conducted in the south of Italy from a fire that occurred on 10 August 2017, affecting both the protected natural site of Pignola (Potenza, South of Italy) and agricultural lands. Sentinel 2 data were processed to identify and map different burnt areas and burn severity levels. Local Index for Statistical Analyses LISA were used to overcome the limits of fixed threshold values and to devise an automatic approach that is easier to re-apply to diverse ecosystems and geographic regions. The validation was assessed using 15 random plots selected from in situ analyses performed extensively in the investigated burnt area. The field survey showed a success rate of around 95%, whereas the commission and omission errors were around 3% of and 2%, respectively. Overall, our findings indicate that the use of Sentinel 2 data allows the development of standardized burn severity maps to evaluate fire effects and address post-fire management activities that support planning, decision-making, and mitigation strategies.

scholarly journals Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007

2010 ◽  
Vol 10 (2) ◽  
pp. 305-317 ◽  
Author(s):  
G. P. Petropoulos ◽  
W. Knorr ◽  
M. Scholze ◽  
L. Boschetti ◽  
G. Karantounias
Keyword(s):  
Cost Effective ◽  
Support Vector ◽  
Svm Classifier ◽  
Wildland Fires ◽  
Burnt Area ◽  
Multispectral Data ◽  
Test Region ◽  
Vector Machines ◽  
Aster Image

Abstract. Remote sensing is increasingly being used as a cost-effective and practical solution for the rapid evaluation of impacts from wildland fires. The present study investigates the use of the support vector machine (SVM) classification method with multispectral data from the Advanced Spectral Emission and Reflection Radiometer (ASTER) for obtaining a rapid and cost effective post-fire assessment in a Mediterranean setting. A further objective is to perform a detailed intercomparison of available burnt area datasets for one of the most catastrophic forest fire events that occurred near the Greek capital during the summer of 2007. For this purpose, two ASTER scenes were acquired, one before and one closely after the fire episode. Cartography of the burnt area was obtained by classifying each multi-band ASTER image into a number of discrete classes using the SVM classifier supported by land use/cover information from the CORINE 2000 land nomenclature. Overall verification of the derived thematic maps based on the classification statistics yielded results with a mean overall accuracy of 94.6% and a mean Kappa coefficient of 0.93. In addition, the burnt area estimate derived from the post-fire ASTER image was found to have an average difference of 9.63% from those reported by other operationally-offered burnt area datasets available for the test region.

scholarly journals Exploring the Potential of Sentinels-1 & 2 of the Copernicus Mission in Support of Rapid and Cost-effective Wildfire Assessment

2020 ◽  
Author(s):  
Alexander R. Brown ◽  
George Petropoulos ◽  
Konstantinos P. Ferentinos
Keyword(s):  
Soil Erosion ◽  
Wildland Fire ◽  
Burn Severity ◽  
Burned Area ◽  
Mediterranean Ecosystem ◽  
Support Vector ◽  
Fire Dynamics ◽  
Burnt Area ◽  
The Impact ◽  
Sentinel 2

The present study explores the use of the recently launched Sentinel-1 and -2 data of the Copernicus mission inwildfire mapping with a particular focus on retrieving information on burnt area, burn severity as well as inquantifying soil erosion changes. As study area, the Sierra del Gata wildfire occurred in Spain during the summerof 2015 was selected. First, diverse image processing algorithms for burnt area extraction from Sentinel-2 datawere evaluated. In the next step, burn severity maps were derived from Sentinel-2 data alone, and the synergybetween Sentinel-2 & Sentinel-1 for this purpose was evaluated. Finally, the impact of the wildfire to soilerodibility estimates derived from the Revised Universal Soil Loss Equation (RUSLE) model implemented to theacquired Sentinel images was explored. In overall, the Support Vector Machines (SVMs) classifier obtained themost accurate burned area mapping, with a derived accuracy of 99.38%. An object-based SVMs classificationusing as input both optical and radar data was the most effective approach of delineating burn severity,achieving an overall accuracy of 92.97%. Soil erosion mapping predictions allowed quantifying the impact ofwildfire to soil erosion at the studied site, suggesting the method could be potentially of a wider use. Our resultscontribute to the understanding of wildland fire dynamics in the context of the Mediterranean ecosystem, demonstratingthe usefulness of Sentinels and of their derived products in wildfire mapping and assessment.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

Large-scale pinball twin support vector machines

2021 ◽  
Author(s):  
M. Tanveer ◽  
A. Tiwari ◽  
R. Choudhary ◽  
M. A. Ganaie
Keyword(s):  
Support Vector Machines ◽  
Large Scale ◽  
Support Vector ◽  
Twin Support Vector Machines ◽  
Vector Machines

scholarly journals Fractal properties of forest fires in Amazonia as a basis for modelling pan-tropical burnt area

2014 ◽  
Vol 11 (6) ◽  
pp. 1449-1459 ◽  
Author(s):  
I. N. Fletcher ◽  
L. E. O. C. Aragão ◽  
A. Lima ◽  
Y. Shimabukuro ◽  
P. Friedlingstein
Keyword(s):  
Forest Fires ◽  
Large Scale ◽  
Fire Spread ◽  
Small Scale ◽  
Burnt Area ◽  
Active Fire ◽  
Fractal Properties ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Current methods for modelling burnt area in dynamic global vegetation models (DGVMs) involve complex fire spread calculations, which rely on many inputs, including fuel characteristics, wind speed and countless parameters. They are therefore susceptible to large uncertainties through error propagation, but undeniably useful for modelling specific, small-scale burns. Using observed fractal distributions of fire scars in Brazilian Amazonia in 2005, we propose an alternative burnt area model for tropical forests, with fire counts as sole input and few parameters. This model is intended for predicting large-scale burnt area rather than looking at individual fire events. A simple parameterization of a tapered fractal distribution is calibrated at multiple spatial resolutions using a satellite-derived burnt area map. The model is capable of accurately reproducing the total area burnt (16 387 km2) and its spatial distribution. When tested pan-tropically using the MODIS MCD14ML active fire product, the model accurately predicts temporal and spatial fire trends, but the magnitude of the differences between these estimates and the GFED3.1 burnt area products varies per continent.

scholarly journals Assessment of Interventions in Fuel Management Zones Using Remote Sensing

2020 ◽  
Vol 9 (9) ◽  
pp. 533 ◽  
Author(s):  
Ricardo Afonso ◽  
André Neves ◽  
Carlos Viegas Damásio ◽  
João Moura Pires ◽  
Fernando Birra ◽  
...  
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fuel Management ◽  
K Nearest Neighbors ◽  
Management Zones ◽  
Vector Machines ◽  
Sentinel 2

Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.

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