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

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.

Download Full-text

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

10.31219/osf.io/2meuw ◽

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.

Download Full-text

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

10.31219/osf.io/n48hd ◽

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.

Download Full-text

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

10.31219/osf.io/vnzgy ◽

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.

Download Full-text

Research Progress of Desertification and Its Prevention in Mongolia

Sustainability ◽

10.3390/su13126861 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6861

Author(s):

Xiya Liang ◽

Pengfei Li ◽

Juanle Wang ◽

Faith Ka Shun Chan ◽

Chuluun Togtokh ◽

...

Keyword(s):

Model Development ◽

Mitigation Strategies ◽

Research Progress ◽

Prevention Measures ◽

Interdisciplinary Approaches ◽

The World ◽

Impacting Factors ◽

Decoupling Analysis ◽

And Control ◽

The Relationship

Mongolia is a globally crucial region that has been suffering from land desertification. However, current understanding on Mongolia’s desertification is limited, constraining the desertification control and sustainable development in Mongolia and even other parts of the world. This paper studied spatiotemporal patterns, driving factors, mitigation strategies, and research methods of desertification in Mongolia through an extensive review of literature. Results showed that: (i) remote sensing monitoring of desertification in Mongolia has been subject to a relatively low spatial resolution and considerable time delay, and thus high-resolution and timely data are needed to perform a more precise and timely study; (ii) the contribution of desertification impacting factors has not been quantitatively assessed, and a decoupling analysis is desirable to quantify the contribution of factors in different regions of Mongolia; (iii) existing desertification prevention measures should be strengthened in the future. In particular, the relationship between grassland changes and husbandry development needs to be considered during the development of desertification prevention measures; (iv) the multi-method study (particularly interdisciplinary approaches) and desertification model development should be enhanced to facilitate an in-depth desertification research in Mongolia. This study provides a useful reference for desertification research and control in Mongolia and other regions of the world.

Download Full-text

Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Download Full-text

Predicting wildfire impacts on the prehistoric archaeological record of the Jemez Mountains, New Mexico, USA

Fire Ecology ◽

10.1186/s42408-021-00103-6 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Megan M. Friggens ◽

Rachel A. Loehman ◽

Connie I. Constan ◽

Rebekah R. Kneifel

Keyword(s):

New Mexico ◽

Fire Severity ◽

Fire Effects ◽

Burn Severity ◽

Fire Season ◽

Archaeological Sites ◽

Archaeological Record ◽

Human Environment ◽

Jemez Mountains ◽

Archaeological Materials

Abstract Background Wildfires of uncharacteristic severity, a consequence of climate changes and accumulated fuels, can cause amplified or novel impacts to archaeological resources. The archaeological record includes physical features associated with human activity; these exist within ecological landscapes and provide a unique long-term perspective on human–environment interactions. The potential for fire-caused damage to archaeological materials is of major concern because these resources are irreplaceable and non-renewable, have social or religious significance for living peoples, and are protected by an extensive body of legislation. Although previous studies have modeled ecological burn severity as a function of environmental setting and climate, the fidelity of these variables as predictors of archaeological fire effects has not been evaluated. This study, focused on prehistoric archaeological sites in a fire-prone and archaeologically rich landscape in the Jemez Mountains of New Mexico, USA, identified the environmental and climate variables that best predict observed fire severity and fire effects to archaeological features and artifacts. Results Machine learning models (Random Forest) indicate that topography and variables related to pre-fire weather and fuel condition are important predictors of fire effects and severity at archaeological sites. Fire effects were more likely to be present when fire-season weather was warmer and drier than average and within sites located in sloped, treed settings. Topographic predictors were highly important for distinguishing unburned, moderate, and high site burn severity as classified in post-fire archaeological assessments. High-severity impacts were more likely at archaeological sites with southern orientation or on warmer, steeper, slopes with less accumulated surface moisture, likely associated with lower fuel moistures and high potential for spreading fire. Conclusions Models for predicting where and when fires may negatively affect the archaeological record can be used to prioritize fuel treatments, inform fire management plans, and guide post-fire rehabilitation efforts, thus aiding in cultural resource preservation.

Download Full-text

The history and characteristics of the 1980–2005 Portuguese rural fire database

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-3343-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 3343-3358 ◽

Cited By ~ 78

Author(s):

M. G. Pereira ◽

B. D. Malamud ◽

R. M. Trigo ◽

P. I. Alves

Keyword(s):

Urban Areas ◽

Unit Area ◽

Fire Regime ◽

Minimum Area ◽

Burnt Area ◽

Forested Areas ◽

Large Fires ◽

Burnt Areas

Abstract. We focus here on a mainland Continental Portuguese Rural Fire Database (PRFD) that includes 450 000 fires, the largest such database in Europe in terms of total number of recorded fires in the 1980–2005 period. In this work, we (a) list the most important factors for triggering and controlling the fire regime in mainland Continental Portugal, (b) describe the dataset's production, (c) discuss procedures adopted to identify and correct different fire data inconsistencies, creating a modified PRFD which we use here and make available as Supplement, (d) explore some basic temporal and completeness properties of the data. We find that the dataset's minimum measured burnt areas have changed with time between AF = 0.1 ha (1980–1990), AF = 0.01 ha (1991–1992), and AF = 0.001 ha (1992–2005), with varying degrees of completeness down to these values. These changes in minimum area measured are responsible for greater numbers of fires being recorded. A relatively small number of large fires in the PRFD are responsible for the majority of the burnt area. For example, fires with AF > 100 ha represent about 1% of all fire records but 75% of total burnt area. Finally, we consider for each Continental Portugal district and for the 26-yr period, the total number of rural fires and area burnt in forests and shrublands, each normalized by district areas. We find that the highest numbers of fires per unit area are in highly populated districts, and that the largest fraction of burnt area is in forested areas, coinciding with large parcels of continuous forests (predominantly rural and moderately urban areas).

Download Full-text

Size of wildfires in the Euro-Mediterranean region: observations and theoretical analysis

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-3-1203-2015 ◽

2015 ◽

Vol 3 (2) ◽

pp. 1203-1230 ◽

Cited By ~ 1

Author(s):

C. Hernandez ◽

P. Drobinski ◽

S. Turquety ◽

J.-L. Dupuy

Keyword(s):

Wind Speed ◽

Percolation Threshold ◽

Eastern Europe ◽

Mediterranean Region ◽

Burnt Area ◽

Fire Propagation ◽

Wind Speeds ◽

The Mediterranean ◽

Hot Weather ◽

Burnt Areas

Abstract. MODIS satellite observations of fire size and ERA-Interim meteorological reanalysis are used to derive a relationship between burnt area and wind speed over the Mediterranean region and Eastern Europe. As intuitively expected, the burnt area associated to the largest wildfires is an increasing function of wind speed in most situations. It is always the case in Eastern Europe. It is also the case in the Mediterranean for moderate temperature anomaly. In situations of severe heatwaves and droughts, the relationship between burnt area and wind speed displays bimodal shape. Burnt areas are large for low 10 m wind speed (lower than 2 m s−1), decrease for moderate wind speed values (lower than 5 m s−1 and larger than 2 m s−1) and increase again for large wind speed (larger than 5 m s−1). To explain such behavior fire propagation is investigated using a probabilistic cellular automaton model. The observed relationship between burnt area and wind speed can be interpreted in terms of percolation threshold which mainly depends on local terrain slope and vegetation state (type, density, fuel moisture). In eastern Europe, the percolation threshold is never exceeded for observed wind speeds. In the Mediterranean Basin we see two behaviors. During moderately hot weather, the percolation threshold is passed when the wind grows strong. On the other hand, in situations of severe Mediterranean heatwaves and droughts, moderate wind speed values impair the propagation of the wildfire against the wind and do not sufficiently accelerate the forward propagation to allow a growth of wildfire size.

Download Full-text

Spatial and Temporal Distribution of Biomass Open Burning Emissions in the Greater Mekong Subregion

Climate ◽

10.3390/cli8080090 ◽

2020 ◽

Vol 8 (8) ◽

pp. 90

Author(s):

Agapol Junpen ◽

Jirataya Roemmontri ◽

Athipthep Boonman ◽

Penwadee Cheewaphongphan ◽

Pham Thi Bich Thao ◽

...

Keyword(s):

Fine Particulate Matter ◽

Temporal Distribution ◽

Landsat 8 ◽

Total Biomass ◽

Burnt Area ◽

Fire Emissions ◽

Vegetation Fires ◽

Agricultural Burning ◽

Burnt Areas ◽

Greater Mekong Subregion

Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area products are widely used to assess the damaged area after wildfires and agricultural burning have occurred. This study improved the accuracy of the assessment of the burnt areas by using the MCD45A1 and MCD64A1 burnt area products with the finer spatial resolution product from the Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) surface reflectance data. Thus, more accurate wildfires and agricultural burning areas in the Greater Mekong Subregion (GMS) for the year 2015 as well as the estimation of the fire emissions were reported. In addition, the results from this study were compared with the data derived from the fourth version of the Global Fire Emissions Database (GFED) that included small fires (GFED4.1s). Upon analysis of the data of the burnt areas, it was found that the burnt areas obtained from the MCD64A1 and MCD45A1 had lower values than the reference fires for all vegetation fires. These results suggested multiplying the MCD64A1 and MCD45A1 for the GMS by the correction factors of 2.11−21.08 depending on the MODIS burnt area product and vegetation fires. After adjusting the burnt areas by the correction factor, the total biomass burnt area in the GMS during the year 2015 was about 33.3 million hectares (Mha), which caused the burning of 109 ± 22 million tons (Mt) of biomass. This burning emitted 178 ± 42 Mt of CO2, 469 ± 351 kilotons (kt) of CH4, 18 ± 3 kt of N2O, 9.4 ± 4.9 Mt of CO, 345 ± 206 kt of NOX, 46 ± 25 kt of SO2, 147 ± 117 kt of NH3, 820 ± 489 kt of PM2.5, 60 ± 32 kt of BC, and 350 ± 205 kt of OC. Furthermore, the emission results of fine particulate matter (PM2.5) in all countries were slightly lower than GFED4.1s in the range between 0.3 and 0.6 times.

Download Full-text

Detecting Burn Severity across Mediterranean Forest Types by Coupling Medium-Spatial Resolution Satellite Imagery and Field Data

Remote Sensing ◽

10.3390/rs12040741 ◽

2020 ◽

Vol 12 (4) ◽

pp. 741 ◽

Cited By ~ 2

Author(s):

Luigi Saulino ◽

Angelo Rita ◽

Antonello Migliozzi ◽

Carmine Maffei ◽

Emilia Allevato ◽

...

Keyword(s):

Fire Effects ◽

Mediterranean Forest ◽

Burn Severity ◽

Landsat 8 ◽

Mediterranean Countries ◽

Medium Resolution ◽

Severity Levels ◽

The Mediterranean ◽

Sentinel 2A ◽

Normalized Burn Ratio

In Mediterranean countries, in the year 2017, extensive surfaces of forests were damaged by wildfires. In the Vesuvius National Park, multiple summer wildfires burned 88% of the Mediterranean forest. This unprecedented event in an environmentally vulnerable area suggests conducting spatial assessment of the mixed-severity fire effects for identifying priority areas and support decision-making in post-fire restoration. The main objective of this study was to compare the ability of the delta Normalized Burn Ratio (dNBR) spectral index obtained from Landsat-8 and Sentinel-2A satellites in retrieving burn severity levels. Burn severity levels experienced by the Mediterranean forest communities were defined by using two quali-quantitative field-based composite burn indices (FBIs), namely the Composite Burn Index (CBI), its geometrically modified version CBI (GeoCBI), and the dNBR derived from the two medium-resolution multispectral remote sensors. The accuracy of the burn severity map produced by using the dNBR thresholds developed by Key and Benson (2006) was first evaluated. We found very low agreement (0.15 < K < 0.21) between the burn severity class obtained from field-based indices (CBI and GeoCBI) and satellite-derived metrics (dNBR) from both Landsat-8 and Sentinel-2A. Therefore, the most appropriate dNBR thresholds were rebuilt by analyzing the relationships between two field-based (CBI and GeoCBI) and dNBR from Landsat-8 and Sentinel-2A. By regressing alternatively FBIs and dNBRs, a slightly stronger relationship between GeoCBI and dNBR metrics obtained from the Sentinel-2A remote sensor (R2 = 0.69) was found. The regressed dNBR thresholds showed moderately high classification accuracy (K = 0.77, OA = 83%) for Sentinel-2A, suggesting the appropriateness of dNBR-Sentinel 2A in assessing mixed-severity Mediterranean wildfires. Our results suggest that there is no single set of dNBR thresholds that are appropriate for all burnt biomes, especially for the low levels of burn severity, as biotic factors could affect satellite observations.

Download Full-text