Fire Reference Perimeters Extracted from Sentinel-2 Data for Validation of Burned Area Products in Africa Biomes

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

Sustainability ◽

10.3390/su13010432 ◽

2021 ◽

Vol 13 (1) ◽

pp. 432

Author(s):

Aru Han ◽

Song Qing ◽

Yongbin Bao ◽

Li Na ◽

Yuhai Bao ◽

...

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Vegetation Change ◽

Fire Severity ◽

Dynamic Change ◽

Burned Area ◽

Fire Event ◽

Vegetation Dynamic ◽

Area Index ◽

Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

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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 ◽

10.3390/s21123982 ◽

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.

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Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

PLoS ONE ◽

10.1371/journal.pone.0232962 ◽

2020 ◽

Vol 15 (5) ◽

pp. e0232962 ◽

Cited By ~ 4

Author(s):

Fiona Ngadze ◽

Kudzai Shaun Mpakairi ◽

Blessing Kavhu ◽

Henry Ndaimani ◽

Monalisa Shingirayi Maremba

Keyword(s):

Burned Area ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Burned Area Mapping ◽

Sentinel 2

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A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data

Remote Sensing ◽

10.3390/rs12152422 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2422

Author(s):

Lisa Knopp ◽

Marc Wieland ◽

Michaela Rättich ◽

Sandro Martinis

Keyword(s):

Deep Learning ◽

Network Performance ◽

Near Infrared ◽

Remote Sensing Data ◽

Economic Consequences ◽

Validation Dataset ◽

Burned Area ◽

Burned Areas ◽

Spectral Bands ◽

Sentinel 2

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.

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Assessment of Burned Area Mapping Methods for Smoke Covered Sentinel-2 Data

2020 13th International Conference on Communications (COMM) ◽

10.1109/comm48946.2020.9141999 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alexandru-Cosmin Grivei ◽

Corina Vaduva ◽

Mihai Datcu

Keyword(s):

Burned Area ◽

Burned Area Mapping ◽

Sentinel 2

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Landsat-8 and Sentinel-2 burned area mapping - A combined sensor multi-temporal change detection approach

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111254 ◽

2019 ◽

Vol 231 ◽

pp. 111254 ◽

Cited By ~ 31

Author(s):

David P. Roy ◽

Haiyan Huang ◽

Luigi Boschetti ◽

Louis Giglio ◽

Lin Yan ◽

...

Keyword(s):

Change Detection ◽

Temporal Change ◽

Burned Area ◽

Landsat 8 ◽

Detection Approach ◽

Burned Area Mapping ◽

Multi Temporal ◽

Combined Sensor ◽

Sentinel 2

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Wildfire Risk Models for western Greenland: Geostatistical Considerations

10.5194/egusphere-egu2020-12660 ◽

2020 ◽

Author(s):

Jessica McCarty ◽

Robert Francis ◽

Justin Fain ◽

Keelin Haynes

Keyword(s):

Risk Model ◽

Fire Risk ◽

The Arctic ◽

Burned Area ◽

Risk Models ◽

High Northern Latitude ◽

Wildfire Risk ◽

Landscape Characteristics ◽

Significant Difference ◽

Sentinel 2

<p>The municipalities of Qeqertalik and Qeqqata in western Greenland experienced two wildfires in July 2017 and July 2019, respectively. Both fires occurred near Sisimiut, the second largest city in Greenland, with the ignition site of the July 2019 wildfire along the Arctic Circle Trail. These Arctic fires vary in fuels and burning behaviour from other high northern latitude fires due to unique flora, specifically the lack of extensive grasses, shrubbery, and more vascular vegetation, and presence of deep vertical beds of carbon-rich humus. The purpose of this research was to create wildfire risk models scalable across the Arctic landscapes of Greenland. We test multiple wildfire risk models based on expert-derived weighted matrix and four geostatistical techniques: Equal Influence (eq_infl), Multiple Logistic Regression (MLR), Geographically Weighted Regression (GWR) and Generalized Geographically Weighted Regression.The eq_infl model applied an even influence of each landscape characteristics. Two MLR models were developed, one using all the available data for the peninsula where the wildfire occurred (MLR_full) and the other which used an equal randomly chosen 50,000 pixel subset of both the burned area and unburned area (MLR_sub) immediately surrounding the 2017 Qeqertalik wildfire.The optimum model was selected in a stepwise fashion for both MLR models using AIC. GWR and GGWR models were derived from the MLR_sub, to avoid multicollinearity. Landscape characteristics for the wildfire risk models relied on open source remotely sensed data like ~20 m synthetic aperture radar imagery from the European Space Agency Sentinel-1 for soil moisture; elevation, slope, and aspect derived from the 10 m Arctic DEM provided by the U.S. National Geospatial Intelligence Agency (NGA) and National Science Foundation (NSF); vegetation fuel beds from the Global Fuelbed Dataset; normalized difference vegetation indices (NDVI) from 20 m Sentinel-2 served as proxies for vegetation condition; and soil carbon information from the 250 m SoilsGrid product was used to indicate likelihood of humus combustion. The nominal spatial resolution of each wildfire risk model was 20 m, after resampling of data. The optimum wildfire risk model was the model that displayed the greatest fire risk within the 2017 burned area. The average fire risks for each model were compared for significant difference in the mean fire risk using an ANOVA and Tukey's Post hoc. Average predicted fire risks by our models were compared to 2017 and 2019 burned areas visually digitized from 10 m Sentinel-2 data. The MLR_full model best represented the burned area of the 2017 Qeqertalik wildfire, though with an R<sup>2</sup> of 0.232, this leaves large amounts of variation unexplained. This is not surprising as wildfires in Greenland are uncommon and applying traditional fire risk approaches may not accurately represent the real-world. We can interpret from the results of the MLR_full model that landscapes across western Greenland have the potential to burn in a similar manner to the 2017 and 2019 wildfires.</p>

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African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2011160118 ◽

2021 ◽

Vol 118 (9) ◽

pp. e2011160118

Author(s):

Ruben Ramo ◽

Ekhi Roteta ◽

Ioannis Bistinas ◽

Dave van Wees ◽

Aitor Bastarrika ◽

...

Keyword(s):

Greenhouse Gases ◽

Spatial Resolution ◽

Carbon Emissions ◽

Sub Saharan Africa ◽

Burned Area ◽

Fire Emissions ◽

Coarse Spatial Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Sub Saharan ◽

Sentinel 2

Fires are a major contributor to atmospheric budgets of greenhouse gases and aerosols, affect soils and vegetation properties, and are a key driver of land use change. Since the 1990s, global burned area (BA) estimates based on satellite observations have provided critical insights into patterns and trends of fire occurrence. However, these global BA products are based on coarse spatial-resolution sensors, which are unsuitable for detecting small fires that burn only a fraction of a satellite pixel. We estimated the relevance of those small fires by comparing a BA product generated from Sentinel-2 MSI (Multispectral Instrument) images (20-m spatial resolution) with a widely used global BA product based on Moderate Resolution Imaging Spectroradiometer (MODIS) images (500 m) focusing on sub-Saharan Africa. For the year 2016, we detected 80% more BA with Sentinel-2 images than with the MODIS product. This difference was predominately related to small fires: we observed that 2.02 Mkm2 (out of a total of 4.89 Mkm2) was burned by fires smaller than 100 ha, whereas the MODIS product only detected 0.13 million km2 BA in that fire-size class. This increase in BA subsequently resulted in increased estimates of fire emissions; we computed 31 to 101% more fire carbon emissions than current estimates based on MODIS products. We conclude that small fires are a critical driver of BA in sub-Saharan Africa and that including those small fires in emission estimates raises the contribution of biomass burning to global burdens of (greenhouse) gases and aerosols.

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