Post-Fire Assessment of Burned Areas with Landsat-8 and Sentinel-2 Imagery Together with Modis and Viirs Active Fire Products

2020 ◽  
Author(s):  
Cesario Vincenzo Angelino ◽  
Luca Cicala ◽  
Sara Parrilli ◽  
Nicomino Fiscante ◽  
Silvia Liberata Ullo
Keyword(s):  
Landsat 8 ◽  
Burned Areas ◽  
Active Fire ◽  
Sentinel 2

scholarly journals Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets

2020 ◽  
Vol 12 (22) ◽  
pp. 3827
Author(s):  
Yosio Edemir Shimabukuro ◽  
Andeise Cerqueira Dutra ◽  
Egidio Arai ◽  
Valdete Duarte ◽  
Henrique Luís Godinho Cassol ◽  
...  
Keyword(s):  
Climate Change ◽  
Fire Severity ◽  
Brazilian Amazon ◽  
Landsat 8 ◽  
Mato Grosso ◽  
Burned Areas ◽  
Active Fire ◽  
Brazilian Amazon Region ◽  
Fraction Images ◽  
Sentinel 2

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

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.

An improved rule-based approach to map burned areas using Landsat and Sentinel-2 images

2020 ◽  
Author(s):  
Symeon Kanaropoulos ◽  
Nikos Koutsias
Keyword(s):  
Spectral Properties ◽  
Threshold Value ◽  
Landsat 8 ◽  
Rule Based ◽  
Spectral Signatures ◽  
Burned Areas ◽  
Multi Temporal ◽  
Rule Based Approach ◽  
Spectral Patterns ◽  
Sentinel 2

<p>This study presents an improvement of an old rule-based semi-automatic method to map burned areas by using multi-temporal Landsat and Seninel-2 images. The rule-based approach consists of a set of rules developed based on spectral properties of burned areas as compared to the pre-fire unburned vegetation and to the spectral signatures of other land cover types found in post-fire satellite scene. Actually, the spectral properties based on which the rules have been developed are presented in two graphs, one that corresponds to spectral signatures plots and the second that corresponds to the histogram data plots. The spectral patterns based on which the rule-based approach has been developed are not always the same. For example, depending on the type of the fire-affected vegetation (e.g. dry vegetation instead of green) the spectral pattern of the SWIR channel that correspond to channel 7 in Landsat 4-7 and 8 is not valid. Instead, there is a similar spectral behaviour but in the SWIR channel that correspond to channel 5 in Landsat 4-7, or channel 6 in Landsat 8. Additionally, the threshold value of 0.10-0.25 of the second rule seems not to be sufficient to cover all variability since there are cases that this value should be higher. Two characteristic examples of the insufficiencies found on the old-rules are concerned in the current analysis, one that presents limitations concerning the rule 5 (Serifos) and one that represents limitations concerning the rule 2 (Portugal). In this study we present a further improvement of the method and also its application to several cases spread out in Greek islands using both Landsat and Sentinel-2 images.</p>

A view of recent forest fire in Australia by satellite derived indices

2021 ◽  
Author(s):  
Kim-Anh Nguyen ◽  
Yuei-An Liou ◽  
Le-Thu Ho
Keyword(s):  
Air Quality ◽  
Optical Sensors ◽  
Forest Fires ◽  
Remote Sensing Data ◽  
Heat Index ◽  
Burned Area ◽  
Landsat 8 ◽  
Burned Areas ◽  
High Severity ◽  
Sentinel 2

<p>Bushfire is one of the dangerous natural manmade hazards. It can cause great damges to the air quality, human health, environment and bio-diversity. In addition, forest fires may be a potential and signigicant source of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans. In early 2020, Australia experienced serious bushfires with over an area of estimated 18.6 million hectares burned, over 5,900 buidlings (including 2, 779 homes) destroyed, and at least 34 people (including three fire fighters) and billion animals and some endangered species killed. Subsequently, air quality was degraded to hazardous levels. It was estimated that about 360 million tonnes of CO<sub>2</sub> was emitted as of 2 Jan. 2020 by NASA. Remote sensing data has been instrumental for the environmental monitoring in particular the bushfire. Many methods and algorithms have been proposed to detect the burned areas in the forest. However, it is challenging or even infeasible to routinely apply them by non-experts due to a chain of sophisticated schemes during their implementation. Here, we present a simple and effective method for mapping a burned area. The performances of different optical sensors and indices are conducted. Sentinel-2 MSI and Landsat 8 data are ultilized for the comparison of burned forest by analyzing different indices (including NDVI, NDBR and newly development index Nomarlized Difference Laten Heat Index (NDLI)). The forest damages are estimated over the Katoombar, Austrialia and the burning severity map is generated and classified into eight levels (none, high regrowth, lowregrowth, unburned, low severity, moderate low severity, moderate high severity, and high severity). The comparision in results from Sentinel-2 MSI data and Landsat image is performed and presented.</p>

scholarly journals REMOTE SENSING & GIS INTEGRATION FOR MONITORING THE AREAS AFFECTED BY FOREST FIRES: A CASE STUDY IN IZMIR, TURKEY

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 165-170 ◽  
Author(s):  
E. Çolak ◽  
A. F. Sunar
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Landsat 8 ◽  
Area Index ◽  
Burned Areas ◽  
Digital Elevation ◽  
Elevation Model ◽  
Normalized Burn Ratio ◽  
Sentinel 2

<p><strong>Abstract.</strong> A forest fire is stated as an ecological disaster whether it is man-made or caused naturally. İzmir is one of the regions where forest fires are most intensified in Turkey. The study area located at Aegean region of Turkey suffered two forest fires in 2017; Menderes and Bayındır areas. This study presents the integration of remote sensing (Sentinel 2 and Landsat 8 satellite images) and GIS data to map and evaluate the forest burned areas due to both forest fires. For this purpose, different indexes such as Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) Burn Index are applied besides different classification algorithms. The results showed that different vegetation types/zones are being affected. Sentinel 2 and Landsat 8 data are integrated to the GIS established with fieldwork data to analyse and also validate the results. Digital Elevation Model (DEM) data produced from ASTER satellite is also overlaid to the outcomes to emphasize the destructed forest areas. The efficiency of using two different satellites are outlined by comparing the accuracy of forest fire maps produced.</p>

scholarly journals Feasibility of Burned Area Mapping Based on ICESAT−2 Photon Counting Data

2019 ◽  
Vol 12 (1) ◽  
pp. 24 ◽  
Author(s):  
Meng Liu ◽  
Sorin Popescu ◽  
Lonesome Malambo
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Carbon Emissions ◽  
Classification Accuracy ◽  
Photon Counting ◽  
Burn Severity ◽  
Landsat 8 ◽  
Burned Areas ◽  
Forest Classification ◽  
Sentinel 2

Accurately mapping burned areas is crucial for the analysis of carbon emissions and wildfire risk as well as understanding the effects of climate change on forest structure. Burned areas have predominantly been mapped using optical remote sensing images. However, the structural changes due to fire also offer opportunities for mapping burned areas using three-dimensional (3D) datasets such as Light detection and ranging (LiDAR). This study focuses on the feasibility of using photon counting LiDAR data from National Aeronautics and Space Administration’s (NASA) Ice, Cloud, and land Elevation Satellite-2 (ICESat−2) mission to differentiate vegetation structure in burned and unburned areas and ultimately classify burned areas along mapped ground tracks. The ICESat−2 mission (launched in September 2018) provides datasets such as geolocated photon data (ATL03), which comprises precise latitude, longitude and elevation of each point where a photon interacts with land surface, and derivative products such as the Land Water Vegetation Elevation product (ATL08), which comprises estimated terrain and canopy height information. For analysis, 24 metrics such as the average, median and standard deviation of canopy height were derived from ATL08 data over forests burned by recent fires in 2018 in northern California and western New Mexico. A reference burn map was derived from Sentinel−2 images based on the differenced Normalized Burn Ratio (dNBR) index. A landcover map based on Sentinel−2 images was employed to remove non-forest classes. Landsat 8 based dNBR image and landcover map were also used for comparison. Next, ICESat−2 data of forest samples were classified into burned and unburned ATL08 100-m segments by both Random Forest classification and logistic regression. Both Sentinel−2 derived and Landsat 8 derived ATL08 samples got high classification accuracy, 83% versus 76%. Moreover, the resulting classification accuracy by Random Forest and logistic regression reached 83% and 74%, respectively. Among the 24 ICESat−2 metrics, apparent surface reflectance and the number of canopy photons were the most important. Furthermore, burn severity of each ATL08 segment was also estimated with Random Forest regression. R2 of predicted burn severity to observed dNBR is 0.61 with significant linear relationship and moderate correlation (r = 0.78). Overall, the reasonably high accuracies achieved in this study demonstrate the feasibility of employing ICESat−2 data in burned forest classification, opening avenues for improved estimation of burned biomass and carbon emissions from a 3D perspective.

scholarly journals The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 846
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Timothy Dube ◽  
John Odindi ◽  
Paramu L. Mafongoya
Keyword(s):  
Food Security ◽  
Precision Agriculture ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Economic Losses ◽  
Leaf Spot Disease ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maize Crop ◽  
Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

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