Mapping and comparing wildfire progressions using freely available, multi-source satellite data

2020 ◽  
Author(s):  
Morgan Crowley ◽  
Jeffrey Cardille ◽  
Joanne White ◽  
Michael Wulder
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Wildland Fire ◽  
Google Earth ◽  
Optical Data ◽  
Fire Season ◽  
Burned Area ◽  
Burned Areas ◽  
The Individual ◽  
Area Classification

<p>Extreme wildfire seasons are becoming the new normal in Canada, and satellite imagery is a useful way to map these fires as they grow across the vast, fire-prone regions of the country. However, single-date and single-sourced imagery of active fires often contain clouds, flares, smoke, and haze that can create challenges for monitoring  burned areas over time. To address this gap, we applied rapid and scalable methods for synthesizing information on fire progressions using freely available satellite imagery, novel image fusion algorithms, and cloud-based data processing platforms. We identified images from Landsat-7, -8, Sentinel-2, and MODIS (MCD64A1 burned-area dataset) for the 2017 and 2018 British Columbia fire seasons that intersect the buffered extents of Canadian wildfires as determined by Canadian National Fire Database. We classified each raw image individually using a standard burned-area classification protocol related to each data source. We used the Bayesian Updating of Land Cover Classifications (BULC) algorithm to create coherent time series from the single-date classifications of optical data sources in Google Earth Engine. From the BULC classification stack, we calculated within-year, intra-annual fire progression metrics to compare satellite-derived fire behaviours between the 2017 and 2018 fire seasons, both at the whole fire season and the individual fire level. End-of-season burned-area estimates corresponded with estimates derived from the National Burned Area Composite (NBAC) product that is generated retrospectively from best-available fire mapping approaches. Additionally, we compared the BULC time series with fire progression estimates from MCD64A1 burned-area dataset to evaluate the influence of spatial resolution on burned-area estimates. Information outputs from this research enable cross-validation of fire behaviour models for different fire seasons and comparison of fire progression metrics between historic fires and fire seasons in Canada. The approach presented can be used to provide rapid and reliable information about active wildland fire progressions to better understand fire growth and associated drivers.</p>

scholarly journals 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

2019 ◽  
Vol 11 (5) ◽  
pp. 489 ◽  
Author(s):  
Tengfei Long ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Weili Jiao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Google Earth ◽  
Burned Area ◽  
Landsat 8 ◽  
Landsat Images ◽  
Burned Areas ◽  
Random Decision Forests ◽  
Burned Area Mapping ◽  
Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

scholarly journals Estimates of biomass burning emissions in tropical Asia based on satellite-derived data

2010 ◽  
Vol 10 (5) ◽  
pp. 2335-2351 ◽  
Author(s):  
D. Chang ◽  
Y. Song
Keyword(s):  
Biomass Burning ◽  
Atmospheric Chemistry ◽  
Crop Residue ◽  
Fire Season ◽  
Burned Area ◽  
Published Data ◽  
Tropical Asia ◽  
Fire Emissions ◽  
Burned Areas ◽  
In Fire

Abstract. Biomass burning in tropical Asia emits large amounts of trace gases and particulate matter into the atmosphere, which has significant implications for atmospheric chemistry and climatic change. In this study, emissions from open biomass burning over tropical Asia were evaluated during seven fire years from 2000 to 2006 (1 March 2000–31 February 2007). The size of the burned areas was estimated from newly published 1-km L3JRC and 500-m MODIS burned area products (MCD45A1). Available fuel loads and emission factors were assigned to each vegetation type in a GlobCover characterisation map, and fuel moisture content was taken into account when calculating combustion factors. Over the whole period, both burned areas and fire emissions showed clear spatial and seasonal variations. The size of the L3JRC burned areas ranged from 36 031 km2 in fire year 2005 to 52 303 km2 in 2001, and the MCD45A1 burned areas ranged from 54 790 km2 in fire year 2001 to 148 967 km2 in 2004. Comparisons of L3JRC and MCD45A1 burned areas using ground-based measurements and other satellite data were made in several major burning regions, and the results suggest that MCD45A1 generally performed better than L3JRC, although with a certain degree of underestimation in forest areas. The average annual L3JRC-based emissions were 123 (102–152), 12 (9–15), 1.0 (0.7–1.3), 1.9 (1.4–2.6), 0.11 (0.09–0.12), 0.89 (0.63–1.21), 0.043 (0.036–0.053), 0.021 (0.021–0.023), 0.41 (0.34–0.52), 3.4 (2.6–4.3), and 3.6 (2.8–4.7) Tg yr−1 for CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10, respectively, whereas MCD45A1-based emissions were 122 (108–144), 9.3 (7.7–11.7), 0.63 (0.46–0.86), 1.1 (0.8–1.6), 0.11 (0.10–0.13), 0.54 (0.38–0.76), 0.043 (0.038–0.051), 0.033 (0.032–0.037), 0.39 (0.34–0.47), 3.0 (2.6–3.7), and 3.3 (2.8–4.0) Tg yr−1. Forest burning was identified as the major source of the fire emissions due to its high carbon density. Although agricultural burning was the second highest contributor, it is possible that some crop residue combustion was missed by satellite observations. This possibility is supported by comparisons with previously published data, and this result may be due to the small size of the field crop residue burning. Fire emissions were mainly concentrated in Indonesia, India, Myanmar, and Cambodia. Furthermore, the peak in the size of the burned area was generally found in the early fire season, whereas the maximum fire emissions often occurred in the late fire season.

scholarly journals Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 518 ◽  
Author(s):  
Natalia Quintero ◽  
Olga Viedma ◽  
Itziar R. Urbieta ◽  
José M. Moreno
Keyword(s):  
Time Series ◽  
Forest Fires ◽  
Fire Hazard ◽  
Sustainable Use ◽  
Google Earth ◽  
Burned Area ◽  
Change Metrics ◽  
Google Earth Engine ◽  
The Stability

Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard.

scholarly journals MAPPING OF BURNED AREA USING PRESENCE AND BACKGROUND LEARNING FRAMEWORK ON THE GOOGLE EARTH ENGINE PLATFORM

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 37-41
Author(s):  
D. Attaf ◽  
K. Djerriri ◽  
D. Mansour ◽  
D. Hamdadou
Keyword(s):  
Remote Sensing ◽  
Classification Problem ◽  
Google Earth ◽  
The Other ◽  
Burned Area ◽  
Support Vector ◽  
Classification Techniques ◽  
Burned Areas ◽  
Google Earth Engine ◽  
One Class Classification

<p><strong>Abstract.</strong> Mapping of burned areas caused by forest fires was always a main concern to researchers in the field of remote sensing. Thus, various spectral indices and classification techniques have been proposed in the literature. In such a problem, only one specific class is of real interest and could be referred to as a one-class classification problem. One-class classification methods are highly desirable for quick mapping of classes of interest. A common used solution to deal with One-Class classification problem is based on oneclass support vector machine (OC-SVM). This method has proved useful in classification of remote sensing images. However, overfitting problem and difficulty in tuning parameters have become the major obstacles for this method. The new Presence and Background Learning (PBL) framework does not require complicated model selection and can generate very high accuracy results. On the other hand the Google Earth Engine (GEE) portal provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. Therefore, this study mainly aims to investigate the possibility of using the PBL framework within the GEE platform to extract burned areas from freely available Landsat archive in the year 2015. The quality of the results obtained using PBL framework was assessed using ground truth digitized by qualified technicians and compared to other classification techniques: Thresholding burned area spectral Index (BAI) and OC-SVM classifiers. Experimental results demonstrate that PBL framework for mapping the burned areas shows the higher classification accuracy than the other classifiers, and it highlights the suitability for the cases with few positive labelled samples available, which facilitates the tedious work of manual digitizing.</p>

scholarly journals Impact of large wildfires on PM<sub>10</sub> levels and human mortality in Portugal

2021 ◽  
Vol 21 (9) ◽  
pp. 2867-2880
Author(s):  
Patricia Tarín-Carrasco ◽  
Sofia Augusto ◽  
Laura Palacios-Peña ◽  
Nuno Ratola ◽  
Pedro Jiménez-Guerrero
Keyword(s):  
Fire Season ◽  
Burned Area ◽  
Substantial Impact ◽  
Burned Areas ◽  
The North ◽  
Mainland Portugal ◽  
Large Fires ◽  
European Forest ◽  
Air Quality Monitoring Stations ◽  
June July

Abstract. Uncontrolled wildfires have a substantial impact on the environment, the economy and local populations. According to the European Forest Fire Information System (EFFIS), between 2000 and 2013 wildfires burned up to 740 000 ha of land annually in the south of Europe, Portugal being the country with the highest percentage of burned area per square kilometre. However, there is still a lack of knowledge regarding the impacts of the wildfire-related pollutants on the mortality of the country's population. All wildfires occurring during the fire season (June–July–August–September) from 2001 and 2016 were identified, and those with a burned area above 1000 ha (large fires) were considered for the study. During the studied period (2001–2016), more than 2 million ha of forest (929 766 ha from June to September alone) were burned in mainland Portugal. Although large fires only represent less than 1 % of the number of total fires, in terms of burned area their contribution is 46 % (53 % from June to September). To assess the spatial impact of the wildfires, burned areas in each region of Portugal were correlated with PM10 concentrations measured at nearby background air quality monitoring stations. Associations between PM10 and all-cause (excluding injuries, poisoning and external causes) and cause-specific mortality (circulatory and respiratory) were studied for the affected populations using Poisson regression models. A significant positive correlation between burned area and PM10 was found in some regions of Portugal, as well as a significant association between PM10 concentrations and mortality, these being apparently related to large wildfires in some of the regions. The north, centre and inland of Portugal are the most affected areas. The high temperatures and long episodes of drought expected in the future will increase the probabilities of extreme events and therefore the occurrence of wildfires.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

scholarly journals Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and -2 Data Fusion

2020 ◽  
Vol 12 (2) ◽  
pp. 302 ◽  
Author(s):  
Kai Heckel ◽  
Marcel Urban ◽  
Patrick Schratz ◽  
Miguel Mahecha ◽  
Christiane Schmullius
Keyword(s):  
South Africa ◽  
Time Series ◽  
Forest Cover ◽  
Short Wave ◽  
Optical Data ◽  
Biophysical Parameters ◽  
Data Set ◽  
Area Index ◽  
The Individual ◽  
Sentinel 2

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution.

scholarly journals Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

2021 ◽  
Vol 10 (8) ◽  
pp. 511
Author(s):  
Sifiso Xulu ◽  
Nkanyiso Mbatha ◽  
Kabir Peerbhay
Keyword(s):  
South Africa ◽  
Forest Fires ◽  
Google Earth ◽  
Burned Area ◽  
Western Cape ◽  
Planted Forests ◽  
Poor Countries ◽  
Burned Areas ◽  
Forestry Industry ◽  
High Level

Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries.

scholarly journals Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

2018 ◽  
pp. 61 ◽  
Author(s):  
J.A. Anaya ◽  
W.F. Sione ◽  
A.M. Rodriguez-Montellano
Keyword(s):  
Accuracy Assessment ◽  
Global Scale ◽  
Google Earth ◽  
Burned Area ◽  
Amazon Forest ◽  
Burned Areas ◽  
Use Of Time ◽  
Chaco Region ◽  
Relative Form ◽  
Normalized Burn Ratio

<p>There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.</p>

Fire regimes and post-fire evolution of burned areas in selected plant biomes of the planet studying the phenology of the landscape with time series of satellite images

2021 ◽  
Author(s):  
Nikos Koutsias ◽  
Anastasia Karamitsou ◽  
Foula Nioti ◽  
Frank Coutelieris
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Fire History ◽  
Fire Regime ◽  
Fire Regimes ◽  
Burned Areas ◽  
History Of ◽  
Sentinel 2

&lt;p&gt;Plant biomes and climatic zones are characterized by a specific type of fire regime which can be determined from the history of fires in the area and it is a synergy mainly of the climatic conditions and the functional characteristics of the types of vegetation. They correspond also to specific phenology types, a feature that can be useful for various applications related to vegetation monitoring, especially when remote sensing methods are used. Both the assessment of fire regime from the reconstruction of fire history and the monitoring of post-fire evolution of the burned areas can be studied with satellite remote sensing based on satellite time series images. The free availability of (i) Landsat satellite imagery by US Geological Survey (USGS, (ii) Sentinel-2 satellite imagery by ESA and (iii) MODIS satellite imagery by NASA / USGS allow low-cost data acquisition and processing (eg 1984-present) which otherwise would require very high costs. The purpose of this work is to determine the fire regime as well as the patterns of post-fire evolution of burned areas in selected vegetation/climate zones for the entire planet by studying the phenology of the landscape with time series of satellite images. More specifically, the three research questions we are negotiating are: (i) the reconstruction of the history of fires in the period 1984-2017 and the determination of fire regimes with&amp;#160;Landsat and Sentinel-2 satellite data , (ii) the assessment of pre-fire phenological pattern of vegetation and (iii) the monitoring and comparative evaluation of post-fire evolution patterns of the burned areas.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This research has been co-financed by the Operational Program &quot;Human Resources Development, Education and Lifelong Learning&quot; and is co-financed by the European Union (European Social Fund) and Greek national funds.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document