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

Long, Tao; Zhang, Zhaoming; He, Guojin; Jiao, Weili; Tang, Chao; Wu, Bingfang; Zhang, Xiaomei; Wang, Guizhou; Yin, Ranyu

doi:10.3390/rs11050489

Cited by 132 publications

(116 citation statements)

References 51 publications

(67 reference statements)

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“…In addition, for atmospheric modelling, ancillary information becomes important, including the detection date, type of burned land (generally relying on external land cover products with their own uncertainties), and combustion completeness and fraction burned area, i.e., critical parameters for atmospheric emission estimations [18]. To overcome some of these limitations (e.g., small fire size, burned fraction), medium resolution optical sensors have been used for BA detection and mapping with early algorithms being mostly based on Landsat imagery [19][20][21][22][23]. However, detection from Landsat imagery is challenging because of the low temporal resolution of this data (16 days), particularly over areas with persistent cloud cover and short post-fire signal persistence (e.g., savannah fires).…”

Section: Introductionmentioning

confidence: 99%

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

et al. 2020

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This study provides a comparative analysis of two Sentinel-1 and one Sentinel-2 burned area (BA) detection and mapping algorithms over 10 test sites (100 × 100 km) in tropical and sub-tropical Africa. Depending on the site, the burned area was mapped at different time points during the 2015–2016 fire seasons. The algorithms relied on diverse burned area (BA) mapping strategies regarding the data used (i.e., surface reflectance, backscatter coefficient, interferometric coherence) and the detection method. Algorithm performance was compared by evaluating the detected BA agreement with reference fire perimeters independently derived from medium resolution optical imagery (i.e., Landsat 8, Sentinel-2). The commission (CE) and omission errors (OE), as well as the Dice coefficient (DC) for burned pixels, were compared. The mean OE and CE were 33% and 31% for the optical-based Sentinel-2 time-series algorithm and increased to 66% and 36%, respectively, for the radar backscatter coefficient-based algorithm. For the coherence based radar algorithm, OE and CE reached 72% and 57%, respectively. When considering all tiles, the optical-based algorithm provided a significant increase in agreement over the Synthetic Aperture Radar (SAR) based algorithms that might have been boosted by the use of optical datasets when generating the reference fire perimeters. The analysis suggested that optical-based algorithms provide for a significant increase in accuracy over the radar-based algorithms. However, in regions with persistent cloud cover, the radar sensors may provide a complementary data source for wall to wall BA detection.

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Section: Introductionmentioning

confidence: 99%

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

et al. 2020

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“…Fortunately, BA detection and mapping is well established and has been studied since the dawn of satellite imagery [5] with most recent studies focusing on (a) the development and improvement of detection and mapping techniques [6][7][8], (b) enhancement of existing global products both in detection accuracy and spatial detail [9,10] and (c) the inter-comparison and validation in different environmental settings and regions [11]. Additionally, the availability of operational satellite-based products, such as land cover, temperature, rainfall, tree cover, etc., provide prospects for assessing and quantifying the impact of wildfires on the ecosystems and biodiversity.…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Kganyago

Shikwambana

2019

Sustainability

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Globally, wildfires are considered the most commonly occurring disasters, resulting from natural and anthropogenic ignition sources. Wildfires consist of burning standing biomass at erratic degrees of intensity, severity, and frequency. Consequently, wildfires generate large amounts of smoke and other toxic pollutants that have devastating impacts on ambient air quality and human health. There is, therefore, a need for a comprehensive study that characterizes land-atmosphere interactions with regard to wildfires, critical for understanding the interrelated and multidimensional impacts of wildfires. Current studies have a limited scope and a narrow focus, usually only focusing on one aspect of wildfire impacts, such as air quality without simultaneously considering the impacts on land surface changes and vice versa. In this study, we use several multisource data to determine the spatial distribution, frequency, disturbance characteristics of and variability and distribution of pollutants emitted by wildfires. The specific objectives were to (1) study the sources of wildfires and the period they are prevalent in sub-Saharan Africa over a 9 year period, i.e., 2007-2016, (2) estimate the seasonal disturbance of wildfires on various vegetation types, (3) determine the spatial distribution of black carbon (BC), carbon monoxide (CO) and smoke, and (4) determine the vertical height distribution of smoke. The results show largest burned areas in December-January-February (DJF), June-July-August (JJA) and September-October-November (SON) seasons, and reciprocal high emissions of BC, CO, and smoke, as observed by Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In addition, the results reveal an increasing trend in the magnitude of BC, and CO concentration driven by meteorological conditions such as low precipitation, low relative humidity, and low latent heat flux. Overall, this study demonstrates the value of multisource remotely sensed data in characterising long-term wildfire patterns and associated emissions. The results in this study are critical for informing better regional fire management and air quality control strategies to preserve endangered species and habitats, promote sustainable land management, and reduce greenhouse gases (GHG) emissions.

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“…The RF algorithm was selected to create the LTDR BA classification because it has been shown to be quite robust in many land cover classification studies [45][46][47] that also includes BA discrimination [48][49][50]. RF is a machine learning algorithm that is based on generating a combination of decision trees that are independent of each other.…”

Section: Random Forest Modelmentioning

confidence: 99%

Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data

et al. 2019

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This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images. Daily images were combined in monthly composites using the maximum temperature criterion to enhance the burned signal and eliminate clouds and artifacts. A synthetic BA index was created to improve the detection of the BA signal. This index included red and near infrared reflectance, surface temperature, two spectral indices, and their temporal differences. Monthly models were generated using the random forest classifier, using the twelve monthly composites of each year as the predictors. Training data were obtained from the NASA MCD64A1 collection 6 product (500 m spatial resolution) for eight years of the overlapping period (2001–2017). This included some years with low and high fire occurrence. Results were tested with the remaining eight years. Pixels classified as burned were converted to burned proportions using the MCD64A1 product. The final product (named FireCCILT10) estimated BA in 0.05-degree cells for the 1982 to 2017 period (excluding 1994, due to input data gaps). This product is the longest global BA currently available, extending almost 20 years back from the existing NASA and ESA BA products. BA estimations from the FireCCILT10 product were compared with those from the MCD64A1 product for continental regions, obtaining high correlation values (r2 > 0.9), with better agreement in tropical regions rather than boreal regions. The annual average of BA of the time series was 3.12 Mkm2. Tropical Africa had the highest proportion of burnings, accounting for 74.37% of global BA. Spatial trends were found to be similar to existing global BA products, but temporal trends showed unstable annual variations, most likely linked to the changes in the AVHRR sensor and orbital decays of the NOAA satellites.

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

Cited by 132 publications

References 51 publications

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data

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