Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa

. This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer
(MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly
data, thus providing the highest spatial resolution (approx. 250 m) among
the existing global BA datasets. The product includes the full times series
(2001–2016) of the Terra-MODIS archive. The BA detection algorithm was based
on monthly composites of daily images, using temporal and spatial distance
to active fires. The algorithm has two steps, the first one aiming to reduce
commission errors by selecting the most clearly burned pixels (seeds), and
the second one targeting to reduce omission errors by applying contextual
analysis around the seed pixels. This product was developed within the
European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the
Fire Disturbance project (Fire_cci). The final output
includes two types of BA files: monthly full-resolution continental tiles
and biweekly global grid files at a degraded resolution of 0.25 ∘ .
Each set of products includes several auxiliary variables that were defined
by the climate users to facilitate the ingestion of the product into global
dynamic vegetation and atmospheric emission models. Average annual burned
area from this product was 3.81 Mkm 2 , with maximum burning in 2011 (4.1 Mkm 2 )
and minimum in 2013 (3.24 Mkm 2 ). The validation was based on
a stratified random sample of 1200 pairs of Landsat images, covering the
whole globe from 2003 to 2014. The validation indicates an overall accuracy
of 0.9972, with much higher errors for the burned than the unburned category
(global omission error of BA was estimated as 0.7090 and global commission
as 0.5123). These error values are similar to other global BA products, but
slightly higher than the NASA BA product (named MCD64A1, which is produced
at 500 m resolution). However, commission and omission errors are better
compensated in our product, with a tendency towards BA underestimation
(relative bias −0.4033 ), as most existing global BA products. To understand
the value of this product in detecting small fire patches (  ha),
an additional validation sample of 52 Sentinel-2 scenes was generated
specifically over Africa. Analysis of these results indicates a better
detection accuracy of this product for small fire patches (  ha)
than the equivalent 500 m MCD64A1 product, although both have high errors for
these small fires. Examples of potential applications of this dataset to
fire modelling based on burned patches analysis are included in this paper.
The datasets are freely downloadable from the Fire_cci
website ( https://www.esa-fire-cci.org/ , last access: 10 November 2018) and their repositories (pixel at
full resolution: https://doi.org/cpk7 , and grid: https://doi.org/gcx9gf ).

/pdf/generation-and-analysis-of-a-new-global-burned-area-product-2xy26m45et.pdf

Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies

Global burned area mapping from ENVISAT-MERIS and MODIS active fire data

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.

https://www.pnas.org/content/pnas/118/9/e2011160118.full.pdf

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

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.

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

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGISTM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool’s structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

Amaia Mesanza

Papers

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data