Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination (original) (raw)
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Burnt Area Detection Using Medium Resolution Sentinel 2 and Landsat 8 Satellites
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Forest fire is one of the most serious environmental problems in Kenya that influences human activities, climate change and biodiversity. The main goal of this study is to apply medium resolution sensors (Landsat 8 OLI and Sentinel 2 MSI) to produce burnt area severity maps that will include small fires (<100 ha) in order to improve burnt area detection and mapping in Kenya. Normalized burnt area indices were generated for specified pre-and post-fire periods. The difference between pre-and post-fire Normalized Burnt Ration (NBR) was used to compute δNBR index depicting forest disturbance by fire events. Thresholded classes were derived from the computed δNBR indices to obtain burnt severity maps. The spatial and temporal agreements of the Burnt area detection dates were validated by comparing against the MODIS MCD641 500 m products and MODIS Fire Information for Resource Management System (FIRMS) 1 km daily product hot-spot acquisition dates. This approach was implemented on Google Earth Engine (GEE) platform with a simple user interface that allows users to auto-generate burnt area maps and statistics. The operational GEE application developed can be used to obtain burnt area severity maps and statistics that allow for initial accurate approximation of fire damage.
Remote Sensing, 2020
Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.
Forest fires are an integral part of Mediterranean ecosystems and a key factor in forest fire management. Accurate information regarding the spatial extent of burned areas is essential for the quantification of the environmental impact of forest fires while at long term such information could be used in improving existing forest fire management plans. The aim of this study was to evaluate the efficiency of several spectral indices in burned area mapping using object-based image analysis (OBIA) and medium (Landsat5 TM-30m) and very high (IKONOS pan-sharpened-1m) resolution satellite imagery. In the case) were additionally employed. The multiresolution segmentation algorithm was selected and applied to all layers generated from the computation of the aforementioned spectral indices. Training samples were defined based on the multispectral pansharpened IKONOS image and used in the classification process in all different cases. Results were statistically and spatially compared with the ...
An Unsupervised Burned Area Mapping Approach Using Sentinel-2 Images
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The frequency and severity of large, destructive fires have increased in the recent past, with extended impacts on the landscape, the human population, and ecosystems. Earth observations provide a means for the frequent, wide coverage and accurate monitoring of fire impacts. This study describes an unsupervised approach for the mapping of burned areas from Sentinel-2 satellite imagery, which is based on multispectral thresholding, and introduces an adaptive thresholding method. It takes into account the localized variability of the spectral responses in a two-phase approach. The first phase detects areas that are burned with a high probability, while the second phase adaptively adjusts this preliminary mapping by expanding and refining its boundaries. The resulting classification contains two main classes of interest: burned and unburned. The latter is further classified into four (4) fire impact severity classes, according to the Copernicus Emergency Management Service (CEMS) and t...
A NEW OPTIMAL INDEX FOR BURNT AREA DISCRIMINATION IN SATELLITE IMAGERY
Biomass burning is a significant global source of greenhouse gases (e.g. carbon dioxide and methane) as well as of nitric and carbon monoxides, methyl bromide and hydrocarbons that lead to acid rain and the photochemical production of tropospheric ozone and destruction of stratospheric ozone which affect global climate. Other impacts relate to the biogeochemical cycling of nitrogen and carbon compounds, the hydrological cycle, the reflectivity and emissivity of the land, the stability of ecosystems and ecosystem biodiversity. An accurate identification of burnt areas is therefore of paramount importance and we present a new vegetation index with optimal properties for burnt area discrimination. We begin by demonstrating the advantages of using the reflective part of the middle infrared (MIR) signal for burnt area detection. This is achieved by evaluating the performance of MODIS visible (bands 1 to 7) and MIR (band 20) channels in burnt area detection. Performance of channel 20 data is both evaluated using surface full normalized radiances (i.e. the sum of emitted and reflected components of the signal) and restricting to the reflected component. A comparison is then performed on the ability of a set of indices to discriminate burnt areas. The set includes the well known normalized difference vegetation index (NDVI), the global environment monitoring index (GEMI) and the enhanced vegetation index (EVI) that are traditionally defined in the red-NIR space; as well as VI20, GEMI20 and EVI20 that were obtained by adapting the previous indices to the MIR-NIR space. Performance is evaluated based on confusion matrices and on signal-to-noise ratio measurements. Obtained results show that vegetation indices defined in the red-NIR space are not suitable to detect burnt surfaces and that better alternatives are available, namely if using solely channel 2. All vegetation indices defined in the MIR-NIR space exhibit an improvement in performance when compared to single-channels and to indices based on the red signal. Vegetation indices based on the reflective part of channel 20 show better results than those derived based on the normalized total radiance of channel 20. However the largest improvement in ability to detect burnt surfaces was obtained with the newly proposed EVI20 index, which consists of a modified EVI that uses the reflective part of MODIS channel 20 in place of channel 1. The new index has the advantage of significantly decreasing the number of omission errors which was still too high in the cases of GEMI20 and VI20.
Utilization of Hyperspectral Remote Sensing Imagery for Improving Burnt Area Mapping Accuracy
Remote Sensing
Wildfires pose a direct threat when occurring close to populated areas. Additionally, their significant carbon and climate feedbacks represent an indirect threat on a global, long-term scale. Monitoring and analyzing wildfires is therefore a crucial task to increase the understanding of interconnections between fire and ecosystems, in order to improve wildfire management activities. This study investigates the suitability of 232 different red/near-infrared band combinations based on hyperspectral imagery of the DESIS sensor with regard to burnt area detection accuracy. It is shown that the selection of wavelengths greatly influences the detection quality, and that especially the utilization of lower near-infrared wavelengths increases the yielded accuracy. For burnt area analysis based on the Normalized Difference Vegetation Index (NDVI), the optimal wavelength range has been found to be 660–670 nm and 810–835 nm for the red band and near-infrared band, respectively.
Remote Sensing of Environment, 2014
Fire severity EnMAP HyspIRI PRISMA Hyperspectral Wildland fire Forest fire AVIRIS OLI Weighted multiple endmember spectral mixture analysis (wMESMA) Fire severity, the degree of environmental change caused by a fire, is traditionally assessed by broadband spectral indices, such as the differenced Normalized Burn Ratio (dNBR) from Landsat imagery. Here, we used an alternative indicator, the burned fraction derived from spectral mixture analysis (SMA), to evaluate and compare the performance for assessing fire severity of broadband and narrowband imaging spectroscopy (IS) data in the visible to shortwave infrared (VSWIR, 0.35-2.5 μm). We used the band specifications of the broadband Operational Land Imager (OLI) and the narrowband Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). We integrated two techniques to account for endmember variability in the unmixing process, spectral weighting and iterative unmixing, in a model referred to as weighted multiple endmember SMA (wMESMA). Based on a separability index, we evaluated the separability between the different ground components, or endmembers, that comprise post-fire environments (char, green vegetation (GV), non-photosynthetic vegetation (NPV) and substrate). We found that the near infrared region (0.7-1.3 μm) had the highest discriminatory power, followed by the shortwave infrared 2 (SWIR2, 2-2.4 μm), SWIR1 (1.5-1.7 μm) and visible (0.35-0.7 μm) regions. Individual narrowbands did not substantially outperform individual broadbands, however, the higher data dimensionality of IS resulted in significantly improved post-fire fractional cover and burned fraction estimates compared to multispectral data. Multispectral data captured a fair amount of the variability in fire severity conditions as represented by the different fractional cover estimates of the endmembers in both a multispectral narrow-and broadband scenario, however, fractional cover estimates derived from IS data using all viable bands were significantly better. This demonstrated the benefits of IS over traditional multispectral data to assess fire severity and also showed that the additional information gain was the result of higher data dimensionality and not because of certain narrowbands capturing narrow spectral features. In addition, we found that the burned fraction derived from all viable AVIRIS bands over a fire in California, USA, was highly correlated with two field measures of fire severity (Geo Composite Burn Index: R 2 = 0.86, and the percentage black trees and shrubs: R 2 = 0.65). Formal quantification of potential improvements of IS over multispectral methods is important with the advent of upcoming spaceborne IS missions (e.g. the Environmental Mapping and Analysis Program and Hyperspectral Infrared Imager). Our analysis showed that IS data when combined with advanced analysis techniques significantly improved fire severity assessments. The improvements of using IS data require higher computational cost and advanced processing, thus multispectral data might still suit the needs of certain applications such as rapid fire damage assessments and global analysis of spatio-temporal fire severity patterns.
International Journal of Remote Sensing, 2002
A new spectral index named Burned Area Index (BAI), speci cally designed for burned land discrimination in the red-near-infrared spectral domain, was tested on multitemporal sets of Landsat Thematic Mapper (TM) and NOAA Advanced Very High Resolution Radiometer (AVHRR) images. The utility of BAI for burned land discrimination was assessed against other widely used spectral vegetation indices: Normalized Di V erence Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Global Environmental Monitoring Index (GEMI). BAI provided the highest discrimination ability among the indices tested. It also showed a high variability within scorched areas, which reduced the average normalized distances with respect to other indices. A source of potential confusion between burned land areas and low-re ectance targets, such as water bodies and cloud shadows, was identi ed. Since BAI was designed to emphasize the charcoal signal in post-re images, this index was highly dependent on the temporal permanence of charcoal after res.
International Journal of Remote Sensing
The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current (post-fire) and historical (pre-fire) data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data beco...
Sentinel-2 MSI data for active fire detection in major fire-prone biomes: A multi-criteria approach
International Journal of Applied Earth Observation and Geoinformation
Sentinel-2 MultiSpectral Instrument (MSI) data exhibits the great potential of enhanced spatial and temporal coverage for monitoring biomass burning which could complement other coarse active fire detection products. This paper aims to investigate the use of reflective wavelength Sentinel-2 data to classify unambiguous active fire areas from inactive areas at 20 m spatial resolution. A multi-criteria approach based on the reflectance of several bands (i.e. B4, B11, and B12) is proposed to demonstrate the boundary constraints in several representative biomes. It is a fully automatic algorithm based on adaptive thresholds that are statistically determined from 11 million Sentinel-2 observations acquired over corresponding summertime (June 2019 to September 2019) across 14 regions or countries. Biome-based parameterizations avoid high omission errors (OE) caused by small and cool fires in different landscapes. It also takes advantage of the multiple criteria whose intersection could reduce the potential commission errors (CE) due to soil dominated pixels or highly reflective building rooftops. Active fire detection performance was mainly evaluated through visual inspection on eight illustrative subsets because of unavailable ground truth. The detection results revealed that CE and OE could be kept at a low level with 0.14 and 0.04 as an acceptable trade-off. The proposed algorithm can be employed for rapid active fire detection as soon as the image is obtained without the requirement of using multi-temporal imagery, and can even be adapted to onboard processing in the future.