Fire detection and temperature retrieval using EO-1 Hyperion data over selected Alaskan boreal forest fires

Waigl, Christine F.; Prakash, Anupma; Stuefer, Martin; Verbyla, David L.; Dennison, Philip E.

doi:10.1016/j.jag.2019.03.004

Cited by 26 publications

(35 citation statements)

References 49 publications

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“…In particular, hyperspectral remote sensing of fire damage enables the accurate discrimination and quantification of burned areas, burn severity, and vegetation recovery [5]. Hyperspectral imagery has been successfully used in different fire studies [6,7]; the Hyperion sensor onboard the Earth-Observing One (EO-1) platform provided data that have been successfully utilized for fire detection [8,9] and burn severity mapping [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Fernández–Manso

Quintano

2020

Remote Sensing

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Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI 750 ), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.

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Section: Introductionmentioning

confidence: 99%

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Fernández–Manso

Quintano

2020

Remote Sensing

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“…The spatial resolution of satellite hyperspectral sensors (typical 30 m) means that a fire can occupy a fraction of the pixel, weakening the radiance measured at the sensor down to a level similar to that of the solar radiance background [6]. Hyperspectral techniques developed for active fire characterization focus on fire detection [2,[7][8][9] and temperature retrieval [6,10]. While great potential is reported in the literature concerning the use of airborne sensors, the exploitation [6,7] of imaging spectroscopy from space has been limited to sensors on just one satellite, EO1-Hyperion, operating between 2000 and 2017.…”

Section: Introductionmentioning

confidence: 99%

“…This study analyzed, for the first time, the scene of a wildfire provided by the new PRISMA mission to test a range of fire detection indices. By using an approach similar to that developed by Waigl [10], we examined three diverse fire detection methods (CO 2 -continuum interpolated band ratio (CIBR), hyperspectral fire detection index (HFDI) and advanced K band difference (AKBD) and explored PRISMA's ability to characterize biomass burning. The metrics were originally developed for airborne sensors [2,5,6] and subsequently tested on data from space [10].…”

Section: Introductionmentioning

confidence: 99%

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Amici

Piscini

2021

Remote Sensing

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Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.

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“…Optical sensors use the emission properties of materials to characterize land surface objects including forest fires, thus the forest fires were detected from space using the unique spectra characteristics of wood (Souza Jr et al 2005;Waigl et al 2019). In the Infrared region of the Electromagnetic radiation (EMR) spectrum, burned soils absorb the incoming energy, whereas the surrounding land features including vegetation are comparatively high reflective.…”

Section: Introductionmentioning

confidence: 99%

“…The data decorrelation produced in this process is extremely significant in change detection analysis in multitemporal Landsat multi-spectral image data (Elhag 2016;Li et al 2013). This specific method is known as multi-temporal PCA and discriminates the differences of the burnt areas using the pre and post-fire differences of the area of interest (Lanorte et al 2015;Singh and Harrison 1985;Waigl et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

et al. 2020

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Forest fires are a common feature in the Mediterranean forests through the years, as a wide tract of forest fortune is lost because of the incendiary fires in the forests. The enormous damages caused by forest fires enhanced the efforts of scientists towards the attenuation of the negative effects of forest fire and consequently the minimization of biodiversity losses by searching more for the adequate distribution of attempts on forest fire prevention and, suppression. The multi-temporal Principal Components Analysis is applied to a pair of images of consecutive years obtained from Landsat-8 satellite to unconventional map and assess the spatial extent of the burned areas on the island of Thasos, Greece. First, the PCA was applied on the before fire image, and then a multi-temporal image is created from the 3rd, 4th, and 5th band of before and after images including Normalized Difference Vegetation Index to enhance the results. The results from the different steps of this analysis robustly mapped the burned areas by 82.28 ha confirmed by almost 85%. Are compared with data provided by the local forest service in order to assess their accuracy. The multi-temporal PCA outputs including NDVI (PC 4, PC %, and PC 6) give better accuracy due to its ability to distinguish the burned areas of older years and to the Normalized Difference Vegetation Index that gives better variance to the image.

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Fire detection and temperature retrieval using EO-1 Hyperion data over selected Alaskan boreal forest fires

Cited by 26 publications

References 49 publications

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots

Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Evaluation of Optical Remote Sensing Data in Burned Areas Mapping of Thasos Island, Greece

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