Hyperspectral remote sensing of fire: State-of-the-art and future perspectives

Veraverbeke, Sander; Dennison, Philip E.; Gitas, Ioannis Z.; Hulley, Glynn; Калашникова, О. В.; Katagis, Thomas; Kuai, Le; Meng, Ran; Roberts, Dar A.; Stavros, Natasha

doi:10.1016/j.rse.2018.06.020

Cited by 127 publications

(74 citation statements)

References 175 publications

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“…In addition, an adequate post-fire management policy that prevents soil losses and promotes vegetation regeneration can only be based on accurate fire damage maps (burned area / burn severity) [4]. Nowadays, reliable burned area estimates at all scales are obtained using remote sensing data and techniques [5]. In particular, hyperspectral remote sensing of fire damage enables the accurate discrimination and quantification of burned areas, burn severity, and vegetation recovery [5].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, reliable burned area estimates at all scales are obtained using remote sensing data and techniques [5]. 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%

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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%

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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“…Their success is related to the characteristics of the short-term post-fire domain that usually produces a mix of remnants of vegetation, ash and burned soil, essentially constituting a sub-pixel question at the spatial resolution of commonly used multispectral sensors [50]. In addition, most spectral indices are based on just two spectral bands, neglecting the information included using all bands, whereas fraction images are typically computed using all spectral bands [51].…”

Section: Introductionmentioning

confidence: 99%

Vegetation and Soil Fire Damage Analysis Based on Species Distribution Modeling Trained with Multispectral Satellite Data

et al. 2019

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Forest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers.

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“…Similarly, remote sensing techniques provide a widely used alternative to field-measured burn damage [14,[48][49][50][51][52][53]. Operational examples include the European Forest Fire Information System (EFFIS) and the Monitoring Trends in Burn Severity (MTBS) project in the USA.…”

Section: Introductionmentioning

confidence: 99%

Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?

2020

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Forest managers rely on accurate burn severity estimates to evaluate post-fire damage and to establish revegetation policies. Burn severity estimates based on reflective data acquired from sensors onboard satellites are increasingly complementing field-based ones. However, fire not only induces changes in reflected and emitted radiation measured by the sensor, but also on energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo (LSA) are greatly affected by wildfires. In this study, we examine the usefulness of these elements of energy balance as indicators of burn severity and compare the accuracy of burn severity estimates based on them to the accuracy of widely used approaches based on spectral indexes. We studied a mega-fire (more than 450 km2 burned) in Central Portugal, which occurred from 17 to 24 June 2017. The official burn severity map acted as a ground reference. Variations induced by fire during the first year following the fire event were evaluated through changes in ET, LST and LSA derived from Landsat data and related to burn severity. Fisher’s least significant difference test (ANOVA) revealed that ET and LST images could discriminate three burn severity levels with statistical significance (uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate (κ = 0.63 and 0.57, respectively), similar to the accuracy of the estimate based on dNBR (κ = 0.66). We conclude that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting as useful indicators of burn severity for mega-fires in Mediterranean ecosystems, may provide critical information about how energy balance changes due to fire.

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Hyperspectral remote sensing of fire: State-of-the-art and future perspectives

Cited by 127 publications

References 175 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

Vegetation and Soil Fire Damage Analysis Based on Species Distribution Modeling Trained with Multispectral Satellite Data

Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?

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