Wildfire temperature and land cover modeling using hyperspectral data

Dennison, Philip E.; Charoensiri, Kraivut; Roberts, Dar A.; Peterson, S.; Green, Robert O.

doi:10.1016/j.rse.2005.10.007

Cited by 130 publications

(65 citation statements)

References 21 publications

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“…Thus, it can provide daily information but is limited to low-spatial resolution. Therefore, downscaling of MODIS LST must be investigated to enhance the spatial resolution of thermal images with relatively low resolution [13].…”

Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

et al. 2017

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Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis-desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 • C) and highest temperatures (higher than 37 • C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 • C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions.

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Section: Introductionmentioning

confidence: 99%

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

et al. 2017

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“…As a result, for a flaming combustion region, the disaggregated BT was relatively higher in the margin area in the proposed disaggregation method due to the input of the SWIR-2 band, and the smoldering effect was also reflected by the tendency of the TIR band. However, temperature of the combustion region is not equal to the disaggregated BT due to a land surface temperature range from 1000 K to 1500 K in flaming combustion conditions [40]. Also, conversion the BT into land surface temperature was not conducted in this work.…”

Section: Discussionmentioning

confidence: 98%

“…Based on both theorems, as the temperature of a blackbody increases, the wavelength of peak radiance shifts to shorter wavelengths and the total emitted radiance from the blackbody increases as shown in Figure 1, and the shorter wavelength is more sensitive to higher temperatures. In terms of emitted radiation, Figure 1 shows that the maximum radiation at a temperature of 1300 K appears at a wavelength of 2.2 μm, which means that it is highly efficient in detecting wildfires, considering land surface temperatures range from 1000 K to 1500 K under flaming combustion [40], and the SWIR-2 spectral response function of the Landsat-8 OLI includes this wavelength. However, the detected radiance by the sensor is composed of reflected solar radiation and emitted thermal radiation.…”

Section: Characteristics Of Swir Wavelengthmentioning

confidence: 99%

“…Practical applications of thermal data, for example, wildfires [40][41][42], volcanic activity [43,44], and land cover classification [45], however, are still limited in medium resolution sensors like Landsat-8 or advanced spaceborne thermal emission and reflection radiometer (ASTER) products. Some previous work reported that the VI-based disaggregation method of the TIR was limited in heterogeneous areas [12,22,46]; however, the limitations of previous VI-based disaggregation on fire affected areas have not been reported.…”

Section: Introductionmentioning

confidence: 99%

“…To deal with these issues, this study chose a SWIR band as the input of the alternative disaggregation algorithm. The SWIR band imagery has a finer resolution and can be used to estimate high temperatures in flaming combustion situations due to its radiometric feature [40]. In addition, in order to preserve spectral information in the disaggregated result, a guided image filter (GF) [47], that is, an edge-preserving smoothing operator, may be employed.…”

Section: Introductionmentioning

confidence: 99%

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Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

Cho

Kim

2018

Remote Sensing

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Thermal data products derived from remotely sensed data play significant roles as key parameters for biophysical phenomena. However, a trade-off between spatial and spectral resolutions has existed in thermal infrared (TIR) remote sensing systems, with the end product being the limited resolution of the TIR sensor. In order to treat this problem, various disaggregation methods of TIR data, based on the indices from visible and near-infrared (VNIR), have been developed to sharpen the coarser spatial resolution of TIR data. Although these methods were reported to exhibit sufficient performance in each study, preservation of thermal variation in the original TIR data is still difficult, especially in fire areas due to the distortion of the VNIR reflectance by the impact of smoke. To solve this issue, this study proposes an efficient and improved disaggregation algorithm of TIR imagery on wildfire areas using guided shortwave infrared (SWIR) band imagery via a guided image filter (GF). Radiometric characteristics of SWIR wavelengths could preserve spatially high frequency temperature components in flaming combustion, and the GF preserved thermal variation of the original TIR data in the disaggregated result. The proposed algorithm was evaluated using Landsat-8 operational land imager (OLI) and thermal infrared sensor (TIRS) images on wildfire areas, and compared with other algorithms based on a vegetation index (VI) originating from VNIR. In quantitative analysis, the proposed disaggregation method yielded the best values of root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), erreur relative globale adimensionelle de synthèse (ERGAS), and universal image quality index (UIQI). Furthermore, unlike in other methods, the disaggregated temperature map in the proposed method reflected the thermal variation of wildfire in visual analysis. The experimental results showed that the proposed algorithm was successfully applied to the TIR data, especially to wildfire areas in terms of quantitative and visual assessments.

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Water‐Enhancing Gels Exhibiting Heat‐Activated Formation of Silica Aerogels for Protection of Critical Infrastructure During Catastrophic Wildfire

Dong,

d'Aquino,

Sen

et al. 2024

Advanced Materials

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A promising strategy to address the pressing challenges with wildfire, particularly in the wildland‐urban interface (WUI), involves developing new approaches for preventing and controlling wildfire within wildlands. Among sprayable fire‐retardant materials, water‐enhancing gels have emerged as exceptionally effective for protecting civil infrastructure. They possess favorable wetting and viscoelastic properties that reduce the likelihood of ignition, maintaining strong adherence to a wide array of surfaces after application. Although current water‐enhancing hydrogels effectively maintain surface wetness by creating a barricade, they rapidly desiccate and lose efficacy under high heat and wind typical of wildfire conditions. To address this limitation, unique biomimetic hydrogel materials from sustainable cellulosic polymers crosslinked by colloidal silica particles are developed that exhibit ideal viscoelastic properties and facile manufacturing. Under heat activation, the hydrogel transitions into a highly porous and thermally insulative silica aerogel coating in situ, providing a robust protective layer against ignition of substrates, even when the hydrogel fire suppressant becomes completely desiccated. By confirming the mechanical properties, substrate adherence, and enhanced substrate protection against fire, these heat‐activatable biomimetic hydrogels emerge as promising candidates for next‐generation water‐enhancing fire suppressants. These advancements have the potential to dramatically improve the ability to protect homes and critical infrastructure during wildfire.

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Wildfire temperature and land cover modeling using hyperspectral data

Cited by 130 publications

References 21 publications

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression

Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

Water‐Enhancing Gels Exhibiting Heat‐Activated Formation of Silica Aerogels for Protection of Critical Infrastructure During Catastrophic Wildfire

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