Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

Harris, Sarah; Veraverbeke, Sander; Hook, Simon J.

doi:10.3390/rs3112403

Cited by 74 publications

(66 citation statements)

References 73 publications

(161 reference statements)

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“…Other studies obtained similar results, e.g., better discrimination capabilities of BAI [18,22,28], Greenness [24,62], or MIRBI in shrub-savannah ecosystems [25,27,52]. Although NBR is often considered the best SI for burned area mapping short time after fire and therefore widely used for burn severity assessments [7,19,21,23,[27][28][29], our results demonstrated that in certain vegetation types other SI would offer a better option. The limitations of NBR for immediate post-fire assessment were already pointed out by Roy et al [76].…”

Section: Summary Of Results Which Si Should We Choose?supporting

confidence: 79%

“…Initially designed for burned area extraction, NBR is the most popular spectral index used for burn severity assessments with different sensors in several ecosystems around the world. In numerous comparative analyses, NBR proved to be one of the most efficient SI [14,19,21,23,28]. In time series analyses, NBR showed good correlations with field-based composite burn index scores several years after a fire, thus representing an efficient tool for vegetation recovery monitoring [29,30].…”

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 94%

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Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018

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Abstract:Remote mountainous regions are among the Earth's last remaining wild spots, hosting rare ecosystems and rich biodiversity. Because of access difficulties and low population density, baseline information about natural and human-induced disturbances in these regions is often limited or nonexistent. Landsat time series offer invaluable opportunities to reconstruct past land cover changes. However, the applicability of this approach strongly depends on the availability of good quality, cloud-free images, acquired at a regular time interval, which in mountainous regions are often difficult to find. The present study analyzed burn scar detection capabilities of 11 widely used spectral indices (SI) at 1 to 5 years after fire events in four dominant vegetation groups in a mountainous region of northwest Yunnan, China. To evaluate their performances, we used M-statistic as a burned-unburned class separability index, and we adapted an existing metric to quantify the SI residual burn signal at post-fire dates compared to the maximum severity recorded soon after the fire. Our results show that Normalized Burn Ratio (NBR) and Normalized Difference Moisture Index (NDMI) are always among the three best performers for the detection of burn scars starting 1 year after fire but not for the immediate post-fire assessment, where the Mid Infrared Burn Index, Burn Area Index, and Tasseled Cap Greenness were superior. Brightness and Wetness peculiar patterns revealed long-term effects of fire in vegetated land, suggesting their potential integration to assist other SI in burned area detection several years after the fire event. However, in general, class separability of most of the SI was poor after one growing season, due to the seasonal rains and the relatively fast regrowth rate of shrubs and grasses, confirming the difficulty of assessment in mountainous ecosystems. Our findings are meaningful for the selection of a suitable SI to integrate in burned area detection workflows, according to vegetation type and time lag between image acquisitions.

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Section: Summary Of Results Which Si Should We Choose?supporting

confidence: 79%

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 94%

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

2018

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“…Spectral vegetation indices have been proven useful in monitoring seasonal variations in vegetation development (phenological cycle) [43,44], as well as post-fire plant regeneration [45,46]: strong correlations were observed between the NDVI and various biophysical vegetation parameters, such as Leaf Area Index (LAI), the fraction of photosynthetically active radiation (fPAR) or vegetation abundance [47]. Although relationships between burn severity, NDVI and LST values seem quite clear, few studies have explored these [8,48,49]. There are indications that the inclusion of thermal information in spectral indices for severity mapping improves their performance [48,49].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

et al. 2014

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Abstract:The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

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“…Thermal infrared (TIR) remote sensing provides spatially distributed estimates of land-surface temperature (LST) that can be used for detecting wild fires [1,2], mapping land surface energy fluxes and evapotranspiration [3][4][5][6][7][8][9][10], monitoring urban heat fluxes [11][12][13][14][15] and detecting drought [7,8,16]. For many of these applications, TIR data are required at relatively fine resolution.…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

2012

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Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS) technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the OPEN ACCESS Remote Sens. 2012, 4 3288 sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that provide high quality thermal band imagery at high temporal and spatial resolution critical for many agricultural, land use and water resource management applications.

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Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

Cited by 74 publications

References 73 publications

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

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