Improving thematic mapper based classification of wildfire induced vegetation mortality

Medler, Michael J.; Yool, Stephen R.

doi:10.1080/10106049709354573

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…Third, sub-canopy surface burn (underburn) is practically undetectable in satellite imagery when upper-canopy foliar matter is unaltered by fire (Caetano et al, 1994;Medler and Yool, 1997). Authors have typically assigned the classification label of 'light burn' to areas where only ground fuels have been fire-altered, while canopy crowns remain unaltered (White et al, 1996;Fung and Jim, 1998).…”

Section: Challenges In Detecting Burn Severitymentioning

confidence: 98%

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Rogan¹,

Franklin²

2001

Geocarto International

107

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Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non-photosynthetic vegetation) and a single (charcoal-ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire-affected areas due to its ability to extract sub-pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.

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Section: Challenges In Detecting Burn Severitymentioning

confidence: 98%

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Rogan¹,

Franklin²

2001

Geocarto International

107

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“…The strength of the KT transform is that the coeYcients of brightness, greenness and especially wetness appear associated with 'minority' burned area scene characteristics (Patterson and Yool 1998). Medler and Yool (1997) combined composite terrain and TM imagery in a supervised classi cation to map wild re mortality. Error matrices demonstrate d this combination of satellite and ancillary data provided a 40% improvement in accuracy compared to TM data alone.…”

Section: Moderate Spatial Resolution Sensorsmentioning

confidence: 99%

Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico

Rogan¹,

Yool²

2001

International Journal of Remote Sensing

131

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Forest depletions caused by re can be detected by remote sensors if the change event causes a change in surface re ective or thermal properties. Pre and post-re TM imagery of a semi-arid region was used to map the burn scar of a management-ignited re into three classes of re severity. The multitemporal imagery was enhanced using several brightness, greenness, and wetness indices. The wetness indices were most accurate at delineating re severity because re severity appears related to changes in plant and soil moisture content. Overall kappa for Kauth Thomas D wetness, the TM 7/4 index, and the second standardised Principal Component were 0.62, 0.59, and 0.61 respectively. The highest overall kappa of 0.66 was achieved using combined Kauth Thomas D brightness, D greenness and D wetness indices.

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“…Historic fire regimes are often studied using dendrochronological techniques to date fire scars (Batek et al, 1999;Heyerdahl et al, 2001;González, 2005;Guyette et al, 2006) and infer fire return interval, fire size and frequency. Current fire regime is often studied by using remote sensing techniques (Mollicone et al, 2002) based on vegetation succession stages and spatial patterns of burnt areas to infer fire return intervals and to map fire intensity (White et al, 1996;Melder and Yool, 1997;Rollins et al, 2004). However, these research approaches were limited in spatial extents and time scale.…”

Section: Introductionmentioning

confidence: 99%

Historic and current fire regimes in the Great Xing’an Mountains, northeastern China: Implications for long-term forest management

Chang

et al. 2008

Forest Ecology and Management

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Improving thematic mapper based classification of wildfire induced vegetation mortality

Cited by 19 publications

References 10 publications

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Mapping Wildfire Burn Severity in Southern California Forests and Shrublands Using Enhanced Thematic Mapper Imagery

Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico

Historic and current fire regimes in the Great Xing’an Mountains, northeastern China: Implications for long-term forest management

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