Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper Data

Patterson, Mark W.; Yool, Stephen R.

doi:10.1016/s0034-4257(98)00018-2

Cited by 138 publications

(82 citation statements)

References 19 publications

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“…Similar criteria were used by previous studies (e.g. Jakubauskas et al, 1990;Patterson and Yool, 1998;Rogan and Yool, 2001) since a standard method to field measure burn severity, such as the Composite Burn Index, was not com monly accepted until approximately 2006 (Key and Benson, 2006). Each plot was assigned to one of the four defined burn severity levels considering the majority burn severity class observed in each plot.…”

Section: Ground Measured Datamentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999-2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

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Section: Ground Measured Datamentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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“…Moderately severe burned categories described a wide range of conditions from some litter present to all surface organic material consumed. This was particularly true for post-fire assessment protocols [7,8,38,39]. The most severely burned categories were often reserved for areas where the forest floor was totally consumed [3,7,40].…”

Section: Resultsmentioning

confidence: 99%

“…Prescribed fire 4 [8] Remote sensing 4 [35] Remote sensing 3 --- [37] Physical setting 3 --- [38] Remote Underlying rock may be exposed when surface organic material is consumed during a fire. Because this is site specific and not necessarily common, we did not include exposed rock in the PFI.…”

Section: S U B G U B G U B G O Biological Effectsmentioning

confidence: 99%

Index for Characterizing Post-Fire Soil Environments in Temperate Coniferous Forests

Jain

Pilliod

Graham

et al. 2012

Forests

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Many scientists and managers have an interest in describing the environment following a fire to understand the effects on soil productivity, vegetation growth, and wildlife habitat, but little research has focused on the scientific rationale for classifying the post-fire environment. We developed an empirically-grounded soil post-fire index (PFI) based on available science and ecological thresholds. Using over 50 literature sources, we identified a minimum of five broad categories of post-fire outcomes: (a) unburned, (b) abundant surface organic matter (>85% surface organic matter), (c) moderate amount of surface organic matter (≥40 through 85%), (d) small amounts of surface organic matter (<40%), and (e) absence of surface organic matter (no organic matter left). We then subdivided each broad category on the basis of post-fire mineral soil colors providing a more fine-tuned post-fire soil index. We related each PFI category to characteristics such as soil temperature and duration of heating during fire, and physical, chemical, and biological responses. Classifying or describing post-fire soil conditions consistently will improve interpretations of fire effects research and facilitate communication of potential responses or outcomes (e.g., erosion potential) from fires of varying severities. OPEN ACCESSForests 2012, 3 446

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“…A escala regional, las técnicas de teledetección han demostrado ser una herramienta muy adecuada para estudiar los procesos de regeneración vegetal debido a los cambios generados por el fuego en el comportamiento espectral de la vegetación (Patterson y Yool, 1998;Díaz-Delgado y Pons, 2001). Índices espectrales como el Normalized Differenced Vegetation Index (NDVI) aplicados a imágenes de satélite han sido ampliamente empleados en el seguimiento de los cambios espaciales y temporales de la vegetación y en la estimación de características del fuego (Díaz-Delgado y Pons, 2001;Miller y Yool, 2002;Key y Benson, 2004).…”

Section: Introductionunclassified

Estudio de los procesos de regeneración vegetal postincendio en parcelas experimentales mediante radiometría de campo

Llovería

Pérez‐Cabello

García-Martín

et al. 2013

CIG

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Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper Data

Cited by 138 publications

References 19 publications

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Index for Characterizing Post-Fire Soil Environments in Temperate Coniferous Forests

Estudio de los procesos de regeneración vegetal postincendio en parcelas experimentales mediante radiometría de campo

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