Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors

Bastarrika, Aitor; Chuvieco, Emilio; Martín, M. Pilar

doi:10.1016/j.rse.2010.12.005

Cited by 229 publications

(209 citation statements)

References 47 publications

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“…In terms of overall accuracies, omission and commission errors and indices of agreement, the results obtained with our method were at a minimum, as accurate as previous studies which used a variety of automatic or semiautomatic fire scar detection methods from remote sensing in the Mediterranean Basin and other regions (see, e.g., [14,29,[35][36][37][38] and references therein). Some methods to detect fire scars from remotely sensed time series data incorporate, as is the case with our method, two different phases: first detecting possible abrupt changes in the temporal data for some pixels, usually by jointly considering several spectral indices, and then using some algorithm for region growing to finally delimitate the perimeter of the fire scar.…”

Section: Discussionsupporting

confidence: 66%

“…Setting strict thresholds or conditions for a pixel to be selected as a seed for the second phase lowers commission errors. Then, omission errors are reduced by using, explicitly or implicitly, lower requirements to incorporate additional pixels in the extension phase [35][36][37]. In our method, detection of abrupt changes in NDVI as a consequence of vegetation burning was best achieved Table 5.…”

Section: Discussionmentioning

confidence: 99%

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Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

et al. 2014

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Abstract:Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

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Section: Discussionsupporting

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

et al. 2014

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“…The first approach is a rule-based semi-automatic algorithm that makes use of uni-temporal and multi-temporal rules based on distinct spectral patterns of burned areas (Koutsias, Pleniou, Mallinis, Nioti, & Sifakis, under review. The second approach uses the Automatic Burned Area Mapping Software (Bastarrika, Chuvieco, & Martin, 2011), a tool that applies a two-phased algorithm to generate fire scar perimeters. Additionally, we used the familiar maximum likelihood classifier on selected Landsat TM and ETM+ images provided by the FUME project, ESA cat-1 images, and others (Table 2).…”

Section: Methodsmentioning

confidence: 99%

Maps of fire occurrence – spatially explicit reconstruction of recent fire history using satellite remote sensing

et al. 2012

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Maps depicting the spatially explicit fire history of an area, including variables such as fire frequency and fire return interval, are important tools promoting the understanding of processes associated with wildfires (fire ignition and spread), the assessment of the impacts of wildland fires on landscape dynamics, and decisions on appropriate management practices. Remote sensing is a cost-and time-effective alternative to automatically assess a vast amount of spatial information and produce various thematic maps. The aim of this study was to reconstruct the recent fire history of Attica region (Greece), in a spatially explicit mode by means of remote sensing techniques using a series of Landsat images acquired from 1984 to 2011. The results show that the fire scar perimeters were captured with high accuracy. Regression modelling shows that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained (86.3% of the variance) by the number of satellite images used (standardized coefficient 0.752) followed by the date of the first image (standardized coefficient 0.705). The use of satellite data as the basic source of information alongside automated classification methods should be promoted for the creation of fire history maps. The latter is further supported when considering the long history of data capture from Landsat satellites, which provide a huge, global historical archive of repeat images of the Earth's surface.

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“…NDMI is very similar to NBR, but uses the shorter SWIR band instead of the longer one, which has revealed to be equally sensitive to wet/dry content of soils and vegetation. However, it has been rarely tested for burned area discrimination [11,19,29]. BAIML and BAIMs are a modified version of the BAI tailored for the MODIS sensors which offer SWIR channels, but they are also used with Landsat images [11,27,61].…”

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

“…However, it has been rarely tested for burned area discrimination [11,19,29]. BAIML and BAIMs are a modified version of the BAI tailored for the MODIS sensors which offer SWIR channels, but they are also used with Landsat images [11,27,61]. Following the same logic as the one defined for BAI, convergence point values for NIR and SWIR bands were chosen by their creators based on the analysis of MODIS time series, and were set to 0.05 for NIR and 0.2 for SWIR.…”

Section: Index Full Name Abbreviation Equation Referencementioning

confidence: 99%

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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Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors

Cited by 229 publications

References 47 publications

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Maps of fire occurrence – spatially explicit reconstruction of recent fire history using satellite remote sensing

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

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