A rule-based semi-automatic method to map burned areas: exploring the USGS historical Landsat archives to reconstruct recent fire history

Koutsias, Nikos; Pleniou, Magdalini; Mallinis, Giorgos; Nioti, Foula; Sifakis, N.

doi:10.1080/01431161.2013.816452

Cited by 35 publications

(37 citation statements)

References 64 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: 59%

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: 59%

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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“…This suggest that there is merit in designing algorithms that may allow local experts to set specific parameters in the algorithm to better account for local vegetation properties and the spatial and spectral properties of local burned area. Efforts are also underway to develop automated burned area algorithms that operate on high-resolution imagery, such as Satellite Pour l'Observation de la Terre (SPOT)-4 and Landsat-7 Enhanced Thematic Mapper plus (ETM+), have been successfully demonstrated on a regional scale [44][45][46][47]. Developing operational automated burned area products based on Sentinel-2 and Landsat-8 could alleviate issues related to the detection of small and spatially fragmented burns with coarser resolution satellite data.…”

Section: Discussionmentioning

confidence: 99%

Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa

et al. 2014

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Abstract:The 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area products, MCD45A1, MCD64A1, and a merged product were validated across six study sites in South Africa using independently-derived Landsat burned-area reference data during the fire season of 2007. The objectives of this study were to: (i) investigate the likelihood of the improved detection of small burns through an empirically-derived merged product; (ii) quantify the probability of detection by each product using sub-pixel burned area measures; and, (iii) compare the mean percent concurrence of burned pixels between the standard products over a ten-year time series in each site. Results show that MCD45A1 presented higher detection probabilities (i.e., 3.0%-37.9%) for small fractions ≤50%, whereas MCD64A1 appeared more reliable (i.e., 12.0%-89.2%) in detecting large fractions >50% of a burned MODIS pixel, respectively. Overall, the merged product demonstrated improved detection of the burned area in all fractions. This paper also demonstrates that, on average, >50% of MODIS burned pixels temporally concur between OPEN ACCESSRemote Sens. 2014, 6 1276 the MCD45A1 and MCD64A1 products in each site. These findings have significant implications for fire monitoring in southern Africa and contribute toward the understanding of the range and of the sources of errors present in the MODIS burned area products.

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“…In addition, more than 25 fire researchers have also used the tool for burned area mapping in diverse parts of the world so far, supporting the idea that a tool to speed up the way to obtain this cartography was needed. Some algorithms have been assessed for automatic or semi-automatic burned area mapping with Landsat data [23][24][25], but these are difficult to implement and the software has not been released in an operative way for the public.…”

Section: Discussionmentioning

confidence: 99%

“…Although automatic Landsat classification and thresholding techniques provide useful information on the spatial extent and characteristics of the burned patches [22][23][24][25], different authors have recognized the difficulty in developing a global algorithm that can cope with the great variety of global burning conditions: old and new burns, spatially fragmented small patches, low severity fires, etc. [12,26].…”

Section: Introductionmentioning

confidence: 99%

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

et al. 2014

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Abstract:A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS TM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation OPEN ACCESSRemote Sens. 2014, 6 12361 errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

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A rule-based semi-automatic method to map burned areas: exploring the USGS historical Landsat archives to reconstruct recent fire history

Cited by 35 publications

References 64 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

Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

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