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

Tsela, Philemon Lehlohonolo; Wessels, Konrad J; Botai, Joel Ongego; Archibald, Sally; Swanepoel, Derick; Steenkamp, K; Frost, Philip

doi:10.3390/rs6021275

Cited by 59 publications

(42 citation statements)

References 43 publications

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“…Regardless of the algorithmic strategy used to tell burned and non-burned pixels apart, all methods are normally designed to be as accurate as possible; that it to say, to obtain low commission errors, even if that means omitting a larger number of burned pixels [39,79]. In this respect, results of this study are in agreement with most previous published works using MODIS data for various regions and ecosystems [33,34,39,58,[80][81][82][83][84][85][86][87][88]. However, a great variability was found in Burned Area percentages detected by the products under consideration for the various fires (Tables 5 and 6).…”

Section: Discussionsupporting

confidence: 82%

“…This product failed to identify a single pixel as burned in the five fires with fewer than 400 ha. Similar findings were reported by Tsela et al [82], who suggested the combination of the two MODIS BA products (MCD45A1 and MCD64A1) would improve detection of small burned scars in South Africa. Fornacca et al [34] also report the worse performance of MCD64A1 in the detection of small burned areas in the mountainous region of Yunnan (China) in the years 2006 and 2009, compared to the product that does not use active fires (MCD45A1).…”

Section: Discussionsupporting

confidence: 78%

“…This situation makes it harder for BY-MODIS or MCD45A1 products, which are based on algorithms seeking to sense drastic changes in post-fire reflectances, to be effective for this fire. Tsela et al [82] obtained comparable results for pine forest plantations in Sabie (South Africa), with omission errors exceeding 80%.…”

Section: Discussionsupporting

confidence: 58%

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Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

et al. 2018

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Burned Area (BA) is deemed as a primary variable to understand the Earth's climate system. Satellite remote sensing data have allowed for the development of various burned area detection algorithms that have been globally applied to and assessed in diverse ecosystems, ranging from tropical to boreal. In this paper, we present a Bayesian algorithm (BY-MODIS) that detects burned areas in a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2002 to 2012 of the Canary Islands' dry woodlands and forests ecoregion (Spain). Based on daily image products MODIS, MOD09GQ (250 m), and MOD11A1 (1 km), the surface spectral reflectance and the land surface temperature, respectively, 10 day composites were built using the maximum temperature criterion. Variables used in BY-MODIS were the Global Environment Monitoring Index (GEMI) and Burn Boreal Forest Index (BBFI), alongside the NIR spectral band, all of which refer to the previous year and the year the fire took place in. Reference polygons for the 14 fires exceeding 100 hectares and identified within the period under analysis were developed using both post-fire LANDSAT images and official information from the forest fires national database by the Ministry of Agriculture and Fisheries, Food and Environment of Spain (MAPAMA). The results obtained by BY-MODIS can be compared to those by official burned area products, MCD45A1 and MCD64A1. Despite that the best overall results correspond to MCD64A1, BY-MODIS proved to be an alternative for burned area mapping in the Canary Islands, a region with a great topographic complexity and diverse types of ecosystems. The total burned area detected by the BY-MODIS classifier was 64.9% of the MAPAMA reference data, and 78.6% according to data obtained from the LANDSAT images, with the lowest average commission error (11%) out of the three products and a correlation (R 2 ) of 0.82. The Bayesian algorithm-originally developed to detect burned areas in North American boreal forests using AVHRR archival data Long-Term Data Record-can be successfully applied to a lower latitude forest ecosystem totally different from the boreal ecosystem and using daily time series of satellite images from MODIS with a 250 m spatial resolution, as long as a set of training areas adequately characterising the dynamics of the forest canopy affected by the fire is defined.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionsupporting

confidence: 78%

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Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

et al. 2018

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“…The images already have atmospheric corrections for gases, thin cirrus clouds and aerosols [55]. The MODIS data is increasingly used to monitor burned areas and active fire over large geographic areas (e.g., [56][57][58][59][60][61][62][63]). The study area is contained in just one MODIS scene (tile h13v10).…”

Section: Modis/terra Time-series Datamentioning

confidence: 99%

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

et al. 2015

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Typically, digital image processing for burned-areas detection combines the use of a spectral index and the seasonal differencing method. However, the seasonal differencing has many errors when applied to a long-term time series. This article aims to develop and test two methods as an alternative to the traditional seasonal difference. The study area is the Chapada dos Veadeiros National Park (Central Brazil) that comprises different vegetation of the Cerrado biome. We used the MODIS/Terra Surface Reflectance 8-Day composite data, considering a 12-year period. The normalized burn ratio was calculated from the band 2 (250-meter resolution) and the band 7 (500-meter resolution reasampled to 250-meter). In this context, the normalization methods aim to eliminate all possible sources of spectral variation and highlight the burned-area features. The proposed normalization methods were the standardized time-series and the interannual phenological deviation. The standardized time-series calculate for each pixel the z-scores of its temporal curve, obtaining a mean of 0 and a standard deviation of 1. The second method establishes a reference curve for each pixel from the average interannual phenology that is subtracted for every year of its respective time series. Optimal threshold value between burned and unburned area for each method was determined from accuracy assessment curves, which compare different threshold values and its accuracy indices with a reference classification using Landsat TM. The different methods have similar accuracy for the burning event, where the standardized method has slightly better results. However, the seasonal difference OPEN ACCESS Remote Sens. 2015, 7 6951 method has a very false positive error, especially in the period between the rainy and dry seasons. The interannual phenological deviation method minimizes false positive errors, but some remain. In contrast, the standardized time series shows excellent results not containing this type of error. This precision is due to the design method that does not perform a subtraction with a baseline (prior year or average phenological curve). Thus, this method allows a high stability and can be implemented for the automatic detection of burned areas using long-term time series.

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“…Today, global-long term systematic-mapping of burned areas are provided by MODIS MOD45 and MOD64 products [19]. These products have been validated globally [20] and regionally by different research groups [21][22][23][24]. However, the performance of these algorithms for mapping sub-canopy burning, particularly in tropical regions, is deficient in accurately detecting the timing and extent of understory forest fires.…”

Section: Sampling Design Rationalementioning

confidence: 99%

Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia

Anderson¹,

Cheek²,

Aragão³

et al. 2017

J Remote Sensing & GIS

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Forest fires and their associated emissions are a key component for the efficient implementation of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) policy. The most suitable method for quantifying large scale fire-associated impacts is by mapping burned areas using remote sensing data. However, to provide robust quantification of the impacts of fire and support coherent policy decisions, these thematic maps must have their accuracy quantitatively assessed. The aim of this research is to present a point-based validation method developed for quantifying the accuracy of burned area thematic maps and test this method in a study case in the Amazon. The method is general; it can be applied to any thematic map consisting of two land cover classes. A stratified random sampling scheme is used to ensure that each class is represented adequately. The confidence intervals for the user's accuracies and for both overall accuracy and area error are calculated using the Wilson Score method and Jeffrey Perks interval, respectively. Such interval methods are novel in the context of map accuracy assessment. Despite the complexity of calculation of the confidence intervals, their use is recommended. A spreadsheet to calculate point and interval estimates is provided for users.

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

Cited by 59 publications

References 43 publications

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Evaluation of a Bayesian Algorithm to Detect Burned Areas in the Canary Islands’ Dry Woodlands and Forests Ecoregion Using MODIS Data

Standardized Time-Series and Interannual Phenological Deviation: New Techniques for Burned-Area Detection Using Long-Term MODIS-NBR Dataset

Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia

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