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

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 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.

[1]  S. A. Lewis,et al.  The Relationship of Multispectral Satellite Imagery to Immediate Fire Effects , 2007 .

[2]  Mingguo Ma,et al.  Comparison of Eight Techniques for Reconstructing Multi-Satellite Sensor Time-Series NDVI Data Sets in the Heihe River Basin, China , 2014, Remote. Sens..

[3]  Sander Veraverbeke,et al.  Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data , 2011, Remote. Sens..

[4]  J. W. Wagtendonk,et al.  Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity , 2004 .

[5]  Toon Spanhove,et al.  Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX) , 2014, Remote. Sens..

[6]  Ross A. Bradstock,et al.  Remote sensing of fire severity in the Blue Mountains: influence of vegetation type and inferring fire intensity , 2006 .

[7]  J. Morisette,et al.  Accuracy Assessment Curves for Satellite-Based Change Detection , 2000 .

[8]  E. Chuvieco,et al.  COMPARACIÓN DE DISTINTAS TÉCNICAS DE ANÁLISIS DIGITAL PARA LA CARTOGRAFÍA DE ÁREAS QUEMADAS CON IMÁGENES LANDSAT ETM , 2003 .

[9]  Eric S. Kasischke,et al.  Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+ data , 2008 .

[10]  I. Jonckheere,et al.  Relating Time-Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics , 2006 .

[11]  Jose Raul Romo-Leon,et al.  Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments , 2012, Remote. Sens..

[12]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[13]  Yosio Edemir Shimabukuro,et al.  Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest , 2014, Remote. Sens..

[14]  Sander Veraverbeke,et al.  Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper , 2011 .

[15]  S. Escuin,et al.  Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images , 2008 .

[16]  S. Ekstrand,et al.  Landsat TM-based forest damage assessment : correction for topographic effects , 1996 .

[17]  Renato Fontes Guimarães,et al.  Spatial Patterns of Fire Recurrence Using Remote Sensing and GIS in the Brazilian Savanna: Serra do Tombador Nature Reserve, Brazil , 2014, Remote. Sens..

[18]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[19]  D. Roy,et al.  An active-fire based burned area mapping algorithm for the MODIS sensor , 2009 .

[20]  Xavier Pons,et al.  Spatial patterns of forest fires in Catalonia (NE of Spain) along the period 1975–1995: Analysis of vegetation recovery after fire , 2001 .

[21]  E. Kasischke,et al.  Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests , 2008 .

[22]  Beniamino Murgante,et al.  Article in Press G Model International Journal of Applied Earth Observation and Geoinformation Multiscale Mapping of Burn Area and Severity Using Multisensor Satellite Data and Spatial Autocorrelation Analysis , 2022 .

[23]  N. C. Fiedler,et al.  Ocorrência de incêndios florestais no parque nacional da Chapada dos Veadeiros, Goiás. , 2006 .

[24]  J. A. Ratter,et al.  Neotropical Savannas and Seasonally Dry Forests : Plant Diversity, Biogeography, and Conservation , 2006 .

[25]  H. Eva,et al.  Remote Sensing of Biomass Burning in Tropical Regions: Sampling Issues and Multisensor Approach , 1998 .

[26]  J. A. Ratter,et al.  2 Biodiversity Patterns of the Woody Vegetation of the Brazilian Cerrado , 2006 .

[27]  N. Benson,et al.  Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio , 2006 .

[28]  S. Hook,et al.  Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data , 2011 .

[29]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[30]  Gareth Roberts,et al.  Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product , 2014, Remote. Sens..

[31]  Joshua J. Picotte,et al.  Timing Constraints on Remote Sensing of Wildland Fire Burned Area in the Southeastern US , 2011, Remote. Sens..

[32]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[33]  R. Hall,et al.  Using Landsat data to assess fire and burn severity in the North American boreal forest region: an overview and summary of results , 2008 .

[34]  A. McGuire,et al.  Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data. , 2010 .

[35]  J. Meert,et al.  The making and unmaking of a supercontinent: Rodinia revisited , 2003 .

[36]  Dario Simonetti,et al.  Interannual Changes of Fire Activity in the Protected Areas of the SUN Network and Other Parks and Reserves of the West and Central Africa Region Derived from MODIS Observations , 2010, Remote. Sens..

[37]  Mark A. Friedl,et al.  Mapping Crop Cycles in China Using MODIS-EVI Time Series , 2014, Remote. Sens..

[38]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[39]  Jan de Leeuw,et al.  Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series , 2013, Remote. Sens..

[40]  B. A. Park,et al.  Assessing the differenced Normalized Burn Ratio ’ s ability to map burn severity in the boreal forest and tundra ecosystems of Alaska ’ s national parks , 2008 .

[41]  Ioannis Z. Gitas,et al.  Contribution of remote sensing to disaster management activities: A case study of the large fires in the Peloponnese, Greece , 2008 .

[42]  Sander Veraverbeke,et al.  The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece. , 2010 .

[43]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[44]  R. Mittermeier,et al.  Biodiversity hotspots for conservation priorities , 2000, Nature.

[45]  Alfonso Calera,et al.  Application of remote sensing and GIS to locate priority intervention areas after wildland fires in Mediterranean systems: a case study from south-eastern Spain , 2004 .

[46]  D. Verbyla,et al.  Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM , 2005 .

[47]  Maria Teresa Pareschi,et al.  The Vegetation Resilience After Fire (VRAF) index: Development, implementation and an illustration from central Italy , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[48]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[49]  W. Verstraeten,et al.  Evaluating Landsat Thematic Mapper spectral indices for estimating burn severity of the 2007 Peloponnese wildfires in Greece. , 2010 .

[50]  J. Bednar,et al.  Alpha-trimmed means and their relationship to median filters , 1984 .

[51]  Amr H. Abd-Elrahman,et al.  Modeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forests , 2011, Remote. Sens..

[52]  P. Fulé,et al.  Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data , 2005 .

[53]  Vicente Caselles,et al.  Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS Images , 2014, Remote. Sens..

[54]  Renato Fontes Guimarães,et al.  IDENTIFICAÇÃO REGIONAL DA FLORESTA ESTACIONAL DECIDUAL NA BACIA DO RIO PARANÃ A PARTIR DA ANÁLISE MULTITEMPORAL DE IMAGENS MODIS , 2006 .

[55]  T. Loboda,et al.  Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data , 2007 .

[56]  C. Justice,et al.  Atmospheric correction of MODIS data in the visible to middle infrared: first results , 2002 .

[57]  Susan L. Ustin,et al.  Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data , 1993 .

[58]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[59]  Variações sazonais nas concentrações de pigmentos e nutrientes em folhas de espécies de cerrado com diferentes estratégias fenológicas , 2007 .

[60]  E. Kasischke,et al.  Controls on variations in MODIS fire radiative power in Alaskan boreal forests: Implications for fire severity conditions , 2013 .

[61]  David P. Roy,et al.  Remote sensing of fire severity: assessing the performance of the normalized burn ratio , 2006, IEEE Geoscience and Remote Sensing Letters.

[62]  Martin J. Wooster,et al.  A Decade Long, Multi-Scale Map Comparison of Fire Regime Parameters Derived from Three Publically Available Satellite-Based Fire Products: A Case Study in the Central African Republic , 2014, Remote. Sens..

[63]  Pieter Kempeneers,et al.  Increasing Spatial Detail of Burned Scar Maps Using IRS-AWiFS Data for Mediterranean Europe , 2012, Remote. Sens..

[64]  Xulin Guo,et al.  Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review , 2013, Remote. Sens..

[65]  Juli G. Pausas,et al.  Fire regime and post-fire Normalized Difference Vegetation Index changes in the eastern Iberian peninsula (Mediterranean basin) , 2006 .

[66]  Jan Verbesselt,et al.  Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index , 2011 .

[67]  L. Giglio,et al.  Mapping burned area in Alaska using MODIS data: a data limitations-driven modification to the regional burned area algorithm , 2011 .

[68]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[69]  G. Goldstein,et al.  Leaf functional traits of Neotropical savanna trees in relation to seasonal water deficit , 2005, Trees.

[70]  John Rogan,et al.  Mapping fire-induced vegetation depletion in the Peloncillo Mountains, Arizona and New Mexico , 2001 .

[71]  Jay D. Miller,et al.  Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR) , 2007 .

[72]  Carol Miller,et al.  A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio , 2014, Remote. Sens..

[73]  F. M. Danson,et al.  Use of a radiative transfer model to simulate the postfire spectral response to burn severity , 2006 .

[74]  Carl H. Key,et al.  Ecological and Sampling Constraints on Defining Landscape Fire Severity , 2006 .

[75]  Konrad J. Wessels,et al.  Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa , 2014, Remote. Sens..

[76]  J. F. Ribeiro,et al.  Fitofisionomias do bioma cerrado. , 1998 .

[77]  E. LeDrew,et al.  Application of principal components analysis to change detection , 1987 .

[78]  K. Murphy,et al.  Evaluating the ability of the differenced Normalized Burn Ratio (dNBR) to predict ecologically significant burn severity in Alaskan boreal forests , 2008 .

[79]  M. Fulk,et al.  Burned area in Kalimantan, Indonesia mapped with NOAA-AVHRR and Landsat TM imagery , 2001 .

[80]  Nilton Correia da Silva,et al.  Classificação de padrões de savana usando assinaturas temporais NDVI do sensor MODLS no Parque Nacional Chapada dos Veadeiros , 2008 .

[81]  Alan R. Gillespie,et al.  A New Approach to Change Vector Analysis Using Distance and Similarity Measures , 2011, Remote. Sens..

[82]  Yosio Edemir Shimabukuro,et al.  Combining noise-adjusted principal components transform and median filter techniques for denoising modis temporal signatures , 2012 .

[83]  Mark Noonan,et al.  The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires , 2004 .

[84]  Carlos A. Klink,et al.  A conservação do Cerrado brasileiro , 2005 .