Effects of band combinations and GIS masking on fire-scar mapping at local scales in east-central Florida, USA

The fire-adapted vegetation in east-central Florida provides habitat for many threatened and endangered species, such as the Florida scrub-jay (Aphelocoma coerulescens). Accurate fire occurrence records are critically important for better understanding the relationship between fire and vegetation structure. The rapid growth rates of fire-adapted vegetation in east-central Florida make it difficult to capture detailed fire scars with remote sensing data acquired weeks after the fires. The objective of this study is to develop a reliable remote sensing approach for accurately mapping burned areas in Florida scrub vegetation at the National Aeronautics and Space Administration (NASA) John F. Kennedy Space Center (KSC) and Merritt Island National Wildlife Refuge (MINWR). Landsat thematic mapper (TM) data acquired on 21 April 1987 were used for classification experiments. Geographic information system (GIS) data layers of fire management units (FMUs) with known fire occurrence (presence or absence) were used to mask the original remote sensing data or thematic maps following classification. A separation index (SI) was used to evaluate each individual band for its power to discriminate unburned and burned areas. Twelve classifications with selected band groups derived from Landsat TM data with different geographic extents were compared using an error matrix method. The classification of the four most suitable bands derived for the entire KSC-MINWR area resulted in the highest accuracy. The final map product was generated by overlaying the classified map with the FMU data layer and masking out FMUs that did not burn. This paper addresses a number of issues relevant to the classification of burned areas and includes the effect of geographic extent (GE effect) of remote sensing data on classification, determining the best bands for classification, and cleaning classification results using GIS masking. It also serves as the first published effort to map fire scars in the Florida scrub and flatwoods vegetative communities of the southeastern US using image processing techniques.

[1]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[2]  Guofan Shao,et al.  An explicit index for assessing the accuracy of cover-class areas , 2003 .

[3]  George Alan Blackburn,et al.  Introducing New Indices for Accuracy Evaluation of Classified Images Representing Semi-Natural Woodland Environments. , 2001 .

[4]  J. Grégoire,et al.  Lessons to be learned from the comparison of three satellite‐derived biomass burning products , 2004 .

[5]  David R. Breininger,et al.  LINKING HABITAT SUITABILITY TO DEMOGRAPHIC SUCCESS IN FLORIDA SCRUB-JAYS , 1998 .

[6]  C. Justice,et al.  Global fire activity from two years of MODIS data , 2005 .

[7]  X. Pons,et al.  A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series , 2000 .

[8]  G. E. Woolfenden,et al.  NEST SITE SELECTION BY FLORIDA SCRUB-JAYS IN NATURAL AND HUMAN-MODIFIED HABITATS , 2002 .

[9]  Kenneth J. Ranson,et al.  Disturbance recognition in the boreal forest using radar and Landsat-7 , 2003 .

[10]  D. Fuller.  Satellite remote sensing of biomass burning with optical and thermal sensors , 2000 .

[11]  Jing Chen,et al.  Forest-fire-scar aging using SPOT-VEGETATION for Canadian ecoregions , 2003 .

[12]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[13]  M. Maggi,et al.  Advantages and drawbacks of NOAA-AVHRR and SPOT-VGT for burnt area mapping in a tropical savanna ecosystem , 2002 .

[14]  David R. Breininger,et al.  Coupling past management practice and historic landscape change on John F. Kennedy Space Center, Florida , 1999, Landscape Ecology.

[15]  F. M. Danson,et al.  Satellite remote sensing of forest resources: three decades of research development , 2005 .

[16]  F. Sunar,et al.  Forest fire analysis with remote sensing data , 2001 .

[17]  J. Pereira,et al.  Vegetation burning in the year 2000: Global burned area estimates from SPOT VEGETATION data , 2004 .

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

[19]  Ioannis Z. Gitas,et al.  A semi-automated object-oriented model for burned area mapping in the Mediterranean region using Landsat-TM imagery , 2004 .

[20]  D. Breininger,et al.  FLORIDA SCRUB-JAY DEMOGRAPHY IN DIFFERENT LANDSCAPES , 1996 .

[21]  Brean W. Duncan,et al.  Anthropogenic influences on potential fire spread in a pyrogenic ecosystem of Florida, USA , 2004, Landscape Ecology.

[22]  Ruiliang Pu,et al.  Determination of Burnt Scars Using Logistic Regression and Neural Network Techniques from a Single Post-Fire Landsat-7 ETM+ Image , 2004 .

[23]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[24]  Geoffrey M. Carter,et al.  TERRITORY QUALITY TRANSITIONS AND SOURCE–SINK DYNAMICS IN A FLORIDA SCRUB‐JAY POPULATION , 2003 .

[25]  Mark W. Patterson,et al.  Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper Data: A Comparison of Linear Transformation Techniques , 1998 .

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

[27]  Andrew T. Hudak,et al.  Mapping fire scars in a southern African savannah using Landsat imagery , 2004 .

[28]  A. Setzer,et al.  Spectral characteristics of fire scars in Landsat-5 TM images of Amazonia , 1993 .

[29]  P. Schmalzer,et al.  Recovery of Oak-Saw Palmetto Scrub after Fire , 1992 .

[30]  E. Chuvieco,et al.  Application of remote sensing and geographic information systems to forest fire hazard mapping. , 1989 .