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Special Issue "Advances in Remote Sensing of Wildland Fires"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2011)

Special Issue Editor

Guest Editor
Prof. Ioannis Gitas (Website)

Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece
Fax: +30 2310 992677
Interests: forest fires; pre-fire planning and post-fire assessment; land use/land cover mapping; soil erosion risk assessment/desertification; other environmental applications of remote sensing and GIS

Special Issue Information

Dear Colleagues,

To introduce new policies that would reduce fire risk and/or its impacts, it is necessary to have a good understanding of how fire affects the structure and functioning of ecosystems. To achieve these goals a thorough understanding is required of not only the prefire distribution of the specific resource features, conditions and characteristics, but what is also required is the collection of post-fire data (e.g. biomass, burn severity, species regeneration, vegetation-type succession) in order to detect and specify environmental changes and trends.

Assessment and analysis of these changes and trends at the local scale are increasingly considered a critical aspect of ecosystem research, since fire plays a crucial role in vegetation composition, biodiversity, soil erosion, and the hydrological cycle. Also at the global scale, they are believed to be sensitive indicators of the impact of changes in the global environment. Whether these changes are mainly caused by land use change or climate warming, greater efforts are demanded to manage wildland fires at different temporal and spatial scales.

Remotely sensed data are used in all the three phases of a fire management program, namely, pre-fire planning, fire detection and monitoring, and post-fire impact assessment. Since the initiation of the Landsat satellite program, several projects have been conducted to test the potential efficacy and reliability of satellite data in collecting information related to forest fire management. However, during the last decade, the range of applications has increased significantly as a result of the following:

  • an increase in the number of airborne and satellite sensors with different characteristics suitable for studying aspects of fire, some of which have been designed specifically for fire monitoring;
  • improvement of our understanding of the role of fire in ecosystems functioning;
  • progress in computer technology (hardware, software);
  • development of new advanced digital image analysis techniques; and
  • improved access to and availability of satellite data and derived products.

This special issue aims to focus on remote sensing applications that make use of new sensors and/or advanced image analysis techniques in order to provide accurate information related to fire management.

Dr. Ioannis Gitas
Guest Editor

Keywords

  • fuel mapping
  • fire risk
  • fire danger
  • active fire mapping
  • biomass burning
  • burned area mapping
  • burn severity
  • post-fire forest regeneration
  • vegetation succession monitoring

Published Papers (7 papers)

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Research

Open AccessArticle Utility of Remotely Sensed Imagery for Assessing the Impact of Salvage Logging after Forest Fires
Remote Sens. 2012, 4(7), 2112-2132; doi:10.3390/rs4072112
Received: 1 February 2012 / Revised: 4 July 2012 / Accepted: 5 July 2012 / Published: 13 July 2012
Cited by 2 | PDF Full-text (950 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed imagery provides a useful tool for land managers to assess the extent and severity of post-wildfire salvage logging disturbance. This investigation uses high resolution QuickBird and National Agricultural Imagery Program (NAIP) imagery to map soil exposure after ground-based salvage operations. [...] Read more.
Remotely sensed imagery provides a useful tool for land managers to assess the extent and severity of post-wildfire salvage logging disturbance. This investigation uses high resolution QuickBird and National Agricultural Imagery Program (NAIP) imagery to map soil exposure after ground-based salvage operations. Three wildfires with varying post-fire salvage activities and variable ground truth data were used to evaluate the utility of remotely sensed imagery for disturbance classification. The Red Eagle Fire in northwestern Montana had intensive ground truthing with GPS-equipment logging equipment to map their travel paths, the Tripod Fire in north central Washington had ground truthed disturbance transects, and the School Fire in southeastern Washington had no salvage-specific ground truthing but pre-and post-salvage images were available. Spectral mixture analysis (SMA) and principle component analysis (PCA) were used to evaluate the imagery. Our results showed that soil exposure (disturbance) was measureable when pre-and post-salvage QuickBird images were compared at one site. At two of the sites, only post-salvage imagery was available, and the soil exposure correlated well to salvage logging equipment disturbance at one site. When ground disturbance transects were compared to NAIP imagery two years after the salvage operation, it was difficult to identify disturbance due to vegetation regrowth. These results indicate that soil exposure (ground disturbance) by salvage operation can be detected with remotely sensed imagery especially if the images are taken less than two years after the salvage operation. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
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Open AccessArticle Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments
Remote Sens. 2012, 4(3), 598-621; doi:10.3390/rs4030598
Received: 31 January 2012 / Revised: 29 February 2012 / Accepted: 29 February 2012 / Published: 2 March 2012
Cited by 9 | PDF Full-text (1164 KB) | HTML Full-text | XML Full-text
Abstract
Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response. We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
Open AccessArticle How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods
Remote Sens. 2012, 4(2), 456-483; doi:10.3390/rs4020456
Received: 10 January 2012 / Revised: 1 February 2012 / Accepted: 1 February 2012 / Published: 9 February 2012
Cited by 23 | PDF Full-text (2125 KB) | HTML Full-text | XML Full-text
Abstract
Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in [...] Read more.
Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotely sensed indices of burn severity developed in one location did not show the same improvement when used in a new location. We evaluated three methods of assessing burn severity in the field: the Composite Burn Index (CBI)—a standardized method of assessing burn severity that combines ecologically significant variables related to burn severity into one numeric site index—and two modifications of the CBI that weight the plot CBI score by the percentage cover of each stratum. Unexpectedly, models using the CBI had higher R2 and better classification accuracy than models using the weighted versions of the CBI. We suggest that the weighted versions of the CBI have lower accuracies because weighting by percentage cover decreases the influence of the dominant tree stratum, which should have the strongest relationship to optically sensed reflectance, and increases the influence of the substrates strata, which should have the weakest relationship with optically sensed reflectance in forested ecosystems. Using a large data set of CBI plots (n = 251) from four fires and CBI scores derived from additional field-based assessments of burn severity (n = 388), we predicted two metrics of image-based burn severity, the Relative differenced Normalized Burn Ratio (RdNBR) and the differenced Normalized Burn Ratio (dNBR). Predictive models for RdNBR showed slightly better classification accuracy than for dNBR (overall accuracy = 62%, Kappa = 0.40, and overall accuracy = 59%, Kappa= 0.36, respectively), whereas dNBR had slightly better explanatory power, but strong differences were not apparent. RdNBR may provide little or no improvement over dNBR in systems where pre-fire reflectance is not highly variable, but may be more appropriate for comparing burn severity among regions. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
Open AccessArticle Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis
Remote Sens. 2012, 4(2), 424-438; doi:10.3390/rs4020424
Received: 6 December 2011 / Revised: 17 January 2012 / Accepted: 21 January 2012 / Published: 3 February 2012
Cited by 14 | PDF Full-text (1114 KB) | HTML Full-text | XML Full-text
Abstract
The devastating series of fire events that occurred during the summers of 2007 and 2009 in Greece made evident the need for an operational mechanism to map burned areas in an accurate and timely fashion to be developed. In this work, Système [...] Read more.
The devastating series of fire events that occurred during the summers of 2007 and 2009 in Greece made evident the need for an operational mechanism to map burned areas in an accurate and timely fashion to be developed. In this work, Système pour l’Observation de la Terre (SPOT)-4 HRVIR images are introduced in an object-based classification environment in order to develop a classification procedure for burned area mapping. The development of the procedure was based on two images and then tested for its transferability to other burned areas. Results from the SPOT-4 HRVIR burned area mapping showed very high classification accuracies ( 0.86 kappa coefficient), while the object-based classification procedure that was developed proved to be transferable when applied to other study areas. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
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Open AccessArticle Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data
Remote Sens. 2011, 3(11), 2403-2419; doi:10.3390/rs3112403
Received: 20 September 2011 / Revised: 18 October 2011 / Accepted: 18 October 2011 / Published: 11 November 2011
Cited by 12 | PDF Full-text (1021 KB) | HTML Full-text | XML Full-text
Abstract
Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data [...] Read more.
Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
Open AccessArticle Modeling Relationships among 217 Fires Using Remote Sensing of Burn Severity in Southern Pine Forests
Remote Sens. 2011, 3(9), 2005-2028; doi:10.3390/rs3092005
Received: 20 July 2011 / Revised: 20 August 2011 / Accepted: 29 August 2011 / Published: 7 September 2011
Cited by 9 | PDF Full-text (580 KB) | HTML Full-text | XML Full-text
Abstract
Pine flatwoods forests in the southeastern US have experienced severe wildfires over the past few decades, often attributed to fuel load build-up. These forest communities are fire dependent and require regular burning for ecosystem maintenance and health. Although prescribed fire has been [...] Read more.
Pine flatwoods forests in the southeastern US have experienced severe wildfires over the past few decades, often attributed to fuel load build-up. These forest communities are fire dependent and require regular burning for ecosystem maintenance and health. Although prescribed fire has been used to reduce wildfire risk and maintain ecosystem integrity, managers are still working to reintroduce fire to long unburned areas. Common perception holds that reintroduction of fire in long unburned forests will produce severe fire effects, resulting in a reluctance to prescribe fire without first using expensive mechanical fuels reduction techniques. To inform prioritization and timing of future fire use, we apply remote sensing analysis to examine the set of conditions most likely to result in high burn severity effects, in relation to vegetation, years since the previous fire, and historical fire frequency. We analyze Landsat imagery-based differenced Normalized Burn Ratios (dNBR) to model the relationships between previous and future burn severity to better predict areas of potential high severity. Our results show that remote sensing techniques are useful for modeling the relationship between elevated risk of high burn severity and the amount of time between fires, the type of fire (wildfire or prescribed burn), and the historical frequency of fires in pine flatwoods forests. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)
Open AccessArticle Timing Constraints on Remote Sensing of Wildland Fire Burned Area in the Southeastern US
Remote Sens. 2011, 3(8), 1680-1690; doi:10.3390/rs3081680
Received: 29 June 2011 / Revised: 5 August 2011 / Accepted: 9 August 2011 / Published: 15 August 2011
Cited by 6 | PDF Full-text (400 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire [...] Read more.
Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78–90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Wildland Fires)

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