Delineating a managed fire regime and exploring its relationship to the natural fire regime in East Central Florida, USA: A remote sensing and GIS approach

Duncan, Brean W.; Shao, Guofan; Adrian, Frederic W.

doi:10.1016/j.foreco.2009.03.053

Cited by 41 publications

(51 citation statements)

References 43 publications

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“…This degradation has resulted in catastrophic impacts to biological diversity; fire managers and ecologists agree that introducing fire back into the system is a priority [24,25]. Prescribed fires differ from wildfires, because of their size, spatial pattern, season and differences in weather when these events occur [26]. One reason that prescribed fires often have limited ecological benefit is that fire managers often burn out the flammable parts of a landscape so that patches are less likely to reignite or produce smoke and other hazards.…”

Section: Management Of Florida Scrub: Fuel Management Vs Ecological mentioning

confidence: 99%

“…Although not a part of the iterative ARM process, we note that we can also use time series of satellite imagery to maintain a fire history database and to map fire boundaries [26,49]. Such data are used during the deliberative or set-up phase of ARM in order to obtain estimates that populate the transition probability matrices described above.…”

Section: Monitoringmentioning

confidence: 99%

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Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Breininger

Duncan

Eaton

et al. 2014

Land

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Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require tradeoffs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire OPEN ACCESSLand 2014, 3 875 behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making.

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Section: Management Of Florida Scrub: Fuel Management Vs Ecological mentioning

confidence: 99%

Section: Monitoringmentioning

confidence: 99%

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Breininger

Duncan

Eaton

et al. 2014

Land

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“…In the prolonged absence of burning, oak scrub vegetation becomes fire-resistant and undergoes structural changes toward xeric hammock [23]. Wildfire was suppressed throughout much of the KSC/MINWR area from the 1950s until 1981, when the National Aeronautics and Space Administration (NASA) instituted a prescribed burning program to reduce the buildup of vegetative fuel loads in the area [25].…”

Section: Study Areamentioning

confidence: 99%

Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida

2010

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Disturbance plays a fundamental role in determining the vertical structure of vegetation in many terrestrial ecosystems, and knowledge of disturbance histories is vital for developing effective management and restoration plans. In this study, we investigated the potential of using vertical vegetation profiles derived from discrete-return lidar to predict time since fire (TSF) in a landscape of oak scrub in east-central Florida. We predicted that fire influences vegetation structure at the mesoscale (i.e., spatial scales of tens of meters to kilometers). To evaluate this prediction, we binned lidar returns into 1m vertical by 5 × 5 m horizontal cells and averaged the resulting profiles over a range of horizontal window sizes (0 to 500 m on a side). We then performed a series of resampling tests to compare the performance of support vector machine (SVM), k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA) classifiers and to estimate the amount of training data necessary to achieve satisfactory performance. Our results indicate that: (1) the SVMs perform significantly better than the other classifiers, (2) SVM classifiers may require relatively small training data sets, and (3) the highest classification accuracies occur with averaging over windows representing sizes in the mesoscale range.

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“…Satellite remote sensing has been widely employed for fire history reconstruction both at the local/regional/national [17,18], as well as the continental/global [19,20], level. More specifically, the most recent (post-2000) global fire history reconstruction is based mainly on the use of MODIS [20,21] and SPOT VEGETATION [22] data, while the creation of fire history over longer time periods involved the extensive use of the Advanced Very High Resolution Radiometer (AVHRR) [11,23].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, since the initiation of the Landsat satellite program, numerous land management projects, including fire-related ones, have been based on data from the three main sensors on board the different Landsat satellites, namely the Multispectral Scanner (MSS), the Thematic (TM), and Enhanced Thematic Mapper (ETM+) sensors [15,18,25]. The recent launch of Landsat-8, as part of the Landsat Data Continuity Mission (LDCM) [26], ensures open access to Landsat data for the coming years, both for operational and scientific applications.…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

2014

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Abstract:In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall) were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that -spatial misplacement‖ was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year) fire history in the Mediterranean, based on extended time series of Landsat or similar data.

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Delineating a managed fire regime and exploring its relationship to the natural fire regime in East Central Florida, USA: A remote sensing and GIS approach

Cited by 41 publications

References 43 publications

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Using Lidar-Derived Vegetation Profiles to Predict Time since Fire in an Oak Scrub Landscape in East-Central Florida

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

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