Visibility analysis of fire lookout towers in the Boyabat State Forest Enterprise in Turkey

Küçük, Ömer; Topaloglu, Ozer; Altunel, Arif Oguz; Çetin, Mehmet

doi:10.1007/s10661-017-6008-1

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…The natural characteristics are suitable for tree growth in this region, however the effects of climate change have started to unprecedentedly change the dynamics even in forestry sector (Tayanc, Im, Dogruel & Karaca, 2009;Ozcan, Musaoglu & Turkes, 2018). The never before seen forest fires have started to hit the region, and the strategies are devised to cope with the problem (Kucuk, Topaloglu, Altunel & Cetin, 2017). KRDF was ravaged by a wind-storm in March 2013, causing 1.5 million m 3 timber loss (Torun, 2018).…”

Section: Discussionmentioning

confidence: 99%

Determination of Some Stand and Site Parameters Influencing the Snow Damage in Trees: A Case Study of Boyabat Forest Enterprise, Turkey

Altunel¹

2019

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of Study: Determination of the snow damage triggering stand and site parameter on trees. Area of Study: Damaged compartments of Yedicam and Burnuk Forest Directorates, #234 and #116, within Boyabat Forest Enterprise of Kastamonu Regional Directorate of Forestry.Material and Method: Two damaged and two control compartments from each directorate were compared to find if any site or stand parameters are related to the snow damage occurred in the area during 2014-2015 winter. Statistical analysis were carried out to test the significance of the parameters.Main results: Age, relative density, the number of trees, basal area and stand volume of the stand parameters, and slope, site class, terrain roughness and soil depth of the site parameters were statistically linked to the snow damage.Research highlights: The climate is a force to be reckoned with. New strategies must be devised and put into action in forest management. Studies have shown the mishaps. The lessons should be taken from the past experiences. Apparent mistakes must gradually be eliminated.

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Section: Discussionmentioning

confidence: 99%

Determination of Some Stand and Site Parameters Influencing the Snow Damage in Trees: A Case Study of Boyabat Forest Enterprise, Turkey

Altunel¹

2019

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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“…Exposure Analysis Simulation modeling [21]; [22]; [30]; [31]; [32]; [33]; [34]; [37]; [69]; [70]; [92]; [93]; [94] Effects Analysis Fire effects [19]; [21]; [35]; [36]; [37]; [38]; [39]; [42] Fire Management Visibility analysis [24]; [43]; [44]; [45]; [46]; [47]; [48]; [49]; [81]; [95]; [96] Fire Management / Emergency Response Network analysis [51]; [52]; [53]; [54]; [55]; [56]; [57]; [58]; [59]; [60]; [61]; [62];…”

Section: Fire Modulementioning

confidence: 99%

Review of Geoinformatics-Based Forest Fire Management Tools for Integrated Fire Analysis

Sakellariou¹,

Sfougaris²,

Christopoulou³

2021

Pol. J. Environ. Stud.

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Wildfires of high severity can have profound implications on natural and human environment affecting the quality of life; the health of living beings; and the prosperity of any society. Consequently, specific strategies, tactics and techniques should always be adopted for the alleviation of this critical phenomenon. Hence, the aim of the paper is the review of the most common geoinformatics-based techniques contributing to an integrated fire analysis through four pillars. The first one is related with the fire exposure on the ground, primarily analyzing the fire susceptibility in terms of fire risk and burn probability maps; The second one examines the fire effects on the most critical ecological and anthropogenic resources and infrastructures. The third pillar combines two effective geospatial tools supporting the wildfire prevention and suppression, such as the visibility analysis for early detection of fire hotspots and the network analysis for strategic and operational planning of fire events. Last, the Earth-Observation module, through the spatiotemporal monitoring and prediction of land use changes, permits the planners to evaluate the underlying pressures (fires, urbanization) against forests developing the appropriate planning guidelines. In the meantime, new perspectives emerge. Novel machine learning algorithms and remote sensing data techniques are expected to improve the fire risk/probability credibility enhancing the more precise identification of fire effects to any resource. The integration of specific geographic criteria (e.g. topography, accessibility) and programming techniques (e.g. maximizing the visible area) to visibility analysis would empower the immediate fire detection. New technologies such as the adoption of drones would be a cost-effective tool for quick retrieval of vital geo-data. Network analysis could propose financially and environmentally efficient location schemes of fire agencies and resources.

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“…A single fuel model can be applied to numerous vegetation types whose fuel definitions are similar to those represented by the model (Dimitrakopoulos, 2002;Kucuk et al, 2015). The National Forest Fire Laboratory (NFFL) in the US has 13 fuel types developed for BEHAVE system (Andrews, 2007), while Canadian Forest Fire Behavior Prediction System uses 16 discrete fuel types (Lawson et al, 1985). The American and Canadian systems are inspired by the researchers around Europe and they have developed seven fuel models based on the same NFFL fuel types (Arroyo et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The conifer forests grown in the Western Black Sea region have large areas of Anatolian black pine stands (Pinus nigra); thus this region has been reassessed a little while back as being a high potential forest fire risk area. Developing fuel models and determining their fire behavior potential are of vital importance in forest, land, and fire management in the Western Black Sea region (Kucuk et al, 2017;Mitsopoulos et al, 2017). Therefore, the aim of this study is to assess three fire behavior descriptors (fireline intensity, rate of fire spread and flame length) using the fuel load, weather and topographic characteristics for the Bayam Forest District in Kastamonu, Turkey with the help of remote sensing techniques and FlamMap software.…”

Section: Introductionmentioning

confidence: 99%

Batı Karadeniz Bölgesinde FlamMap yazılımı ve uzaktan algılama teknikleri kullanılarak orman yangın davranışı simülasyonunun değerlendirilmesi

Yavuz¹,

Sağlam²,

Küçük³

et al. 2018

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of study: Forest fuels are very critical for fire behavior models and hazard maps. Relationship among wind speed, fuel moisture content, slope, and fuel type directs us to predict fire behavior of a given region. For this study, we evaluated fire behavior parameters such as fireline intensity and rate of fire spread using the fuel moisture content, slope, fuel load, and wind speed for the Bayam Forest District with the help of remote sensing techniques and FlamMap software. Area of study: The study area is located in Bayam Forest District in the city of Taskopru, Kastamonu, a Western Black Sea region of Turkey. Material and Methods: In order to estimate and map forest fuel load of the study area, fuel models were developed using the parameters of the average vegetation height, 1-hr, 10-hr, and 100-hr fuel load, foliage, total fuel load, litter load and litter depth. Three basic fire descriptors (fireline intensity, rate of fire spread, and flame length) were calculated using FlamMap software with the parameters fuel load, wind speed, fuel moisture, and slope. Using the descriptors above, the historical fire data was overlaid with the fireline intensity maps to determine fire potential areas within the remote sensing and GIS framework. Main results: The results of this study showed that 20.0% of the region had low (<2 m min-1), 43.2% had moderate (2-15 m min-1), 12.0% had high (15-30 m min-1), and 24.8% had very high (>30 m min-1) rate of fire spread, respectively. The fireline intensity map showed that 60.7% of the area was in low (0-350 kW m-1), 24.9% was in moderate (350-1700 kW m-1), 1.3% was in high (1700-3500 kW m-1), and 13.0% was in very high (>3500 kW m-1) fireline intensity. Highlights: The spatial extent of fuel types was observed and three of the potential fire behavior predictors (fire intensity, rate of fire spread and flame length) were estimated using remote sensing and GIS techniques. The overlaid historical fire data showed that the most fire-prone areas are in the mixed young Anatolian black pine-Scots pine tree stands that have 40-70% canopy cover and that are in the young Anatolian black pine tree stands that have more than 70% canopy cover.

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Visibility analysis of fire lookout towers in the Boyabat State Forest Enterprise in Turkey

Cited by 28 publications

References 17 publications

Determination of Some Stand and Site Parameters Influencing the Snow Damage in Trees: A Case Study of Boyabat Forest Enterprise, Turkey

Determination of Some Stand and Site Parameters Influencing the Snow Damage in Trees: A Case Study of Boyabat Forest Enterprise, Turkey

Review of Geoinformatics-Based Forest Fire Management Tools for Integrated Fire Analysis

Batı Karadeniz Bölgesinde FlamMap yazılımı ve uzaktan algılama teknikleri kullanılarak orman yangın davranışı simülasyonunun değerlendirilmesi

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