Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements

González-Olabarría, J. R.; Rodríguez, Francisco; Fernández-Landa, Alfredo; Mola‐Yudego, Blas

doi:10.1016/j.foreco.2012.06.056

Cited by 82 publications

(70 citation statements)

References 39 publications

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“…In this regard, our findings are in accordance with previous studies on the effect of plot positioning errors on ALS-based estimations [23] and the effect of co-registration error on the estimation of ALS metrics [7]. The applied SUR method resulted in better fitting statistics than those reported in previous research for the same study area [33]. For the case of diameter distribution models, our results showed that predictions based on ALS data were slightly less reliable than the diameter distribution models published by [4] based on growing attributes retrieved from field measurements only.…”

Section: Effect Of Plot Positioning Errors On the Estimation Of Growisupporting

confidence: 92%

“…Each of the five models was first fitted separately using stepwise regression analysis [32]. The stepwise procedure was used iteratively until all models had a maximum of three predictor variables, except the model for H o , which had only one predictor based on the results of [33]. The final models were selected based on the following goodness-of-fit statistics: predictors' significance, root mean square error (RMSE), degree of explained variance (R 2 ), normality and homogeneity of variance of the residuals checked visually from Q-Q and scatter plots.…”

Section: Estimation Of Forest Stand Attributes From Als Datamentioning

confidence: 99%

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Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Pascual

Pukkala

de-Miguel

2018

Forests

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Forest management planning is increasingly relying on airborne laser scanning (ALS) in forest inventory. The area-based method to interpret ALS data requires sample plots measured in the field. The aim of this study was to assess and trace the impacts of the positioning errors of field plots along the entire forest management planning process, from their effect on forest inventory results to the outcome of forest management planning. This research links plot positioning errors with the spatio-temporal allocation of forest treatments and calculates the inoptimality losses arising from plot positioning errors in ALS-based forest inventory. The study area was a forest management unit in Central Spain. Growing stock attributes were predicted for a grid of square-shaped cells. Alternative management schedules were simulated for the grid cells by using growth and yield models. Then, a spatial forest planning problem aiming at maximizing timber production with even-flow cuttings was formulated. Spatial objective variables were used to cluster management prescriptions into dynamic treatment units. We used simulated annealing to conduct spatial optimization. First, the true plot locations were used and then the whole process was repeated with normally distributed random errors with standard deviation equal to 2.5, 5 and 10 m, resulting in an average error of 1.47, 3.06 and 8.34 m, respectively. Increasing the level of positioning errors resulted in higher variability in the estimated growing stock attributes and in the achieved values of management goals. Sub-optimal prescriptions caused by the tested plot positioning errors caused up to 4.62% losses in timber production and up to 3.35% losses in utility. The losses increased with increasing plot positioning error.

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Section: Effect Of Plot Positioning Errors On the Estimation Of Growisupporting

confidence: 92%

Section: Estimation Of Forest Stand Attributes From Als Datamentioning

confidence: 99%

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Pascual

Pukkala

de-Miguel

2018

Forests

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“…Advances in laser imaging detection and ranging (LiDAR) remote sensing technologies have facilitated the creation of high-resolution spatially explicit maps of canopy fuel metrics (i.e., canopy cover, canopy height, canopy base height and canopy bulk density; Scott and Reinhardt 2001), which improve these input metrics for wildfire behavior modeling (Andersen et al 2005;Erdody and Moskal 2010;García et al 2011;Gonzalez-Olabarria et al 2012a;Hermosilla et al 2014). Other remote sensing technologies such as near-infrared aerial imagery have been also used for fuel model mapping (Fallowski et al 2005;Arroyo et al 2008), but only small-footprint discrete-return airborne LiDAR pulses can penetrate beneath the tree canopies to allow pixel-based reconstruction of three-dimensional forest structure characteristics for large regions.…”

Section: Introductionmentioning

confidence: 99%

A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain

2015

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Wildfires are a growing threat to socioeconomic and natural resources in the wildland-rural-urban intermix in central Navarra (Spain), where recent fastspreading and spotting short fire events have overwhelmed suppression capabilities. A fire simulation modeling approach based on the minimum travel time algorithm was used to analyze the wildfire exposure of highly valued resources and assets (HVRAs) in a 28,000 ha area. We replicated 30,000 fires at fine resolution (20 m), based on wildfire season and recent fire weather and moisture conditions, historical ignition patterns and spatially explicit canopy fuels derived from low-density airborne light detection and ranging (LiDAR). Detailed maps of simulated fire likelihood, fire intensity and fire size were used to assess spatial patterns of HVRA exposure to fire and to analyze large fire initiation and spread through source-sink ratio and fire potential index. Crown fire activity was estimated and used to identify potential spotting-emission hazardous stands. The results revealed considerable variation in fire risk causative factors among and within HVRAs. Exposure levels across HVRAs were mainly related to the combined effects of anthropic ignition locations, fuels, topography and weather conditions. We discuss the potential of fire management strategies such as prioritizing mitigation treatment and fire ignition prevention monitoring, informed by fine-scale geospatial quantitative risk assessment outcomes.

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“…Crown fire behaviour modelling is also used within the framework of wildfire research to assess the efficacy of fuel treatments (e.g. Arca et al, 2007;Duguy et al, 2007;González-Olabarría et al, 2012;Jiménez et al, 2016;Oliveira et al, 2016;Madrigal et al, 2017). Nevertheless, crown fire models have not been tested by experimental burns in Mediterranean conditions and the predictions may therefore be misleading (Cruz & Alexander, 2010;Benali et al, 2016), leading to the application of inappropriate forest and fire management activities.…”

Section: Introductionmentioning

confidence: 99%

Assessment of crown fire initiation and spread models in Mediterranean conifer forests by using data from field and laboratory experiments

et al. 2017

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Aim of study: To conduct the first full-scale crown fire experiment carried out in a Mediterranean conifer stand in Spain; to use different data sources to assess crown fire initiation and spread models, and to evaluate the role of convection in crown fire initiation.Area of study: The Sierra Morena mountains (Coordinates ETRS89 30N: X: 284793-285038; Y: 4218650-4218766), southern Spain, and the outdoor facilities of the Lourizán Forest Research Centre, northwestern Spain. Material and methods:The full-scale crown fire experiment was conducted in a young Pinus pinea stand. Field data were compared with data predicted using the most used crown fire spread models. A small-scale experiment was developed with Pinus pinaster trees to evaluate the role of convection in crown fire initiation. Mass loss calorimeter tests were conducted with P. pinea needles to estimate residence time of the flame, which was used to validate the crown fire spread model.Main results: The commonly used crown fire models underestimated the crown fire spread rate observed in the full-scale experiment, but the proposed new integrated approach yielded better fits. Without wind-forced convection, tree crowns did not ignite until flames from an intense surface fire contacted tree foliage. Bench-scale tests based on radiation heat flux therefore offer a limited insight to full-scale phenomena.Research highlights: Existing crown fire behaviour models may underestimate the rate of spread of crown fires in many Mediterranean ecosystems. New bench-scale methods based on flame buoyancy and more crown field experiments allowing detailed measurements of fire behaviour are needed.

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Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements

Cited by 82 publications

References 39 publications

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

Effects of Plot Positioning Errors on the Optimality of Harvest Prescriptions When Spatial Forest Planning Relies on ALS Data

A fire modeling approach to assess wildfire exposure of valued resources in central Navarra, Spain

Assessment of crown fire initiation and spread models in Mediterranean conifer forests by using data from field and laboratory experiments

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