Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data

2014 ◽  
Vol 23 (3) ◽  
pp. 350 ◽  
Author(s):  
Eduardo González-Ferreiro ◽  
Ulises Diéguez-Aranda ◽  
Felipe Crecente-Campo ◽  
Laura Barreiro-Fernández ◽  
David Miranda ◽  
...  
Keyword(s):  
Pinus Radiata ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Low Density ◽  
Crown Fire ◽  
Fire Behaviour ◽  
Fuel Characteristics ◽  
Fire Initiation ◽  
Behaviour Simulation ◽  
Simulation Systems

Crown fire initiation and spread are key elements in gauging fire behaviour potential in conifer forests. Crown fire initiation and spread models implemented in widely used fire behaviour simulation systems such as FARSITE and FlamMap require accurate spatially explicit estimation of canopy fuel complex characteristics. In the present study, we evaluated the potential use of very low-density airborne LiDAR (light detection and ranging) data (0.5 first returns m–2) – which is freely available for most of the Spanish territory – to estimate canopy fuel characteristics in Pinus radiata D. Don stands in north-western Spain. Regression analysis indicated strong relationships (R2=0.82–0.98) between LiDAR-derived metrics and field-based fuel estimates for stand height, canopy fuel load, and average and effective canopy base height Average and effective canopy bulk density (R2=0.59–0.70) were estimated indirectly from a set of previously modelled forest variables. The LiDAR-based models developed can be used to elaborate geo-referenced raster files to describe fuel characteristics. These files can be generated periodically, whenever new freely available airborne LiDAR data are released by the Spanish National Plan of Aerial Orthophotography, and can be used as inputs in fire behaviour simulation systems.

Simulated fire behaviour in young, postfire lodgepole pine forests

2017 ◽  
Vol 26 (10) ◽  
pp. 852 ◽  
Author(s):  
Kellen N. Nelson ◽  
Monica G. Turner ◽  
William H. Romme ◽  
Daniel B. Tinker
Keyword(s):  
Pinus Contorta ◽  
Stand Structure ◽  
Lodgepole Pine ◽  
Pine Forests ◽  
Fuel Moisture ◽  
Crown Fire ◽  
Fire Behaviour ◽  
Fire Initiation ◽  
Moisture Conditions ◽  
Simulated Fire

Early-seral forests are expanding throughout western North America as fire frequency and annual area burned increase, yet fire behaviour in young postfire forests is poorly understood. We simulated fire behaviour in 24-year-old lodgepole pine (Pinus contorta var. latifolia) stands in Yellowstone National Park, Wyoming, United States using operational models parameterised with empirical fuel characteristics, 50–99% fuel moisture conditions, and 1–60kmhr−1 open winds to address two questions: [1] How does fireline intensity, and crown fire initiation and spread vary among young, lodgepole pine stands? [2] What are the contributions of fuels, moisture and wind on fire behaviour? Sensitivity analysis indicated the greatest contributors to output variance were stand structure mediated wind attenuation, shrub fuel loads and 1000-h fuel moisture for fireline intensity; crown base height for crown fire initiation; and crown bulk density and 1-h fuel moisture for crown fire spread. Simulation results predicted crown fire (e.g. passive, conditional or active types) in over 90% of stands at 50th percentile moisture conditions and wind speeds greater than 3kmhr−1. We conclude that dense canopy characteristics heighten crown fire potential in young, postfire lodgepole pine forests even under less than extreme wind and fuel moisture conditions.

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

2017 ◽  
Vol 26 (2) ◽  
pp. e02S ◽  
Author(s):  
Francisco Rodríguez y Silva ◽  
Mercedes Guijarro ◽  
Javier Madrigal ◽  
Enrique Jiménez ◽  
Juan R. Molina ◽  
...  
Keyword(s):  
Fire Spread ◽  
Full Scale ◽  
Research Centre ◽  
Crown Fire ◽  
Fire Behaviour ◽  
Bench Scale ◽  
Fire Experiment ◽  
Fire Initiation ◽  
Scale Experiment

Aims 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.

Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height

2012 ◽  
Vol 21 (2) ◽  
pp. 95 ◽  
Author(s):  
Martin E. Alexander ◽  
Miguel G. Cruz
Keyword(s):  
Wildland Fire ◽  
Stem Bark ◽  
Flame Length ◽  
Flame Height ◽  
Crown Fire ◽  
Fire Behaviour ◽  
Surface Fire ◽  
Crown Scorch ◽  
Fire Initiation ◽  
Bark Char

This state-of-knowledge review examines some of the underlying assumptions and limitations associated with the inter-relationships among four widely used descriptors of surface fire behaviour and post-fire impacts in wildland fire science and management, namely Byram’s fireline intensity, flame length, stem-bark char height and crown scorch height. More specifically, the following topical areas are critically examined based on a comprehensive review of the pertinent literature: (i) estimating fireline intensity from flame length; (ii) substituting flame length for fireline intensity in Van Wagner’s crown fire initiation model; (iii) the validity of linkages between the Rothermel surface fire behaviour and Van Wagner’s crown scorch height models; (iv) estimating flame height from post-fire observations of stem-bark char height; and (v) estimating fireline intensity from post-fire observations of crown scorch height. There has been an overwhelming tendency within the wildland fire community to regard Byram’s flame length–fireline intensity and Van Wagner’s crown scorch height–fireline intensity models as universal in nature. However, research has subsequently shown that such linkages among fire behaviour and post-fire impact characteristics are in fact strongly influenced by fuelbed structure, thereby necessitating consideration of fuel complex specific-type models of such relationships.

Corrigendum to: Interdependencies between flame length and fireline intensity in predicting crown fire initiation and crown scorch height

2017 ◽  
Vol 26 (4) ◽  
pp. 345 ◽  
Author(s):  
Martin E. Alexander ◽  
Miguel G. Cruz
Keyword(s):  
Wildland Fire ◽  
Stem Bark ◽  
Flame Length ◽  
Flame Height ◽  
Crown Fire ◽  
Fire Behaviour ◽  
Surface Fire ◽  
Crown Scorch ◽  
Fire Initiation ◽  
Bark Char

This state-of-knowledge review examines some of the underlying assumptions and limitations associated with the inter-relationships among four widely used descriptors of surface fire behaviour and post-fire impacts in wildland fire science and management, namely Byram's fireline intensity, flame length, stem-bark char height and crown scorch height. More specifically, the following topical areas are critically examined based on a comprehensive review of the pertinent literature: (i) estimating fireline intensity from flame length; (ii) substituting flame length for fireline intensity in Van Wagner's crown fire initiation model; (iii) the validity of linkages between the Rothermel surface fire behaviour and Van Wagner's crown scorch height models; (iv) estimating flame height from post-fire observations of stem-bark char height; and (v) estimating fireline intensity from post-fire observations of crown scorch height. There has been an overwhelming tendency within the wildland fire community to regard Byram's flame length–fireline intensity and Van Wagner's crown scorch height–fireline intensity models as universal in nature. However, research has subsequently shown that such linkages among fire behaviour and post-fire impact characteristics are in fact strongly influenced by fuelbed structure, thereby necessitating consideration of fuel complex specific-type models of such relationships.

scholarly journals Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics

2019 ◽  
Vol 28 (11) ◽  
pp. 823 ◽  
Author(s):  
Brigite Botequim ◽  
Paulo M. Fernandes ◽  
José G. Borges ◽  
Eduardo González-Ferreiro ◽  
Juan Guerra-Hernández
Keyword(s):  
Bulk Density ◽  
Forest Type ◽  
Classification Tree ◽  
Airborne Lidar ◽  
Fuel Load ◽  
Mediterranean Forests ◽  
Fire Behaviour ◽  
Fuel Loading ◽  
Fuel Characteristics ◽  
Stand Height

Wildfires cause substantial environmental and socioeconomic impacts and threaten many Spanish forested landscapes. We describe how LiDAR-derived canopy fuel characteristics and spatial fire simulation can be integrated with stand metrics to derive models describing fire behaviour. We assessed the potential use of very-low-density airborne LiDAR (light detection and ranging) data to estimate canopy fuel characteristics in south-western Spain Mediterranean forests. Forest type-specific equations were used to estimate canopy fuel attributes, namely stand height, canopy base height, fuel load, bulk density and cover. Regressions explained 61–85, 70–85, 38–96 and 75–95% of the variability in field estimated stand height, canopy fuel load, crown bulk density and canopy base height, respectively. The weakest relationships were found for mixed forests, where fuel loading variability was highest. Potential fire behaviour for typical wildfire conditions was predicted with FlamMap using LiDAR-derived canopy fuel characteristics and custom fuel models. Classification tree analysis was used to identify stand structures in relation to crown fire likelihood and fire suppression difficulty levels. The results of the research are useful for integrating multi-objective fire management decisions and effective fire prevention strategies within forest ecosystem management planning.

scholarly journals Sub-Compartment Variation in Tree Height, Stem Diameter and Stocking in a Pinus radiata D. Don Plantation Examined Using Airborne LiDAR Data

2014 ◽  
Vol 6 (8) ◽  
pp. 7592-7609 ◽  
Author(s):  
Hanieh Saremi ◽  
Lalit Kumar ◽  
Christine Stone ◽  
Gavin Melville ◽  
Russell Turner
Keyword(s):  
Pinus Radiata ◽  
Tree Height ◽  
Airborne Lidar ◽  
Stem Diameter ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis

2016 ◽  
pp. 103 ◽  
Author(s):  
J. Guerra-Hernández ◽  
M. Tomé ◽  
E. González-Ferreiro
Keyword(s):  
Regression Models ◽  
Deciduous Forest ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Mediterranean Forest ◽  
Lidar Data ◽  
Low Density ◽  
Airborne Lidar Data

<p>This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m<sup><span lang="EN-US">–2</span></sup>) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystems.</p>

scholarly journals Assessing the probability of crown fire initiation based on fire danger indices

2003 ◽  
Vol 79 (5) ◽  
pp. 976-983 ◽  
Author(s):  
Miguel G Cruz ◽  
Martin E Alexander ◽  
Ronald H Wakimoto
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Logistic Models ◽  
Fire Danger ◽  
Research Database ◽  
Crown Fire ◽  
Fire Behaviour ◽  
Wide Range ◽  
Fire Initiation ◽  
Fire Danger Indices

The initiation of crown fires in conifer stands was modelled through logistic regression analysis by considering as independent variables a basic physical descriptor of the fuel complex structure and selected components of the Canadian Forest Fire Weather Index (FWI) System. The study was based on a fire behaviour research database consisting of 63 experimental fires covering a relatively wide range of burning conditions and fuel type characteristics. Four models were built with decreasing input needs. Significant predictors of crown fire initiation were: canopy base height, wind speed measured at a height of 10 m in the open, and four components of the FWI System (i.e., Fine Fuel Moisture Code, Drought Code, Initial Spread Index and Buildup Index). The models predicted correctly the type of fire (i.e., surface or crown) between 90% and 66% of the time. The C index, a statistical measure, varied from 0.94 to 0.71, revealing good concordance between predicted probabilities and observed events. A comparison between the logistic models and Canadian Forest Fire Behaviour Prediction System models did not show any conclusive differences. The results of a limited evaluation involving two independent experimental fire data sets for distinctly different fuel complexes were encouraging. The logistic models built may have applicability in fire management decision support systems, allowing for the estimation of the probability of crown fire initiation at small and large spatial scales from commonly available fire environment and fire danger rating information. The relationships presented are considered valid for free-burning fires on level terrain in coniferous forests that have reached a pseudo steady-state and are not deemed applicable to dead conifer forests (i.e., insect-killed stands). Key words: Canadian Forest Fire Danger Rating System, crown fire initiation, fire behaviour, fire danger indices, logistic regression

scholarly journals Estimation of Tree Height by Combining Low Density Airborne LiDAR Data and Images Using the 3D Tree Model: A Case Study in a Subtropical Forest in China

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1252
Author(s):  
Xiaocheng Zhou ◽  
Wenjun Wang ◽  
Liping Di ◽  
Lin Lu ◽  
Liying Guo
Keyword(s):  
High Resolution ◽  
Tree Height ◽  
Airborne Lidar ◽  
Estimation Accuracy ◽  
Tree Level ◽  
Lidar Data ◽  
Low Density ◽  
Tree Model ◽  
Single Tree ◽  
Airborne Lidar Data

In general, low density airborne LiDAR (Light Detection and Ranging) data are typically used to obtain the average height of forest trees. If the data could be used to obtain the tree height at the single tree level, it would greatly extend the usage of the data. Since the tree top position is often missed by the low density LiDAR pulse point, the estimated forest tree height at the single tree level is generally lower than the actual tree height when low density LiDAR data are used for the estimation. To resolve this problem, in this paper, a modified approach based on three-dimensional (3D) parameter tree model was adopted to reconstruct the tree height at the single tree level by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. The approach was applied to two coniferous forest plots in the subtropical forest region, Fujian Province, China. The following conclusions were reached after analyzing the results: The marker-controlled watershed segmentation method is able to effectively extract the crown profile from sub meter-level resolution images without the aid of the height information of LiDAR data. The adaptive local maximum method satisfies the need for detecting the vertex of a single tree crown. The improved following-valley approach is available for estimating the tree crown diameter. The 3D parameter tree model, which can take advantage of low-density airborne LiDAR data and high resolution images, is feasible for improving the estimation accuracy of the tree height. Compared to the tree height results from only using the low density LiDAR data, this approach can achieve higher estimation accuracy. The accuracy of the tree height estimation at the single tree level for two test areas was more than 80%, and the average estimation error of the tree height was 0.7 m. The modified approach based on the three-dimensional parameter tree model can effectively increase the estimation accuracy of individual tree height by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data.

scholarly journals Identifying Forest Structural Types along an Aridity Gradient in Peninsular Spain: Integrating Low-Density LiDAR, Forest Inventory, and Aridity Index

2022 ◽  
Vol 14 (1) ◽  
pp. 235
Author(s):  
Julián Tijerín-Triviño ◽  
Daniel Moreno-Fernández ◽  
Miguel A. Zavala ◽  
Julen Astigarraga ◽  
Mariano García
Keyword(s):  
Random Forest ◽  
Forest Structure ◽  
Forest Inventory ◽  
Aridity Index ◽  
Airborne Lidar ◽  
Random Forest Model ◽  
Lidar Data ◽  
Low Density ◽  
Forest Model ◽  
Structural Types

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer’s accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region.

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