Article

Generation of high-resolution fuel model maps from discrete airborne laser scanner and Landsat-8 OLI: a low-cost and highly updated methodology for large areas

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Wildfire risk is increasing in the context of global change, and the need for accurate fuel model maps in broader areas is becoming urgent to manage large wildfires. Among remote sensing technologies, Airborne Laser Scanner (ALS) is extremely useful for fuel mapping as it provides 3D information on vegetation distribution. A cost-effective methodology to obtain high-resolution fuel model maps in large forest areas from ALS data (1 pulse/m2) and Landsat-8 OLI images is presented. A two-phase approach was used to generate the fuel model maps: i) ad-hoc vegetation classification derived from ALS and Landsat-8 OLI, and ii) fuel model assignment based on fuel complex structure from a limited number of ALS-derived metrics: fractional canopy cover, fuel height, and canopy relief ratio. Fuel model maps for the Canary Islands (Spain) were generated for two fuel classification systems, standard Northern Forest Fire Laboratory (NFFL) and specific Canarian fuel models (CIFM), at 25 m resolution (3678 km2) according to decision rules based on ALS-derived metrics developed for each vegetation type. Fieldwork was used to validate the fuel model maps, obtaining an overall accuracy of 82% (kappa = 0.777) and 70% (kappa = 0.679) for the standard NFFL and CIFM fuel models respectively. Discrimination between fuel models associated to forests with and without understory was satisfactory, showing higher errors due to species composition classification rather than to ALS-derived fuel structure. Errors due to underestimation of ALS-derived fuel cover and height were more evident in mixed grassland and shrubland fuels. Results demonstrated the potential of combining imagery and ALS for fuel model mapping at a large scale from existing data sources, even with low laser pulse density and temporarily mismatched data sets. The proposed methodology may be applied for fuel mapping in other large areas provided that ALS information is available and that fuel model definition has explicit structure characteristics allowing decision rules based on ALS data. Once algorithms are defined for fuel model assignment, the low number of ALS-derived metrics and the semi-automated processing ensures that fuel model maps can be easily updated as new data sources become available providing managers with useful spatial information in large areas.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... Based on this study, our hypothesis is that characterizing pre-fire forest structure from fine spatial resolution LiDAR data could potentially complement the information of the MESMA Hyperion post-fire char fraction image about burn severity. Some studies have already combined multispectral/hyperspectral data and LiDAR data to map fuel types (Erdody and Moskal, 2010;Marino et al., 2016;Mutlu et al., 2008a;Sánchez-Sánchez et al., 2018); to classify tree species (Naidoo et al., 2012); to predict forest height (Ahmed et al., 2015;Gu et al., 2018); or to relate canopy structure to burn severity patterns (Kane et al., 2014b(Kane et al., , 2015Mutlu et al., 2008b;García-Llamas et al., 2019). Some studies have used postfire LiDAR data alone or as a complement to multispectral/hyperspectral imagery (Bolton et al., 2015;Kane et al., 2014b;Montealegre et al., 2014), and a few of them have employed a multitemporal LiDAR to map burn severity (McCarley et al., 2017;Wang and Glenn, 2009;White and Dietterick, 2012;Wulder et al., 2009;Stavros et al., 2016) but none of them have employed pre-fire LiDAR data to complement the information from multispectral/hyperspectral imagery in order to quantify burn severity, to our knowledge. ...
... This decision was based on previous studies that recommended discarding this kind of potentially erroneous laser returns (e.g. Erdody and Moskal, 2010;Estornell et al., 2011;Marino et al., 2016;). Height values higher than 20 m were also discarded to avoid undesired returns from power lines, assuming that 20 m is the maximum height of vegetation. ...
... Finally, CR (Smith, 1986), related to tree vigor, is the ratio of canopy height depth (20th percentile height of vegetation points) to total tree height (90th percentile height of vegetation points). All the calculated LiDAR-derived metrics were identified in previous studies as highly correlated with vegetation structure and/or fuel load (Cao et al. 2019;Erdody and Moskal, 2010;Kane et al., 2015;Naidoo et al., 2012;Marino et al., 2016Marino et al., , 2018. ...
Article
All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR.
... La generación de cartografías de alta resolución que incorporen información sobre los diferentes descriptores del estrato arbóreo es indispensable para el análisis del comportamiento esperado de los incendios mediante el uso de simuladores del comportamiento del fuego. En este sentido, la integración de la información ambiental previa existente a escala regional con datos disponibles de otras fuentes, en especial LiDAR aéreo y teledetección satelital, suponen una oportunidad para mejorar la caracterización de los complejos de combustible forestal a gran escala y con un alto nivel de detalle (MARINO et al. 2016). ...
... La modelización y tipificación del combustible forestal demanda una actualización periódica y un seguimiento, tanto por la propia evolución de la vegetación, especialmente las estructuras vegetales menos maduras, como por las perturbaciones que acontezcan debidas a incendios 12/14 forestales, aprovechamientos o cambios de uso. Agresta cuenta con una metodología propia de trabajo basada en datos LiDAR, de rápida actualización a partir de nuevas fuentes de información, para la generación de cartografía de alta resolución de modelos de combustible forestal a escala regional (MARINO et al., 2016). Esta metodología, previamente validada para la identificación de los modelos de combustible NFFL-Rothermel, así como para otros sistemas de clasificación, se ha utilizado con éxito para la generación de cartografía de detalle de modelos de combustible actualizadas tanto a nivel regional (ej. ...
Conference Paper
Full-text available
Las cartografías de alta resolución de los combustibles forestales se han convertido en una información espacial imprescindible para la simulación de incendios, herramienta básica tanto para la gestión preventiva como para la extinción. La generación y actualización de estas cartografías a escala regional supone un importante reto para las administraciones públicas al requerir no solo información detallada de la vegetación existente en todo el territorio en relación a su estructura y comportamiento potencial frente al fuego, sino a la necesidad de disponer de mecanismos ágiles de actualización de estos complejos de combustible. En el marco del proyecto CILIFO se está desarrollando una mejora de la caracterización de los combustibles superficiales y arbóreos presentes en la comunidad autónoma de Andalucía. Este trabajo incorpora tanto la generación de cartografías de calidad de modelos de combustible (clasificación UCO40 y Rothermel) y de combustibles de copas (altura de base de copa, carga disponible y densidad aparente) basadas en datos LIDAR del PNOA y datos de campo, como una herramienta para su revisión, actualización e integración en la Red de Información Ambiental de Andalucía (REDIAM) a través del “Módulo Seguimiento” de la plataforma SIPNA (Sistema de Información del Patrimonio Natural de Andalucía).
... Kramer et al. (2014) used ALS data to differentiate between high and low levels of ladder fuels after forest fuel treatments. The canopy release ratio (CRR) suggested by Parker and Russ (2004) has been also used to differentiate the presence or absence of vertical fuel continuity for ALS-derived fuel model mapping (Marino et al., 2016). ...
... CRR also performed better than h_mean, which is expected considering that this index includes a ratio of the differences between h_mean, h_max and h_min (Table 2). Our results are consistent with previous works suggesting CRR as a useful ALS metric to assess the vertical structure in forest areas (Marino et al., 2016;Parker and Russ, 2004). ...
Article
Vertical fuel structure is critical for fire hazard assessment in forest ecosystems. Forest stands with ladder fuels are more prone to crown fires because of canopy fuel continuity. However, characterization of ladder fuels is difficult in the field and few studies have developed explicit measurement procedures to account for these hazardous fuel situations. This study compares vertical profiles derived from airborne laser scanning (ALS) data and stereoscopic hemispherical images obtained in Pinus sylvestris stands in central Spain to test their ability to detect the presence or absence of vertical fuel continuity (VFC). Vertical fuel profiles based on canopy cover fraction estimations at different height strata were assessed at plot level and compared with field observations. The quadratic form distance (QFD) was the metric used to quantify the similarity between histogram distributions defined by the vertical profiles from different datasets. Logistic regression analysis was tested to discriminate areas with and without VFC from ALS data at two threshold levels (15% and 30%). The vertical fuel profiles of canopy cover showed a different level of correspondence depending on the relative amount of ladder fuels. Significant logistic models were found (p < 0.0001, c-index > 090) for different combination of ALS metrics, with low percentiles (up to P30), canopy relief ratio (CRR) and the percentage of returns normalized by height strata (PRN) up to 8 m as the best predictors to identify the presence of VFC. Results indicated that both datasets were useful in retrieving variability of forest fuel distribution, but further methodological improvements (e.g. understory segmentation in stereoscopic images, new algorithms to better account for occlusions, or ground calibration for laser attenuation in ALS) are needed to increase accuracy in highly continuous areas.
... Their final fuel type classification yielded an overall accuracy of 88.24%, with a kappa coefficient of 0.86. Marino et al. (2016) also proposed a two-phase approach combining ALS data and Landsat-8 OLI. Their approach was conducted using low density ALS data 405 and lower spatial resolution multispectral data than the previous approaches. ...
... Finally, the integration of LiDAR and spectral information from very high spectral resolution data or hyperspectral images presents a good synergetic alternative to deal with the complex nature of fuels. Previous studies already integrated information from these two kinds of data before to map fuel 505 types, but their approaches are site specific (Riaño et al. 2007;Mutlu et al. 2008;García et al. 2011;Jakubowksi et al. 2013;Marino et al. 2016). ...
Article
Fuel types are one of the key variables that drive wildfire ignition and propagation. A new method is proposed to automatically classify and map fuel types from LiDAR data. The 3-dimensional Discrete Anisotropic Radiative Transfer (DART) model generated a fuel type LiDAR signature library. These simulations provided reference endmembers and additional data to demonstrate the feasibility to classify fuel types using spectral matching algorithms, like multiple endmember spectral mixture analysis (MESMA) and spectral angle mapper (SAM). When choosing a single endmember per fuel type, MESMA outperformed SAM with 63.3% and 48.9% agreement, respectively. Multiple endmembers per fuel type improved the classification results to 85.3% in SAM and 86.5 % in MESMA. Endmembers need to identify different scan angles that account for the variability in height and number of trees for better results. Contrary to empirical models, a fuel type LiDAR signature library provides a comprehensive suite of solutions to classify fuel types from LiDAR data that is less study site dependent and applicable to multiple sensors.
... This system is quite similar to the fuel types of coniferous, deciduous, mixed wood, slash, and open grassland, as defined in the Canadian Fire Behavior Prediction System (FBPS) [36]. Various research has demonstrated the feasibility of integrating ALS and optical images to map the fuel types (alternatively fuel models), such as the ones defined by the Northern Forest Fire Laboratory (NFFL) [36,41,49,50,60,61]. The proposed algorithm for mapping the surface fuel load is therefore most likely able to substitute the fuel types of the FBPS, NFFL, and NFDRS classification systems to moderately improve the mapping performance for forests with undulating terrain morphology in mountainous area. ...
Article
Full-text available
Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.
... The abandonment of the field crops leads to the proliferation of bushes increasing the fuel load [4]. Consequently, fuel type mapping has been accomplished by several authors using remote sensed data [4,5], mostly based on multispectral mediumresolution sensors [5-8] but also using hyperspectral images captured by sensors on board of aircrafts [9,10], airborne and satellite LiDAR (Light Detection and Ranging) data [11][12][13] and the combination of different types of sensors [14][15][16][17][18][19]. ...
Article
Full-text available
Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radi-ative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.
... Once the points have been grouped in cells and the corresponding ground value is known, a gate filter was applied to classify the points in two groups: ground points and vegetation points. Taking into account the precision of height values, the threshold used for the classification of ground points was the DTM altitude value plus 0.4 m [34]. So, those points with a zcoordinate greater than the established threshold were classified as vegetation points. ...
Article
Full-text available
The optimization of forest management in the surroundings of roads is a necessary task in term of wildfire prevention and the mitigation of their effects. One of the reasons why a forest fire spreads is the presence of contiguous flammable material, both horizontally and vertically and, thus, vegetation management becomes essential in preventive actions. This work presents a methodology to detect the continuity of vegetation based on aerial Light Detection and Ranging (LiDAR) point clouds, in combination with point cloud processing techniques. Horizontal continuity is determined by calculating Cover Canopy Fraction (CCF). The results obtained show 50% of shrubs presence and 33% of trees presence in the selected case of study, with an error of 5.71%. Regarding vertical continuity, a forest structure composed of a single stratum represents 81% of the zone. In addition, the vegetation located in areas around the roads were mapped, taking into consideration the distances established in the applicable law. Analyses show that risky areas range from a total of 0.12 ha in a 2 m buffer and 0.48 ha in a 10 m buffer, representing a 2.4% and 9.5% of the total study area, respectively.
... Siguiendo la experiencia de Marino et al. (2016) en el análisis de la estructura tridimensional de la vegetación con LiDAR, se han seleccionado los siguientes estadísticos: Porcentaje de primeros retornos (FCC), Altura media (Hm), Porcentaje del Número de Retornos normalizado (PNR), calculado a partir del número de retornos a distintas alturas (Skowronski et al., 2007) para analizar la densidad de la vegetación bajo arbolado, y Canopy Relief Ratio (CRR), un estadístico tal como se puede ver en la ecuación 1, relacionado con la longitud libre de copa de la masa (Parker y Russ, 2004). ...
Conference Paper
Full-text available
La transitabilidad de la vegetación leñosa por parte del ganado es uno de los factores que condiciona la admisibilidad de muchas superficies de bosque y matorral de cara a las ayudas de la PAC (Política Agraria Común). La metodología actual del Fondo Español de Garantía Agraria (FEGA) diseñada en base a vuelos LIDAR del Plan Nacional de Ortofotografía Aérea (PNOA) de 0,5 pulsos/m 2 no permite discriminar la penetrabilidad bajo el dosel de copas con respecto al ganado. A partir de vuelos LiDAR con mayor densidad, como el disponible en La Rioja (2 pulsos/m 2), hemos desarrollado dos metodologías alternativas para solucionar este problema, con una fiabilidad global del 86% y el 78% respectivamente. Estos resultados suponen una oportunidad técnica para identificar con mejor precisión las zonas poten-cialmente aptas para el pastoreo, cuya aplicación implicaría sin duda una importante repercusión económica en el sector de la ganadería extensiva. Palabras clave: Coeficiente de Admisibilidad de Pastos, Pastos, ganadería extensiva, estructura de la vegetación, teledetección.
... In this regard, improvements in remote sensing methods have provided an inexpensive and faster alternative to field surveys. Recently, studies have been combining spectral and lidar data to more accurately map and quantify fuel types not only based on their optical properties but also their vertical structure at multiple scales [46,47]. The NASA's Global Ecosystems Dynamics Investigation spaceborne lidar [48] launched in 2018 is currently collecting threedimensional measurements of forest structure that will be used for estimating forest aboveground biomass globally, and could potentially be applied for estimating canopy fuel properties, including canopy fuel load and bulk density. ...
Article
Full-text available
p>Simulation frameworks are necessary to facilitate decision-making to many fire agencies. An accurate estimation of fire behavior is required to analyze potential impact and risk. Applied research and technology together have improved the implementation of fire modeling, and decision-making in operational environments.</p
... El desarrollo de la tecnología LiDAR y la disponibilidad de datos en formato abierto, como los proporcionados por el Plan Nacional de Ortofotografía Aérea (PNOA) en España, han permitido el avance en el conocimiento de la utilidad de este tipo de información para la monitorización de las masas forestales (MORSDORF et al., 2004;HUDAK et al., 2008;MARTIN-ALCON, et al., 2015). En particular, los datos LiDAR aéreos son una fuente de datos extremadamente útil para la caracterización estructural de la vegetación en grandes superficies, permitiendo generar cartografía de variables descriptivas de los combustibles (MARINO et al. 2016;PRICE & GORDON, 2016;MAURO et al., 2021). ...
Conference Paper
Full-text available
La caracterización de la cantidad y distribución espacial de la biomasa aérea en masas arboladas es clave para evaluar el riesgo de incendio y su severidad potencial, así como para planificar actuaciones preventivas orientadas a reducir su impacto. La tecnología LiDAR permite generar cartografía de variables descriptivas de la estructura de los combustibles de copas en grandes superficies. Sin embargo, para obtener estimaciones fiables es necesario disponer de modelos adaptados a las particularidades de cada especie, calibrándolos adecuadamente con datos de campo. Este estudio utiliza información LiDAR de la segunda cobertura del PNOA (1 p/m2) para la modelización de la altura de la base de las copas (CBH), la carga de combustible disponible (CFL) y la densidad aparente de combustible de copas (CBD) en parcelas de Pinus sylvestris L. Se exploran distintas formulaciones, encontrando mejores ajustes para la variable CBH, seguida de CFL, siendo CBD la más difícil de estimar. Los modelos se emplearon para generar cartografía de alta resolución de los combustibles de copas en los pinares de Valsaín (Segovia), input requerido para simulación de incendios, comparando los resultados con modelos obtenidos a partir de datos LiDAR de la primera cobertura del PNOA (0,5 p/m2) en la misma zona.
... The methodology applied here is based solely on LiDAR data. Previous results have discriminated FTs using LiDAR data combined with optical remote sensing data (Marino et al., 2016;Jakubowski et al., 2013;Mutlu et al., 2008). The synergy of structural information from LiDAR together with spectral information from passive remote sensing data could improve the accuracy of the results. ...
Article
Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.
... A su vez, algoritmos altamente eficientes (Finney, 2002) nos permiten saturar el paisaje con miles de igniciones (>10 4 igniciones) y determinar en alta resolución (< 50 m de píxel) la intensidad del fuego y su probabilidad de quema (Alcasena et al., 2015). Actualmente, la disponibilidad de los datos requeridos para la simulación de incendios es cada vez mayor y los avances tecnológicos permiten caracterizar con precisión las variables geoespaciales del paisaje (topografía, modelos de combustible de superficie y parámetros de copas) en alta resolución Marino et al., 2016). A partir de los registros en estaciones meteorológicas resulta posible determinar localmente cuales son las condiciones meteorológicas extremas asociadas a los grandes incendios forestales y caracterizar así los escenarios más frecuentes durante el periodo estival (Bradshaw y McCormick, 2000;Duane y Brotons, 2018). ...
Article
Full-text available
In Mediterranean areas, the combined effects of the rural exodus, lack of forest management, and fire suppression policies have substantially contributed to increased forest fuel loadings and continuity over large areas. The result is a growing incidence of wildfires that exceed fire suppression capacity. Economic resources for landscape management are limited, and thus they must be prioritized towards the protection of valued assets where there is a high expectation of loss and the fuel treatments on strategic locations that restrict fires spreading into communities. We completed a case study in the Juslapeña Valley (Navarra, Spain) to demonstrate prioritization of fuel management activities. The study area has frequent and large forest fires that have caused significant damage to forest values and assets in rural communities. We first generated a wildfire risk map for valued assets, and then designed the optimal treatment mosaic within the community fireshed considering the wildfire exposure to forestlands and fire transmission to residential housing. We also identified overstocked stands where the timber or firewood production might supply the needs of local communities and partially cover the treatment cost. We found that the highest economic losses were obtained in residential houses located in the southern portion of the study area, mainly due to a higher burn probability. Fires ignited outside of the study area also exposed communities, and thus the extent considered in wildfire management plans needs to be adjusted to reflect the source and scale of risk to communities. The assessment framework presented in this study can be adapted to the multifunctional forest management in any fire-prone Mediterranean region elsewhere.
... They found that fusing LiDAR with imagery and/or LiDAR intensity led to small increases in estimation accuracy over LiDAR alone. More recently, Marino et al. (2016) took an approach using spectral reflectance data, LANDSAT 8 in this case, to develop a map of fuel classifications and then ALS data to derive fuel structure and loading data. As we advance our ability to fuse these two types of data, it is likely that our landscape-scale estimates of fuel classifications and of fuel loading will continue to improve. ...
... These techniques are quickly proving to have direct application to AGB and fuel loading assessment of shrublands in moderately-size areas (e.g., Alonso-Rego et al., 2020;Anderson et al., 2018;Cunliffe et al., 2016;Eisfelder et al., 2012;Lamelas-Gracia et al., 2019;Li et al., 2017;Poley et al., 2020;Schrader-Patton and Underwood, 2021), although their use also posed some challenges (Anderson et al., 2018;Cooper et al., 2020;Estornell et al., 2011;Li et al., 2017). This information can be used together with aerial and satellite imagery to generate fuel maps for large areas (D'Este et al., 2021;Greaves et al., 2015;Lin et al., 2021;Lippitt et al., 2018;Marino et al., 2016;Riaño et al., 2007), which provide essential data for forest and land management. Nonetheless, all remote sensing methods must be validated with ground data Duncanson et al., 2019), supported by ad hoc destructive sampling surveys or, alternatively, the application of allometric models such as those fitted in the present study. ...
Article
Full-text available
Shrub-dominated ecosystems cover large areas globally and play essential roles in ecological processes. Aboveground biomass expressed on an area basis (AGB) is central to many of the ecological processes and services provided by shrublands and is important as the main fuel source for wildfires. Hence, its accurate estimation in shrublands is crucial for ecologists and land managers. This is especially relevant in fire-prone regions such as NW Spain, where shrublands are an important part of the landscape, providing multiple services, but are severely impacted by wildfires. Although biomass models are available for numerous shrub species at the individual plant level, operational models based directly on easily measured shrub stand attributes are scarce. In this study, equations for estimating AGB and loads of different fuel components by size and condition (live and dead) from stand biometric variables were developed for the nine most prevalent shrub communities in NW Spain. Non-linear iterative seemingly unrelated regression was used to fit compatible systems of equations for estimating fuel loads, with shrub stand height and cover and litter depth as predictors for individual shrub communities and all data combined. In general, the goodness-of-fit statistics indicated that the estimates were reasonably accurate for all communities (grouped and ungrouped). The best results were obtained for AGB and total fuel load, including litter, whereas the poorest results were obtained for standing live and dead fine fuel load. Model performance was reduced when height was the only independent variable, although the reduction was small for most fuel categories, except litter load for which the variability was adequately explained by the litter depth. These results illustrate the feasibility of the stand level approach for constructing operational models of shrub fuel load that are accurate for most of fuel components, while also highlighting the ongoing challenges in live and dead fine fuel modelling. The equations developed represent an appreciable advance in shrubland biomass assessment in the region and areas with similar characteristics and may be instrumental in generating fuel maps, fire management improvement and better C storage assessment by vegetation, among other many uses.
... As such an important prerequisite metric, accurate measurement of P gap is critical to subsequent applications. In recent years, researchers have used airborne LiDAR data with a large scan angle range (up to ±45°) in forests (Cao et al., 2016;Marino et al., 2016;Morsdorf et al., 2010). With this configuration, there is high variation in scan angles, even in the same flight. ...
Article
Gap fraction (P gap) and vertical gap fraction profile (vertical P gap profile) are important forest structural metrics. Accurate estimation of P gap and vertical P gap profile is therefore critical for many ecological applications , including leaf area index (LAI) mapping, LAI profile estimation and wildlife habitat modelling. Although many studies estimated P gap and vertical P gap profile from airborne LiDAR data, the scan angle was often overlooked and a nadir view assumed. However, the scan angle can be off-nadir and highly variable in the same flight strip or across different flight strips. In this research, the impact of off-nadir scan angle on P gap and vertical P gap profile was evaluated, for several forest types. Airborne LiDAR data from nadir (0°$7°), small off-nadir (7°$23°), and large off-nadir (23°$38°) directions were used to calculate both P gap and vertical P gap profile. Digital hemispherical photographs (DHP) acquired during fieldwork were used as references for validation. Our results show that angular P gap from airborne LiDAR correlates well with angular P gap from DHP (R 2 = 0.74, 0.87, and 0.67 for nadir, small off-nadir and large off-nadir direction). But underestimation of P gap from LiDAR amplifies at large off-nadir scan angle. By comparing P gap and vertical P gap profiles retrieved from different directions, it is shown that scan angle impact on P gap and vertical P gap profile differs amongst different forest types. The difference is likely to be caused by different leaf angle distribution and canopy architecture in these forest types. Statistical results demonstrate that the scan angle impact is more severe for plots with discontinuous or sparse canopies. These include coniferous plots, and deciduous or mixed plots with between-crown gaps. In these discontinuous plots, P gap and vertical P gap profiles are maximum when observed from nadir direction , and then rapidly decrease with increasing scan angle. The results of this research have many important practical implications. First, it is suggested that large off-nadir scan angle of airborne LiDAR should be avoided to ensure a more accurate P gap and LAI estimation. Second, the angular dependence of vertical P gap profiles observed from airborne LiDAR should be accounted for, in order to improve the retrieval of LAI profiles, and other quantitative canopy structural metrics. This is especially necessary when using multi-temporal datasets in discontinuous forest types. Third, the anisotropy of P gap and vertical P gap profile observed by airborne LiDAR, can potentially help to resolve the anisotropic behavior of canopy reflectance, and refine the inversion of biophysical and biochemical properties from passive multispectral or hyperspectral data.
... El uso de la tecnología LiDAR para el análisis de la estructura de la vegetación permite generar información útil para multitud de campos de interés forestal. Su utilización está muy extendida en distintos ámbitos de la ingeniería forestal, desde la generación de mapas de modelos de combustible orientados a mejorar la planificación de selvicultura preventiva y gestión de los incendios (Gonzalez-Olabarria et al, 2012;Marino et al, 2016), estimación de biomasa y sumideros de carbono (García et al., 2009), hasta el inventario forestal (Hyyppä et al 2008;Rodriguez et al 2014) pasando por el análisis de crecimientos . ...
Conference Paper
Full-text available
La Ley 30/2014, de Parques Nacionales, otorga al Estado el cometido de realizar un seguimiento general de la Red de Parques Nacionales. El Organismo Autónomo Parques Nacionales desarrolla para ello un Plan de Seguimiento y Evaluación, con iniciativas de seguimiento ecológico, sociológico y funcional, en colaboración con las comunidades autónomas y con asesoramiento científico. Algunos de los proyectos de seguimiento ecológico en marcha son la elaboración de una cartografía continua y comparable de los sistemas naturales de la Red, y el seguimiento de la estructura de los ecosistemas mediante la tecnología LiDAR, al que se ha dado un enfoque a largo plazo, aprovechando la finalización en 2015 de la primera cobertura LiDAR nacional por parte del proyecto PNOA-LiDAR del Instituto Geográfico Nacional, obtenida mediante sensores aerotransportados. La presente comunicación describe la metodología desarrollada para el procesamiento y análisis de los datos LiDAR, así como para la integración de la información resultante en la cartografía de los sistemas naturales, enriqueciendo las bases de datos de la unidad SIG de la Red de Parques Nacionales. La segunda cobertura LiDAR, con periodicidad sexenal prevista, garantizaría otra actualización de la cartografía y un seguimiento de la evolución de la estructura de los sistemas naturales.
... El uso de la tecnología LiDAR para el análisis de la estructura de la vegetación permite generar información útil para multitud de campos de interés forestal. Su utilización está muy extendida en distintos ámbitos de la ingeniería forestal, desde la generación de mapas de modelos de combustible orientados a mejorar la planificación de selvicultura preventiva y gestión de los incendios (GonzalezOlabarria et al, 2012;Marino et al, 2016), estimación de biomasa y sumideros de carbono (García et al., 2009), hasta el inventario forestal (Hyyppä et al 2008;Rodriguez et al 2014) pasando por el análisis de crecimientos (). Paralelamente, en los últimos años se han empezado a desarrollar multitud de estudios basados en datos LiDAR orientados al seguimiento y conservación de hábitats, con aplicaciones tan variadas como son los índices de calidad de ribera (Magdaleno et al, 2010), la conectividad de hábitats de especies como el oso pardo (Mateo-Sánchez, et al;, la detección de madera muerta (Kim, et al;) y, en combinación con otros sensores, en la propia conservación y el análisis de hábitats (Mundt et al;2006)., cambios en la vegetación (Nyström et al, 2012) y crecimientos (Hopkinson et al;).Es de vital importancia que desde el sector forestal se continúe dando uso a estos datos LIDAR e, incidiendo en la importancia y el potencial de los mismos, se trabaje para que se tengan en cuenta los requerimientos forestales en la configuración de futuros vuelos LiDAR del PNOA, poniéndolos en valor. ...
Conference Paper
La Ley 30/2014, de Parques Nacionales, otorga al Estado el cometido de realizar un seguimiento general de la Red de Parques Nacionales. El Organismo Autónomo Parques Nacionales desarrolla para ello un Plan de Seguimiento y Evaluación, con iniciativas de seguimiento ecológico, sociológico y funcional, en colaboración con las comunidades autónomas y con asesoramiento científico. Algunos de los proyectos de seguimiento ecológico en marcha son la elaboración de una cartografía continua y comparable de los sistemas naturales de la Red, y el seguimiento de la estructura de los ecosistemas mediante la tecnología LiDAR, al que se ha dado un enfoque a largo plazo, aprovechando la finalización en 2015 de la primera cobertura LiDAR nacional por parte del proyecto PNOA-LiDAR del Instituto Geográfico Nacional, obtenida mediante sensores aerotransportados. La presente comunicación describe la metodología desarrollada para el procesamiento y análisis de los datos LiDAR, así como para la integración de la información resultante en la cartografía de los sistemas naturales, enriqueciendo las bases de datos de la unidad SIG de la Red de Parques Nacionales. La segunda cobertura LiDAR, con periodicidad sexenal prevista, garantizaría otra actualización de la cartografía y un seguimiento de la evolución de la estructura de los sistemas naturales.
Article
Full-text available
Fire is one of the most important degradation agents in Amazonian terrestrial ecosystems and is widely used to manage and transform land cover for many purposes. Uncontrolled fire use increases the vulnerability of affected environments to climate and anthropogenic impacts. During the burning process, the combustion of organic material releases carbon dioxide, the main responsible for the greenhouse effect. Although fire use control is of upmost importance, uncertainties regarding the results of burned areas detection and monitoring remote sensing tools are still lacking. One of the main used approaches for burned area mapping in the Brazilian Amazon is the Linear Spectral Mixture Model (LSMM), which considers the spectral response of pure endmembers. In this work we aimed to assess the performance of the integration of the Fmask algorithm with LSMM, considering that this algorithm may refine data from LSMM. We tested scenes from sensor Operational Land Imager (OLI - Landsat 8) and sensor Multispectral Instrument (MSI - Sentinel-2A) in Forest and Non Forest land covers in order to support the detection of burned areas. The study area is located in the south of Pará State, in Amazon biome. The mapping results yielded overall accuracy above 99% for the mapping from LSMM refined by Fmask algorithm in both sensors. The producer's accuracy was above 97%, whilst user's accuracy ranges from 77.54% and 95%. The result of the Kappa index for OLI was 0.93 for Forest and 0.97 in Non Forest, while for MSI, the results are 0.87 and 0.88, respectively. The integration of the Fmask algorithm with LSMM enabled accurate and less time-consuming data processing results. This approach is considered a potential automation tool to refine burned areas mapping in the Brazilian Amazon in a multi-sensors perspective.
Article
Full-text available
Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data.
Article
Full-text available
Leaf biomass distribution is a key factor for modeling energy and carbon fluxes in forest canopies and for assessing fire behavior. We propose a new method to estimate 3D leaf bulk density distribution, based on a calibration of indices derived from T-LiDAR. We applied the method to four contrasted plots in a mature Quercus pubescens forest. Leaf bulk densities were measured inside 0.7 m-diameter spheres, referred to as Calibration Volumes. Indices were derived from LiDAR point clouds and calibrated over the Calibration Volume bulk densities. Several indices were proposed and tested to account for noise resulting from mixed pixels and other theoretical considerations. The best index and its calibration parameter were then used to estimate leaf bulk densities at the grid nodes of each plot. These LiDAR-derived bulk density distributions were used to estimate bulk density vertical profiles and loads and above four meters compared well with those assessed by the classical inventory-based approach. Below four meters, the LiDAR-based approach overestimated bulk densities since no distinction was made between wood and leaf returns. The results of our method are promising since they demonstrate the possibility to assess bulk density on small plots at a reasonable operational cost.
Article
Full-text available
The canopy density model (CDM), a new product interpolated from airborne laser scanner (ALS) data and dedicated to forest structure characterization is presented. It exploits both the multiecho capability of the ALS and a nonparametric density estimation technique called kernel density estimators (KDEs). The CDM is used to delineate the outmost perimeter of vegetation features and to compute forest crown cover (CrCO). Contrary to other works that focus on single-layer forest canopies, CrCo is derived here for each layer, namely, the overstory, the understory, and ground vegetation. The root-mean-square error of prediction determined by using field data acquired over 44 forest stands in a forest in Portugal allows the testing of the reliability of the method: It ranges from 6.21% (overstory) to 13.76% (ground vegetation). In addition, we investigate the ability of the CDM to map the CrCo for individual trees. Finally, two existing methods have been applied to our study site in order to assess improvements, advantages, and drawbacks of our approach.
Article
Full-text available
Although knowledge of surface fuel loads is critical for evaluating potential fire behaviour and effects, their inherent variability makes these difficult to quantify. Several studies relate fuel loads to vegetation type, topography and spectral imaging, but little work has been done examining relationships between forest overstorey variables and surface fuel characteristics on a small scale (<0.05ha). Within-stand differences in structure and composition would be expected to influence fuel bed characteristics, and thus affect fire behaviour and effects. We used intensive tree and fuel measurements in a fire-excluded Sierra Nevada mixed conifer forest to assess relationships and build predictive models for loads of duff, litter and four size classes of downed woody fuels to overstorey structure and composition. Overstorey variables explained a significant but somewhat small percentage of variation in fuel load, with marginal R2 values for predictive models ranging from 0.16 to 0.29. Canopy cover was a relatively important predictor for all fuel components, although relationships varied with tree species. White fir abundance had a positive relationship with total fine woody fuel load. Greater pine abundance was associated with lower load of fine woody fuels and greater load of litter. Duff load was positively associated with total basal area and negatively associated with oak abundance. Knowledge of relationships contributing to within-stand variation in fuel loads can increase our understanding of fuel accumulation and improve our ability to anticipate fine-scale variability in fire behaviour and effects in heterogeneous mixed species stands.
Article
Full-text available
We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r2 = 0.87), canopy cover (r2 = 0.83), basal area (r2 = 0.82), shrub cover (r2 = 0.62), shrub height (r2 = 0.59), combined fuel loads (r2 = 0.48), and fuel bed depth (r2 = 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and < 50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management.
Technical Report
Full-text available
We investigated the spatial variability of a number of wildland fuel characteristics for the major fuel components found in six common northern Rocky Mountain ecosystems. Surface fuel characteristics of loading, particle density, bulk density, and mineral content were measured for eight fuel components—four downed dead woody fuel size classes (1, 10, 100, 1000 hr), duff, litter, shrub, and herb—on nested plots located within sampling grids to describe their variability across spatial scales. We also sampled canopy bulk density, biomass, and cover for each plot in the grid. The spatial distribution and variability of surface and canopy fuel characteristics are described using the variance, spatial autocorrelation, semi-variograms, and Moran’s I. We found that all fuels had high variability in loading (two to three times the mean), and this variability increased with the size of fuel particle. We also found that fuel components varied at different scales, with fine fuels varying at scales of 1 to 5 m, coarse fuels at 10 to 150 m, and canopy fuels at 100 to 500 m. Findings and data from this study can be used to sample, describe, and map fuel characteristics, such as loading, at the appropriate spatial scales to accommodate the next generation of fire behavior prediction models.
Article
Full-text available
Identification of clouds, cloud shadows and snow in optical images is often a necessary step toward their use. Recently a new program (named Fmask) designed to accomplish these tasks was introduced for use with images from Landsats 4–7 (Zhu & Woodcock, 2012). In this paper, there are the following: (1) improvements in the Fmask algorithm for Landsats 4–7; (2) a new version for use with Landsat 8 that takes advantage of the new cirrus band; and (3) a prototype algorithm for Sentinel 2 images. Though Sentinel 2 images do not have a thermal band to help with cloud detection, the new cirrus band is found to be useful for detecting clouds, especially for thin cirrus clouds. By adding a new cirrus cloud probability and removing the steps that use the thermal band, the Sentinel 2 scenario achieves significantly better results than the Landsats 4–7 scenario for all 7 images tested. For Landsat 8, almost all the Fmask algorithm components are the same as for Landsats 4–7, except a new cirrus cloud probability is calculated using the new cirrus band, which improves detection of thin cirrus clouds. Landsat 8 results are better than the Sentinel 2 scenario, with 6 out of 7 test images showing higher accuracies.
Article
Full-text available
High resolution and low uncertainty deforestation maps covering large spatial areas in tropical countries are needed to plan efficient forest conservation and management programs such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Using an open-source free software (R, GRASS and QGis) and an original statistical approach combining multi-date land cover observations based on Landsat satellite images and the random forests classifier, we obtained up-to-date deforestation maps for the periods 2000–2005 and 2005–2010 with a minimum mapping unit of 0.36 ha for 7.7 M hectares, i.e. 40.3% of the tropical humid forest and 20.6% of the tropical dry forest in Madagascar. Uncertainty in deforestation on the maps was calculated by comparing the results of the classification to more than 30,000 visual interpretation points on a regular grid. We assessed accuracy on a per-pixel basis (confusion matrix) and by measuring the relative surface difference between wall-to-wall approach and point sampling. At the pixel level, user accuracy was 84.7% for stable land cover and 60.7% for land cover change. On average for the whole study area, we obtained a relative difference of 2% for stable land cover categories and 21.1% land cover change categories respectively between the wallto-wall and the point sampling approach. Depending on the study area, our conservative assessment of annual deforestation rates ranged from 0.93 to 2.33%·yr−1 for the humid forest and from 0.46 to 1.17%·yr−1 for the dry forest. Here we describe an approach to obtain deforestation maps with reliable uncertainty estimates that can be transposed to other regions in the tropical world.
Article
Full-text available
Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR’s ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with LiDAR data. Prior to applying LiDAR in operational management, it is necessary to understand the errors in LiDAR-derived estimates of forest inventory metrics (i.e., tree height). Most LiDAR-based forest inventory metrics require creation of digital elevation models (DEM), and because metrics are calculated relative to the DEM surface, errors within the DEMs propagate into delivered metrics. This study combines LiDAR DEMs and 54 ground survey plots to investigate how surface morphology and vegetation structure influence DEM errors. The study further compared two LiDAR classification algorithms and found no significant difference in their performance. Vegetation structure was found to have no influence, whereas increased variability in the vertical error was observed on slopes exceeding 30°, illustrating that these algorithms are not limited by high-biomass western coniferous forests, but that slope and sensor accuracy both play important roles. The observed vertical DEM error translated into ±1%–3% error range in derived timber volumes, highlighting the potential of LiDAR-derived inventories in forest management.
Article
Full-text available
A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time.(...) However, to maximize the usefulness of data from multitemporal point of view, an easy-to-use, cost-efective, and accurate radiometric calibration and correction procedure is needed.
Article
Full-text available
This paper presents methods to generate fuel type maps from remote sensing data at a spatial and temporal scale adequate for operational fire management applications. Fuel type maps account for structural characteristics of vegetation related to fire behaviour and fire propagation. A fuel type classification system adapted to the ecological characteristics of the European Mediterranean basin was adopted for this study. The Cabañeros National Park (in cen- tral Spain) area was selected for testing and validating the methods. Fuel type maps were derived from two Landsat TM satellite images and digital elevation data. Atmospheric and topographic corrections of the satellite images were performed to reduce spectral variability. A sensitivity analysis was carried out to determine the most appropriate bands for fuel type mapping. The final classification was checked by an intense field survey, the final classification accuracy being estimated at 83%. The main problem was discriminating among those fuel types that differ only in vegetation height or composition of the understory layer. The mean mapping accuracy was 15 m (0.6 pixels), and no areal dis- crepancy or boundary displacement with vegetation maps was apparent.
Article
Full-text available
A fuel-type map of a predominantly shrub-land area in central Portugal was generated for a fire research experimental site, by combining airborne light detection and ranging (LiDAR), and simultaneous color infrared ortho imaging. Since the vegetation canopy and the ground are too close together to be easily discerned by LiDAR pulses, standard methods of processing LiDAR data did not provide an accurate estimate of shrub height. It was demonstrated that the standard process to generate the digital ground model (DGM) sometimes contained height values for the top of the shrub canopy rather than from the ground. Improvement of the DGM was based on separating canopy from ground hits using color infrared ortho imaging to detect shrub cover, which was measured simultaneously with the LiDAR data. Potentially erroneous data in the DGM was identified using two criteria: low vegetation height and high Normalized Difference Vegetation Index (NDVI), a commonly used spectral index to identify vegetated areas. Based on the height of surrounding pixels, a second interpolation of the DGM was performed to extract those erroneously identified as ground in the standard method. The estimation of the shrub height improved significantly after this correction, and increased deter-mination coefficients from R 2 = 0.48 to 0.65. However, the estimated shrub heights were still less than those observed in the field.
Article
Full-text available
The distribution of 44 common vascular plant species on Tenerife, The Canary Islands, has been related to the variation in temperature and precipitation. Frequency values for these speceis were obtained using a stratified sampling system with 200 10 m x 10 m plots distributed all over the island. A model is proposed relating variation in vegetation to altitude according to two main climatic gradients occurring between the 3718 m summit and the coastline of Tenerife. The vascular plants have been classified into guilds according to their presumed tolerance to cold and drought; one remainder group includes species which are randomly distributed without any relation to climatic variation. Differences between the guild structure suggested here and common knowledge of plant species occurrence in the well-known vegetation belts of Tenerife are explained by assuming disturbance and local ground-water availability as additional decisive factors.
Article
Full-text available
Due to increased fuel loading as a result of fire suppression, land managers in the American west are in need of precise information about the fuels they manage, including canopy fuels. Canopy fuel metrics such as canopy height (CH), canopy base height (CBH), canopy bulk density (CBD) and available canopy fuel (ACF) are specific inputs for wildfire behavior models such as FARSITE and emission models such as FOFEM. With finer spatial resolution data, accurate quantification of these metrics with detailed spatial heterogeneity can be accomplished. Light Detection and Ranging (LiDAR) and color near-infrared imagery are active and passive systems, respectively, that have been utilized for measuring a range of forest structure characteristics at high resolution. The objective of this research was to determine which remote sensing dataset can estimate canopy fuels more accurately and whether a fusion of these datasets produces more accurate estimates. Regression models were developed for ponderosa pine (Pinus ponderosa) stand representative of eastern Washington State using field data collected in the Ahtanum State Forest and metrics derived from LiDAR and imagery. Strong relationships were found with LiDAR alone and LiDAR was found to increase canopy fuel accuracy compared to imagery. Fusing LiDAR with imagery and/or LiDAR intensity led to small increases in estimation accuracy over LiDAR alone. By improving the ability to estimate canopy fuels at higher spatial resolutions, spatially explicit fuel layers can be created and used in wildfire behavior and smoke emission models leading to more accurate estimations of crown fire risk and smoke related emissions.
Article
Full-text available
Vegetation indices and transformations have been used extensively in forest change detection studies. In this study, we processed multitemporal normalized difference moisture index (NDMI) and tasseled cap wetness (TCW) data sets and compared their statistical relationships and relative efficiencies in detecting forest disturbances associated with forest type and harvest intensity at five, two and one year Landsat acquisition intervals. The NDMI and TCW were highly correlated (>0.95 r2) for all five image dates. There was no significant difference between TCW and NDMI for detecting forest disturbance. Using either a NDMI or TCW image differencing method, when Landsat image acquisitions were 5 years apart, clear cuts could be detected with nearly equal accuracy compared to images collected 2 years apart. Partial cuts had much higher commission and omission errors compared to clear cut. Both methods had 7–8% higher commission and 12–22% higher omission error to detect hardwood disturbance when it occurred in the first year of the 2-year interval (as compared to 1-year interval). Softwood and hardwood change detection errors were slightly higher at 2-year Landsat acquisition intervals compared to 1-year interval. For images acquired 1 and 2 years apart, NDMI forest disturbance commission and omission errors were slightly lower than TCW. The NDMI can be calculated using any sensor that has near-infrared and shortwave bands and is at least as accurate as TCW for detecting forest type and intensity disturbance in biomes similar to the Maine forest, particularly when Landsat images are acquired less than 2 years apart. Where partial cutting is the most dominant harvesting system as is currently the case in northern Maine, we recommend images collected every year to minimize (particularly omission) errors. However, where clear cuts or nearly complete canopy removal occurs, Landsat intervals of up to 5 years may be nearly as accurate in detecting forest change as 1 or 2 year intervals.
Article
The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related Vls (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When Vls were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies.
Article
Abstract Scientists and managers critically need ways to assess how fuel treatments alter fire behavior, yet few tools currently exist for this purpose. We present a spatially-explicit-fuel-modeling system, FuelManager, which models fuels, vegetation growth, fire behavior (using a physics-based model, FIRETEC), and fire effects. FuelManager's flexible approach facilitates modeling fuels across a wide range of detail. Large trees or shrubs with specific coordinates are modeled as individual “Plants”, while understory plants are modeled as collections of plants called “LayerSets”. Both Plants and LayerSets contain various fuel particles (leaves, needles, twigs) with various properties including shape, size and surface area to volume ratio. A wide range of vegetation and treatments can be modeled, analyzed quantitatively and visualized in a 3D viewer. We describe the modeling approach and demonstrate fuel modeling at different levels of detail, fuel treatment and fire effects capabilities. Detailed model equations are provided in the Appendices. http://www.sciencedirect.com/science/article/pii/S1364815216300676
Article
This study proposed modifying the conceptual approach that is commonly used to model development of stand attribute estimates using airborne LiDAR data. New models were developed using an area-based approach to predict wood volume, stem volume, aboveground biomass, and basal-area across a wide range of canopy structures, sites and LiDAR characteristics. This new modeling approach does not adopt standard approaches of stepwise regression using a series of height metrics derived from airborne LiDAR. Rather, it used four metrics describing complementary 3D structural aspects of the stand canopy. The first three metrics were related to mean canopy height, height heterogeneity, and horizontal canopy distribution. A fourth metric was calculated as the coefficient of variation of the leaf area density profile. This fourth metric provided information on understory vegetation. The models that were developed with the four structural metrics provided higher estimation accuracy on stand attributes than models using height metrics alone, while also avoiding data over-fitting. Overall, the models provided prediction error levels ranging from 12.4% to 24.2%, depending upon forest type and stand attribute. The more homogeneous coniferous stand provided the highest estimation accuracy. Estimation errors were significantly reduced in mixed forest when separate models were developed for individual stand types (coniferous, mixed and deciduous stands) instead of a general model for all stand types. Model robustness was also evaluated in leaf-off and leaf-on conditions where both conditions provided similar estimation errors.
Article
The characteristics and spatial distribution of fuels are critical for assessing fire hazard, fuel consumption, greenhouse gas emissions and other fire effects. However, fuel maps are difficult to generate and update, because many regions of the world lack fuel descriptions or adequate mapped vegetation attributes to assign these fuelbeds spatially acrossthelandscape.Thispaperpresentsaprocesstogeneratefuelmapsforlargeareasusingremotelysensedinformation and ancillary fuel characteristic data. The Fuel Characteristic Classification System was used to build fuelbeds for South America and predict potential fire hazard using a set of default environmental variables. A land-cover map was combined with abiomemap todefine98fuelbeds,and their parameterswereassignedbased oninformationfromglobaldatasetsand existing Fuel Characteristic Classification System fuelbeds or photo series. The indices of potential surface fire behaviour ranged from1.32to 9,whereas indices ofpotentialcrown fireand availablefuel forcombustion had lowto mediumvalues (0–6). This paper presents a geospatial fuels map for South America. This map could be used to assess fire hazard, predict fire behaviour under defined environmental conditions or calculate fuel consumption and greenhouse gas emissions. It could also be easily updated as new remotely sensed information on vegetation becomes available.
Article
Deriving individual tree information from discrete return, small footprint LiDAR data may improve forest aboveground biomass estimates, and provide tree-level information that is important in many ecological studies. Several crown delineation algorithms have been developed to extract individual tree information from LiDAR point clouds or rasterized canopy height models (CHM), but many of these algorithms have difficulty discriminating between overlapping crowns, and also may fail to detect understory trees. Our approach uses a watershed-based delineation of a CHM, which is subsequently refined using the LiDAR point cloud. Individual tree detection was validated with stem mapped field data from the Smithsonian Environmental Research Center (SERC), Maryland, and on a plot and stand level through comparisons of stem density and basal area to delineated metrics at both SERC and a study area in the Sierra Nevada, California. For individual tree detection, the algorithm correctly identified 70% of dominant trees, 58% of codominant trees, 35% of intermediate trees and 21% of suppressed trees at SERC. The algorithm had difficulty distinguishing between crowns of small, dense understory trees of approximately the same height. Delineated crown volume alone explained 53% and 84% of the variability in basal area at the SERC and Sierra Nevada sites, respectively. The algorithm produced crown area distributions comparable to diameter at breast height (DBH) size class distributions observed in the field in both study sites. The algorithm detected understory crowns better in the conifer-dominated Sierra Nevada site than in the closed-canopy deciduous site in Maryland. The ability for the algorithm to reproduce both accurate tree size distributions and individual crown geometries in two dissimilar and complex forests suggests great promise for applicability to a wide range of forest systems.
Article
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 (R-2=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 (R-2=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.
Article
This paper reports on the,structure of a fire growth simulation model, FARSITE, and its performance under simplified test conditions. FARSITE incorporates existing models of surface fire, crown fire, point-source fire acceleration, spotting, and fuel moisture. This documentation of how the simulation was constructed, and how the individual fire behavior models perform, will be useful to researchers and managers who use FARSITE or are interested in fire growth simulation. The models were integrated using a vector propagation technique for fire perimeter expansion that controls for both space and time resolution of fire growth over the landscape. The model produces vector fire perimeters (polygons) at specified time intervals. The vertices of these polygons contain information on the fire's spread rate and intensity, which are interpolated to produce raster maps of fire behavior. Because fire behavior at each vertex is assumed independent of the others, the simulation outputs illustrate the strict spatial consequences to fire behavior of incorporating the models into a two-dimensional simulation. Simplified test conditions show that surface fire growth and intensity conform to idealized patterns. Similarities also exist between simulated crown fires and observed patterns of extreme wind-driven fires. Complex patterns of fi re growth and behavior result from the spatial and temporal dependencies in the model. The limitations and assumptions of this approach are discussed.
Article
An experimental burning program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. A range of previously developed prediction models were examined, but none provided adequate fire behaviour predictions. Empirical models were then developed to predict rate of fire spread and flame height in flat terrain, using the variables site age, dead fuel moisture content and surface wind speed. The models should provide good predictions for low to moderate intensity fires and adequate predictions for high intensity wildfires.
Article
In this text we describe an initial attempt to incorporate discrete porous element fuel beds into the coupled atmosphere-wildfire behavior model HIGRAD/FIRETEC. First we develop conceptual models for use in translating measured tree data (in this case a ponderosa pine forest) into discrete fuel elements. Then data collected at experimental sites near Flagstaff, Arizona are used to create a discontinuous canopy fuel representation in HIGRAD/FIRETEC. Four simulations are presented with different canopy and understory configurations as described in the text. The results are discussed in terms of the same two discrete locations within the canopy for each simulation. The canopy structure had significant effects on the balance between radiative and convective heating in driving the fire and indeed sometimes determined whether a specific tree burned or not. In our simulations the ground fuel density was the determining factor in the overall spread rate of the fire, even when the overstory was involved in the fire. This behavior is well known in the fire meteorology community. In the future, simulations of this type could help land managers to better understand the role of canopy and understory structure in determining fire behavior, and thus help them decide between the different thinning and fuel treatment strategies available to them.
Article
Four classification algorithms have been assessed and compared with mapped forest fuel types from Terra-ASTER sensor images in a representative area of Tenerife Island (Canary Islands, Spain). A BEHAVE fuel-type map from 2002, together with field data also obtained in 2002 during the Third Spanish National Forest Inventory, was used as reference data. The BEHAVE fuel types of the reference dataset were first converted into the Fire Behaviour Fuel Types described by Scott and Burgan, taking into account the vegetation of the study area. Then, three pixel-based algorithms (Maximum Likelihood, Neural Network and Support Vector Machine) and an Object-Based Image Analysis were applied to classify the Scott and Burgan fire behaviour fuel types from an ASTER image from 3 March 2003. The performance of the algorithms tested was assessed and compared in terms of quantity disagreement and allocation disagreement. Within the pixel-based classifications, the best results were obtained from the Support Vector Machine
Article
Several studies have addressed the biomass and volume of trees using Airborne Light Detection and Ranging (LiDAR) data. However, little research has been conducted into shrub vegetation, which covers a high percentage of Mediterranean forest. We used LiDAR data and an airborne image to estimate biomass and volume of shrub vegetation. Field data were collected in 29 square plots of 100 m2. In each plot, the percentage of the surface covered was measured in the field. Shrub vegetation inside 3 stands for each plot was clear cut to calculate the biomass and volume of the 29 plots. To find the best fit between LiDAR-spectral data and field measurements, stepwise regressions were performed using previously selected variables. The highest accuracy was found when variables derived from LiDAR data and the airborne image were combined (R2 values of 0.78 and 0.84 for biomass and volume, respectively). Biomass and volume were predicted using variables from height metrics of LiDAR data (median and standard deviation); density metrics (percentage of points whose height was between 0.50 m and 0.75 m, 0.75 m–1 m, and higher than 1 m); and spectral data (standard deviation of green band, mean of the vegetation index NDVI). These results revealed the potential of LiDAR and spectral data to characterize shrub structure and make it possible to estimate and map the biomass and volume of this vegetation.
Article
Shrub vegetation is a key element of Mediterranean forest areas and it is necessary to develop tools that allow a precise knowledge of this vegetation. This study aims to predict shrub volume and analyze the factors affecting the accuracy of these estimations in small stands using airborne discrete-return LiDAR data. The study was performed over 83 circular stands with 0.5 m radius located in Chiva (Spain) mainly occupied by Quercus coccifera. The vegetation inside each area was clear cut, and the height and the diameter of each plant was measured to compute the volume of shrub vegetation per stand. Volume values were related with maximum height values derived from LiDAR data reaching a coefficient of determination value R2=0.26. Afterwards, factors affecting the quality of volume estimations were analyzed, i.e., vegetation type, LiDAR density, and accuracy of the digital terrain model (DTM). Significant accuracy improvements (R2=0.71) were detected for stands with 0.5 m, LiDAR data density greater than 8 points/m2, vegetation Q. coccifera, and error associated to the DTM less than 0.20 m. These results show the feasibility of using LiDAR data to predict shrub volume under certain conditions, which can contribute to improved forest management and characterization.
Article
In this work we studied the height and coverage of shrub vegetation using light detection and ranging (LIDAR) data. The maximum dominant heights of vegetation were measured in the field in 83 stands of a 0.5-m radius, and the data were compared with figures for heights obtained from LIDAR data in concentric areas with different radii. The minimum root mean square error (RMSE) between the field measurements and LIDAR data was found for radii between 1.5 and 2.25 m, RMSE being 0.26 m. When the slopes are low and an accurate digital terrain model is obtained, it was shown that the radius can be reduced. Shrub heights were also studied in plots of 100 m2. In this case, the 95th percentile of the LIDAR data included in each plot was the best predictor of height with R2 of 0.71 and a RMSE of 0.13 m. For detecting the presence of shrub vegetation, the highest accuracy was obtained when the canopy height model and a spectral image were combined (overall accuracy of 90%).
Article
Airborne laser altimetry provides an unprecedented view of the forest floor in timber fuel types and is a promising new tool for fuels assessments. It can be used to resolve two fuel models under closed canopies and may be effective for estimating coarse woody debris loads. A simple metric–obstacle density–provides the necessary quantification of fuel bed roughness to make these measures possible. This work highlights the need for more research in the application of laser technology to fuels mapping.
Article
The present study sets a methodological framework to combine LiDAR derived data with fire behaviour models in order to assess fire risk at landscape level for forest management and planning. Two forest areas of the Model Forest in Urbión, Soria (Central Spain) were analyzed, covering 992.7 ha and 221.7 ha. The modelling phase was based in 160 field sample plots as ground data, and the LiDAR data had a density of first returns of 2 pulses/m 2 , which were used to construct 13 models for stand variables (e.g. basal area, stem volume, branch biomass). The coefficients of determination ranged from 0.167 for shrub cover, to 0.906 for dominant height. The modelled variables were used for a classification of fuel types compatible with the continuous data. The simulation phase was performed using the spatialized data on FlamMap in order to assess the potential fire behaviour resulting across the whole landscape for four scenarios of moisture and wind conditions. The results showed maps of fire intensity and prob-ability of fire occurrence, based on the simulation of 500 random ignition points, which allowed the anal-ysis of the spatial relation between the initial state and allocation of forest resources and their risk of fire. The methodology proposed, as well as the results of this research are directly applicable for operational forest planning at landscape level. Ó 2012 Elsevier B.V. All rights reserved.
Article
A first-return laser rangefinder deployed from a low-flying helicopter provided inexpensive, repeatable, and high-resolution measurements of the shape of the outer forest canopy in stands of four successional stages on the Maryland coastal plain. The traces of canopy height in these stands revealed structural characteristics such as cover, maximum and mean heights which were consistent with corresponding ground measurements. Differences in the statistics on canopy structure corresponded to general developmental trends in these stands, including the development of maximum height and surface rugosity. Furthermore, some of the outer canopy statistics related to measures of internal organization, such as the shape of the foliage height profile and the leaf area index. These observations suggest that such measurements could be used readily to classify stand structure and developmental stage, and to deduce some aspects of internal organization of vegetation.
Article
Crown fire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (CFD) and can be used to model fire in heterogeneous crown fuels. However, the potential impacts of variability in crown fuels on fire behavior have not yet been explored. In this study we describe a new model, FUEL3D, which incorporates the pipe model theory (PMT) and a simple 3D recursive branching approach to model the distribution of fuel within individual tree crowns. FUEL3D uses forest inventory data as inputs, and stochastically retains geometric variability observed in field data. We investigate the effects of crown fuel heterogeneity on fire behavior with a CFD fire model by simulating fire under a homogeneous tree crown and a heterogeneous tree crown modeled with FUEL3D, using two different levels of surface fire intensity. Model output is used to estimate the probability of tree mortality, linking fire behavior and fire effects at the scale of an individual tree. We discovered that variability within a tree crown altered the timing, magnitude and dynamics of how fire burned through the crown; effects varied with surface fire intensity. In the lower surface fire intensity case, the heterogeneous tree crown barely ignited and would likely survive, while the homogeneous tree had nearly 80% fuel consumption and an order of magnitude difference in total net radiative heat transfer. In the higher surface fire intensity case, both cases burned readily. Differences for the homogeneous tree between the two surface fire intensity cases were minimal but were dramatic for the heterogeneous tree. These results suggest that heterogeneity within the crown causes more conditional, threshold-like interactions with fire. We conclude with discussion of implications for fire behavior modeling and fire ecology.
Article
Methods for using airborne laser scanning (also called airborne LIDAR) to retrieve forest parameters that are critical for fire behavior modeling are presented. A model for the automatic extraction of forest information is demonstrated to provide spatial coverage of the study area, making it possible to produce 3-D inputs to improve fire behavior models.The Toposys I airborne laser system recorded the last return of each footprint (0.30–0.38 m) over a 2000 m by 190 m flight line. Raw data were transformed into height above the surface, eliminating the effect of terrain on vegetation height and allowing separation of ground surface and crown heights. Data were defined as ground elevation if heights were less than 0.6 m. A cluster analysis was used to discriminate crown base height, allowing identification of both tree and understory canopy heights. Tree height was defined as the 99 percentile of the tree crown height group, while crown base height was the 1 percentile of the tree crown height group. Tree cover (TC) was estimated from the fraction of total tree laser hits relative to the total number of laser hits. Surface canopy (SC) height was computed as the 99 percentile of the surface canopy group. Surface canopy cover is equal to the fraction of total surface canopy hits relative to the total number of hits, once the canopy height profile (CHP) was corrected. Crown bulk density (CBD) was obtained from foliage biomass (FB) estimate and crown volume (CV), using an empirical equation for foliage biomass. Crown volume was estimated as the crown area times the crown height after a correction for mean canopy cover.
Article
We evaluate the potential of deriving fractional cover (fCover) and leaf area index (LAI) from discrete return, small footprint airborne laser scanning (ALS) data. fCover was computed as the fraction of laser vegetation hits over the number of total laser echoes per unit area. Analogous to the concept of contact frequency, an effective LAI proxy was estimated by a fraction of first and last echo types inside the canopy. Validation was carried out using 83 hemispherical photographs georeferenced to centimeter accuracy by differential GPS, for which the respective gap fractions were computed over a range of zenith angles using the Gap Light Analyzer (GLA). LAI was computed by GLA from gap fraction estimations at zenith angles of 0–60°. For ALS data, different data trap sizes were used to compute fCover and LAI proxy, the range of radii was 2–25 m. For fCover, a data trap size of 2 m radius was used, whereas for LAI a radius of 15 m provided best results. fCover was estimated both from first and last echo data, with first echo data overestimating field fCover and last echo data underestimating field fCover. A multiple regression of fCover derived from both echo types with field fCover showed no increase of R2 compared to the regression of first echo data, and thus, we only used first echo data for fCover estimation. R2 for the fCover regression was 0.73, with an RMSE of 0.18. For the ALS LAI proxy, R2 was lower, at 0.69, while the RMSE was 0.01. For LAI larger radii (∼ 15 m ) provided best results for our canopy types, which is due to the importance of a larger range of zenith angles (0–60°) in LAI estimation from hemispherical photographs. Based on the regression results, maps of fCover and LAI were computed for our study area and compared qualitatively to equivalent maps based on imaging spectrometry, revealing similar spatial patterns and ranges of values.
Article
Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R2=0.86; ln(crown bulk density): R2=0.84; canopy base height: R2=0.77; canopy height: R2=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.
Article
Wild-land fires have become intense and more frequent all over the world. Improving the accuracy of mapping fuel models is essential for fuel management decisions and explicit fire behavior prediction for real-time support of suppression tactics and logistics decisions. The overall aim of this paper is to develop the use of lidar (LIght Detection and Ranging) remote sensing to accurately and effectively assess fuel models in East Texas. More specific goals include: (1) developing lidar derived products and the methodology to use them for assessing fuel models; (2) investigating the use of several techniques for data fusion of lidar and multispectral imagery for assessing fuel models; (3) investigating the gain in fuels mapping accuracy when using lidar as opposed to QuickBird imagery alone; and (4) producing spatially explicit digital fuel maps. Estimates of fuel models were compared with in-situ data collected over 62 plots. We employ a unique approach to classify fuel models using a combination of lidar height bins and multispectral image data. Different image processing approaches for fusing lidar and multispectral data, such as the Minimum Noise Fraction (MNF) and Principle Component Analysis (PCA), were used to improve the overall accuracy of image classification. Supervised image classification methods provided better accuracy (90.10%) with the fusion of airborne lidar data with QuickBird data than with QuickBird imagery alone (76.52%).According to our results, lidar derived data provide accurate estimates of surface fuel parameters efficiently and accurately over extensive areas of forests. This study demonstrates the importance of using accurate maps of fuel models derived using new lidar remote sensing techniques.
Article
Two canopy properties, leaf area index (LAI) and covered ground (CoverGnd), were estimated using hemispherical photography of three oak (Quercus pyrenaica) and eight pine (Pinus sylvestris) forest plots in Sierra de Guadarrama (central Spain). Pulses from airborne laser scanner (Lidar) that hit the surface on the exact location (within centimeter resolution) of the photographs were analyzed and separated by different radius size (from 0.5 to 20 m). The correlation between Lidar and hemispherical photography estimates of canopy properties was highly significant, but was affected by the type of forest and the radius size. CoverGnd was better estimated using a small radius size (2.5 m, equivalent to one fourth of canopy height), while LAI was better estimated using a larger radius size (7.5–12.5 m, equivalent to the entire canopy height). In general, the smaller the tree, the shorter the radius was that must be used to select Lidar data, and the best Lidar estimator of canopy properties was the percentage of canopy hits. Overall oak canopies showed better results than pine forest. The poorer estimation in pine forest plots was probably due to the larger foliage and branch clumping of pine versus oak canopies. Lidar data could be used to produce high-resolution regional maps of the canopy properties studied.
Article
A key challenge in modern wildfire mitigation and forest management is accurate mapping of forest fuels in order to determine spatial fire hazard, plan mitigation efforts, and manage active fires. This study quantified forest fuels of the montane zone of Boulder County, CO, USA in an effort to aid wildfire mitigation planning and provide a metric by which LANDFIRE national fuel maps may be compared. Using data from 196 randomly stratified field plots, pre-existing vegetation maps, and derived variables, predictive classification and regression tree models were created for four fuel parameters necessary for spatial fire simulation with FARSITE (surface fuel model, canopy bulk density, canopy base height, and stand height). These predictive models accounted for 56–62% of the variability in forest fuels and produced fuel maps that predicted 91.4% and 88.2% of the burned area of two historic fires simulated in the FARSITE model. Simulations of areas burned based on LANDFIRE national fuel maps were less accurate, burning 77.7% and 40.3% of the historic fire areas. Our results indicate that fuel mapping efforts that utilize local area information and biotic as well as abiotic predictors will more accurately simulate fire spread rates and reflect the inherent variability of forested environments than do current LANDFIRE data products.