Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

Morsdorf, Felix; Kötz, Benjamin; Meier, Erich; Itten, K.I.; Allgöwer, Britta

doi:10.1016/j.rse.2006.04.019

Cited by 397 publications

(318 citation statements)

References 35 publications

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“…Canopy cover was estimated as the ratio of the canopy energy to the total energy [14,29]. Typically, a correction factor of 2 is applied to the intensity of the ground returns to account for the differences in reflectance between canopy and ground at the wavelength of the LiDAR system [15,30]. However soil type or the presence of duff and litter affect this correction factor significantly, thus making it site-dependent.…”

Section: Lidar Data and Processingmentioning

confidence: 99%

“…Likewise, although CC was not measured in the field, numerous previous studies have demonstrated that CC can be accurately estimated from LiDAR data. For example, Morsdorf et al [15] reported an RMSE of 0.18 using only first returns; Hopkinson and Chasmer [14] also reported a mean error less than 0.2 in CC estimation from LiDAR data across multiple ecozones. Using these values as reference for the LiDAR-based CC estimates and an uncertainty of 18% was assumed.…”

Section: Model Performance and Error Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

et al. 2017

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Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R 2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg· ha −1 , 0.09, and 0.02 kg·m −3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels.

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Section: Lidar Data and Processingmentioning

confidence: 99%

Section: Model Performance and Error Assessmentmentioning

confidence: 99%

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

et al. 2017

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“…Korpela (2008) found that lidar intensity values discriminated well between lichens and other ground vegetation. Precise lidar-based estimation of leaf area index (LAI) from small-footprint data (Morsdorf et al, 2006) contributed to the detection of defoliation of Scots pine in Norway due to a severe insect attack (Solberg et al, 2006).…”

Section: Lidar Metrics As Predictors Of Forest Attributesmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

111

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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“…It is worth noting that the quality of the DEM strongly depends on the point density, especially in closed-canopy forests (Reutebuch et al 2003). The flexibility of airborne LiDAR, coupled with a high level of positional accuracy and point density, makes airborne LiDAR systems an attractive data acquisition tool for estimating a wide range of tree and forest parameters (Laes et al 2011) such as tree height (Andersen et al 2006;Detto et al 2013), stem volume (Heurich and Thoma 2008), tree biomass (Li et al 2008), and leaf area index (Morsdorf et al 2006). The use of airborne LiDAR for estimating forest inventory parameters and structural characteristics is reviewed by van Leeuwen and Nieuwenhuis (2010), and a meta-analysis of 70 articles has been conducted by Zolkos et al (2013).…”

Section: Light Detection and Ranging Systemsmentioning

confidence: 99%

An overview of existing and promising technologies for national forest monitoring

Henry

Réjou‐Méchain

Cifuentes-Jara

et al. 2015

Annals of Forest Science

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Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

Cited by 397 publications

References 35 publications

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

An overview of existing and promising technologies for national forest monitoring

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