Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning

Morsdorf, Felix; Mårell, Anders; Koetz, Benjamin; Cassagne, Nathalie; Pimont, François; Rigolot, Éric; Allgöwer, Britta

doi:10.1016/j.rse.2010.01.023

Cited by 129 publications

(101 citation statements)

References 48 publications

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“…Airborne Lidar (Light Detection and Ranging) is an active remote sensing system well suited to measure specific forest information, including tree height [8], basal area [9], leaf area index [10], canopy cover [11,12], vertical vegetation strata [13], successional stages [14], and canopy architecture [15]. In the case of tropical forests, Asner et al [16,17] assessed the capability of mean canopy height (MCH) derived from airborne Lidar to estimate the above-ground carbon in Madagascar and Colombia, demonstrating that Lidar-derived MCH could be used to accurately estimate above ground carbon.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

et al. 2014

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Abstract:In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables). Mean Canopy Height (MCH) was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF) models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R 2 = 0.75 and RMSE = 2.81 m). Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression). The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm.

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Section: Introductionmentioning

confidence: 99%

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

et al. 2014

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“…Most applications of airborne LiDAR to derive fractional cover were focused on tree canopy cover, which can be estimated with high accuracy [25][26][27]. Some attention has also been paid on the estimation of understory cover in forests [28][29][30]. Wing et al [31] received good results for estimating understory cover from high-resolution small-footprint LiDAR data.…”

Section: Lidardata Acquisitionmentioning

confidence: 99%

LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Ewald

Dupke

Heurich

et al. 2014

Forests

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Abstract:The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the OPEN ACCESSForests 2014, 5 1375 potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests.

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“…ALS-based assessment of canopy layering can be either derived from LiDAR waveform data (mostly the case for large or medium-footprint LiDAR systems) [29][30][31], or based on ALS point clouds directly [32,33] or indirectly, e.g., by generating a kind of synthetic waveform using classified echo height distributions or height distribution probability functions [34][35][36]. The assessment of canopy layering with ALS is mainly performed using area-based approaches (ABA).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, however, a fundamental part of their algorithm was the prior definition of a threshold to separate the dominant tree layer and understory trees, which limits the determination of canopy layers to a maximum of two layers. Morsdorf et al [33] applied a cluster analysis to echo heights and echo intensities to stratify the canopy into three layers, whereby each layer consists almost entirely of oak, pine or shrubs, respectively. Although the proposed approach results in high accuracy values, a successful transfer of the method to dense, mixed temperate forests is unlikely.…”

Section: Introductionmentioning

confidence: 99%

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

Leiterer

Torabzadeh

Furrer

et al. 2015

Forests

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Canopy layers form essential structural components, affecting stand productivity and wildlife habitats. Airborne laser scanning (ALS) provides horizontal and vertical information on canopy structure simultaneously. Existing approaches to assess canopy layering often require prior information about stand characteristics or rely on pre-defined height thresholds. We developed a multi-scale method using ALS data with point densities >10 pts/m 2 to determine the number and vertical extent of canopy layers (canopylayer, canopylength), seasonal variations in the topmost canopy layer (canopytype), as well as small-scale heterogeneities in the canopy (canopyheterogeneity). We first tested and developed the method on a small forest patch (800 ha) and afterwards tested transferability and robustness of the method on a larger patch (180,000 ha). We validated the approach using an extensive set of ground data, achieving overall accuracies >77% for canopytype and canopyheterogeneity, and >62% for canopylayer and canopylength. We conclude that our method provides a robust characterization of canopy layering supporting automated canopy structure monitoring. OPEN ACCESSForests 2015, 6 4147

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Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning

Cited by 129 publications

References 48 publications

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia

LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

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