Modeling Mediterranean forest structure using airborne laser scanning data

Bottalico, Francesca; Chirici, Gherardo; Giannini, Raffaello; Mele, Salvatore; Mura, Matteo; Puxeddu, Michele; McRoberts, Ronald E.; Valbuena, Rubén; Travaglini, Davide

doi:10.1016/j.jag.2016.12.013

Cited by 40 publications

(34 citation statements)

References 63 publications

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“…In this sense, the use of automatic methods for variable selection is considerably less time-demanding when modeling. The three ALS-derived variables included in the best model were coherent and analogous to those derived for characterizing forest vertical diversity [22] and aboveground biomass [15,36]. Variables derived from digital surface models (DSM) were not computed in this study.…”

Section: Discussionmentioning

confidence: 96%

“…This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.Forests 2018, 9, 158 2 of 17 tools due to its capability to provide 3-D information of vegetation structure. Vertical forest structure has been estimated with ALS data for several applications, such as forest inventory [16][17][18], forest structural heterogeneity [19][20][21][22], fuel type mapping [23,24] fuel modelling [23][24][25][26] or tree damage detection after natural disasters [27][28][29] for several height strata. However, few studies have focused on shrub biomass characterization with ALS data [30][31][32][33].…”

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confidence: 99%

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Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Domingo

Lamelas

Montealegre

et al. 2018

Forests

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The account of total biomass can assist with the evaluation of climate regulation policies from local to global scales. This study estimates total biomass (TB), including tree and shrub biomass fractions, in Pinus halepensis Miller forest stands located in the Aragon Region (Spain) using Airborne Laser Scanning (ALS) data and fieldwork. A comparison of five selection methods and five regression models was performed to relate the TB, estimated in 83 field plots through allometric equations, to several independent variables extracted from ALS point cloud. A height threshold was used to include returns above 0.2 m when calculating ALS variables. The sample was divided into training and test sets composed of 62 and 21 plots, respectively. The model with the lower root mean square error (15.14 tons/ha) after validation was the multiple linear regression model including three ALS variables: the 25th percentile of the return heights, the variance, and the percentage of first returns above the mean. This study confirms the usefulness of low-density ALS data to accurately estimate total biomass, and thus better assess the availability of biomass and carbon content in a Mediterranean Aleppo pine forest.Forests 2018, 9, 158 2 of 17 tools due to its capability to provide 3-D information of vegetation structure. Vertical forest structure has been estimated with ALS data for several applications, such as forest inventory [16][17][18], forest structural heterogeneity [19][20][21][22], fuel type mapping [23,24] fuel modelling [23][24][25][26] or tree damage detection after natural disasters [27][28][29] for several height strata. However, few studies have focused on shrub biomass characterization with ALS data [30][31][32][33]. Some studies have used low density ALS data to estimate forest biomass [25,[34][35][36][37][38], but little research has been performed including shrub vegetation because of the inherent difficulty in the estimation related to its low height and uniform surface [30]. Several studies state that ALS data tends to underestimate shrub vegetation [39][40][41][42]. Besides, when shrub and tree vegetation cover is high [43] and density of ALS data is low, the accuracy of digital elevation models (DEM) used to normalize return heights decreases [30]. The performed studies use an approach that combines ALS data and harvesting field measurements for biomass estimation [30,33]. In this sense, the lack of more studies to characterize shrub vegetation might have been associated with the necessary destructive sampling to generate forest structure equations, the assumption of simple geometric shapes [44,45], and the additional difficulty to estimate biomass at a regional scale using low-density ALS data. However, the presence of shrub biomass in the understory or the existence of shrubland areas constitutes an important land use in the Mediterranean basin. In this sense, the availability of shrub allometric equations for the main Spanish shrub species [46] have opened new opportunities. These equations allow the estimat...

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Section: Discussionmentioning

confidence: 96%

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confidence: 99%

Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Domingo

Lamelas

Montealegre

et al. 2018

Forests

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“…The results showed that Lorey's mean height (R 2 = 0.87, standard error = 0.69) had the highest accuracies, followed by aboveground biomass (R 2 = 0.74, standard error = 0.20) and stem density (R 2 = 0.67, standard error = 0.36). Bottalico et al (2017) [66] modeled several forest structural attributes using airborne LiDAR-derived metrics in Italy. The model results indicated that Lorey's mean height had the highest accuracy (R 2 = 0.83, rRMSE = 10.5%) among all the extracted attributes.…”

Section: Forest Structural Attribute Modeling and Accuracy Assessmentmentioning

confidence: 99%

Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests

Cao

Hao

et al. 2019

Forests

103

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Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (△Adj-R2 = 0.09, △rRMSE = 4.20%), whereas the lowest difference was for basal area (△Adj-R2 = 0.03, △rRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.

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“…Originally designed for terrestrial mapping and construction of digital terrain models (DTM), ALS was soon discovered to have potential in monitoring and predicting forest inventory variables (Large & Heritage, 2009). Most laser scanners work in the near-infrared (NIR) spectral range, being able to penetrate the green foliage and vegetation to provide data throughout the vertical forest cross-section (Bottalico et al, 2017;Naesset, 1997a). The ability to gather such vertical data enables the prediction of the structure variables of forests (Ellis, Griscom, Walker, Gonçalves, & Cormier, 2016;Korpela, 2008;Naesset & Gobakken, 2005;Wing et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Thinning- and tree-growth-caused changes in canopy cover and stand height and their estimation using low-density bitemporal airborne lidar measurements – a case study in hemi-boreal forests

Arumäe

Lang

Laarmann

2020

European Journal of Remote Sensing

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Repeated airborne laser scanning (ALS) measurements during leaf-on and leaf-off phenophases were studied. A 15 km × 15 km test site located in northern Estonia was used that included a reference set of stands, and 870 stands with thinning carried out before, between, and after two ALS flights. The decrease in ALS-based canopy cover estimate (CC ALS ) caused by thinning was similar for the leaf-off and leaf-on phenophases, and for different height thresholds. The point cloud height percentile (H Px ) values increased in almost all thinned stands, and the increase was present for the leaf-off and leaf-on phenophases. ALS point cloud metrics (skewness, kurtosis, mode, and canopy relief ratio) showed no response to thinning (p-value >0.05). Stand-dominating species had no significant influence on H Px increment or CC ALS change using the leaf-on data (p-value >0.05). The minimum height filter for pulse return selection had a substantial influence on H Px increment in stands thinned between the two ALS measurements. Ground points are usually excluded from H Px calculation, but for stand-level analyses, their inclusion can provide additional information. ARTICLE HISTORY

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Modeling Mediterranean forest structure using airborne laser scanning data

Cited by 40 publications

References 63 publications

Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests

Thinning- and tree-growth-caused changes in canopy cover and stand height and their estimation using low-density bitemporal airborne lidar measurements – a case study in hemi-boreal forests

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