Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest

Montealegre, Antonio Luis; Lamelas, María Teresa; Fernández, Juan Ramón de la Riva; García-Martín, Alberto; Escribano, Francisco

doi:10.1093/forestry/cpw008

Cited by 40 publications

(51 citation statements)

References 47 publications

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“…The location of the field plots was selected, within the limits of the Aleppo pine stands at the unburned area, using a stratified random sampling technique, in order to achieve a representative sample of the variability of the terrain (Naesset & Økland, 2002), forest structure and tree density (Montealegre et al, 2016). Thereby, terrain slopes, tree height and canopy cover of the study area were derived from ALS point cloud to define homogeneous areas.…”

Section: Field Plot Datamentioning

confidence: 99%

“…In this sense, some studies have used low-density ALS data to estimate forest parameters such as tree height, crown diameter, basal area, stem density, volume (Guerra-Hernández, Tomé, & González-Ferreiro, 2016a;Hayashi, Weiskittel, & Sader, 2014;Holopainen et al, 2010;Mehtätalo, Virolainen, Tuomela, & Packalen, 2015;Montealegre, Lamelas, de la Riva, García-Martín, & Escribano, 2016;Naesset, 2002;Naesset & Økland, 2002;Popescu, Randolph, & Ross, 2003) as well as biomass (García-Gutiérrez, Martínez-Álvarez, Troncoso, & Riquelme, 2015;Guerra-Hernández et al, 2016b;Hall, Burke, Box, Kaufmann, & Stoker, 2005;Montagnoli et al, 2015;Shendryk, Margareta, Leif, Natascha, 2014). In addition, some of them have compared different point densities (González-Ferreiro et al, 2013;Singh, Gang, James, & Ross, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Domingo

Lamelas

Gracia

et al. 2017

European Journal of Remote Sensing

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The knowledge of the forest biomass reduction produced by a wildfire can assist in the estimation of greenhouse gases to the atmosphere. This study focuses on the estimation of biomass losses and CO 2 emissions by combustion of Aleppo pine forest in a wildfire occurred in the municipality of Luna (Spain). The availability of low point density airborne laser scanning (ALS) data allowed the estimation of pre-fire aboveground forest biomass. A comparison of nine regression models was performed in order to relate the biomass, estimated in 46 field plots, to several independent variables extracted from the ALS data. The multivariate linear regression selected model, including the percentage of first returns above 2 m and 40th percentile of the return heights, was validated using a leave-one-out cross-validation technique (6.1 ton/ha root mean square error). Biomass losses were estimated in a three-phase approach: (i) wildfire severity was obtained using the difference normalized burn ratio ΔNBR ð Þ, (ii) Aleppo pine forest was delimited using the National Forest Map and ALS data and (iii) burning efficiency factors were applied considering severity levels. Post-fire biomass was then transformed into CO 2 emissions (426,754.8 ton). This study evidences the usefulness of low-density ALS data to accurately estimate pre-fire biomass, in order to assess CO 2 emissions in a Mediterranean Aleppo pine forest. ARTICLE HISTORY

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Section: Field Plot Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Domingo

Lamelas

Gracia

et al. 2017

European Journal of Remote Sensing

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“…Interestingly, for both RF and RT models, height-based variables contributed more than volume-based variables, and the variance of height was one of the most contributing variables. This could be explained through the work of Montealegre et al [48], whose second figure shows the vertical distribution of LiDAR point data of Aleppo pines under three height groupings: short, medium and tall. They reported that the variance of the vertical distribution is larger for the taller Aleppo pines, which implies that the variance of height is closely related to canopy height at a plot or stand level.…”

Section: Comparison Of Plot-level Height Modelsmentioning

confidence: 99%

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Lee

Kim

et al. 2018

Forests

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Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approaches-support vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species.

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“…The CHM metrics with corresponding description and explanation of the calculation are summarized in Table 3. [16], was applied to develop the stand-level volume models. In total, 548 forest stands were used in the statistical analyses, out of which 274 were selected for the models' development and the other 274 stands for the models' validation.…”

Section: Chm Metricsmentioning

confidence: 99%

“…Among a number of potential predictors (CHM metrics and FMP variables), only variables that were highly correlated with field estimated stand volume (r ≥ ±0.5) were included in the backward stepwise regression. This type of regression is commonly used for linear modeling of forest attributes based on either image-based [22,24] or ALS-based [16,56] metrics. To justify the use of linear regression models, four principal statistical assumptions [57] were tested: (i) linearity and additivity of the relationship between dependent and independent variables (scatterplots of Remote Sens.…”

Section: Chm Metricsmentioning

confidence: 99%

A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions

2017

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Digital aerial photogrammetry has recently attracted great attention in forest inventory studies, particularly in countries where airborne laser scanning (ALS) technology is not available. Further research, however, is required to prove its practical applicability in deriving three-dimensional (3D) point clouds and canopy surface and height models (CSMs and CHMs, respectively) over different forest types. The primary aim of this study is to investigate the applicability of image-based CHMs at different spatial resolutions (1 m, 2 m, 5 m) for use in stand-level forest inventory, with a special focus on estimation of stand-level merchantable volume of even-aged pedunculate oak (Quercus robur L.) forests. CHMs are generated by subtracting digital terrain models (DTMs), derived from the national digital terrain database, from corresponding digital surface models (DSMs), derived by the process of image matching of digital aerial images. Two types of stand-level volume regression models are developed for each CHM resolution. The first model is based solely on stand-level CHM metrics, whereas in the second model, easily obtainable variables from forest management databases are included in addition to CHM metrics. The estimation accuracies of the stand volume estimates based on stand-level metrics (relative root mean square error RMSE % = 12.53%-13.28%) are similar or slightly higher than those obtained from previous studies in which stand volume estimates were based on plot-level metrics. The inclusion of stand age as an independent variable in addition to CHM metrics improves the accuracy of the stand volume estimates. Improvements are notable for young and middle-aged stands, and negligible for mature and old stands. Results show that CHMs at the three different resolutions are capable of providing reasonably accurate volume estimates at the stand level.

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Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest

Cited by 40 publications

References 47 publications

Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions

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Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest

Cited by 40 publications

References 47 publications

Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

A Comparison of Stand-Level Volume Estimates from Image-Based Canopy Height Models of Different Spatial Resolutions

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Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest

Comparison of regression models to estimate biomass losses and CO₂ emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest