Growth-Competition-Based Stem Diameter and Volume Modeling for Tree-Level Forest Inventory Using Airborne LiDAR Data

In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can be measured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R2 (R2 = 0.70 to R2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation.

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Sub-Compartment Variation in Tree Height, Stem Diameter and Stocking in a Pinus radiata D. Don Plantation Examined Using Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs6087592 ◽

2014 ◽

Vol 6 (8) ◽

pp. 7592-7609 ◽

Cited By ~ 10

Author(s):

Hanieh Saremi ◽

Lalit Kumar ◽

Christine Stone ◽

Gavin Melville ◽

Russell Turner

Keyword(s):

Pinus Radiata ◽

Tree Height ◽

Airborne Lidar ◽

Stem Diameter ◽

Lidar Data ◽

Airborne Lidar Data

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Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data

Remote Sensing of Environment ◽

10.1016/j.rse.2014.10.004 ◽

2015 ◽

Vol 156 ◽

pp. 322-334 ◽

Cited By ~ 137

Author(s):

Marc Bouvier ◽

Sylvie Durrieu ◽

Richard A. Fournier ◽

Jean-Pierre Renaud

Keyword(s):

Predictive Models ◽

Forest Inventory ◽

Airborne Lidar ◽

Lidar Data ◽

Airborne Lidar Data

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Interpretation of Forest Resources at the Individual Tree Level in Japanese Conifer Plantations Using Airborne LiDAR Data

Remote Sensing ◽

10.3390/rs8030188 ◽

2016 ◽

Vol 8 (3) ◽

pp. 188 ◽

Cited By ~ 1

Author(s):

Songqiu Deng ◽

Masato Katoh

Keyword(s):

Forest Resources ◽

Airborne Lidar ◽

Tree Level ◽

Lidar Data ◽

Individual Tree ◽

Conifer Plantations ◽

The Individual ◽

Airborne Lidar Data

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Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data

Forest Science and Technology ◽

10.1080/21580103.2012.673749 ◽

2012 ◽

Vol 8 (2) ◽

pp. 89-98 ◽

Cited By ~ 4

Author(s):

Taejin Park ◽

Woo-Kyun Lee ◽

Jong-Yeol Lee ◽

Woo-Hyuk Byun ◽

Doo-Ahn Kwak ◽

...

Keyword(s):

Forest Inventory ◽

Forest Type ◽

National Forest Inventory ◽

Volume Estimation ◽

Airborne Lidar ◽

Forest Plot ◽

Lidar Data ◽

National Forest ◽

Airborne Lidar Data

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Lennukilidari ja spektraalse kaugseireandmestiku kasutamine metsa peamiste takseertunnuste hindamiseks Aegviidu katsealal / Estimation of main forest inventory variables from spectral and airborne lidar data in Aegviidu test site, Estonia

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/v10132-012-0003-7 ◽

2012 ◽

Vol 56 (1) ◽

pp. 27-41 ◽

Cited By ~ 4

Author(s):

Mait Lang ◽

Johannes Anniste ◽

Tauri Arumäe

Keyword(s):

Standard Error ◽

Forest Inventory ◽

Remote Sensing Data ◽

Test Site ◽

Airborne Lidar ◽

Stem Volume ◽

Lidar Data ◽

Height Distribution ◽

Understorey Vegetation ◽

Airborne Lidar Data

Abstract Field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data. Up to 7 m random error in location of 15 m radius sample plots within homogeneous stands causes usually about 0.5 m standard deviation in lidar pulse return height distribution percentiles. Forest mean height can be predicted with linear relationship from 80th percentile of lidar pulse return height distribution. Upper percentiles of pulse return height distribution are not significantly affected by omitting returns from ground and forest understorey vegetation. Total stem volume in forest can be predicted by using 80th percentile, 25th percentile and canopy cover as model arguments with less than 70 m3 ha-1 standard error. Best species specific stem volume models had 10 m3 ha-1 smaller standard error.

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Estimation of Tree Height by Combining Low Density Airborne LiDAR Data and Images Using the 3D Tree Model: A Case Study in a Subtropical Forest in China

Forests ◽

10.3390/f11121252 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1252

Author(s):

Xiaocheng Zhou ◽

Wenjun Wang ◽

Liping Di ◽

Lin Lu ◽

Liying Guo

Keyword(s):

High Resolution ◽

Tree Height ◽

Airborne Lidar ◽

Estimation Accuracy ◽

Tree Level ◽

Lidar Data ◽

Low Density ◽

Tree Model ◽

Single Tree ◽

Airborne Lidar Data

In general, low density airborne LiDAR (Light Detection and Ranging) data are typically used to obtain the average height of forest trees. If the data could be used to obtain the tree height at the single tree level, it would greatly extend the usage of the data. Since the tree top position is often missed by the low density LiDAR pulse point, the estimated forest tree height at the single tree level is generally lower than the actual tree height when low density LiDAR data are used for the estimation. To resolve this problem, in this paper, a modified approach based on three-dimensional (3D) parameter tree model was adopted to reconstruct the tree height at the single tree level by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. The approach was applied to two coniferous forest plots in the subtropical forest region, Fujian Province, China. The following conclusions were reached after analyzing the results: The marker-controlled watershed segmentation method is able to effectively extract the crown profile from sub meter-level resolution images without the aid of the height information of LiDAR data. The adaptive local maximum method satisfies the need for detecting the vertex of a single tree crown. The improved following-valley approach is available for estimating the tree crown diameter. The 3D parameter tree model, which can take advantage of low-density airborne LiDAR data and high resolution images, is feasible for improving the estimation accuracy of the tree height. Compared to the tree height results from only using the low density LiDAR data, this approach can achieve higher estimation accuracy. The accuracy of the tree height estimation at the single tree level for two test areas was more than 80%, and the average estimation error of the tree height was 0.7 m. The modified approach based on the three-dimensional parameter tree model can effectively increase the estimation accuracy of individual tree height by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data.

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Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha.44.1.10155 ◽

2016 ◽

Vol 44 (1) ◽

Author(s):

Bogdan APOSTOL ◽

Adrian LORENT ◽

Marius PETRILA ◽

Vladimir GANCZ ◽

Ovidiu BADEA

Keyword(s):

Picea Abies ◽

Norway Spruce ◽

Test Site ◽

Volume Estimation ◽

Airborne Lidar ◽

Lidar Data ◽

Stand Volume ◽

Airborne Lidar Data

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Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

Forest Ecosystems ◽

10.1186/s40663-019-0212-0 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Wuming Zhang ◽

Shangshu Cai ◽

Xinlian Liang ◽

Jie Shao ◽

Ronghai Hu ◽

...

Keyword(s):

Tree Height ◽

Point Clouds ◽

Airborne Lidar ◽

Canopy Height ◽

Lidar Data ◽

Cloth Simulation ◽

Simulation Based ◽

Maximum Tree Height ◽

Airborne Lidar Data ◽

Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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