Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Lai-ping, Luo; Zhai, Qiuping; Su, Yanjun; Ma, Qin; Kelly, Maggi

doi:10.1364/oe.26.00a562

Cited by 50 publications

(40 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accurate individual tree height extraction is the key to AGB estimation for individual trees. As the literature [19,44] shows, the R 2 of extracted tree heights from airborne LiDAR-acquired point cloud data is by far higher than 0.90. Even in some dense seasonal tropical forests, the R 2 is still as high as 0.94 [28].…”

Section: Discussionmentioning

confidence: 94%

Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography

Lin

Wang

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

In tree Aboveground Biomass (AGB) estimation, the traditional harvest method is accurate but unsuitable for a large-scale forest. The airborne Light Detection And Ranging (LiDAR) is superior in obtaining the point cloud data of a dense forest and extracting tree heights for AGB estimation. However, the LiDAR has limitations such as high cost, low efficiency, and complicated operations. Alternatively, the overlapping oblique photographs taken by an Unmanned Aerial Vehicle (UAV)-loaded digital camera can also generate point cloud data using the Aerial Triangulation (AT) method. However, limited by the relatively poor penetrating capacity of natural light, the photographs captured by the digital camera on a UAV are more suitable for obtaining the point cloud data of a relatively sparse forest. In this paper, an electric fixed-wing UAV loaded with a digital camera was employed to take oblique photographs of a sparse subalpine coniferous forest in the source region of the Minjiang River. Based on point cloud data obtained from the overlapping photographs, a Digital Terrain Model (DTM) was generated by filtering non-ground points along with the acquisition of a Digital Surface Model (DSM) of Minjiang fir trees by eliminating subalpine shrubs and meadows. Individual tree heights were extracted by overlaying individual tree outlines on Canopy Height Model (CHM) data computed by subtracting the Digital Elevation Model (DEM) from the rasterized DSM. The allometric equation with tree height (H) as the predictor variable was established by fitting measured tree heights with tree AGBs, which were estimated using the allometric equation on H and Diameter at Breast Height (DBH) in sample tree plots. Finally, the AGBs of all of the trees in the test site were determined by inputting extracted individual tree heights into the established allometric equation. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted individual tree heights were 0.92 and 1.77 m, and the R2 and RMSE of the estimated AGBs of individual trees were 0.96 and 54.90 kg. The results demonstrated the feasibility and effectiveness of applying UAV-acquired oblique optical photographs to the tree AGB estimation of sparse subalpine coniferous forests.

show abstract

Section: Discussionmentioning

confidence: 94%

Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography

Lin

Wang

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Remote sensing has the potential to provide consistent, reproducible and up-to-date information on various forest parameters in order to make inventories more efficient [6]. Previous studies mostly applied a specific remote sensing system to a single area and demonstrated how remote sensing can be used for quick assessment of forests at large spatial scales (see, e.g., [7][8][9][10][11][12][13][14][15][16]). Nevertheless, comparing novel remote sensing methods for measuring tree height usually involves a high degree of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Ganz

Käber

Adler

2019

Forests

View full text Add to dashboard Cite

We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

show abstract

“…We achieved detection rates from 0.75 to 0.90, which is in line with other studies. For instance, Luo et al [35] reported overall accuracy of 0.87 for comparison between automatically segmented trees and visual examination in Tahoe National Forest (USA). Wang et al [7] reported R 2 of 0.8-0.9 for comparison of plot-level field-based and ALS-based tree counts in Heihe River Basin (China).…”

Section: Tree Detectionmentioning

confidence: 99%

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Novotný

Navrátilová

Janoutová

et al. 2020

Forests

View full text Add to dashboard Cite

Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.

show abstract

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Cited by 50 publications

References 54 publications

Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography

Aboveground Tree Biomass Estimation of Sparse Subalpine Coniferous Forest with UAV Oblique Photography

Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Influence of Site-Specific Conditions on Estimation of Forest above Ground Biomass from Airborne Laser Scanning

Contact Info

Product

Resources

About