Comparison of conventional measurement and LiDAR-based measurement for crown structures

Xu, Weiheng; Su, Zhifang; Zhong-ke, Feng; Xu, Hui; Jiao, Youquan; Yan, Fei

doi:10.1016/j.compag.2013.08.015

Cited by 37 publications

(23 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

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

show abstract

“…Alternatively, the method of delimiting and measuring the projected area of the tree crown [ 11 ] has been proposed as a manual measurement process. Vertical crown projection onto the soil can be related to canopy volume [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimized Method of Olive Tree Crown Volume Measurement

Miranda‐Fuentes

Llorens

Gamarra-Diezma

et al. 2015

Sensors

View full text Add to dashboard Cite

Accurate crown characterization of large isolated olive trees is vital for adjusting spray doses in three-dimensional crop agriculture. Among the many methodologies available, laser sensors have proved to be the most reliable and accurate. However, their operation is time consuming and requires specialist knowledge and so a simpler crown characterization method is required. To this end, three methods were evaluated and compared with LiDAR measurements to determine their accuracy: Vertical Crown Projected Area method (VCPA), Ellipsoid Volume method (VE) and Tree Silhouette Volume method (VTS). Trials were performed in three different kinds of olive tree plantations: intensive, adapted one-trunked traditional and traditional. In total, 55 trees were characterized. Results show that all three methods are appropriate to estimate the crown volume, reaching high coefficients of determination: R2 = 0.783, 0.843 and 0.824 for VCPA, VE and VTS, respectively. However, discrepancies arise when evaluating tree plantations separately, especially for traditional trees. Here, correlations between LiDAR volume and other parameters showed that the Mean Vector calculated for VCPA method showed the highest correlation for traditional trees, thus its use in traditional plantations is highly recommended.

show abstract

“…TLS is a fast, affordable and accurate method used in the extraction of forest inventory parameters (e.g. DBH, tree heights and crown shape parameters) at the individual tree level by capturing three-dimensional (3D) point cloud of the forest area [2][3][4][5] . Also, dense point cloud data from TLS is used to determine stand/plot level attributes, such as leaf area index 6 , non-destructive tree volume estimates 7 , which aid in the calibration of satellite-based remote sensing estimations 8 .…”

mentioning

confidence: 99%

Automatic Estimation of Tree Stem Attributes Using Terrestrial Laser Scanning in Central Indian Dry Deciduous Forests

et al. 2018

View full text Add to dashboard Cite

Forest inventories are critical for effective management of forest resources. Recently, the use of terrestrial laser scanning (TLS) to automatically extract forest inventory parameters at tree level (e.g. tree location, diameter at breast height (DBH) and height) has gained significant importance. TLS using both single-scan and multi-scan techniques, not only helps in detailed and accurate measurements of tree objects but also helps increase the measurement frequency. In the current study, we develop an automated solution to extract forest inventory parameters at individual tree level from TLS data by using random sample consensus (RANSAC)-based circle fitting algorithm. The method was evaluated on both single-and multiscan data by characterizing four circular plots of radius 20 m in dry deciduous forests of Betul, Madhya Pradesh (India). Over all the plots, tree detection rates of 75% and 97% were obtained using single-and multi-scan TLS data respectively. Tree detection rates were significantly affected by increase in distance from the scanner, in single-scan approach when compared to multi-scan approach. Field based DBH measurements correlated well using both single (R 2 = 0.96) and multiple scans (R 2 = 0.99). The DBH estimates from multi-scan TLS data resulted in low root-meansquare error (RMSE) of 2.2 cm compared to that of 4.1 cm using single-scan. Further, tree heights were extracted from TLS data and validated with selectively measured trees on field (R 2 = 0.98; N = 65). The RMSE of tree height was estimated to be 1.65 m. The current results show the potential use of TLS in automatically deriving forest inventory parameters with reliable accuracy at individual tree level.

show abstract

Comparison of conventional measurement and LiDAR-based measurement for crown structures

Cited by 37 publications

References 18 publications

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

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

Towards an Optimized Method of Olive Tree Crown Volume Measurement

Automatic Estimation of Tree Stem Attributes Using Terrestrial Laser Scanning in Central Indian Dry Deciduous Forests

Contact Info

Product

Resources

About