Tree Height Estimation Using Field Measurement and Low-Cost Unmanned Aerial Vehicle (UAV) at Phnom Kulen National Park of Cambodia

Mot, Ly; Hong, Su Young; Charoenjit, Kitsanai; Zhang, Haoran

doi:10.1109/agro-geoinformatics50104.2021.9530357

Cited by 6 publications

(7 citation statements)

References 27 publications

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“…This RMSE was higher than our value (1.45–1.73 m), although it was calculated using a different method, while we obtained a lower R 2 value (0.71–0.77). In a study of a cashew plantation, Mot et al [ 80 ] reported that the highest R 2 (0.60) was derived from a 50 m UAV flight, whereas the 200 m UAV flight only achieved an R 2 of 0.50. They also revealed that their proposed method was only applicable to open terrain where the TH was <12 m due to a design limitation of the pipe meters (i.e., a straight tube to measure height).…”

Section: Discussionmentioning

confidence: 99%

“…First, an LAS dataset was used as input to ArcGIS. Then, a raster was created using the LAS dataset [ 80 ], with the file value set to elevation. For DSM generation, the LAS file was filtered to the first return, and the value was set to maximum.…”

Section: Methodsmentioning

confidence: 99%

“…First, an LAS dataset was used as input to ArcGIS. Then, a raster was created using the LAS dataset [80], with the file value set to elevation. For DSM generation, the LAS file The GCPs were added to each corresponding image for optimization of the camera locations and orientations, as well as other internal camera parameters.…”

Section: Generation Of the Chmmentioning

confidence: 99%

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UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Karthigesu,

Owari,

Tsuyuki

et al. 2023

Sensors

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Old plantations are iconic sites, and estimating stand parameters is crucial for valuation and management. This study aimed to estimate stand parameters of a 115-year-old Japanese larch (Larix kaempferi (Lamb.) Carrière) plantation at the University of Tokyo Hokkaido Forest (UTHF) in central Hokkaido, northern Japan, using unmanned aerial vehicle (UAV) photogrammetry. High-resolution RGB imagery was collected using a DJI Matrice 300 real-time kinematic (RTK) at altitudes of 80 and 120 m. Structure from motion (SfM) technology was applied to generate 3D point clouds and orthomosaics. We used different filtering methods, search radii, and window sizes for individual tree detection (ITD), and tree height (TH) and crown area (CA) were estimated from a canopy height model (CHM). Additionally, a freely available shiny R package (SRP) and manually digitalized CA were used. A multiple linear regression (MLR) model was used to estimate the diameter at breast height (DBH), stem volume (V), and carbon stock (CST). Higher accuracy was obtained for ITD (F-score: 0.8–0.87) and TH (R2: 0.76–0.77; RMSE: 1.45–1.55 m) than for other stand parameters. Overall, the flying altitude of the UAV and selected filtering methods influenced the success of stand parameter estimation in old-aged plantations, with the UAV at 80 m generating more accurate results for ITD, CA, and DBH, while the UAV at 120 m produced higher accuracy for TH, V, and CST with Gaussian and mean filtering.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Karthigesu,

Owari,

Tsuyuki

et al. 2023

Sensors

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show abstract

“…In ArcGIS Pro (version 2.8), the LAS data set to raster tool within the LAS conversion tool was employed to create the DSM, using the LAS point clouds as input. Parameters including the value field, interpolation type, cell assignment, void fill method, and sampling value were configured as max, binning, nearest neighbor, and five, respectively [48]. The UAV digital terrain model (DTM) exhibited low accuracy due to the occlusion effect from the top canopy.…”

Section: Generation Of the Chmsmentioning

confidence: 99%

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Karthigesu,

Owari,

Tsuyuki

et al. 2024

Remote Sensing

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Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.

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“…Remote sensing (RS) methods such as terrestrial laser scanning (TLS), mobile laser scanning (MLS), airborne laser scanning (ALS), unmanned aerial vehicle (UAV) LiDAR, and digital aerial photogrammetry (DAP) have proved to be efficient and accurate for estimating tree parameters [12][13][14][15][16][17][18][19]. In recent years, UAV LiDAR has become increasingly popular [20][21][22][23][24][25][26] as it provides 3D information with a higher spatial resolution due to its low flying altitude [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

et al. 2022

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Biomass stored in young forests has enormous potential for the reduction of fossil fuel consumption. However, to ensure long-term sustainability, the measurement accuracy of tree height is crucial for forest biomass and carbon stock monitoring, particularly in young forests. Precise height measurement using traditional field measurements is challenging and time consuming. Remote sensing (RS) methods can, however, replace traditional field-based forest inventory. In our study, we compare individual tree height estimation from Light Detection and Ranging (LiDAR) and Digital Aerial Photogrammetry (DAP) with field measurements. It should be noted, however, that there was a one-year temporal difference between the field measurement and LiDAR/DAP scanning. A total of 130 trees (32 Scots Pine, 29 Norway Spruce, 67 Silver Birch, and 2 Eurasian Aspen) were selected for height measurement in a young private forest in south-east Finland. Statistical correlation based on paired t-tests and analysis of variance (ANOVA, one way) was used to compare the tree height measured with the different methods. Comparative results between the remote sensing methods and field measurements showed that LiDAR measurements had a stronger correlation with the field measurements and higher accuracy for pine (R2 = 0.86, bias = 0.70, RMSE = 1.44) and birch (R2 = 0.81, bias = 0.86, RMSE = 1.56) than DAP, which had correlation values of (R2 = 0.71, bias = 0.82, RMSE = 2.13) for pine and (R2 = 0.69, bias = 1.19, RMSE = 2.08) for birch. The correlation of the two remote sensing methods with the field measurements was very similar for spruce: LiDAR (R2 = 0.83, bias = 0.30, RMSE = 1.17) and DAP (R2 = 0.83, bias = 0.44, RMSE = 1.26). Moreover, the correlation was highly significant, with minimum error and mean difference (R2 = 0.79–0.98, MD = 0.12–0.33, RMSD = 0.45–1.67) between LiDAR and DAP for all species. However, the paired t-test suggested that there is a significant difference (p < 0.05) in height observation between the field measurements and remote sensing for pine and birch. The test showed that LiDAR and DAP output are not significantly different for pine and spruce. Presumably, the time difference in field campaign between the methods was the reason for these significant results. Additionally, the ANOVA test indicated that the overall means of estimated height from LiDAR and DAP were not significantly different from field measurements in all species. We concluded that utilization of LiDAR and DAP for estimating individual tree height in young forests is possible with acceptable error and comparable accuracy to field measurement. Hence, forest inventory in young forests can be carried out using LiDAR or DAP for height estimation at the individual tree level as an alternative to traditional field measurement approaches.

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Tree Height Estimation Using Field Measurement and Low-Cost Unmanned Aerial Vehicle (UAV) at Phnom Kulen National Park of Cambodia

Cited by 6 publications

References 27 publications

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

UAV Photogrammetry for Estimating Stand Parameters of an Old Japanese Larch Plantation Using Different Filtering Methods at Two Flight Altitudes

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

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