Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Fekry, Reda; Yao, Wei; Cao, Lin; Shen, Xin

doi:10.1016/j.fecs.2022.100065

Cited by 27 publications

(23 citation statements)

References 41 publications

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“…These two have the same geographic reference and coordinate system, which can be directly integrated with the software. The fusion of UAV-LS point cloud data and TLS point cloud data can greatly improve the accuracy of individual tree parameters 23 . Merged point cloud data are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

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Combining multisource remote sensing data to calculate individual tree biomass in complex stands

Lian,

Zhang,

Wang

et al. 2024

J. Appl. Rem. Sens.

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Accurate estimation of forest individual tree characteristics and biomass is very important for monitoring global carbon storage and carbon cycle. In order to solve the problem of calculating individual biomass of various tree species in complex stands, we take terrestrial laser scanning data, unmanned aerial vehicle-laser scanning data, and multispectral data as data sources and extract spectral characteristics, vegetation index characteristics, texture characteristics, and tree height characteristics of diverse forest areas through multispectral classification of tree species. Based on the random forest (RF) algorithm, the extracted features were superimposed and optimized, and the tree species were classified according to the multispectral data combined with field investigation. Then multispectral classification data combined with light detection and ranging (LIDAR) point cloud data were used to classify point cloud species, and then individual tree parameters were extracted for the divided point cloud species, and stand biomass was obtained using the tree biomass calculation model. The results showed that all kinds of tree species could be identified based on RF algorithm by combining multispectral data and LIDAR data. The overall classification accuracy was 66% and the kappa coefficient was 0.59. The recall rate of poplar, cypress, and lacebark-pine was about 75%, except for willow and clove trees, which were blocked by large crown width and caused multiple detection and missed detection. The R 2 of diameter at breast height was 0.85, and the root-mean-square error (RMSE) was 5.90 cm. The R 2 of the tree height was 0.90, and the RMSE was 1.78 m. Finally, the biomass of each tree species was calculated, and the stand biomass was 66.76 t∕hm 2 , which realized the classification of the whole stand and the measurement of the biomass of each tree. Our study proves that the application of combined multisource remote sensing data to forest biomass estimation has good feasibility.

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Section: Methodsmentioning

confidence: 99%

“…The fusion of UAV-LS point cloud data and TLS point cloud data can greatly improve the accuracy of individual tree parameters. 23 Merged point cloud data are shown in Fig. 1.…”

Section: Data Processingmentioning

confidence: 99%

Combining multisource remote sensing data to calculate individual tree biomass in complex stands

Lian,

Zhang,

Wang

et al. 2024

J. Appl. Rem. Sens.

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“…BEV images are usually obtained through perspective transformation of the original image obtained by the camera. This requires a known relationship between the camera and the ground, and through this transformation, the size and position of the object in the BEV image can directly correspond to the actual physical size and position [9].…”

Section: Generation Of Bev Datamentioning

confidence: 99%

Comparative Study on BEV Vision and LiDAR Point Cloud Data Fusion Methods

Zhou

2023

TCSISR

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With the gradual maturity of autonomous driving technology, efficient fusion and processing of multimodal sensor data has become an important research direction. This study mainly explores the strategy of integrating BEV (Bird's Eye View) vision with LiDAR point cloud data. We evaluated the performance and applicability of each of the three main data fusion methods through in-depth comparison: early fusion, mid-term fusion, and late fusion. First of all, we summarize the working principle and data characteristics of BEV vision and LiDAR, and emphasize their key roles in auto drive system. Then, the theoretical basis and implementation methods of the three fusion strategies were described in detail. The experimental results show that different fusion strategies exhibit their own advantages in different application scenarios and requirements. For example, early fusion performs well in high-precision tasks, but has a high demand for computing resources. And mid-term fusion is more suitable in scenarios with high real-time requirements. Overall, this study provides in-depth insights and practical suggestions on the fusion of BEV vision and LiDAR data in the field of autonomous driving, laying a solid foundation for future research and applications.

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“…Moreover, other researchers tried to integrate ground-based and UAV-LiDAR (ULS) data to better compute tree parameters based on quantitative structure modelling (QSM). The specific fusion was performed in three steps [14]. At first, the ground-based/ULS LiDAR data was co-registered trough the local density peaks of the clustered canopy.…”

Section: Introductionmentioning

confidence: 99%

“…Afterwards, redundancy and noise were removed from the ground-based/ULS LiDAR data fusion. Finally, tree modelling and biophysical parameter retrieval were based on QSM [14]. Other studies combined terrestrial laser scanner and unmanned aerial vehicle with a GNSS receiver [15].…”

Section: Introductionmentioning

confidence: 99%

Point cloud density enhancement within PROION project

Kyriou,

Nikolakopoulos

2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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Over the past few years Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanner (TLS) have emerged as paramount remote sensing approaches for the generation of exceptional three-dimensional models. The current study aims to examine the synergistic use of UAV and TLS point clouds created in the context of "PROION" project. In particular, the project co-funded by the European Union and the Hellenic government, combines in situ measurements, remote sensing data and soft computing methods aiming at evaluating the possible deformation on national infrastructure located in Western Greece. 3D representations of the observation sites derived from UAV and TLS sensors constitute a key part of the validation procedure. In light of this, the spatial reconstruction of the observed infrastructure should be implemented in the best possible spatial resolution. Hence, each observation site was surveyed twice in a regular basis, i.e. a UAV flight was performed to collect points on the roof while a scanning was executed for the acquisition of facades. Afterwards the generated UAV and TLS 3D representations were fused in order to produce a more comprehensive 3D model of each infrastructure. The point clouds generated at the different processing stages were compared in terms of their density.

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Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Cited by 27 publications

References 41 publications

Combining multisource remote sensing data to calculate individual tree biomass in complex stands

Combining multisource remote sensing data to calculate individual tree biomass in complex stands

Comparative Study on BEV Vision and LiDAR Point Cloud Data Fusion Methods

Point cloud density enhancement within PROION project

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