Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment

Zhou, Longfei; Meng, Ran; Tan, Yiyang; Lv, Zhengang; Zhao, Yongqiang; Xu, Bin; Zhao, Feng

doi:10.1016/j.ufug.2022.127489

Cited by 18 publications

(13 citation statements)

References 77 publications

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“…LiDAR method has been considered the ideal method for DTM generation due to its ability to penetrate vegetation [18]. However, LiDAR presents some drawbacks in comparison to SfM.…”

Section: Introductionmentioning

confidence: 99%

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

Lee,

Gou,

Park

et al. 2024

Preprint

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Developing algorithms for generating accurate Digital Terrain Model (DTM) of rivers is necessary due to the limitations of traditional field survey methods, which are time-consuming and costly and do not provide continuous data. The objective of this study was to develop an advanced algorithm for generating high-quality DTM of rivers using Structur from Motion (SfM) data. A leveling survey was conducted on four cross-sections of the Bokha stream in Icheon City, S. Korea, and SfM-based DTM was produced using the Pix4Dmapper program and Phantom 4 multispectral drone. Two vegetation filters (NDVI and ExG) and two morphological filters (ATIN and CSF) were applied to the data, and the best filter combination was identified based on MAE and RMSE analyses. The integration of NDVI and CSF showed the best performance for the vegetation area, while a single application of NDVI showed the lowest MAE for the bare area. The effectiveness of the SfM method in eliminating waterfront vegetation was confirmed, with an overall MAE of 0.299 m RMSE of 0.375 m. These findings suggest that generating DTMs of riparian zones can be achieved efficiently with a limited budget and time using the proposed methodology.

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“…LiDAR method has been considered the ideal method for DTM generation due to its ability to penetrate vegetation [18]. However, LiDAR presents some drawbacks in comparison to SfM.…”

Section: Introductionmentioning

confidence: 99%

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

Lee,

Gou,

Park

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Textural measurements (for example, the grey level co-occurrence matrix [GLCM]), a measure of variability in pixel values among surrounding pixels for a specific analysis window, 29,30 can provide information about canopy biophysical properties regarding the shape, size, and attitude of leaves for weed mapping in corn and rice fields. 23,31,32 In addition, structural measurements, such as plant height (PH) derived from three-dimensional (3D) point clouds reflecting spatial-detailed 3D information on the vegetation canopy column, [33][34][35] have been successfully used for weed mapping. 36,37 To further improve the accuracy of weed mapping, researchers have developed many methods based on the fusion of multisource remote sensing measurements (VI, GLCM, PH), which show higher mapping accuracy than using a single measurement alone.…”

Section: Introductionmentioning

confidence: 99%

Improved weed mapping in corn fields by combining UAV‐based spectral, textural, structural, and thermal measurements

Meng

Chen

et al. 2023

Pest Management Science

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BACKGROUND Spatial‐explicit weed information is critical for controlling weed infestation and reducing corn yield losses. The development of unmanned aerial vehicle (UAV)‐based remote sensing presents an unprecedented opportunity for efficient, timely weed mapping. Spectral, textural, and structural measurements have been used for weed mapping, whereas thermal measurements—for example, canopy temperature (CT)—were seldom considered and used. In this study, we quantified the optimal combination of spectral, textural, structural, and CT measurements based on different machine‐learning algorithms for weed mapping. RESULTS CT improved weed‐mapping accuracies as complementary information for spectral, textural, and structural features (up to 5% and 0.051 improvements in overall accuracy [OA] and Marco‐F1, respectively). The fusion of textural, structural, and thermal features achieved the best performance in weed mapping (OA = 96.4%, Marco‐F1 = 0.964), followed by the fusion of structural and thermal features (OA = 93.6%, Marco‐F1 = 0.936). The Support Vector Machine‐based model achieved the best performance in weed mapping, with 3.5% and 7.1% improvements in OA and 0.036 and 0.071 in Marco‐F1 respectively, compared with the best models of Random Forest and Naïve Bayes Classifier. CONCLUSION Thermal measurement can complement other types of remote‐sensing measurements and improve the weed‐mapping accuracy within the data‐fusion framework. Importantly, integrating textural, structural, and thermal features achieved the best performance for weed mapping. Our study provides a novel method for weed mapping using UAV‐based multisource remote sensing measurements, which is critical for ensuring crop production in precision agriculture. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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“…Photogrammetry works on the principle of using 2D image data, most often in the form of photographs. Therefore, classic compact cameras, mobile cameras, 3D or 360 cameras, laser 3D sceners, drones, or tablets with LiDar technologies, which are now available to the general public, are used for this method [6], [9], [10], [20]. The quality of the scanned image depends on the resolution, color gamut, lighting conditions, and attributes are affect the demand for additional outputs [12], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Use of scanning devices for object 3D reconstruction by photogrammetry and visualization in virtual reality

et al. 2023

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This article aims to compare two different scanning devices (360 camera and digital single lens reflex (DSLR) camera) and their properties in the three-dimensional (3D) reconstruction of the object by the photogrammetry method. The article first describes the various stages of the process of 3D modeling and reconstruction of the object. A point cloud generated to the 3D model of the object, including textures, is created in the following steps. The scanning devices are compared under the same conditions and time from capturing the image of a real object to its 3D reconstruction. The attributes of the scanned image of the recon-structed 3D model, which is a mandarin tree in a citrus greenhouse in a daylight environment, are also compared. Both created models are also compared visually. That visual comparison reveals the possibilities in the application of both scanning devices can be found in the process of 3D reconstruction of the object by photogrammetry. The results of this research can be applied in the field of 3D modeling of a real object using 3D models in virtual reality, 3D printing, 3D visualization, image analysis, and 3D online presentation.

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Comparison of UAV-based LiDAR and digital aerial photogrammetry for measuring crown-level canopy height in the urban environment

Cited by 18 publications

References 77 publications

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

Improved weed mapping in corn fields by combining UAV‐based spectral, textural, structural, and thermal measurements

Use of scanning devices for object 3D reconstruction by photogrammetry and visualization in virtual reality

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