3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

Çakir, Gursel Y.; Post, Christopher J.; Mikhailova, Elena A.; Schlautman, Mark A.

doi:10.3390/urbansci5040088

Cited by 9 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These instruments cost USD 50,000-125,000 [7][8][9] and require a high degree of technical expertise to process the resulting point cloud data. More recently, low-cost (USD < 1000), short-range (3-5 m) LiDAR on mobile phones and tablets, originally intended for augmented reality applications, has been found suitable for measuring DBH and tree locations in certain forest environments [10][11][12][13][14][15]. Other researchers have used structure from motion and stereography to create point clouds or depth maps with only handheld color cameras, though they find that these systems require longer processing and data collection times and do not match the depth map accuracy of mobile LiDAR [12,16].…”

Section: Introductionmentioning

confidence: 99%

“…These technologies are needed in diverse forest environments, including those with occlusion from branches, leaves, and low-lying vegetation. Recent work does not focus on these environments [10,13,17], requires manual intervention [11], or uses processing pipelines that are run offline for several hours on a powerful desktop computer [15,16] to identify the tree trunks to be measured within each image scan. There are also ease-of-use limitations: almost all existing mobile systems require the user to walk in a prescribed path around each tree to scan it from every angle, though as Cakir et al [11] note, in the case of "thorns, bushes, tall grasses, etc., it becomes physically difficult to walk around and between individual trees, making the scanning challenging in some forest conditions".…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Single-Image Tree Diameter Estimation with Mobile Phones

2023

View full text Add to dashboard Cite

Ground-based forest inventories are reliable methods for forest carbon monitoring, reporting, and verification schemes and the cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else require manual intervention or offline processing to identify and measure tree trunks. In this paper, we designed an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate the trunk diameter automatically from a single image in complex and realistic field conditions. We implemented our design and built it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational costs to run on this commodity platform in near real-time. We evaluated our app in 3 different forests across 3 seasons and found that in a corpus of 97 sample tree images, our app estimated the trunk diameter with a RMSE of 3.7 cm (R2 = 0.97; 8.0% mean absolute error) compared to manual DBH measurement. It achieved a 100% tree detection rate while reducing the surveyor time by up to a factor of 4.6. Our work contributes to the search for a low-cost, low-expertise alternative to terrestrial laser scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating the trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurements outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Single-Image Tree Diameter Estimation with Mobile Phones

2023

View full text Add to dashboard Cite

show abstract

“…The final statements confirmed similar results (RMSE for iPad Pro = 0.087 m and for FARO = 0.070 m). Similar research was performed in references [32,33]. The credible overview concerning the data acquisition methods including the iPad device for scientific forest management is presented in reference [31].…”

Section: Problem Overviewmentioning

confidence: 81%

“…A considerable body of published articles focuses on analyzing the integration of LiDAR technology into iPads, particularly concerning forestry-related issues [28][29][30][31][32][33]. In reference [29], the authors focused on the determination of the tree diameter parameters and compared the cross-sections resulting from the individual point clouds as outputs from selected applications: 3D Scanner App, Polycam, and SiteScape (3D Scanner App-LAAN LABS-New York, NY, USA; Polycam-Polycam Inc.-New York, NY, USA; SiteScape-FARO-Lake Mary, FL, USA).…”

Section: Problem Overviewmentioning

confidence: 99%

Usage of a Conventional Device with LiDAR Implementation for Mesh Model Creation

Smrčková,

Chromčák,

Ižvoltová

et al. 2024

Buildings

View full text Add to dashboard Cite

The trend of using conventional devices like mobile phones, tablets, and the other devices is gaining traction in improving customer service practices. This coincides with the growing popularity of building information modeling (BIM), which has led to increased exploration of various 3D object capture methods. Additionally, the technological boom has resulted in a surge of applications working with different 3D model formats including mesh models, point cloud, and TIN models. Among these, the usage of mesh models is experiencing particularly rapid growth. The main objective advantages of mesh models are their efficiency, scalability, flexibility, sense of detail, user-friendliness, and compatibility. The idea of this paper is to use a conventional device, specifically an iPad Pro equipped with light detection and ranging (LiDAR) technology, for creating mesh models. The different data capture methods employed by various applications will be compared to evaluate the final models´ precision. The accuracy of the 3D models generated by each application will be assessed by comparing the spatial coordinates of identical points distributed irregularly across the entire surface of the chosen object. Various available currently most-used applications were utilized in the process of data collection. In general, 3D representations of the object/area, etc., may be visualized, analyzed, and further processed in more formats such as TIN models, point cloud, or mesh models. Mesh models provide a visualization of the object mirroring the solid design of the real object, thus approximating reality in the closest way. This fact, along with automatized postprocessing after data acquisition, the ability to capture and visualize both convex and concave objects, and the possibility to use this type of 3D visualization for 3D printing, contribute to the decision to test and analyze mesh models. Consequently, the mesh models were created via the automatic post-processing, i.e., without external intervention. This fact leads to the problems of random coordinate systems being automatically pre-defined by every application. This research must deal with the resulting obstacles in order to provide a valid and credible comparative analysis. Various criteria may be applied to the mesh models’ comparisons, including objective qualitative and quantitative parameters and also the subjective ones. The idea of this research is not to analyze the data acquisition process in detail, but instead to assess the possibilities of the applications for the basic users.

show abstract

“…On the other hand, its drawbacks are a small reach of 5 to 6 m and a tendency to misalign repeatedly scanned objects [18]. This method was already successfully used in forestry research with feasible accuracy for forestry inventory purposes [19,20]. A detailed model of each tree stem was not the goal of these studies.…”

Section: Introductionmentioning

confidence: 99%

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

Hrdina,

Surový

2023

Remote Sensing

View full text Add to dashboard Cite

The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models.

show abstract

3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

Cited by 9 publications

References 24 publications

Robust Single-Image Tree Diameter Estimation with Mobile Phones

Robust Single-Image Tree Diameter Estimation with Mobile Phones

Usage of a Conventional Device with LiDAR Implementation for Mesh Model Creation

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

Contact Info

Product

Resources

About