Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives

Mokroš, Martin; Mikita, Tomáš; Singh, Arunima; Tomaštík, Julián; Chudá, Juliána; Wężyk, Piotr; Kuželka, Karel; Surový, Peter; Klimánek, Martin; Zięba-Kulawik, Karolina; Bobrowski, Rogério; Liang, Xinlian

doi:10.1016/j.jag.2021.102512

Cited by 66 publications

(80 citation statements)

References 30 publications

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“…A LiDAR scanner ubiquitously available on consumer-grade devices was presented with the introduction of the iPad Pro 2020 11-inch and 12.9-inch display (hereafter iPad) on March 25, 2020 and the iPhone 12 Pro and iPhone 12 Pro Max (hereafter iPhone) on October 23, 2020 by Apple Inc.. Comparisons between the iPad LiDAR sensor, a hand-held personal laser scanning approach and traditional forest inventory equipment already demonstrated a high detection rate of tree stems above a threshold of 10 cm diameter 23 , 24 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Luetzenburg

Kroon

Bjørk

2021

Sci Rep

150

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Traditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Luetzenburg

Kroon

Bjørk

2021

Sci Rep

150

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“…Other studies have used repeated ALS flights to assess relative growth or damage following disturbances [47,48]. Most recently, unmanned aerial vehicles (UAVs) and drones have been used to assess similar individual tree structural attributes as a potential lower cost alternative to ALS [49][50][51][52]. However, the delay that was observed between initial research applications and the widespread use of ALS in supporting operational forest products supply chains and resource management suggests that it may take at least a decade for UAV-and drone-based approaches to replace ALS as the standard approach to acquire these measurements [2,7].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Sparks

Smith

2021

Forests

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Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

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“…It was found that both the scanning device and segmentation procedure had some limitations. It has been documented in other papers that the higher bias on lower values in DBH is mainly due to the noise in the pointcloud generated by the mobile scanner [41,49,51,52]. This bias is explained by noise effects especially on lower DBH.…”

Section: Discussionmentioning

confidence: 85%

Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform

Niţă

2021

Forests

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Climate-smart forestry is a sustainable forest management approach for increasing positive climate impacts on society. As climate-smart forestry is focusing on more sustainable solutions that are resource-efficient and circular, digitalization plays an important role in its implementation. The article aimed to validate an automatic workflow of processing 3D pointclouds to produce digital twins for every tree on large 1-ha sample plots using a GeoSLAM mobile LiDAR scanner and VirtSilv AI platform. Specific objectives were to test the efficiency of segmentation technique developed in the platform for individual trees from an initial cloud of 3D points observed in the field and to quantify the efficiency of digital twinning by comparing the automatically generated results of (DBH, H, and Volume) with traditional measurements. A number of 1399 trees were scanned with LiDAR to create digital twins and, for validation, were measured with traditional tools such as forest tape and vertex. The segmentation algorithm developed in the platform to extract individual 3D trees recorded an accuracy varying between 95 and 98%. This result was higher in accuracy than reported by other solutions. When compared to traditional measurements the bias for diameter at breast height (DBH) and height was not significant. Digital twinning offers a blockchain solution for digitalization, and AI platforms are able to provide technological advantage in preserving and restoring biodiversity with sustainable forest management.

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Novel low-cost mobile mapping systems for forest inventories as terrestrial laser scanning alternatives

Cited by 66 publications

References 30 publications

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform

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