Tree Measurements in the Urban Environment: Insights from Traditional and Digital Field Instruments to Smartphone Applications

Pace, Rocco; Masini, Emanuela; Giuliarelli, Diego; Biagiola, Luca; Tomao, Antonio; Guidolotti, Gabriele; Agrimi, Mariagrazia; Portoghesi, Luigi; Angelis, Paolo De; Calfapietra, Carlo

doi:10.48044/jauf.2022.009

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…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%

Robust Single-Image Tree Diameter Estimation with Mobile Phones

2023

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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.

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Section: Introductionmentioning

confidence: 99%

Robust Single-Image Tree Diameter Estimation with Mobile Phones

2023

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“…Notable examples are KIRI Innovations (2023) and ForestScanner (Tatsumi et al, 2022). When scaled, these apps may allow communities to create high-resolution 3D maps of trees and vegetation at a lower cost than traditional approaches (i.e., flyovers from low-flying aircraft) (Pace et al, 2022).…”

Section: Tablementioning

confidence: 99%

Measuring the 3-30-300 rule to help cities meet nature access thresholds

Browning,

Locke,

Konijnendijk

et al. 2024

Science of The Total Environment

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“…Notable examples are KIRI 89 and ForestScanner 90 . When scaled, these apps may allow communities to create high-resolution 3D maps of trees and vegetation at a lower cost than traditional approaches (i.e., flyovers from lowflying aircraft) 91 .…”

Section: Land Cover Mapsmentioning

confidence: 99%

Measuring the 3-30-300 Rule to Help Cities Meet Nature Access Thresholds

Browning¹,

Locke²,

Bosch³

et al. 2023

Preprint

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The 3-30-300 rule offers benchmarks for cities to promote equitable nature access. It dictates that individuals should see three trees from their dwelling, have 30% tree canopy in their neighborhood, and live within 300 meters of a high-quality green space. Implementing this demands thorough measurement, monitoring, and evaluation methods. Seven data and processes exist to assess these components, including vegetation indices, street level analyses, tree inventories, questionnaires, window view analyses, land cover maps, and green space maps. Our panel of expert’s consensus recommends surveys and window-view analyses for the '3', high-resolution land cover maps for the '30', and green space maps with network analyses for the '300'. These methods, responsive to local situations and resources, not only implement the 3-30-300 rule but foster broader dialogue on local desires and requirements. Consequently, these techniques can guide strategic investments in urban greening for health, equity, biodiversity, and climate adaptation.

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Tree Measurements in the Urban Environment: Insights from Traditional and Digital Field Instruments to Smartphone Applications

Cited by 9 publications

References 30 publications

Robust Single-Image Tree Diameter Estimation with Mobile Phones

Robust Single-Image Tree Diameter Estimation with Mobile Phones

Measuring the 3-30-300 rule to help cities meet nature access thresholds

Measuring the 3-30-300 Rule to Help Cities Meet Nature Access Thresholds

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