Evaluating Carbon Sequestration and PM2.5 Removal of Urban Street Trees Using Mobile Laser Scanning Data

Street trees are an important part of urban facilities, and they can provide both aesthetic benefits and ecological benefits for urban environments. Ecological benefits of street trees now are attracting more attention because of environmental deterioration in cities. Conventional methods of evaluating ecological benefits require a lot of labor and time, and establishing an efficient and effective evaluating method is challenging. In this study, we investigated the feasibility to use mobile laser scanning (MLS) data to evaluate carbon sequestration and fine particulate matter (PM2.5) removal of street trees. We explored the approach to extract individual street trees from MLS data, and street trees of three streets in Nantong City were extracted. The correctness rates and completeness rates of extraction results were both over 92%. Morphological parameters, including tree height, crown width, and diameter at breast height (DBH), were measured for extracted street trees, and parameters derived from MLS data were in a good agreement with field-measured parameters. Necessary information about street trees, including tree height, DBH, and tree species, meteorological data and PM2.5 deposition velocities were imported into i-Tree Eco model to estimate carbon sequestration and PM2.5 removal. The estimation results indicated that ecological benefits generated by different tree species were considerably varied and the differences for trees of the same species were mainly caused by the differences in morphological parameters (tree height and DBH). This study succeeds in estimating the amount of carbon sequestration and PM2.5 removal of individual street trees with MLS data, and provides researchers with a novel and efficient way to investigate ecological benefits of urban street trees or urban forests.

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CARBON SEQUESTRATION ESTIMATION OF STREET TREES BASED ON POINT CLOUD FROM VEHICLE-BORNE LASER SCANNING SYSTEM

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-313-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 313-319

Author(s):

Y. Zhao ◽

Q. Hu

Keyword(s):

Carbon Sequestration ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Scanning System ◽

Street Trees ◽

Ecological Benefits ◽

Cloud Data ◽

Crown Width ◽

Laser Scanning System

Continuous development of urban road traffic system requests higher standards of road ecological environment. Ecological benefits of street trees are getting more attention. Carbon sequestration of street trees refers to the carbon stocks of street trees, which can be a measurement for ecological benefits of street trees. Estimating carbon sequestration in a traditional way is costly and inefficient. In order to solve above problems, a carbon sequestration estimation approach for street trees based on 3D point cloud from vehicle-borne laser scanning system is proposed in this paper. The method can measure the geometric parameters of a street tree, including tree height, crown width, diameter at breast height (DBH), by processing and analyzing point cloud data of an individual tree. Four Chinese scholartree trees and four camphor trees are selected for experiment. The root mean square error (RMSE) of tree height is 0.11m for Chinese scholartree and 0.02m for camphor. Crown widths in X direction and Y direction, as well as the average crown width are calculated. And the RMSE of average crown width is 0.22m for Chinese scholartree and 0.10m for camphor. The last calculated parameter is DBH, the RMSE of DBH is 0.5cm for both Chinese scholartree and camphor. Combining the measured geometric parameters and an appropriate carbon sequestration calculation model, the individual tree’s carbon sequestration will be estimated. The proposed method can help enlarge application range of vehicle-borne laser point cloud data, improve the efficiency of estimating carbon sequestration, construct urban ecological environment and manage landscape.

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A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

Remote Sensing ◽

10.3390/rs5020584 ◽

2013 ◽

Vol 5 (2) ◽

pp. 584-611 ◽

Cited By ~ 106

Author(s):

Bin Wu ◽

Bailang Yu ◽

Wenhui Yue ◽

Song Shu ◽

Wenqi Tan ◽

...

Keyword(s):

Laser Scanning ◽

Parameters Estimation ◽

Morphological Parameters ◽

Automated Identification ◽

Street Trees

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Nondestructive Estimation of the Above-Ground Biomass of Multiple Tree Species in Boreal Forests of China Using Terrestrial Laser Scanning

Forests ◽

10.3390/f10110936 ◽

2019 ◽

Vol 10 (11) ◽

pp. 936 ◽

Cited By ~ 3

Author(s):

Chen ◽

Feng ◽

Chen ◽

Khan ◽

Lian

Keyword(s):

Tree Species ◽

Boreal Forests ◽

Point Cloud ◽

Laser Scanning ◽

Tree Height ◽

Point Cloud Data ◽

Above Ground Biomass ◽

Cloud Data ◽

Non Destructive ◽

Multiple Tree

Above-ground biomass (AGB) plays a pivotal role in assessing a forest’s resource dynamics, ecological value, carbon storage, and climate change effects. The traditional methods of AGB measurement are destructive, time consuming and laborious, and an efficient, relatively accurate and non-destructive AGB measurement method will provide an effective supplement for biomass calculation. Based on the real biophysical and morphological structures of trees, this paper adopted a non-destructive method based on terrestrial laser scanning (TLS) point cloud data to estimate the AGBs of multiple common tree species in boreal forests of China, and the effects of differences in bark roughness and trunk curvature on the estimation of the diameter at breast height (DBH) from TLS data were quantitatively analyzed. We optimized the quantitative structure model (QSM) algorithm based on 100 trees of multiple tree species, and then used it to estimate the volume of trees directly from the tree model reconstructed from point cloud data, and to calculate the AGBs of trees by using specific basic wood density values. Our results showed that the total DBH and tree height from the TLS data showed a good consistency with the measured data, since the bias, root mean square error (RMSE) and determination coefficient (R2) of the total DBH were −0.8 cm, 1.2 cm and 0.97, respectively. At the same time, the bias, RMSE and determination coefficient of the tree height were −0.4 m, 1.3 m and 0.90, respectively. The differences of bark roughness and trunk curvature had a small effect on DBH estimation from point cloud data. The AGB estimates from the TLS data showed strong agreement with the reference values, with the RMSE, coefficient of variation of root mean square error (CV(RMSE)), and concordance correlation coefficient (CCC) values of 17.4 kg, 13.6% and 0.97, respectively, indicating that this non-destructive method can accurately estimate tree AGBs and effectively calibrate new allometric biomass models. We believe that the results of this study will benefit forest managers in formulating management measures and accurately calculating the economic and ecological benefits of forests, and should promote the use of non-destructive methods to measure AGB of trees in China.

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USING MOBILE LASER SCANNING POINT CLOUDS TO EXTRACT URBAN ROADSIDE TREES FOR ECOLOGICAL BENEFITS ESTIMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2020-211-2020 ◽

2020 ◽

Vol XLIII-B2-2020 ◽

pp. 211-216

Author(s):

W. Fan ◽

B. Yang ◽

F. Liang ◽

Z. Dong

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Morphological Parameters ◽

Heat Island ◽

Individual Tree ◽

Ecological Benefits ◽

Urban Heat ◽

The City ◽

Tree Extraction ◽

Parameters Calculation

Abstract. Roadside trees in the city play a crucial role in addressing the issues of air pollution, urban heat island effects, road noise, and so on. This paper proposes an efficient and robust method to automatically extract individual roadside trees with morphological parameters from mobile laser scanning (MLS) point clouds for ecological benefits estimation. The proposed method consists of four steps: MLS data pre-processing, pole-like objects classification, individual tree extraction, morphological parameters calculation for ecological benefits estimation. The proposed method is verified using three complex datasets in Shanghai, China. Comprehensive experiments demonstrate that the proposed method achieves good performance in extracting individual tree in terms of average precision and recall (91.83%, 92.60%), and provides detailed information for ecological benefits estimation.

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Drought-forced tree morphological changes facilitate trubs in a semiarid region

10.5194/egusphere-egu2020-2346 ◽

2020 ◽

Author(s):

Jingyu Dai ◽

Hongyan Liu ◽

Yongcai Wang ◽

Qinghua Guo

Keyword(s):

Carbon Sequestration ◽

Laser Scanning ◽

Mechanical Stability ◽

Morphological Changes ◽

Tree Height ◽

Semiarid Region ◽

Morphological Studies ◽

Canopy Area ◽

Sequestration Rate ◽

Vegetation Models

Semiarid forests characterized by the presence of &#8220;trub&#8221; species, which have short heights but large canopy sizes, can maintain a high carbon sequestration rate. By integrating terrestrial laser scanning (TLS), we quantified drought-forced tree morphological variation along a precipitation gradient; annual precipitation (MAP) explained 70.3% of variation in tree height (Height) but did not explain the variation in canopy area (CA). Theoretical CA-Height relationships widely adopted by dynamic global vegetation models (DGVMs)&#160; matched only the 5th percentile of our results, which is problematic for simulating carbon sequestration of open forests in semiarid regions. The trend toward &#8220;trubs&#8221; under a drying climate implies two decoupled functions of stems, mechanical stability and hydraulic efficiency, and can be an important strategy for trees to balance water and carbon. Our results demonstrate the importance of tree morphological studies for both tree environment-acclimation strategies and the improvement of DGVMs.

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Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

Remote Sensing ◽

10.3390/rs13122297 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2297

Author(s):

Jonathon J. Donager ◽

Andrew J. Sánchez Meador ◽

Ryan C. Blackburn

Keyword(s):

Ponderosa Pine ◽

Laser Scanning ◽

Canopy Cover ◽

Basal Area ◽

Tree Height ◽

Absolute Error ◽

Forest Monitoring ◽

Individual Tree ◽

Structural Configurations ◽

Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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