Potential of Internet street-view images for measuring tree sizes in roadside forests

Wang, Wenjie; Lu, Xiao; Zhang, Jinghua; Yang, Yang; Tian, Ping; Wang, Huimei; He, Xingyuan

doi:10.1016/j.ufug.2018.09.008

Cited by 38 publications

(16 citation statements)

References 37 publications

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“…These classifiers are rarely used in urban tree classification as the main processing method, due to the heterogeneity of the landscape and the complexity of the data [35,67]. However, some authors found great accuracy in urban cover classification [44,97], stem measurement [102], object detection [78], and tree identification [108] using this approach. Among the 48 papers focused on urban tree characterization, only 10% of the studies evaluated used analytical approaches such as linear regression, variable correlation, logistic regression, and continuous-time Markov-chain.…”

Section: Traditional Parametric Methodsmentioning

confidence: 99%

“…Roberts et al [102] analyzed the accuracy of their lineal regression model comparing field measurements and predictions derived from PPC. Wang et al [108] used ANOVA and Pearson correlation to relate tree dimensions from a GSV image and information taken from field inventory. Likewise, Aval et al [97] used data processing and marked point process, which is a stochastic method derived from the Continuous-time Markov chain.…”

Section: Traditional Parametric Methodsmentioning

confidence: 99%

“…They employed panoramic images of the streets, which allowed them to use fewer photographs, by downloading images more distantly from each other. On the other hand, a study developed by Wang et al [108] describes the potential of GSV in obtaining information on three dimensions, which is necessary when estimating other ES such as carbon sequestration. They were able to estimate the diameter, height, and crown dimension of street line trees with an accuracy of 92%, 87%, and 80%, respectively.…”

Section: Ground-level Images and Videosmentioning

confidence: 99%

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Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

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Section: Traditional Parametric Methodsmentioning

confidence: 99%

Section: Traditional Parametric Methodsmentioning

confidence: 99%

Section: Ground-level Images and Videosmentioning

confidence: 99%

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Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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“…As the road development of Harbin represents a typical case in China, it has been used to study the effects of urbanization [23]. Many studies in Harbin have focused on CO 2 dynamics [36], remote sensing tree measurements [37], habitat variability [38], mycorrhizal changes [23,39], soil fertility mapping [40], forest carbon sequestrations [31], the biodiversity of trees and birds [32,41], microclimate regulations from urban trees [14,42], and the tree census method development [43]. The associations between road development and forest characteristics were not reported to date, and new studies will provide support for urban green infrastructure development.…”

Section: Study Areamentioning

confidence: 99%

Associations between Road Density, Urban Forest Landscapes, and Structural-Taxonomic Attributes in Northeastern China: Decoupling and Implications

Yang

et al. 2019

Forests

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A better understanding on the associations between road density (RD), urban forest structural-taxonomic attributes, and landscape metrics is vital for forest ecological service evaluations and suitable management in sprawling urban areas with increasing road networks. We chose Harbin, a fast growing provincial capital city in northeast China, as a case study to address this issue. We utilized ArcGIS software (Esri, version 10.0; Redlands, CA, USA) and FRAGSTATS (V4.2.589) to digitize GF-1 images (Gaofen No.1 remote sensing images) to acquire road net characteristic information and landscape metrics of urban forests in Harbin. Together with forest structural-taxonomic attributes from a stratified random sampling survey, statistical methods such as an analysis of variance, a regression analysis, and a redundancy analysis were used to determine the road-dependent differences and to decouple the associations between them. The results indicated that road area percentages, road length/imperious surface area (ISA) ratios, road area/ISA ratios, and road cross-points sharply increased from low to heavy RD areas. This road intensification was strongly associated with increased urban forest area, patch density, and diverse patch shapes; smaller tree sizes, lower tree densities, and diverse tree species compositions were generally observed. Redundancy-based variation partitioning showed that part of the variations in structural-taxonomic attributes of forests could be explained by road intensity characteristics. In low RD (0–1.5 km/km2) regions, the road characteristics significantly affected forest characteristics (Shannon Wiener diversity index, species richness, and evenness index); however, such associations weakened with increasing forest landscape-related associations in medium to heavy RD (1.5–6 km/km2) regions. Our findings highlighted that road development is strongly associated with forest characteristics in Harbin city, and RD-dependent forest landscape regulating management could favor the maximization of forest ecological services that are related to structural and species identities.

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“…Globally, ecological shelterbelt engineering projects, such as the Great Plains Shelterbelt Project (Roosevelt Engineering) in the USA, the Great Plan for the Transformation of Nature in the former Soviet Union, forestry and water conservation projects in Japan, the Green Dam Engineering Project in the five countries of North Africa, and the Three-North Shelterbelt Program in China, have increased the scientific study of shelterbelt forests (Zhang et al, 2016 ). There are numerous forest plantations worldwide, many of which were planted in degraded or abandoned farmlands and are used as agricultural protection forests or bioenergy forests in China (Wang G. Y. et al, 2018 ; Zhang et al, 2018 ) and worldwide (Deniz and Paletto, 2018 ; Jha, 2018 ). The area of shelterbelt forests used for protecting soil and water increased to 330 million ha globally by 2010, accounting for 8% of all forest areas.…”

Section: Introductionmentioning

confidence: 99%

Shelterbelt Poplar Forests Induced Soil Changes in Deep Soil Profiles and Climates Contributed Their Inter-site Variations in Dryland Regions, Northeastern China

Wang

et al. 2019

Front. Plant Sci.

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The influence of shelterbelt afforestation on soils in different-depth profiles and possible interaction with climatic conditions is important for evaluating ecological effects of large-scale afforestation programs. In the Songnen Plain, northeastern China, 720 soil samples were collected from five different soil layers (0–20, 20–40, 40–60, 60–80, and 80–100 cm) in shelterbelt poplar forests and neighboring farmlands. Soil physiochemical properties [pH, electrical conductivity (EC), soil porosity, soil moisture and bulk density], soil carbon and nutrients [soil organic carbon (SOC), N, alkaline-hydrolyzed N, P, available P, K and available K], forest characteristics [tree height, diameter at breast height (DBH), and density], climatic conditions [mean annual temperature (MAT), mean annual precipitation (MAP), and aridity index (ARID)], and soil texture (percentage of silt, clay, and sand) were measured. We found that the effects of shelterbelt afforestation on bulk density, porosity, available K, and total P were observed up to 100 cm deep; while the changes in available K and P were several-fold higher in the 0–20 cm soil layer than that in deeper layers (p < 0.05). For other parameters (soil pH and EC), shelterbelt-influences were mainly observed in surface soils, e.g., EC was 14.7% lower in shelterbelt plantations than that in farmlands in the 0–20 cm layer, about 2.5–3.5-fold higher than 60–100 cm soil inclusion. For soil moisture, shelterbelt afforestation decreased soil water by 7.3–8.7% in deep soils (p < 0.05), while no significant change was in 0–20 cm soil. For SOC and N, no significant differences between shelterbelt and farmlands were found in all five-depth soil profiles. Large inter-site variations were found for all shelterbelt-induced soil changes (p < 0.05) except for total K in the 0–20 cm layer. MAT and silt content provided the greatest explanation powers for inter-site variations in shelterbelt-induced soil properties changes. However, in deeper soils, water (ARID and MAP) explained more of the variation than that in surface soils. Therefore, shelterbelt afforestation in northeastern China could affect aspects of soil properties down to 100 cm deep, with inter-site variations mainly controlled by climate and soil texture, and greater contribution from water characteristics in deeper soils.

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Potential of Internet street-view images for measuring tree sizes in roadside forests

Cited by 38 publications

References 37 publications

Remotely Sensed Tree Characterization in Urban Areas: A Review

Remotely Sensed Tree Characterization in Urban Areas: A Review

Associations between Road Density, Urban Forest Landscapes, and Structural-Taxonomic Attributes in Northeastern China: Decoupling and Implications

Shelterbelt Poplar Forests Induced Soil Changes in Deep Soil Profiles and Climates Contributed Their Inter-site Variations in Dryland Regions, Northeastern China

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