Estimation of urban tree canopy cover using random point sampling and remote sensing methods

Parmehr, Ebadat G.; Amati, Marco; Taylor, Elizabeth; Livesley, Stephen J.

doi:10.1016/j.ufug.2016.08.011

Cited by 102 publications

(64 citation statements)

References 27 publications

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“…i-Tree Canopy random sampling method was created by the US Department of Agriculture (USDA) Forest Service and provides an accurate estimate of tree canopy and other cover classes such as grass, building, and impervious surface within the boundary of areas preferred [16,17]. The tool allows users to select random points on aerial photographs, and the user classifies each point into a cover class (e.g., tree, building, grass) [18].…”

Section: Data Acquisition and Methodologymentioning

confidence: 99%

“…Estimated UTC and ISC can be also derived as percent or area, and also accuracy and precision of cover types can be calculated using i-Tree Canopy tool. The tool suggests 500-1000 random sample points to increase the accuracy of UTC estimation with a confidence level at 95% [16].…”

Section: Data Acquisition and Methodologymentioning

confidence: 99%

“…Non-field-based methods used to estimate UTC can be categorized as random point sampling and remote sensing methods. UTC also differs from other technologies such as LiDAR because it is a cheap and quick method, and estimates more accurate data of temporal depth than satellite-based metrics [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…The software allows users to define points randomly onto Google Earth imagery which provides percentage estimates of cover classes and vegetation benefits (annual economic and air pollutant removal estimates) [17,18]. The photo-interpreted tree canopy estimation has been discussed as an economically efficient and less time-consuming method in several studies [1,16,19,20]. On the other hand, field measurements of tree canopy cover are usually considered as expensive methods and cannot provide a complete coverage of large-scale areas, but it is possible to determine individual trees and their canopy cover with field-based estimation techniques [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…There are several studies focusing on UTC monitoring [12,14,16], and their findings show that UTC calculation is a less complex method for accurately producing maps in contrast to satellite metrics. However, the existing monitoring system is limited for low-resolution aerial photographs in terms of random sampling method application using a rapid and satellite-based metrics.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing spatial structure of urban tree cover (UTC) and impervious surface cover (ISC) density using remotely sensed data in Osmaniye, Turkey

Atasoy

2020

SN Appl. Sci.

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Urban trees provide a wide range of significant benefits, and their degradation can result in air pollution and floods, and can damage public health and decrease social welfare. The purpose of this research was to estimate the urban tree cover (UTC) and impervious surface cover (ISC) density and to evaluate how they influence forest gain and loss in Turkey. Accordingly, i-Tree Canopy random point sampling and remote sensing methods were applied using the most current Landsat 7 Enhanced Thematic Mapper Plus images of Osmaniye City. The results indicate that the majority (n = 217) of random points were overlapped with buildings on aerial photograph with a percent cover estimate of 34.1 ± 1.88%. Also, the second highest number of the randomly selected points (n = 166) overlapped with road cover at estimate of 26.1 ± 1.74%. Grass cover (n = 68), ISC (n = 59), and shrub cover (n = 52) percentages were estimated as 10.7 ± 1.22%, 9.26 ± 1.15%, and 8.16 ± 1.08%, respectively. UTC (n = 41) percentage was estimated as 6.44 ± 0.97% in the urban city center. The lowest percent cover of randomly selected points was parking lots (n = 34) estimated as 5.34 ± 0.89%. Also, the forest loss intensity was unevenly distributed and reflected areas with high population density, and forest cover loss was estimated at the highest level on the east side of the city center. The findings of this research suggest that urbanization around the city center of Osmaniye has altered the local vegetative cover due to deforestation activities to create areas for building construction and new developments.Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Section: Data Acquisition and Methodologymentioning

confidence: 99%

Section: Data Acquisition and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Characterizing spatial structure of urban tree cover (UTC) and impervious surface cover (ISC) density using remotely sensed data in Osmaniye, Turkey

Atasoy

2020

SN Appl. Sci.

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Say where you sample: Increasing site selection transparency in urban ecology

et al. 2023

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Urban ecological studies have the potential to expand our understanding of socioecological systems beyond that of an individual city or region. Cross‐comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50‐question, theory‐based questionnaire and a multiple‐reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers we read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists' ability to assess the internal and external validity of studies' findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields' ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.

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Determination of the optimum number of sample points to classify land cover types and estimate the contribution of trees on ecosystem services using the I‐Tree Canopy tool

Selim

Dönmez

Kılçık

2022

Integr Envir Assess & Manag

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The process of producing information about dynamic land use and land cover and ecosystem health quickly with high accuracy and low cost is important. This information is one of the basic data used for sustainable land management. For this purpose, remote sensing technologies are generally used, and sampling points are mostly assigned. Determination of the optimum number of sampling points using the I-Tree Canopy tool was the main focus of this study. The I-Tree Canopy tool classifies land cover, revealing the effects of tree cover on ecosystem services, such as carbon (C) sequestration and storage, temperature regulation, air pollutant filtering, and air quality improvement, with numerical data. It is used because it is practical, open source, and user-friendly. This software works based on sampling point assignment, but it is unclear how many sampling points should be assigned. Therefore, determining the optimum number of sample points by statistical methods will increase the effectiveness of this tool and guide users. For this purpose, reference data were created for comparison. Then, 31 I-Tree Canopy reports were created with 100-point increments up to 3100. The data obtained from the reports were compared with the reference data, and statistical analysis based on Gaussian and a second-order polynomial fit was performed. At the end of the analysis, the following results were obtained; the results of this study demonstrated that the optimum number of sample points for a 1-ha area is 760 ± 32 from the comparison of the real area and I-Tree Canopy results. Similar results from the Gaussian fit of annually sequestered and stored C and carbon dioxide (CO 2 ) amounts in trees and the reduction in air pollution in grams were obtained as 714 ± 16. Therefore, we may conclude that taking more than 800 sample points will not be statistically significant.

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Estimation of urban tree canopy cover using random point sampling and remote sensing methods

Cited by 102 publications

References 27 publications

Characterizing spatial structure of urban tree cover (UTC) and impervious surface cover (ISC) density using remotely sensed data in Osmaniye, Turkey

Characterizing spatial structure of urban tree cover (UTC) and impervious surface cover (ISC) density using remotely sensed data in Osmaniye, Turkey

Say where you sample: Increasing site selection transparency in urban ecology

Determination of the optimum number of sample points to classify land cover types and estimate the contribution of trees on ecosystem services using the I‐Tree Canopy tool

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