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Error matrix showing accuracy for a categorical raster map of land use land cover in Seattle. Overall accuracy was 74.00%, and the Khat was 0.64.

Error matrix showing accuracy for a categorical raster map of land use land cover in Seattle. Overall accuracy was 74.00%, and the Khat was 0.64.

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Article
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Increasing urbanization around the globe is leading to concern over the loss of tree canopy within cities, but quantifying urban forest canopy cover can be difficult. We discuss methods of assessing canopy cover within cities, and then use a case study of Seattle, WA, USA to examine issues of uncertainty in canopy cover assessment. We find that unc...

Contexts in source publication

Context 1
... points were randomly located within Seattle and assigned a class by a trained photo interpreter using 2009 0.5 ft Aerials Express true color imagery and georeferenced oblique angle aerial photographs. The accuracy assessment is presented as an error matrix (Table 2). ...
Context 2
... line is the best fit least squares regression and the dotted line represents unity. resolution aerial imagery collected in 2009 with a 1 ft pixel resolution, producing a CC estimate of 26.3% (Table 2). ...

Citations

... Accurate canopy cover assessment is dependent on the user's ability to correctly classify each assessment point. This in turn is dependent on the ease of interpretation of the aerial imagery, which can be highly variable due to age of the imagery, presence of shadows and pixel size (Richardson and Moskal, 2014). ...
Technical Report
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Forest Research has made the first estimate of the monetary value of non-woodland trees in the UK. This helps us understand the overall value of our treescape, in which non-woodland trees play a critical role. Non-woodland trees are (i) single trees; (ii) groups of trees covering less than 0.1 hectares; and (iii) small woods covering less than 0.5 hectares. There are an estimated 0.74 million hectares of non-woodland tree canopy cover in Great Britain, and a further 31 thousand hectares in Northern Ireland.
... To estimate tree canopy coverage in the urban centres, we used the land classification algorithm tool i-Tree Canopy [40], which has been used in previous urban greenspace studies [41][42][43]. The urban centre boundaries were loaded into i-Tree Canopy. ...
Article
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Green infrastructure plays a vital role in urban ecosystems. This includes sustaining biodiversity and human health. Despite a large number of studies investigating greenspace disparities in suburban areas, no known studies have compared the green attributes (e.g., trees, greenness, and greenspaces) of urban centres . Consequently, there may be uncharacterised socioecological disparities between the cores of urban areas (e.g., city centres). This is important because people spend considerable time in urban centres due to employment, retail and leisure opportunities. Therefore, the availability of––and disparities in––green infrastructure in urban centres can affect many lives and potentially underscore a socio-ecological justice issue. To facilitate comparisons between urban centres in Great Britain, we analysed open data of urban centre boundaries with a central business district and population of ≥100,000 ( n = 68). Given the various elements that contribute to ‘greenness’, we combine a range of different measurements (trees, greenness, and accessible greenspaces) into a single indicator. We applied the normalised difference vegetation index (NDVI) to estimate the mean greenness of urban centres and the wider urban area (using a 1 km buffer) and determined the proportion of publicly accessible greenspace within each urban centre with Ordnance Survey Open Greenspace data. Finally, we applied a land cover classification algorithm using i-Tree Canopy to estimate tree coverage. This is the first study to define and rank urban centres based on multiple green attributes. The results suggest important differences in the proportion of green attributes between urban centres. For instance, Exeter scored the highest with a mean NDVI of 0.15, a tree coverage of 11.67%, and an OS Greenspace coverage of 0.05%, and Glasgow the lowest with a mean NDVI of 0.02, a tree cover of 1.95% and an OS Greenspace coverage of 0.00%. We also demonstrated that population size negatively associated with greenness and tree coverage, but not greenspaces, and that green attributes negatively associated with deprivation. This is important because it suggests that health-promoting and biodiversity-supporting resources diminish as population and deprivation increase. Disparities in green infrastructure across the country, along with the population and deprivation-associated trends, are important in terms of socioecological and equity justice. This study provides a baseline and stimulus to help local authorities and urban planners create and monitor equitable greening interventions in urban/city centres.
... Canopy tool provides a low-cost, fast, and repeatable method to evaluate tree cover and explain its impact on ecosystem services with numerical data. It is also stated that this software is promising in predicting and evaluating the benefits generated by ecosystem services, providing high accuracy; data produced largely overlap with results obtained by image classification techniques (Olivatto, 2019;Razaghirad, 2021;Richardson & Moskal, 2014). However, there are some restrictions on the use of this method. ...
... The most obvious and time-consuming limitation is the inability to determine the appropriate number of sampling points. Several examples can be given as follows: Omodior et al. (2021) used an average of 280 random points for vegetation cover classification; Jacobs et al. (2014) used 1000 random points to classify landscape features across Australia; 500 points were used to examine the tree cover differences in the study area between 2008 and 2011 by Hwang and Wiseman (2020); Richardson and Moskal (2014) used 1000 points to evaluate the urban forest cover; Ersoy Tonyaloğlu and Atak (2021) used 10 608 points in a study investigating the effects of land use and land cover change on potential regulating ecosystem services. It has been seen that a different number of sample points are used regardless of the size of the land (Benjamin et al., 2015;Walters & Sinnett, 2021;Zho, 2017). ...
Article
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The process of producing information about dynamic land use/land cover and ecosystem health in a short time 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 sequestration and storage, temperature regulation, air pollutant filtering, and air quality improvement, with numerical data. It is used since 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 will 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 showed 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 carbon and CO2 amounts in trees and the reduction of air pollution in grams were obtained as 714±16. Therefore, we may conclude that the sample points taken more than 800 will not create a statistically significant difference. This article is protected by copyright. All rights reserved. Integr Environ Assess Manag 2022;00:0–0.
... Despite being of great value, urban forests are constantly under threat of development as densification and urban expansion continue [1]. Continuing urbanization has raised concerns about the loss of urban tree canopy cover (TCC) on global and regional scales [7,8]. A study looking at 2002 to 2009 across the United States showed that there was an average TCC decline of 0.2% per 6 years [8]. ...
... This mapping can also aid in the identification of areas that are at risk of deforestation or in need of reforestation or afforestation, aiding future development and management decisions. TCC is the percentage of an area covered by tree canopies and is the most common measurement for assessing urban forests, in part because it is easily understood by members of the public and it is a simple proxy to measure the amount of urban forest [3,7]. Urban forests are defined as "forest stands and trees with amenity values situated in or near urban areas" [10]. ...
Article
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The advancement and accessibility of high-resolution remotely sensed data has made it feasible to detect tree canopy cover (TCC) changes over small spatial scales. However, the short history of these high-resolution collection techniques presents challenges when assessing canopy changes over longer time scales (> 50 years). This research shows how using high-resolution LiDAR data in conjunction with historical aerial photos can overcome this limitation. We used the University of British Columbia’s Point Grey campus in Vancouver, Canada, as a case study, using both historical aerial photographs from 1949 and 2015 LiDAR data. TCC was summed in 0.05 ha analysis polygons for both the LiDAR and aerial photo data, allowing for TCC comparison across the two different data types. Methods were validated using 2015 aerial photos, the means (Δ 0.24) and a TOST test indicated that the methods were statistically equivalent (±5.38% TCC). This research concludes the methods outlined is suitable for small scale TCC change detection over long time frames when inconsistent data types are available between the two time periods.
... Following the i-Tree Canopy user guide, the boundaries of AHP were demarcated using the polygon tool on Google Maps satellite imagery (datum and projection WGS 84 Web Mercator) and 1,000 randomly generated point samples were visually assigned to the category of Tree (tree canopy only) or Non-Tree (shrubs, grasses, trail, parking lot) (Fig. 2). Low image resolution or shadows can reduce accuracy in distinguishing vegetation (Richardson and Moskal 2014), thus we confirmed land cover type of any ambiguous point samples using this study's high resolution UAV imagery. Total area and percentage of tree canopy cover and non-tree cover (± Standard Error) were estimated for AHP from the 1,000-point samples. ...
Article
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People protect what they value, yet different people assign value to nature in different ways. The monetary valuation of ecosystem services (ES) is a strategy to estimate the worth of benefits provided by nature to humans and is increasingly common in cities where human populations are densest. Most ES valuation of urban areas are at the city scale with few studies at the parcel level, yet urban land decisions are typically made at the parcel level. Here we approximated the monetary value of ecosystem services for a single nascent urban park in the United States’ second most populous city, Los Angeles. Acknowledging no single method can capture the entire ES value of a location, we use four approaches to approximate a value range for this site. Using a combination of unoccupied aerial vehicle imagery and ground-truthing surveys, the park was partitioned by dominant land cover types to assess values derived from literature estimates, tree canopy features, and collected field-based metrics of all individual trees over 1.5 m height using the ecosystem service valuation functions of the i-Tree software suite. We also applied a more novel market-based approach to approximate the park’s overall value. We found calculated dollar values across and within the land cover types varied by orders of magnitude between assessment approaches yet were generally low due to limited mature vegetation cover. The present study is unique in providing a baseline assessment for a recently opened, highly urban park in a low-income, park-poor neighborhood of Los Angeles. More broadly, it provides ES valuation at the data-lacking parcel scale which is needed to better understand the ecological role and function of green spaces in cities.
... 3. We do not apply discounting in determining costs and benefits. As commonly practiced, benefits and costs realised at later periods are considered as worth less than those realised in earlier periods (Richardson and Moskal 2014). Future research might account for when tree benefits will occur, as well as how much tree coverage would be useful. ...
Article
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This pilot study, set in Brisbane, Australia, provides answers to the following research questions: (1) Is there inter- or intra-suburb inequality in street tree provision, in particular along routes that lead to public transport stops? (2) Are further investments in street trees justifiable on heatwave harm reduction alone? The pilot targets three suburbs away from the urban core, which have different socio-economic levels. The ‘number of street trees per kilometre’ is used as a foundational measure and a labour-intensive (but quite granular and accurate) data collection method is adopted. Our findings point toward inequality in the provision of street trees, especially in the lower income suburb. In the two wealthier suburbs too, street trees are not regarded as a pedestrian transport infrastructure asset. At the same time, our threshold analysis shows that street tree planting is justifiable on heatwave harm reduction alone. In the future, a study of all Brisbane streets would provide more conclusive answers.
... Discrepancies between image interpreters can introduce error into the UTC product (Richardson and Moskal, 2014). To reduce this error, one interpreter carried out manual delineation across the entire study area while another interpreter delineated~10% of the area to validate the replicability of the dataset. ...
Article
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Present-day spatial patterns of urban tree canopy (UTC) are created by complex interactions between various human and biophysical drivers; thus, urban forests represent legacies of past processes. Understanding these legacies can inform municipal tree planting and canopy cover goals while also addressing urban sustainability and inequity. We examined historical UTC cover patterns and the processes that formed them in the cities of Chelsea and Holyoke, Massachusetts using a mixed methods approach. Combining assessments of delineated UTC from aerial photos with historical archival data, we show how biophysical factors and cycles of governance and urban development and decay have influenced the spatiotemporal dynamics of UTC. The spatially explicit UTC layers generated from this research track historical geographic tree distribution and dynamic change over a 62-year period (1952–2014). An inverse relationship was found between UTC and economic prosperity: while canopy gains occurred in depressed economic periods, canopy losses occurred in strong economic periods. A sustainable increase of UTC is needed to offset ongoing losses and overcome historical legacies that have suppressed UTC across decades. These findings will inform future research on residential canopy formation and stability, but most importantly, they reveal how historical drivers can be used to inform multi-decadal UTC assessments and the creation of targeted, feasible UTC goals at neighborhood and city scales. Such analyses can help urban natural resource managers to better understand how to protect and expand their cities’ UTC over time for the benefit of all who live in and among the shade of urban forests.
... Recently, the increasing accessibility of technologies led to a growing number of high spatial resolution (<3 m) experiments in urban contexts [7,29,32]. For example, studies on urban forest canopy managed to identify its extension [34,35] and tree species [21]. UGI assessment and monitoring differs by regional environment, since each contains different structures of vegetation and complex surfaces [36]. ...
... For example, a study assessing urban vegetation biomass classifies urban vegetation through an object-oriented classification method, using remote sensing images and LiDAR data [11]. Object-based image analysis classifies segments of aerial or satellite images based on rules related to color, shape, and texture [35]. Zhong et al. evaluated the effects of urbanization by calculating two greennessrelated spectral indexes-NDVI and the Enhanced Vegetation Index (EVI)-from Landsat images [12]. ...
... The research presented here visually tests and selects NDVI values greater than 0.18, in line with the values indicated in Table 1 [35], and combines them with height data from LiDAR. Specifically, green areas have been classified as low vegetation (0.00-0.40 m), mainly representing pervious surfaces, medium vegetation (0.40-2.00 m), mainly representing bushes and farmlands, and high vegetation (above 2.00 m). ...
Article
Full-text available
Urban green infrastructure (UGI) has a key role in improving human and environmental health in cities and contributes to several services related to climate adaptation. Accurate localization and quantification of pervious surfaces and canopy cover are envisaged to implement UGI, address sustainable spatial planning, and include adaptation and mitigation strategies in urban planning practices. This study aims to propose a simple and replicable process to map pervious surfaces and canopy cover and to investigate the reliability and the potential planning uses of UGI maps. The proposed method combines the normalized difference vegetation index (NDVI), extracted from high-resolution airborne imagery (0.20 m), with digital elevation models to map pervious surfaces and canopy cover. The approach is tested in the Municipality of Trento, Italy, and, according to a random sampling validation, has an accuracy exceeding 80%. The paper provides a detailed map of green spaces in the urban areas, describing quantity and distribution, and proposes a synthesis map expressed as a block-level degree of pervious surfaces and canopy cover to drive urban transformations. The proposed approach constitutes a useful tool to geovisualize critical areas and to compare levels of pervious surfaces and canopy cover in the municipal area. Acknowledging the role of green areas in the urban environment, the paper examines the potential applications of the maps in the policy cycle, such as land use management and monitoring, and in climate-related practices, and discusses their integration into the current planning tools to shift towards performative rather than prescriptive planning.
... The effectiveness of using either approach has been demonstrated in several studies involving assessments of land cover transition (e.g., Bettinger and Merry, 2019;Nowak and Greenfield, 2012;Nowak et al., 2013). Richardson and Moskal (2014) suggested that in comparison to census-based landscape classification methods, random sampling provides more unbiased results in detecting land cover changes, although it may require a large number of point samples to provide an acceptable level of statistical confidence. King and Locke (2013) did not find a significant statistical difference in detecting land cover changes between census-based and sampling approaches, yet Parmehr et al. (2016) found a 4.5% variation in urban tree canopy maps developed using landscape classification techniques that combined multispectral satellite imagery and LiDAR data, and a point sampling approach that used high resolution aerial imagery. ...
Article
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Using imagery available through Google Earth Pro and a point sampling methodology, changes in land cover for three U.S. cities were assessed, beginning during the Great Recession (2007) and extending through to 2018. The cities were Buffalo (New York), Denver (Colorado), and San Diego (California), and 11 land cover classes were used to characterize each. The novel contributions of this work, and the innovative contributions to science include an analysis of urban land cover change in the years since the Great Recession, and the use of point pattern analysis on sample points that changed from non-developed in 2007 to developed in 2018, to determine whether a spatial pattern of land cover class change was evident. An initial assumption was made that forest cover change in these three cities would be minimal since the Great Recession. In fact, forest cover decreased by less than 1% in all three cities with the greatest decrease in Buffalo. Over the post-recession study period, increases in the developed land classes were evident in all three cities at the expense of grasses, tree cover, and other land classes. Some clustering of new development activities was noticed at a relatively small scale in San Diego, while some dispersion of new developed activities was noticed at a larger scale in Denver. Among other factors, changes in population, economics, and land use are factors that influence land cover change with specific impacts on forest cover, and therefore in the provision of urban forest benefits to the environment and society.
... Urban forest generally refers to all woody vegetation, including street trees on public and private lands, urban parks, and other trees located on residential properties, commercial land, and other lands within a city (Nowak et al., 2010, Berland 2012, Richardson and Moskal 2014. The urban forest is a significant component of the urban environment. ...
... They also improve air and water quality and biodiversity and reduce energy use and greenhouse gas effect by facilitating the cooling effect. (Dwyer et al., 1992, Akbari 2002, Konijnendijk and Randrup 2004, Nowak et al., 2010, Berland 2012, Richardson and Moskal 2014, Pasher et al., 2014, Safford et al., 2013, Parmehr et al., 2016, Kaspar et al., 2017. ...
... Ecosystem services provided by urban forests in the city are directly related to the area covered by tree canopy, the ratio of the area covered by the crown of the tree (leaves, branches, and trunk) when viewed from above, and generally estimated as a percentage. It is a simple, well-known, and most often used metric to measure urban forest coverage (Nowak and Greenfield 2012, Berland 2012, Richardson and Moskal 2014, Grove et al., 2006, Doick et al., 2020. Some cities integrated this metric into management plans and policies, such as the city of Los Angeles, CA, USA. ...
Article
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Urban trees and forests are essential components of the urban environment. They can provide numerous ecosystem services and goods, including but not limited to recreational opportunities and aesthetic values, removal of air pollutants, improving air and water quality, providing shade and cooling effect, reducing energy use, and storage of atmospheric CO2. However, urban trees and forests have been in danger of being lost by dense housing resulting from population growth in the cities since the 1950s, leading to increased local temperature, pollution level, and flooding risk. Thus, determining the status of urban trees and forests is necessary for comprehensive understanding and quantifying the ecosystem services and goods. Tree canopy cover is a relatively quick, easy to obtain, and cost-effective urban forestry metric broadly used to estimate ecosystem services and goods of the urban forest. This study aimed to determine urban forest canopy cover areas and monitor the changes between 1984–2015 for the Great Plain Conservation area (GPCA) that has been declared as a conservation Area (GPCA) in 2017, located on the border of Düzce City (Western Black Sea Region of Turkey). Although GPCA is a conservation area for agricultural purposes, it consists of the city center with 250,000 population and most settlement areas. A random point sampling approach, the most common sampling approach, was applied to estimate urban tree canopy cover and their changes over time from historical aerial imageries. Tree canopy cover ranged from 16.0% to 27.4% within the study period. The changes in urban canopy cover between 1984–1999 and 1999–2015 were statistically significant, while there was no statistical difference compared to the changes in tree canopy cover between 1984–2015. The result of the study suggested that an accurate estimate of urban tree canopy cover and monitoring long-term canopy cover changes are essential to determine the current situation and the trends for the future. It will help city planners and policymakers in decision-making processes for the future of urban areas.