Assessing City Greenness using Tree Canopy Cover: The Case of Yogyakarta, Indonesia

The study aims to measure the greenness of an Indonesia city using tree canopy cover data. Rapid physical development brings impacts to the loss of urban trees, which leads to the increase of flooding risk, local temperature and pollution level. To address the issues, a baseline assessment of urban tree canopy existence is necessary as inputs for effective urban environmental management policies. The methods used in this research include 1) remote sensing and spatial analysis, and 2) simple quantitative analysis. Furthermore, three indicators are used in assessing the greenness, including 1) size of the canopy, 2) canopy cover percentage, and 3) canopy per capita. The results found that the city of Yogyakarta has a low level of greenness based on the canopy size in which covers only 467.37 ha or 14.38% of the total area. The second finding is Yogyakarta has an unequal distribution of canopy cover percentage in each district (kecamatan). The third finding is Yogyakarta City has a canopy per capita rate of 10.93 sq m/person. This number is below the UN recommendation of 15sq m / person. It indicates that residents have poor access to urban greenery. Additionally, the article discusses that the three indicators used have strength and weakness in measuring the level of greenness. Therefore, the assessment objectives must be taken into account. We recommend the use of each indicator as follows: 1) the canopy size is used as an initial inventory of the existence and distribution of the canopy, 2) the canopy cover percentage canopy percentage for measuring and comparing the level of greenness spatially and visually between areas, 3) the canopy per capita is used to measure the possibility of access and interaction of residents with the presence of a tree canopy. Cities’ authority can use the information to measure the achievement of SDGs number 11, 13, or 15.

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Assessing City Greenness using Tree Canopy Cover: The Case of Yogyakarta, Indonesia

GEOGRAPHY ENVIRONMENT SUSTAINABILITY ◽

10.24057/2071-9388-2020-212 ◽

2021 ◽

Vol 14 (1) ◽

pp. 71-80

Author(s):

Rendy Bayu Aditya ◽

Muhammad Ulul Lizamun Ningam

Keyword(s):

Canopy Cover ◽

Tree Canopy ◽

Urban Trees ◽

Pollution Level ◽

Unequal Distribution ◽

Urban Greenery ◽

Tree Canopy Cover ◽

Per Capita ◽

Flooding Risk ◽

Canopy Size

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A Case Study Balancing Predetermined Targets and Real-World Constraints to Guide Optimum Urban Tree Canopy Cover for Perth, Western Australia

Forests ◽

10.3390/f11111128 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1128

Author(s):

Jackie Parker ◽

Greg D. Simpson

Keyword(s):

Western Australia ◽

Canopy Cover ◽

Tree Canopy ◽

Urban Trees ◽

Conceptual Tool ◽

Urban Tree ◽

Tree Canopy Cover ◽

Urban Tree Canopy ◽

Social Environmental

Trees in urban settings are becoming increasingly important as mediators to emerging challenges that transect social, environmental, and economic factors. Trees provide shade; absorb and store atmospheric carbon and other pollutants; reduce local temperature fluctuations; provide essential inner-city fauna habitat; assist in reducing over-land stormwater flow; provide amenity; and provide many more social, environmental, and economic benefits. To secure these benefits, tree canopy cover targets are commonly employed by land managers; however, such targets are rarely quantified against the characteristics and limitations of individual urban centers. Through the generation and interrogation of qualitative and quantitative data, this case study of Perth, Western Australia presents a new conceptual tool that integrates eleven factors found to influence the capacity and opportunity for a city to support urban tree canopy cover. This tool is designed to capture and causally weigh urban tree canopy considerations based on individual city characteristics, collective values, and identifiable constraints. The output of the tool provides an “optimum” tree canopy cover result (as a percentage of the urban fabric) to better inform canopy cover targets and recommendations for urban tree strategic planning and management. This tool is valuable for urban land managers, city planners, urban designers, and communities in effective planning, management, valuation, and investment regarding urban trees as a sub-set of urban green infrastructure.

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ASSESSING URBAN FOREST CANOPY COVER IN GREAT PLAIN CONSERVATION AREA (DÜZCE CITY, TURKEY) BETWEEN 1984 AND 2015

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

10.5194/isprs-archives-xlvi-4-w5-2021-539-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 539-544

Author(s):

Z. Uçar ◽

R. Eker ◽

A. Aydin

Keyword(s):

Ecosystem Services ◽

Forest Canopy ◽

Urban Forest ◽

Canopy Cover ◽

Tree Canopy ◽

Urban Trees ◽

Conservation Area ◽

Urban Tree ◽

Tree Canopy Cover ◽

Urban Tree Canopy

Abstract. 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.

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An Assessment of Falling Trees Due Strong Winds in Lisbon: Affected Species and Microclimatic Simulation

10.20944/preprints201710.0177.v1 ◽

2017 ◽

Author(s):

Flavio Mendes ◽

Felipe Petean ◽

Antonio Lopes ◽

Ezequiel Correia

Keyword(s):

Visual Analysis ◽

Hybrid Approach ◽

Canopy Cover ◽

Tree Canopy ◽

Urban Trees ◽

Prunus Cerasifera ◽

Tree Canopy Cover ◽

Approach Method ◽

Strong Winds ◽

Microclimatic Simulation

Fallen trees due to strong winds are well recorded in Lisbon. However, specie identification is needed to increase urban trees management. This paper aimed the identification of the most vulnerable trees to strong winds in Lisbon, through a hybrid approach method by proximity. The occurrence database was compiled together with basic structural city maps. Four criteria were designed to presuppose the trees specie names by approximation: i) Trees must be within 15 m from the street center; ii) At least 3 individuals within 30 m from the occurrence must belong to the same specie; iii) The surrounding specie must be representative in the street (>50%); iv) Visual analysis of street/avenue medians. Microscale analysis through supervised classification and micrometeorological simulations od strong winds were performed. Morus nigra L., Tipuana tipu (Benth.) Kuntze, Liriodendron tulipifera L., Prunus cerasifera Ehrh. and Koelreuteria paniculata Laxm. were identified as the species that fall the most. In 57.7% of cases (425 fallen trees), the wind speed 12-hours before the occurrence was greater than 7 m s−1. Alvalade neighborhood showed 22.7% tree canopy cover while the microclimatic simulation revealed two main vulnerability zones: Brazil and Church avenues, where winds were stronger possibly due acceleration effect.

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Residential housing segregation and urban tree canopy in 37 US Cities

10.31235/osf.io/97zcs ◽

2020 ◽

Cited By ~ 3

Author(s):

Dexter Locke ◽

Billy Hall ◽

J Morgan Grove ◽

Steward T.A. Pickett ◽

Laura A. Ogden ◽

...

Keyword(s):

Housing Stock ◽

Rank Order ◽

Canopy Cover ◽

Urban Environments ◽

Tree Canopy ◽

Urban Trees ◽

Wealth Creation ◽

Socioeconomic Impacts ◽

Tree Canopy Cover ◽

Loan Corporation

Redlining was a racially discriminatory housing policy established by the federal Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect, and may improve quality of life in cities. In our prior research in Baltimore, MD, we discovered that redlining policy influenced the location and allocation of trees and parks. Our analysis of 37 metropolitan areas here shows that areas formerly graded D, which were mostly inhabited by racial and ethnic minorities, have on average ~23% tree canopy cover today. Areas formerly graded A, characterized by U.S.-born white populations living in newer housing stock, had nearly twice as much tree canopy (~43%). Results are consistent across small and large metropolitan regions. The ranking system used by Home Owners’ Loan Corporation to assess loan risk in the 1930s parallels the rank order of average percent tree canopy cover today.

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Residential housing segregation and urban tree canopy in 37 US Cities

npj Urban Sustainability ◽

10.1038/s42949-021-00022-0 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Dexter H. Locke ◽

Billy Hall ◽

J. Morgan Grove ◽

Steward T. A. Pickett ◽

Laura A. Ogden ◽

...

Keyword(s):

Housing Stock ◽

Rank Order ◽

Canopy Cover ◽

Urban Environments ◽

Tree Canopy ◽

Urban Trees ◽

Wealth Creation ◽

Socioeconomic Impacts ◽

Tree Canopy Cover ◽

Loan Corporation

AbstractRedlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remain unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect, and may improve quality of life in cities. In our prior research in Baltimore, MD, we discovered that redlining policy influenced the location and allocation of trees and parks. Our analysis of 37 metropolitan areas here shows that areas formerly graded D, which were mostly inhabited by racial and ethnic minorities, have on average ~23% tree canopy cover today. Areas formerly graded A, characterized by U.S.-born white populations living in newer housing stock, had nearly twice as much tree canopy (~43%). Results are consistent across small and large metropolitan regions. The ranking system used by Home Owners’ Loan Corporation to assess loan risk in the 1930s parallels the rank order of average percent tree canopy cover today.

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Zoning, land use, and urban tree canopy cover: The importance of scale

Urban Forestry & Urban Greening ◽

10.1016/j.ufug.2012.12.005 ◽

2013 ◽

Vol 12 (2) ◽

pp. 191-199 ◽

Cited By ~ 22

Author(s):

Sarah K. Mincey ◽

Mikaela Schmitt-Harsh ◽

Richard Thurau

Keyword(s):

Land Use ◽

Canopy Cover ◽

Tree Canopy ◽

Urban Tree ◽

Tree Canopy Cover ◽

Urban Tree Canopy

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Diversity of Medicinal Plants among Different Tree Canopies

Sustainability ◽

10.3390/su13052640 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2640

Author(s):

Muhammad Zubair ◽

Akash Jamil ◽

Syed Bilal Hussain ◽

Ahsan Ul Haq ◽

Ahmad Hussain ◽

...

Keyword(s):

Medicinal Plants ◽

Coniferous Forest ◽

Canopy Cover ◽

Local Communities ◽

Tree Canopy ◽

Tree Cover ◽

Open Spaces ◽

Floral Diversity ◽

Tree Canopy Cover ◽

Closed Canopy

The moist temperate forests in Northern Pakistan are home to a variety of flora and fauna that are pivotal in sustaining the livelihoods of the local communities. In these forests, distribution and richness of vegetation, especially that of medicinal plants, is rarely reported. In this study, we carried out a vegetation survey in District Balakot, located in Northeastern Pakistan, to characterize the diversity of medicinal plants under different canopies of coniferous forest. The experimental site was divided into three major categories (viz., closed canopy, open spaces, and partial tree cover). A sampling plot of 100 m2 was established on each site to measure species diversity, dominance, and evenness. To observe richness and abundance, the rarefaction and rank abundance curves were plotted. Results revealed that a total of 45 species representing 34 families were available in the study site. Medicinal plants were the most abundant (45%) followed by edible plants (26%). Tree canopy cover affected the overall growth of medicinal plants on the basis of abundance and richness. The site with partial canopy exhibited the highest diversity, dominance, and abundance compared to open spaces and closed canopy. These findings are instrumental in identifying the wealth of the medicinal floral diversity in the northeastern temperate forest of Balakot and the opportunity to sustain the livelihoods of local communities with the help of public/private partnership.

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Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽

10.3390/land10040433 ◽

2021 ◽

Vol 10 (4) ◽

pp. 433

Author(s):

Xiaolan Huang ◽

Weicheng Wu ◽

Tingting Shen ◽

Lifeng Xie ◽

Yaozu Qin ◽

...

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Forest Canopy ◽

Canopy Cover ◽

Google Earth ◽

Tree Canopy ◽

Landsat Data ◽

High Resolution Data ◽

Modis Ndvi ◽

Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

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