Estimating urban greenness index using remote sensing data: A case study of an affluent vs poor suburbs in the city of Johannesburg

Abutaleb, Khaled; Mudede, Marko Freddy; Nkongolo, Nsalambi V.; Newete, Solomon W.

doi:10.1016/j.ejrs.2020.07.002

Cited by 42 publications

(25 citation statements)

References 27 publications

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“…There is an increasing body of evidence that urban ecosystems in good condition contribute to biodiversity conservation (Kowarik 2011, Baldock et al 2015, Hall et al 2016, Lepczyk et al 2017, Egerer et al 2020, Kowarik et al 2020) and to human well-being (Gascon et al 2016, Dadvand et al 2016, van den Berg et al 2016, Tischer et al 2017).…”

Section: Discussionmentioning

confidence: 99%

“…NDVI is calculated from the reflectance ratio of the red and infrared satellite bands (Running 1990). In literature, the NDVI is well known and widely used as a simple, but effective index for quantifying green vegetation (Tucker 1979, Pettorelli et al 2005, Abutaleb et al 2021, for instance, to describe the distribution and amount of green space in population-based epidemiological studies (Balseviciene et al 2014, Gascon et al 2017, Helbich 2019, Song et al 2019, Kua and Lee 2021. The "greenest" values are derived from Landsat annual Top-of-Atmosphere (TOA) reflectance composites with a resolution of 30 m. Data are available in the GEE platform for the period 1996-2018.…”

Section: A Framework For the Analysis Of Changes In Vegetation Cover ...mentioning

confidence: 99%

“…Several scholars who implemented indicators to evaluate urban sprawl in Europe used a horizon of perception of 2-3 km. The horizon of perception (HP) provides a measure of how an area can be visually perceived(EEA 2016, Aurambout et al 2018). This approach is based on the European Landscape Convention, according to which 'Landscape means an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors' (Article 1 of the European Landscape Convention…”

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confidence: 99%

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Green balance in urban areas as an indicator for policy support: a multi-level application

Zulian¹,

Marando²,

Mentaschi³

et al. 2022

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Green spaces are increasingly recognised as key elements in enhancing urban resilience as they provide several ecosystem services. Therefore, their implementation and monitoring in cities are crucial to meet sustainability targets. In this paper, we provide a methodology to compute an indicator that assesses changes in vegetation cover within Urban Green Infrastructure (UGI). Such an indicator is adopted as one of the indicators for reporting on the key area “nature and biodiversity” in the Green City Accord (GCA). In the first section, the key steps to derive the indicator are described and a script, which computes the trends in vegetation cover using Google Earth Engine (GEE), is provided. The second section describes the application of the indicator in a multi-scale, policy-orientated perspective. The analysis has been carried out in 696 European Functional Urban Areas (FUAs), considering changes in vegetation cover inside UGI between 1996 and 2018. Results were analysed for the EU and the United Kingdom. The Municipality of Padua (Italy) is used as a case study to illustrate the results at the local level. Over the last 22 years, a slight upward trend characterised the vegetation growth within UGI in European FUAs. Within core cities and densily built-upcommuting zones, the trend was stable; in non-densely built-up areas, an upward trend was recorded. Vegetation cover in UGI has been relatively stable in European cities. However, a negative balance between abrupt changes in greening and browning has been recorded, affecting most parts of European cities (75% of core cities and 77% of commuting zones in densely built-up areas). This still indicates ongoing land take with no compensation of green spaces that are lost to artificial areas. Focusing on the FUA of Padua, a downward trend was observed in 33.3% and 12.9% of UGI in densely built-up and not-densely built-up areas, respectively. Within the FUA of Padua, most municipalities are characterised by a negative balance between abrupt greening and browning, both in non-densely built-up and densely built-up areas. This approach complements traditional metrics, such as the extent of UGI or tree canopy cover, by providing a valuable measure of condition of urban ecosystems and an instrument to monitor the impact of land take.

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Section: Discussionmentioning

confidence: 99%

Section: A Framework For the Analysis Of Changes In Vegetation Cover ...mentioning

confidence: 99%

mentioning

confidence: 99%

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Green balance in urban areas as an indicator for policy support: a multi-level application

Zulian¹,

Marando²,

Mentaschi³

et al. 2022

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“…In remote sensing field, vegetation indices have been widely used to extract vegetation biophysical information from satellite image data [69]. Previous works have demonstrated the effectiveness of vegetation indices in remote sensing-based green space mapping [7,13,[70][71][72]. erefore, this study relies on such conventional indices as a means of urban green space detection.…”

Section: Remote Sensing-based Vegetation Indicesmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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“…An obvious disadvantage of this type of quantification is the fact that it cannot distinguish between a well-maintained green area and sites such as abandoned terrains with overgrown vegetation. However, through the use of NDVI and the zonal statistics obtained for urban planning units, large differences can be reported and these data can complement existing quantitative and qualitative indicators (Abutaleb, 2020;Fung & Siu, 2000;Nouri et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

How green the urban development units in Sofia are: Earth observation and population time series analysis

Sarafova¹

2021

JBGS

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Over the last decades, the pressure that people and their activities put on the environment has increased. Green areas in many cities are diminishing in size due to urbanization, which inevitably leads to a decrease in quality of life. This study uses remote sensing (RS) data for Sofia, Bulgaria, for a period of nearly four decades, analyzing the dynamics of NDVI of the urban development units (UDUs). Statistics for NDVI per were calculated for each UDU for eleven dates in the following years: 1987, 1990, 1992, 1993, 1996, 2000, 2001, 2002, 2011, 2015, and 2020.  An estimate was made of the amount of green vegetation per capita, similar to other coefficients used for population analysis. NDVI profiles for major urban parks showed differences for the studied period. Sentinel-2 data for 2020 was used for visualization of the current situation, in combination with detailed population data for all UDUs. The obtained data will help the decision-making process for the development of UDUs, while the methodology can be applied in any other city worldwide.

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Estimating urban greenness index using remote sensing data: A case study of an affluent vs poor suburbs in the city of Johannesburg

Cited by 42 publications

References 27 publications

Green balance in urban areas as an indicator for policy support: a multi-level application

Green balance in urban areas as an indicator for policy support: a multi-level application

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

How green the urban development units in Sofia are: Earth observation and population time series analysis

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