Mapping and classifying green infrastructure typologies for climate-related studies based on remote sensing data

Bartesaghi-Koc, Carlos; Osmond, Paul; Peters, Alan

doi:10.1016/j.ufug.2018.11.008

Cited by 60 publications

(38 citation statements)

References 25 publications

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“…This can be accomplished by avoiding the formation of large contiguous areas of high risk and extreme high risk in the urban environment, measuring the minimum ecological safety distance between cities, establishing ecological corridors and ecological networks, and delimiting urban growth boundaries [44]. Therefore, the spatial identification of different levels of urban heat environment risk zone and the spatial-temporal change analysis of topological relationship among the risk zones will help to improve the scientific basis of green infrastructures spatial planning [45,46]. Efficient green infrastructure as nature-based solutions will be of great significance to mitigate the risk of urban heat environment and maintain the sustainable development of the city [47].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

De-chao

Sun

et al. 2019

Sustainability

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For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

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Section: Discussionmentioning

confidence: 99%

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

De-chao

Sun

et al. 2019

Sustainability

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“…Theoretically, NDVI values are represented as a ratio ranging from −1 to +1, where positive values indicate vegetated areas and negative values denote non-vegetated areas or vegetation affected by stress. NDVI values are influenced by several factors (vegetation phenology, climatic conditions, water availability) and thresholds may differ by locations and time of the year [57].…”

Section: Urban Feature Layers 241 Vegetation and Water Bodies Layers Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

et al. 2021

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This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

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“…To address the problem of low temporal and spatial resolution, airborne, and unmanned aerial vehicles (UAV) have been implemented to monitor GI in cities [ 16 , 17 , 18 , 19 ]. However, the use of airborne remote sensing comes with a cost, requiring a pilot and skilled personnel to operate the instrumentation, process the information, and deliver interpreted information to relevant city council personnel for GI management and decision making.…”

Section: Introductionmentioning

confidence: 99%

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

Fuentes

Tongson

Viejo

2021

Sensors

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Climate change forecasts higher temperatures in urban environments worsening the urban heat island effect (UHI). Green infrastructure (GI) in cities could reduce the UHI by regulating and reducing ambient temperatures. Forest cities (i.e., Melbourne, Australia) aimed for large-scale planting of trees to adapt to climate change in the next decade. Therefore, monitoring cities’ green infrastructure requires close assessment of growth and water status at the tree-by-tree resolution for its proper maintenance and needs to be automated and efficient. This project proposed a novel monitoring system using an integrated visible and infrared thermal camera mounted on top of moving vehicles. Automated computer vision algorithms were used to analyze data gathered at an Elm trees avenue in the city of Melbourne, Australia (n = 172 trees) to obtain tree growth in the form of effective leaf area index (LAIe) and tree water stress index (TWSI), among other parameters. Results showed the tree-by-tree variation of trees monitored (5.04 km) between 2016–2017. The growth and water stress parameters obtained were mapped using customized codes and corresponded with weather trends and urban management. The proposed urban tree monitoring system could be a useful tool for city planning and GI monitoring, which can graphically show the diurnal, spatial, and temporal patterns of change of LAIe and TWSI to monitor the effects of climate change on the GI of cities.

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Mapping and classifying green infrastructure typologies for climate-related studies based on remote sensing data

Cited by 60 publications

References 25 publications

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle

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