Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

Huang, Chudong; Ye, Xinyue

doi:10.3390/su7079478

Cited by 29 publications

(14 citation statements)

References 34 publications

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“…Consistent with previous findings [3,18,24,31,35], our results showed that higher NDVI and a larger green space were associated with higher PCI. However, our findings also suggested that this association might not follow a linear pattern.…”

Section: Discussionsupporting

confidence: 83%

“…The range of reduced LST in green spaces is between approximately 1~3 K [18,19], occasionally exceeding 5 K [1]. Research has also found that reduced LSTs in green spaces are influenced by the size of those spaces [20], NDVI [21][22][23][24], wind speed [1], albedo [25], the proportion of available tree-shaded areas [26], configuration of the land cover [27,28], and the vegetation types planted in them [29]. These studies have primarily examined LST variations inside and outside green spaces caused by their physical characteristics or meteorological conditions [29].…”

Section: Introductionmentioning

confidence: 99%

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Examining the Association between Physical Characteristics of Green Space and Land Surface Temperature: A Case Study of Ulsan, Korea

Park

Cho

2016

Sustainability

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Abstract:The rapid increase of impervious surfaces and the dense development that accompanies urban growth has reduced the amount of green space in urban landscapes and increased urban surface temperatures. Accordingly, the greening of urban spaces has been proposed as one approach to mitigating urban heat island (UHI) effects. To find the most practical green space design for reducing land surface temperatures (LSTs), we explored the effects of the physical characteristics of green spaces on cooling intensity and distance. The physical characteristics of green spaces were defined as shape, size, Normalized Difference Vegetation Index (NDVI), and the land-use type of their surroundings. LANDSAT 8 images were used to examine 30 green spaces in Ulsan, Korea. The analytical results showed that the cooling effect was mainly observed within 120 m of a green area and that the intensity of the cooling effects did not exceed 3.0 K. A belt-shaped green space had a greater cooling distance compared to a compact green space. We also found that the NDVI and size of a green space had a positive but non-linear association with cooling intensity.

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Examining the Association between Physical Characteristics of Green Space and Land Surface Temperature: A Case Study of Ulsan, Korea

Park

Cho

2016

Sustainability

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“…The coupling relationship between multiple driving forces in the spatial context has been heatedly debated in a variety of environmental studies [44]. This pilot study demonstrated the promising feature of high-performance computing on the solution of large-scale spatial join to explore geographical relationships.…”

Section: Discussionmentioning

confidence: 92%

A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability

et al. 2016

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Sustainability research faces many challenges as respective environmental, urban and regional contexts are experiencing rapid changes at an unprecedented spatial granularity level, which involves growing massive data and the need for spatial relationship detection at a faster pace. Spatial join is a fundamental method for making data more informative with respect to spatial relations. The dramatic growth of data volumes has led to increased focus on high-performance large-scale spatial join. In this paper, we present Spatial Join with Spark (SJS), a proposed high-performance algorithm, that uses a simple, but efficient, uniform spatial grid to partition datasets and joins the partitions with the built-in join transformation of Spark. SJS utilizes the distributed in-memory iterative computation of Spark, then introduces a calculation-evaluating model and in-memory spatial repartition technology, which optimize the initial partition by evaluating the calculation amount of local join algorithms without any disk access. We compare four in-memory spatial join algorithms in SJS for further performance improvement. Based on extensive experiments with real-world data, we conclude that SJS outperforms the Spark and MapReduce implementations of earlier spatial join approaches. This study demonstrates that it is promising to leverage high-performance computing for large-scale spatial join analysis. The availability of large-sized geo-referenced datasets along with the high-performance computing technology can raise great opportunities for sustainability research on whether and how these new trends in data and technology can be utilized to help detect the associated trends and patterns in the human-environment dynamics.

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“…Múltiples autores han empleado este índice como una medida de verdor en zonas urbanas como resultado de la cantidad de vegetación o coberturas permeables presentes en un área (Zhou, Troy, Grove y Jenkins, 2009;Leslie, Sugiyama, Ierodiaconou y Kremer, 2010). Adicionalmente, diferentes estudios han encontrado una relación inversa entre la temperatura de la superficie y el NDVI (Weng et al, 2004;Weng, 2009;Huang y Ye, 2015). Para calcular el NDVI, se utilizó una imagen multiespectral obtenida por el sensor OLI a bordo de la plataforma Landsat 8 (tabla 1).…”

Section: Tipo De Información Descripción Fuenteunclassified

Distribución de la infraestructura verde y su capacidad de regulación térmica en Bogotá, Colombia

Rubiano

2019

Colomb. for.

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Las ciudades presentan características ambientales particulares que dan lugar a fenómenos como las islas de calor. Aunque las coberturas vegetales son fundamentales en la mitigación de estas condiciones adversas, se ha observado que su distribución no es equitativa. Analizar esta distribución y la oferta de servicios ecosistémicos es importante para la planificación de las ciudades. Por tal razón, se analizó la distribución de la capacidad de regulación térmica de las coberturas vegetales públicas de la ciudad de Bogotá, usando la cobertura arbórea y el verdor como variables proxy, mediante análisis geoestadístico y metodologías propias de los sistemas de información geográfica y la teledetección. El verdor de los parques aumentó con su tamaño y estrato socioeconómico, la cobertura arbórea incrementó en los estratos más altos. Se identificaron coldspots y hotspots de estas variables y del potencial de regulación térmica en los sectores con estratos más bajos y más altos respectivamente.

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Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

Cited by 29 publications

References 34 publications

Examining the Association between Physical Characteristics of Green Space and Land Surface Temperature: A Case Study of Ulsan, Korea

Examining the Association between Physical Characteristics of Green Space and Land Surface Temperature: A Case Study of Ulsan, Korea

A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability

Distribución de la infraestructura verde y su capacidad de regulación térmica en Bogotá, Colombia

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