On the association between land system architecture and land surface temperatures: Evidence from a Desert Metropolis—Phoenix, Arizona, U.S.A

Li, Xiaoxiao; Kamarianakis, Yiannis; Yu, Ouyang; Turner, B. L.; Brazel, Anthony J.

doi:10.1016/j.landurbplan.2017.02.009

Cited by 38 publications

(11 citation statements)

References 67 publications

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“…The SUHI variations have also been widely attributed to landscape composition and configuration metrics at a local scale [81,82,98,101,102,145,146,155,157,163,166,[169][170][171][172]. Some authors even state that the patterns of built-up land and vegetation have the largest impact on urban LST [115].…”

Section: Suhi Variations At a Local Scale And Their Driversmentioning

confidence: 99%

“…It is frequently questioned whether landscape composition or configuration is more important. The answer to this question depends on the city under investigation [173], the land cover/land use, and the types of green space within the study area [99,101,172,174] as well as the landscape metrics considered [171]. In general, composition is often found to be more important than configuration [95,103,157,163,169,170,175].…”

Section: Suhi Variations At a Local Scale And Their Driversmentioning

confidence: 99%

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Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

et al. 2018

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The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

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Section: Suhi Variations At a Local Scale And Their Driversmentioning

confidence: 99%

Section: Suhi Variations At a Local Scale And Their Driversmentioning

confidence: 99%

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

et al. 2018

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“…Though the need for more studies on the estimation of UHI parameters had been emphasized by Weng (2003), the studies on the relationship between LST and urban land use/land cover still prevail. Time series and statistical analysis and modelling (Fu and Weng, 2016;Li et al, 2017;Tran et al, 2017) have been used to better understand the influence of the characteristics of urban surfaces on UHI.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Aina

Adam

Ahmed

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban heat island (UHI) effect is considered to be one of the key indicators of the impacts of urbanization and the climate changes on the environment. Thus, the growing interest in studying the impacts of urbanization on changes in land surface temperature (LST). The literature on LST indicates the need for more studies on the relationship between changes in LST and land use types, especially in the arid environment. This paper examines the spatial and temporal changes in land surface temperature influenced by land use/land cover types in Riyadh, Saudi Arabia. Multi-temporal Landsat images of the study area, 1985, 1995, 2002 and 2015, were processed to derive land surface temperatures. UHI index was computed for the different land use/land cover types (highdensity residential, medium-density residential, low-density residential, industrial, vegetation, and desert) in the study area. The results indicate a trend of rising temperatures in all the land use types in the study area. This is probably due to climate change. The industrial area has the highest temperatures among the land use types. The lowest temperatures are found in the vegetation area as expected. There is a need to implement mitigating measures to reduce the effects of rising temperatures in the study area.

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“…Anomaly detection (Singha, Guntukub, Thakura, & Hota, 2014), filtering (Varatharajan, Manogaran, & Priyan, 2018), outlier removal (Wang et al, 2017), ratios (Xuan, Ligon, Srimani, Ge, & Luo, 2017), metadata (Ghiringhelli et al, 2017) Transformation Format conversion (Kumara, Paik, Zhang, Siriweera, & Koswatte, 2015), imputation, smoothing (Hu, Wen, Chua, & Li, 2014), normalization/transformation, exponential differentiation, classification (Hu et al, 2015;Zhang, He, Zhang, Peng, & Long, 2017), spatial interpolation (Schlapfer, Nieke, & Itten, 2007), coordinate transformation (Xie, Zhou, Vivoni, Hendrickx, & Small, 2005), Evaluation Bias correction (Krakauer, Ghazanfar, Gomez-Marin, MacIver, & Poeppel, 2017), sensitivity analysis (Delen, Tomak, Topuz, & Eryarsoy, 2017), uncertainty evaluation (Matott, Babendreier, & Purucker, 2009) Reduction Compression (Sayood, 2017), redundancy elimination, machine learning (Stateczny & Wlodarczyk-Sielicka, 2014), clustering (Manogaran et al, 2018), classification (Camara et al, 2016), feature selection (Rehman et al, 2016), data fusion (Ahmad, Paul, Rathore, & Chang, 2016), factor analysis (Hayton, Allen, & Scarpello, 2004), revised averaging scheme Augmentation Flipping, rotating, scaling, cropping, translation (Yu, Wu, Luo, & Ren, 2017;Kravchenko, Tishaieva, & Perkhaliuk, 2018) Data transformation methods (e.g. normalization, smoothing, interpolation, coordinate transformation, time series) are necessary to prepare data for further data analytics in terms of spatiotemporal resolution, data format, coordinate projection, and others (Friedman, Hastie, & Tibshirani, 2008;Jenerette et al, 2016;Li, Kamarianakis, Ouyang, Turner, & Brazel, 2017;Sugumaran, Burnett, & Blinkmann, 2012).…”

Section: Extractionmentioning

confidence: 99%

Big Earth data analytics: a survey

Yang

et al. 2019

Big Earth Data

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Big Earth data are produced from satellite observations, Internet-of-Things, model simulations, and other sources. The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding, responding, and addressing challenges of Earth sciences and applications. In the past years, new technologies (such as cloud computing, big data and artificial intelligence) have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable. This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain. We first introduce the concepts of big Earth data. The architecture, various functionalities, and supporting modules are then reviewed from a generic methodology aspect. Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools. The driven questions are exemplified through cutting-edge Earth science researches and applications. A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.

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On the association between land system architecture and land surface temperatures: Evidence from a Desert Metropolis—Phoenix, Arizona, U.S.A

Cited by 38 publications

References 67 publications

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Big Earth data analytics: a survey

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