Evaluating the Potential of Landsat Satellite Data to Monitor the Effectiveness of Measures to Mitigate Urban Heat Islands: A Case Study for Stuttgart (Germany)

Seeberg, Gereon; Hostlowsky, Antonia; Huber, Julia; Kamm, Julia; Lincke, Lucia; Schwingshackl, Clemens

doi:10.3390/urbansci6040082

Cited by 6 publications

(1 citation statement)

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“…The majority of the aforementioned studies propose solutions to tackle the challenge of UHI by utilizing remote sensing, which includes the deployment of satellite, aerial, and ground-based sensors as valuable methods for quantifying UHIs, monitoring their spatiotemporal dynamics, and implementing targeted mitigation strategies. This includes: Satellite-based thermal infrared imaging, such as data from MODIS (Moderate Resolution Imaging Spectroradiometer) [105], [113], or Landsat [114], [115], [116] for monitoring over large urban areas. The various scales allow the global reach of the UHI phenomenon and also enable the identification of hotspots [117], [118].…”

Section: Monitoring Climate Variabilitymentioning

confidence: 99%

A review on how Big Data can help to monitor the environment and to mitigate risks due to climate change

Montillet,

Kermarrec,

Forootan

et al. 2024

Preprint

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Climate change triggers a wide range of hydrometeorological, glaciological and geophysical processes that span across vast spatiotemporal scales. With the advances in technology and analytics, a multitude of remote sensing, geodetic and in situ instruments have been developed to effectively monitor and help comprehend the Earth's system including its climate variability and the recent anomalies associated with global warming. A huge volume of data is generated by recording these observations, resulting in the need for novel methods to handle and interpret such Big Datasets. Managing this enormous amount of data extends beyond current computer storage considerations; it also encompasses the complexities of processing, modeling, and analysing. Big Datasets present unique characteristics that set them apart from smaller datasets, thereby posing challenges to traditional approaches. Moreover, computational time plays a crucial role, especially in the context of geohazard warning and response systems which necessitate low latency requirements. In this review, we delve into the monitoring and analysis of various climate change-related phenomena, including, but not limited to, droughts, floods, cyclones-induced storm surges, urban heat islands, ice mass balance, sea-level rise, and the modelling of the influence of solar variability on the Earth’s climate. By examining these phenomena, we explore some of the current and future trends in Big Data, aiming to encourage and speed-up the development of such techniques and promoting their benefits to timely monitor and towards achieving climate sustainability, thereby addressing its threat to humanity.

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