A Conceptual List of Indicators for Urban Planning and Management Based on Earth Observation

Chrysoulakis, Nektarios; Feigenwinter, Christian; Triantakonstantis, Dimitris; Penyevskiy, Igor; Tal, Abraham; Parlow, Eberhard; Fleishman, Guy; Düzgün, Şebnem; Esch, Thomas; Marconcini, Mattia

doi:10.3390/ijgi3030980

Cited by 41 publications

(33 citation statements)

References 62 publications

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“…Regarding the growing urban population and therefore increasing city size, combined with the increasing number of heat waves expected in the near future, the study of urban climate with a focus on human thermal comfort is inevitable for future urban planning. Nevertheless, the understanding and integration of this issue in urban planning is still expandable [24,26].…”

Section: Motivationmentioning

confidence: 99%

“…The investigation area contains the metropolitan area of Basel with approximately 730,000 inhabitants in total, including the bordering suburbs located in the Canton of Basel-Land (BL), the German cities Weil-am-Rhein and Lörrach, the French city Saint-Louis, and the city of Basel [26,68]. From north to south and west to east the area measures 21 by 20 km 2 , respectively, spanning from the upper left (47°38′35′′N, 7°29′17′′E) to the lower right (47°27′25′′N, 7°45′32′′E).…”

Section: Site Descriptionmentioning

confidence: 99%

“…These values are projected to the underlying topography using a piecewise cubic interpolation ( Figure 3). Meteorological ground truth data from the measurement station Basel Klingelbergstrasse [63] representing the surface conditions (i.e., relative humidity, air temperature, and pressure) are The climate is considered oceanic, with mild winters and warm and sunny summers (Köppen Cfb climate) [26,66]. The annual mean temperature is 10.5 • C with an annual precipitation rate of 842 mm according to the suburban weather Station Basel/Binningen maintained by the Federal Office of Meteorology and Climatology for the reference period 1981 to 2010 [67].…”

Section: Land Surface Temperaturementioning

confidence: 99%

“…with a distinct topography (Figure 2). The climate is considered oceanic, with mild winters and warm and sunny summers (Köppen Cfb climate) [26,66]. The annual mean temperature is 10.5 °C with an annual precipitation rate of 842 mm according to the suburban weather Station Basel/Binningen maintained by the Federal Office of Meteorology and Climatology for the reference period 1981 to 2010 [67].…”

Section: Site Descriptionmentioning

confidence: 99%

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Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

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Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data.

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Section: Motivationmentioning

confidence: 99%

Section: Site Descriptionmentioning

confidence: 99%

Section: Land Surface Temperaturementioning

confidence: 99%

Section: Site Descriptionmentioning

confidence: 99%

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Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Wicki

Parlow

2017

Remote Sensing

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“…Their uses are numerous and widely diverse. They cover areas of urban and regional planning [1]- [6], environmental monitoring and assessment [7]- [10], change detection [11]- [17], simulation and prediction of future spatial and man-made phenomena [18]- [21], and others. They are time sensitive as well as content important.…”

Section: Introductionmentioning

confidence: 99%

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

Ayad¹

2016

GEP

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The aim of this project is to create high resolution land cover classification as well as tree canopy density maps at a regional level using high resolution spatial data. Modeling and the data manipulation and analysis of LiDAR LAS point cloud dataset as well as multispectral aerial photographs from the National Agriculture Imagery Program (NAIP) were carried out. Using geoprocessing modeling, a land cover map is created based on filtered returns from LiDAR point cloud data (LAS dataset) to extract features based on their class and return values, and traditional classification methods of high resolution multi-spectral aerial photographs of the remaining ground cover for Clarion County in Pennsylvania. The newly developed model produced 7 classes at 10 ft × 10 ft spatial resolution, namely: water bodies, structures, streets and paved surfaces, bare ground, grassland, trees, and artificial surfaces (e.g. turf). The model was tested against areas with different sizes (townships and municipalities) which revealed a classification accuracy between 94% and 96%. A visual observation of the results shows that some tree-covered areas were misclassified as built up/structures due to the nature of the available LiDAR data, an area of improvement for further studies. Furthermore, a geoprocessing service was created in order to disseminate the results of the land cover classification as well as the tree canopy density calculation to a broader audience. The service was tested and delivered in the form of a web application where users can select an area of interest and the model produces the land cover and/or the tree canopy density results (http://maps.clarion.edu/LandCoverExtractor). The produced output can be printed as a final map layout with the highlighted area of interest and its corresponding legend. The interface also allows the download of the results of an area of interest for further investigation and/or analysis.

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Air Pollutants-Induced Environmental Critical Zones in Capital City of India

Nahid

Mahato²,

Anees³

et al. 2023

GIScience and Geo-Environmental Modelling

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A Conceptual List of Indicators for Urban Planning and Management Based on Earth Observation

Cited by 41 publications

References 62 publications

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment

Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery

Air Pollutants-Induced Environmental Critical Zones in Capital City of India

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