@article{Wicki2017bc,

title = {Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment},

author = {Andreas Wicki and Eberhard Parlow },

editor = {MDPI},

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Wicki_Parlow_RemSens.pdf},

year  = {2017},

date = {2017-07-04},

journal = {Remote Sensing},

volume = {Vol 9},

pages = {684},

abstract = {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. Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment (PDF Download Available). Available from: https://www.researchgate.net/publication/318206742_Multiple_Regression_Analysis_for_Unmixing_of_Surface_Temperature_Data_in_an_Urban_Environment [accessed Jan 18 2018].},

keywords = {atmospheric corrections, land surface temperature, land use/land cover, Landsat 8, LST analysis, multiple linear regression, thermal infrared data, urban},

pubstate = {published},

tppubtype = {article}

}

@article{Wicki2017b,

title = {Attribution of local climate zones using a multitemporal land use/land cover classification scheme},

author = {Andreas Wicki and Eberhard Parlow},

editor = {SPIE },

url = {https://urbanfluxes.eu/wp-content/uploads/2018/01/2017_Wicki_Parlow_JARS.pdf},

doi = {DOI: 10.1117/1.JRS.11.026001},

year  = {2017},

date = {2017-04-03},

journal = {J. Appl. Remote Sens. 11(2), 026001 (2017)},

volume = {Vol. 11},

pages = {026001-1 – 026001-16},

abstract = {Worldwide, the number of people living in an urban environment exceeds the rural population with increasing tendency. Especially in relation to global climate change, cities play a major role considering the impacts of extreme heat waves on the population. For urban planners, it is important to know which types of urban structures are beneficial for a comfortable urban climate and which actions can be taken to improve urban climate conditions. Therefore, it is essential to differ between not only urban and rural environments, but also between different levels of urban densification. To compare these built-up types within different cities worldwide, Stewart and Oke developed the concept of local climate zones (LCZ) defined by morphological characteristics. The original LCZ scheme often has considerable problems when adapted to European cities with historical city centers, including narrow streets and irregular patterns. In this study, a method to bridge the gap between a classical land use/land cover (LULC) classification and the LCZ scheme is presented. Multitemporal Landsat 8 data are used to create a high accuracy LULC map, which is linked to the LCZ by morphological parameters derived from a high-resolution digital surface model and cadastral data. A bijective combination of the different classification schemes could not be achieved completely due to overlapping threshold values and the spatially homogeneous distribution of morphological parameters, but the attribution of LCZ to the LULC classification was successful. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.},

keywords = {land use/land cover, Landsat 8, local climate zones, morphology, urban},

pubstate = {published},

tppubtype = {article}

}