Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns

With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.

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Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Remote Sensing ◽

10.3390/rs13183672 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3672

Author(s):

Johannes H. Uhl ◽

Stefan Leyk ◽

Zekun Li ◽

Weiwei Duan ◽

Basel Shbita ◽

...

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Color Space ◽

Area Under The Curve ◽

Land Development ◽

Historical Maps ◽

Human Settlement ◽

Historical Building ◽

Seamless Integration

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

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Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images

International Journal of Organizational and Collective Intelligence ◽

10.4018/ijoci.2018010104 ◽

2018 ◽

Vol 8 (1) ◽

pp. 46-62 ◽

Cited By ~ 1

Author(s):

Djelloul Mokadem ◽

Abdelmalek Amine ◽

Zakaria Elberrichi ◽

David Helbert

Keyword(s):

Remote Sensing ◽

Genetic Algorithms ◽

Urban Areas ◽

Satellite Image ◽

Initial Population ◽

Landsat Images ◽

Kohonen Maps ◽

Multispectral Satellite Image

In this article, the detection of urban areas on satellite multispectral Landsat images. The goal is to improve the visual interpretations of images from remote sensing experts who often remain subjective. Interpretations depend deeply on the quality of segmentation which itself depends on the quality of samples. A remote sensing expert must actually prepare these samples. To enhance the segmentation process, this article proposes to use genetic algorithms to evolve the initial population of samples picked manually and get the most optimal samples. These samples will be used to train the Kohonen maps for further classification of a multispectral satellite image. Results are obtained by injecting genetic algorithms in sampling phase and this paper proves the effectiveness of the proposed approach.

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Can climate adaptation solutions fix the urban heat island? An assessment of the thermal conditions during heat waves in Vienna impacted by climate change and urban development scenarios for the mid-21st-century

10.5194/egusphere-egu2020-8668 ◽

2020 ◽

Author(s):

Paul Hamer ◽

Heidelinde Trimmel ◽

Philipp Weihs ◽

Stéphanie Faroux ◽

Herbert Formayer ◽

...

Keyword(s):

Climate Change ◽

Urban Heat Island ◽

Heat Wave ◽

Air Temperature ◽

Urban Areas ◽

Climate Adaptation ◽

Heat Waves ◽

Heat Island ◽

Human Thermal Comfort ◽

Urban Heat

Climate change threatens to exacerbate existing problems in urban areas arising from the urban heat island. Furthermore, expansion of urban areas and rising urban populations will increase the numbers of people exposed to hazards in these vulnerable areas. We therefore urgently need study of these environments and in-depth assessment of potential climate adaptation measures.We present a study of heat wave impacts across the urban landscape of Vienna for different future development pathways and for both present and future climatic conditions. We have created two different urban development scenarios that estimate potential urban sprawl and optimized development concerning future building construction in Vienna and have built a digital representation of each within the Town Energy Balance (TEB) urban surface model. In addition, we select two heat waves of similar frequency of return representative for present and future conditions (following the RCP8.5 scenario) of the mid 21st century and use the Weather Research and Forecasting Model (WRF) to simulate both heat wave events. We then couple the two representations urban Vienna in TEB with the WRF heat wave simulations to estimate air temperature, surface temperatures and human thermal comfort during the heat waves. We then identify and apply a set of adaptation measures within TEB to try to identify potential solutions to the problems associated with the urban heat island.Global and regional climate change under the RCP8.5 scenario causes the future heat wave to be more severe showing an increase of daily maximum air temperature in Vienna by 7 K; the daily minimum air temperature will increase by 2-4 K. We find that changes caused by urban growth or densification mainly affect air temperature and human thermal comfort local to where new urbanisation takes place and does not occur significantly in the existing central districts.Exploring adaptation solutions, we find that a combination of near zero-energy standards and increasing albedo of building materials on the city scale accomplishes a maximum reduction of urban canyon temperature of 0.9 K for the minima and 0.2 K for the maxima. Local scale changes of different adaption measures show that insulation of buildings alone increases the maximum wall surface temperatures by more than 10 K or the maximum mean radiant temperature (MRT) in the canyon by 5 K.&#160; Therefore, additional adaptation to reduce MRT within the urban canyons like tree shade are needed to complement the proposed measures.This study concludes that the rising air temperatures expected by climate change puts an unprecedented heat burden on Viennese inhabitants, which cannot easily be reduced by measures concerning buildings within the city itself. Additionally, measures such as planting trees to provide shade, regional water sensitive planning and global reduction of greenhouse gas emissions in order to reduce temperature extremes are required.We are now actively seeking to apply this set of tools to a wider set of cases in order to try to find effective solutions to projected warming resulting from climate change in urban areas.

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Validation of Pleiades Tri-Stereo DSM in Urban Areas

10.20944/preprints201801.0030.v1 ◽

2018 ◽

Author(s):

Manolis Panagiotakis ◽

Nektarios Chrysoulakis ◽

Vasiliki Charalampopoulou ◽

Dimitris Poursanidis

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

Relevant Literature ◽

Simulation Models ◽

Cost Effective ◽

Stereo Images ◽

Cost Effective Approach ◽

Spatial Coverage ◽

Temporal Coverage

A very high-resolution DSM covering an area of 400km2 over the Athens Metropolitan Area has been produced using Pleiades 1B 0,5m panchromatic tri-stereo images. Applied Remote Sensing and Photogrammetry tools have been used resulted in a 1x1m DSM over the study area. DSM accuracy has been evaluated by comparison with measured elevations with D-GPS and a reference DSM provided by the National Cadaster & Mapping Agency S.A. In addition, different combinations of stereo images have been prepared for further exploitation of the quality of the produced DSM by stereo vs. tri-stereo images. Results show that the produced by the tri-stereo images DSM has an RMSE of 1.17m in elevation (z), which is among the best reported in the relevant literature. Stereo based DSMs from the same sensor have worst performance to this end. Satellite Remote Sensing (SRS) based DSMs over urban areas provide the best cost-effective approach in comparison to airborne-based datasets due to high spatial coverage, lower cost and high temporal coverage. Pleiades-based high-quality DSM products can serve the domains of urban planning/climate, hydrological modelling and natural hazards, as major input for simulation models and morphological analysis at local scale.

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Data and techniques for studying the urban heat island effect in Johannesburg

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-203-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 203-206 ◽

Cited By ~ 3

Author(s):

C. H. Hardy ◽

A. L. Nel

Keyword(s):

Remote Sensing ◽

Urban Heat Island ◽

Urban Areas ◽

Remote Sensing Data ◽

Urban Heat Island Effect ◽

Heat Island ◽

Sensing Data ◽

Island Effect ◽

Urban Heat ◽

The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

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Global Harmonization of Urbanization Measures: Proceed with Care

Remote Sensing ◽

10.3390/rs13244973 ◽

2021 ◽

Vol 13 (24) ◽

pp. 4973

Author(s):

Deborah Balk ◽

Stefan Leyk ◽

Mark R. Montgomery ◽

Hasim Engin

Keyword(s):

Remote Sensing ◽

Urban Areas ◽

The United States ◽

Population Census ◽

Urban Rural ◽

Global Harmonization ◽

United States Mexico ◽

Remote Sensing Methods ◽

Urban Places ◽

Derived Data

By 2050, two-thirds of the world’s population is expected to be living in cities and towns, a marked increase from today’s level of 55 percent. If the general trend is unmistakable, efforts to measure it precisely have been beset with difficulties: the criteria defining urban areas, cities and towns differ from one country to the next and can also change over time for any given country. The past decade has seen great progress toward the long-awaited goal of scientifically comparable urbanization measures, thanks to the combined efforts of multiple disciplines. These efforts have been organized around what is termed the “statistical urbanization” concept, whereby urban areas are defined by population density, contiguity and total population size. Data derived from remote-sensing methods can now supply a variety of spatial proxies for urban areas defined in this way. However, it remains to be understood how such proxies complement, or depart from, meaningful country-specific alternatives. In this paper, we investigate finely resolved population census and satellite-derived data for the United States, Mexico and India, three countries with widely varying conceptions of urban places and long histories of debate and refinement of their national criteria. At the extremes of the urban–rural continuum, we find evidence of generally good agreement between the national and remote sensing-derived measures (albeit with variation by country), but identify significant disagreements in the middle ranges where today’s urban policies are often focused.

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A REVIEW ON SPATIAL QUALITY ASSESSMENT METHODS FOR EVALUATION OF PAN-SHARPENED SATELLITE IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-255-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 255-261

Author(s):

F. Dadras Javan ◽

F. Samadzadegan ◽

S. Mehravar ◽

A. Toosi

Keyword(s):

Remote Sensing ◽

Quality Assessment ◽

Satellite Imagery ◽

Urban Areas ◽

Large Scale ◽

Spatial Distortion ◽

Sensing Applications ◽

Fused Image ◽

Spatial Quality

Abstract. Nowadays, high-resolution fused satellite imagery is widely used in multiple remote sensing applications. Although the spectral quality of pan-sharpened images plays an important role in many applications, spatial quality becomes more important in numerous cases. The high spatial quality of the fused image is essential for extraction, identification and reconstruction of significant image objects, and will result in producing high-quality large scale maps especially in the urban areas. This paper introduces the most sensitive and effective methods in detecting the spatial distortion of fused images by implementing a number of spatial quality assessment indices that are utilized in the field of remote sensing and image processing. In this regard, in order to recognize the ability of quality assessment indices for detecting the spatial distortion quantity of fused images, input images of the fusion process are affected by some intentional spatial distortions based on non-registration error. The capabilities of the investigated metrics are evaluated on four different fused images derived from Ikonos and WorldView-2 initial images. Achieved results obviously explicate that two methods namely Edge Variance Distortion and the spatial component of QNR metric called Ds are more sensitive and responsive to the imported errors.

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The Potential of Utilizing Air Temperature Datasets from Non-Professional Meteorological Stations in Brno and Surrounding Area

Sensors ◽

10.3390/s19194172 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4172 ◽

Cited By ~ 1

Author(s):

Karel Dejmal ◽

Petr Kolar ◽

Josef Novotny ◽

Alena Roubalova

Keyword(s):

Air Temperature ◽

Missing Values ◽

Meteorological Parameter ◽

Surrounding Area ◽

Quality Of Data ◽

Current State ◽

Recorded Data ◽

Urban Heat ◽

The Impact

An increasing number of individuals and institutions own or operate meteorological stations, but the resulting data are not yet commonly used in the Czech Republic. One of the main difficulties is the heterogeneity of measuring systems that puts in question the quality of outcoming data. Only after a thorough quality control of recorded data is it possible to proceed with for example a specific survey of variability of a chosen meteorological parameter in an urban or suburban region. The most commonly researched element in the given environment is air temperature. In the first phase, this paper focuses on the quality of data provided by amateur and institutional stations. The following analyses consequently work with already amended time series. Due to the nature of analyzed data and their potential use in the future it is opportune to assess the appropriateness of chronological and possibly spatial interpolation of missing values. The evaluation of seasonal variability of air temperature in the scale of Brno city and surrounding area in 2015–2017 demonstrates, that the enrichment of network of standard (professional) stations with new stations may significantly refine or even revise the current state of knowledge, for example in the case of urban heat island phenomena. A cluster analysis was applied in order to assess the impact of localization circumstances (station environment, exposition, etc.) as well as typological classification of the set of meteorological stations.

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Cool Pavement Strategies for Urban Heat Island Mitigation in Suburban Phoenix, Arizona

Sustainability ◽

10.3390/su11164452 ◽

2019 ◽

Vol 11 (16) ◽

pp. 4452 ◽

Cited By ~ 7

Author(s):

Sushobhan Sen ◽

Jeffery Roesler ◽

Benjamin Ruddell ◽

Ariane Middel

Keyword(s):

Urban Heat Island ◽

Air Temperature ◽

Urban Areas ◽

Meteorological Data ◽

Heat Island ◽

Land Use Patterns ◽

Reflective Coating ◽

Air Temperatures ◽

Artificial Surfaces ◽

Urban Heat

Urban areas are characterized by a large proportion of artificial surfaces, such as concrete and asphalt, which absorb and store more heat than natural vegetation, leading to the Urban Heat Island (UHI) effect. Cool pavements, walls, and roofs have been suggested as a solution to mitigate UHI, but their effectiveness depends on local land-use patterns and surrounding urban forms. Meteorological data was collected using a mobile platform in the Power Ranch community of Gilbert, Arizona in the Phoenix Metropolitan Area, a region that experiences harsh summer temperatures. The warmest hour recorded during data collection was 13 August 2015 at 5:00 p.m., with a far-field air temperature of about 42 ∘ C and a low wind speed of 0.45 m/s from East-Southeast (ESE). An uncoupled pavement-urban canyon Computational Fluid Dynamics (CFD) model was developed and validated to study the microclimate of the area. Five scenarios were studied to investigate the effects of different pavements on UHI, replacing all pavements with surfaces of progressively higher albedo: New asphalt concrete, typical concrete, reflective concrete, making only roofs and walls reflective, and finally replacing all artificial surfaces with a reflective coating. While new asphalt surfaces increased the surrounding 2 m air temperatures by up to 0.5 ∘ C, replacing aged asphalt with typical concrete with higher albedo did not significantly decrease it. Reflective concrete pavements decreased air temperature by 0.2–0.4 ∘ C and reflective roofs and walls by 0.4–0.7 ∘ C, while replacing all roofs, walls, and pavements with a reflective coating led to a more significant decrease, of up to 0.8–1.0 ∘ C. Residences downstream of major collector roads experienced a decreased air temperature at the higher end of these ranges. However, large areas of natural surfaces for this community had a significant effect on downstream air temperatures, which limits the UHI mitigation potential of these strategies.

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Automated detection of urban heat islands based on satellite imagery, digital surface models, and a low-cost sensor network

10.5194/egusphere-egu21-14143 ◽

2021 ◽

Author(s):

Sebastian Schlögl ◽

Nico Bader ◽

Julien Gérard Anet ◽

Martin Frey ◽

Curdin Spirig ◽

...

Keyword(s):

Climate Change ◽

Air Temperature ◽

Rural Areas ◽

Urban Areas ◽

Low Cost ◽

Urban Heat Islands ◽

Heat Islands ◽

Micro Scale ◽

Air Temperatures ◽

Urban Heat

Today, more than half of the world&#8217;s population lives in urban areas and the proportion is projected to increase further in the near future. The increased number of heatwaves worldwide caused by the anthropogenic climate change may lead to heat stress and significant economic and ecological damages. Therefore, the growth of urban areas in combination with climate change can increase future mortality rates in cities, given that cities are more vulnerable to heatwaves due to the greater heat storage capacity of artificial surfaces towards higher longwave radiation fluxes.To detect urban heat islands and resolve the micro-scale air temperature field in an urban environment, a low-cost air temperature network, including 450 sensors, was installed in the Swiss cities of Zurich and Basel in 2019 and 2020. These air temperature data, complemented with further official measurement stations, force a statistical air temperature downscaling model for urban environments, which is used operationally to calculate hourly micro-scale air temperatures in 10 m horizontal resolution. In addition to air temperature measurements from the low-cost sensor network, the model is further forced by albedo, NDVI, and NDBI values generated from the polar-orbiting satellite Sentinel-2, land surface temperatures estimated from Landsat-8, and high-resolution digital surface and elevation models.Urban heat islands (UHI) are processed averaging hourly air temperatures over an entire year for each grid point, and comparing this average to the overall average in rural areas. UHI effects can then be correlated to high-resolution local climate zone maps and other local factors.Between 60-80 % of the urban area is modeled with an accuracy below 1 K for an hourly time step indicating that the approach may work well in different cities. However, the outcome may depend on the complexity of the cities. The model error decreases rapidly by increasing the number of spatially distributed sensor data used to train the model, from 0 to 70 sensors, and then plateaus with further increases. An accuracy below 1 K can be expected for more than 50 air temperature measurements within the investigated cities and the surrounding rural areas.&#160;A strong statistical air temperature model coupled with atmospheric boundary layer models (e.g. PALM-4U, MUKLIMO, FITNAH) will aid to generate highly resolved urban heat island prediction maps that help decision-makers to identify local heat islands easier. This will ensure that financial resources will be invested as efficiently as possible in mitigation actions.

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