scholarly journals Measuring Urban Land Cover Influence on Air Temperature through Multiple Geo-Data—The Case of Milan, Italy

2018 ◽  
Vol 7 (11) ◽  
pp. 421 ◽  
Author(s):  
Daniele Oxoli ◽  
Giulia Ronchetti ◽  
Marco Minghini ◽  
Monia Molinari ◽  
Maryam Lotfian ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Satellite Imagery ◽  
Air Temperature ◽  
Urban Areas ◽  
Urban Land ◽  
Mitigation Strategies ◽  
Landsat 8 ◽  
Local Climate ◽  
Urban Land Cover

Climate issues are nowadays one of the most pressing societal challenges, with cities being identified among the landmarks for climate change. This study investigates the effect of urban land cover composition on a relevant climate-related variable, i.e., the air temperature. The analysis exploits different big geo-data sources, namely high-resolution satellite imagery and in-situ air temperature observations, using the city of Milan (Northern Italy) as a case study. Satellite imagery from the Landsat 8, Sentinel-2, and RapidEye missions are used to derive Local Climate Zone (LCZ) maps depicting land cover compositions across the study area. Correlation tests are run to investigate and measure the influence of land cover composition on air temperature. Results show an underlying connection between the two variables by detecting an average temperature offset of about 1.5 ∘ C between heavily urbanized and vegetated urban areas. The approach looks promising in investigating urban climate at a local scale and explaining effects through maps and exploratory graphs, which are valuable tools for urban planners to implement climate change mitigation strategies. The availability of worldwide coverage datasets, as well as the exclusive use of Free and Open Source Software (FOSS), provide the analysis with a potential to be empowered, replicated, and improved.

scholarly journals Comparison of the impacts of urban development and climate change in exposing European cities to pluvial flooding

2017 ◽  
Author(s):  
Per Skougaard Kaspersen ◽  
Nanna Høegh Ravn ◽  
Karsten Arnbjerg-Nielsen ◽  
Henrik Madsen ◽  
Martin Drews
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Urban Development ◽  
Extreme Precipitation ◽  
Urban Areas ◽  
Urban Land ◽  
European Cities ◽  
Urban Land Cover ◽  
Pluvial Flooding ◽  
Rcp 8.5

Abstract. The economic and human consequences of extreme precipitation and the related flooding of urban areas have increased rapidly over the past decades. Some of the key factors that affect the risks to urban areas include climate change, the densification of assets within cities and the general expansion of urban areas. In this paper, we examine and compare quantitatively the impact of climate change and recent urban development patterns on the exposure of four European cities to pluvial flooding. In particular, we investigate the degree to which pluvial floods of varying severity and in different geographical locations are influenced to the same extent by changes in urban land cover and climate change. We have selected the European cities of Odense, Vienna, Strasbourg and Nice for analyses to represent, different climatic conditions, trends in urban development and topographical characteristics. We develop and apply a combined remote-sensing and flood-modelling approach to simulate the extent of pluvial flooding for a range of extreme precipitation events for historical (1984) and present-day (2014) urban land cover and for two climate-change scenarios (RCP 4.5 and RCP 8.5). Changes in urban land cover are estimated using Landsat satellite imagery for the period 1984–2014. We combine the remote-sensing analyses with regionally downscaled estimates of precipitation extremes of current and expected future climate to enable 2D overland flow simulations and flood-hazard assessments. The individual and combined impacts of urban development and climate change are quantified by examining the variations in flooding between the different simulations along with the corresponding uncertainties. For all four cities, we find an increase in flood exposure corresponding to an observed absolute growth in impervious surfaces of 7–12 % during the past thirty years of urban development. Similarly, we find that climate change increases exposure to pluvial flooding under both the RCP 4.5 and RCP 8.5 scenarios. The relative importance of urban development and climate change on flood exposure varies considerably between the cities. For Odense, the impact of urban development is comparable to that of climate change under an RCP 8.5 scenario (2081–2100), while for Vienna and Strasbourg it is comparable to the impacts of an RCP 4.5 scenario. For Nice, climate change dominates urban development as the primary driver of changes in exposure to flooding. The variation between geographical locations is caused by differences in soil infiltration properties, historical trends in urban development and the projected regional impacts of climate change on extreme precipitation.

scholarly journals Anticipating urbanization-led land cover change and its impact on local climate using time series model: a study on Dhaka city

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Ripan Debnath
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Arima Model ◽  
Urban Land ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Dhaka City ◽  
Local Climate ◽  
Urban Land Cover

Urbanization-led changes in natural landscape often result in environmental degradation and subsequently contribute to local climate variability. Therefore, apart from global climate change, Dhaka city’s ongoing rapid urban growth may result in altering future local climate patterns significantly. This study explores transition relationships between urbanization (population), land cover, and climate (temperature) of Dhaka city beginning in 1975 through to forecast scenarios up to 2035. Satellite image, geographic, demographic, and climatic data were analyzed. Change in core urban land cover (area) was regarded as a function of population growth and was modeled using linear regression technique. The study developed and validated a time series (ARIMA) model for predicting mean maximum temperature change where (forecasted) land cover scenarios were regressors. Throughout the studied period, the city exhibited an increasing urbanization trend that indicated persistent growth of core urban land cover in future. As a result, the city’s mean maximum temperature was found likely to increase by around 1.5-degree Celsius during 2016–2035 on average from that of observed 1996–2015 period. It is expected that findings of this study may help in recognizing urbanization-led climate change easily, which is crucial to effective climate change management actions and urban planning.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1417-1421
Author(s):  
D. Amarsaikhan
Keyword(s):  
Land Cover ◽  
Urban Land ◽  
Landsat 8 ◽  
Data Sets ◽  
Contextual Knowledge ◽  
Rule Based ◽  
Land Cover Types ◽  
Urban Land Cover ◽  
Land Cover Map

Abstract. The aim of this research is to classify urban land cover types using an advanced classification method. As the input bands to the classification, the features derived from Landsat 8 and Sentinel 1A SAR data sets are used. To extract the reliable urban land cover information from the optical and SAR features, a rule-based classification algorithm that uses spatial thresholds defined from the contextual knowledge is constructed. The result of the constructed method is compared with the results of a standard classification technique and it indicates a higher accuracy. Overall, the study demonstrates that the multisource data sets can considerably improve the classification of urban land cover types and the rule-based method is a powerful tool to produce a reliable land cover map.

scholarly journals Urban Bare Land Classification Using NDBaI Index Based on Combination of Sentinel 2 MSI and Landsat 8 Multiresolution Images

2020 ◽  
Vol 36 (2) ◽  
Author(s):  
Trinh Le Hung
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Urban Land ◽  
Landsat 8 ◽  
Land Classification ◽  
Infrared Band ◽  
Urban Land Cover ◽  
Bare Land ◽  
Sentinel 2

The classification of urban land cover/land use is a difficult task due to the complexity in the structure of the urban surface. This paper presents the method of combining of Sentinel 2 MSI and Landsat 8 multi-resolution satellite image data for urban bare land classification based on NDBaI index. Two images of Sentinel 2 and Landsat 8 acquired closely together, were used to calculate the NDBaI index, in which sortware infrared band (band 11) of Sentinel 2 MSI image and thermal infrared band (band 10) of Landsat 8 image were used to improve the spatial resolution of NDBaI index. The results obtained from two experimental areas showed that, the total accuracy of classifying bare land from the NDBaI index which calculated by the proposed method increased by about 6% compared to the method using the NDBaI index, which is calculated using only Landsat 8 data. The results obtained in this study contribute to improving the efficiency of using free remote sensing data in urban land cover/land use classification.

scholarly journals Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

2018 ◽  
Vol 7 (12) ◽  
pp. 453 ◽  
Author(s):  
Mst Ilme Faridatul ◽  
Bo Wu
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Urban Land Cover ◽  
Svm Algorithm ◽  
Bare Land ◽  
Land Covers

Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94-96%, which is superior to the accuracy of the SVM algorithm.

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