scholarly journals The use of combined Landsat and Radarsat data for urban ecosystem accounting in Canada

2020 ◽  
Vol 36 (3) ◽  
pp. 823-839
Author(s):  
Marcelle Grenier ◽  
Nicholas Lantz ◽  
François Soulard ◽  
Jennie Wang
Keyword(s):  
Random Forest ◽  
Urban Areas ◽  
Practical Method ◽  
Urban Ecosystem ◽  
Mitigation Measures ◽  
Support Vector ◽  
Landsat Images ◽  
Local Climate ◽  
Local Climate Zones ◽  
Ecosystem Accounting

This paper describes an approach for combining Landsat and Radarsat satellite images to generate national statistics for urban ecosystem accounting. These accounts will inform policy related to the development of mitigation measures for climatic and hydrologic events in Canada. Milton, Ontario was used as a test case for the development of an approach identifying urban ecosystem types and assessing change from 2001 to 2019. Methods included decomposition of Radarsat images into polarimetric parameters to test their usefulness in characterizing urban areas. Geographic object-based image analysis (GEOBIA) was used to identify urban ecosystem types following an existing classification of local climate zones. Three supervised classifiers: decision tree, random forest and support vector machine, were compared for their accuracy in mapping urban ecosystems. Ancillary geospatial datasets on roads, buildings, and Landsat-based vegetation were used to better characterize individual ecosystem assets. Change detection focused on the occurrence of changes that can impact ecosystem service supply – i.e., conversions from less to more built-up urban types. Results demonstrate that combining Radarsat polarimetric parameters with the Landsat images improved urban characterization using the GEOBIA random forest classifier. This approach for mapping urban ecosystem types provides a practical method for measuring and monitoring changes in urban areas.

scholarly journals GIS-based delineation of local climate zones: The case of medium-sized Central European cities

2016 ◽  
Vol 24 (3) ◽  
pp. 2-12 ◽  
Author(s):  
Jan Geletič ◽  
Michal Lehnert
Keyword(s):  
Decision Making ◽  
Urban Areas ◽  
Expert Knowledge ◽  
Mapping Method ◽  
Rural Settlements ◽  
Climate Zones ◽  
Local Climate ◽  
Central European ◽  
European Cities ◽  
Local Climate Zones

Abstract Stewart and Oke (2012) recently proposed the concept of Local Climate Zones (LCZ) to describe the siting of urban meteorological stations and to improve the presentation of results amongst researchers. There is now a concerted effort, however, within the field of urban climate studies to map the LCZs across entire cities, providing a means to compare the internal structure of urban areas in a standardised way and to enable the comparison of cities. We designed a new GIS-based LCZ mapping method for Central European cities and compiled LCZ maps for three selected medium-sized Central European cities: Brno, Hradec Králové, and Olomouc (Czech Republic). The method is based on measurable physical properties and a clearly defined decision-making algorithm. Our analysis shows that the decision-making algorithm for defining the percentage coverage for individual LCZs showed good agreement (in 79–89% of cases) with areas defined on the basis of expert knowledge. When the distribution of LCZs on the basis of our method and the method of Bechtel and Daneke (2012) was compared, the results were broadly similar; however, considerable differences occurred for LCZs 3, 5, 10, D, and E. It seems that Central European cities show a typical spatial pattern of LCZ distribution but that rural settlements in the region also regularly form areas of built-type LCZ classes. The delineation and description of the spatial distribution of LCZs is an important step towards the study of urban climates in a regional setting.

scholarly journals PREDICTION OF POLLUTANT CONCENTRATIONS BY METEOROLOGICAL DATA USING MACHINE LEARNING ALGORITHMS

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 21-27
Author(s):  
K. Alpan ◽  
B. Sekeroglu
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Smart City ◽  
Urban Areas ◽  
Meteorological Data ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Pollutant Concentrations

Abstract. Air pollution, which is one of the biggest problems created by the developing world, reaches severe levels, especially in urban areas. Weather stations established at certain points in countries regularly obtain data and inform people about air quality. In Smart City applications, it is aimed to perform this process with higher speed and accuracy by collecting data with thousands of sensors based on the Internet of Things. At this stage, artificial intelligence and machine learning plays a vital role in analyzing the data to be obtained. In this study, six pollutant concentrations; particulate matters (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), Ozone (O3), and carbon monoxide (CO), were predicted using three basic machine learning algorithms, namely, random forest, decision tree and support vector regression, by considering only meteorological data. Experiments on two different datasets showed that the random forest has a high prediction capacity (R2: 0.74–0.86), and high-accuracy predictions can be performed on pollutant concentrations using only meteorological data. This and further studies based on meteorological data would help to reduce the number of devices in Smart City applications and will make it more cost-effective.

scholarly journals ON THE CLASSIFIER PERFORMANCE FOR SIMULATION BASED DEBRIS DETECTION IN SAR IMAGERY

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 45-50
Author(s):  
S. Kuny ◽  
H. Hammer ◽  
K. Schulz
Keyword(s):  
Random Forest ◽  
Damage Detection ◽  
Urban Areas ◽  
Detection Algorithm ◽  
Performance Comparison ◽  
Random Forest Classifier ◽  
Training Data ◽  
Support Vector ◽  
Effective Performance ◽  
Sar Imagery

Abstract. Urban areas struck by disasters such as earthquakes are in need of a fast damage detection assessment. A post-event SAR image often is the first available image, most likely with no matching pre-event image to perform change detection. In previous work we have introduced a debris detection algorithm for this scenario that is trained exclusively with synthetically generated training data. A classification step is employed to separate debris from similar textures such as vegetation. In order to verify the use of a random forest classifier for this context, we conduct a performance comparison with two alternative popular classifiers, a support vector machine and a convolutional neural network. With the direct comparison revealing the random forest classifier to be best suited, the effective performance on the prospect of debris detection is investigated for the post-earthquake Christchurch scene. Results show a good separation of debris from vegetation and gravel, thus reducing the false alarm rate in the damage detection operation considerably.

scholarly journals Impact of urban environment on land surface temperature in local climate zones

2021 ◽  
Vol 333 ◽  
pp. 02008
Author(s):  
Anna Gosteva ◽  
Sofia Ilina ◽  
Aleksandra Matuzko
Keyword(s):  
Surface Temperature ◽  
Urban Environment ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Landsat 8 ◽  
Local Climate ◽  
Spatial Coverage ◽  
Artificial Environment ◽  
Local Climate Zones

The replacement of the natural landscape by artificial environment has led to changes in the ecosystem and physical properties of the surface, such as heat storage capacity, and thermal conductivity properties. These changes increase the difficulty of heat transfer between urban areas and the environment. Land surface temperature (LST) images from various satellites are widely used to represent urban thermal environments, which are more convenient and intuitive way. LST maps provide full spatial coverage, which distinguishes them from air temperature data obtained from meteorological stations. The study of LST according to the Landsat 8 data of Krasnoyarsk city over the past 10 years allowed the authors to talk about the observation of constant seasonal urban heat islands (UHI). For a more detailed consideration of the urban environment, this study further considers urban landscapes, thus the idea of local climate zone (LCZ) is introduced to study these diverse impacts in addition to the traditional map of LST. And analysis of the interaction of UHI and LCZ.

scholarly journals Urban Landscape Change Analysis Using Local Climate Zones and Object-Based Classification in the Salt Lake Metro Region, Utah, USA

2019 ◽  
Vol 11 (13) ◽  
pp. 1615 ◽  
Author(s):  
Jed Collins ◽  
Iryna Dronova
Keyword(s):  
Urban Areas ◽  
Salt Lake ◽  
Remote Sensing Data ◽  
Climate Trends ◽  
Local Cooling ◽  
Change Analysis ◽  
Study Region ◽  
Local Climate ◽  
Object Based ◽  
Local Climate Zones

Urban areas globally are vulnerable to warming climate trends exacerbated by their growing populations and heat island effects. The Local Climate Zone (LCZ) typology has become a popular framework for characterizing urban microclimates in different regions using various classification methods, including a widely adopted pixel-based protocol by the World Urban Database and Access Portal Tools (WUDAPT) Project. However, few studies to date have explored the potential of object-based image analysis (OBIA) to facilitate classification of LCZs given their inherent complexity, and few studies have further used the LCZ framework to analyze land cover changes in urban areas over time. This study classified LCZs in the Salt Lake Metro Region, Utah, USA for 1993 and 2017 using a supervised object-based analysis of Landsat satellite imagery and assessed their change during this time frame. The overall accuracy, measured for the most recent classification period (2017), was equal to 64% across 12 LCZs, with most of the error resulting from similarities among highly developed LCZs and non-developed classes with sparse or low-stature vegetation. The observed 1993–2017 changes in LCZs indicated a regional tendency towards primarily suburban, open low-rise development, and large low-rise and paved classes. However, despite the potential for local cooling with landscape transitions likely to increase vegetation cover and irrigation compared to pre-development conditions, summer averages of Landsat-derived top-of-atmosphere brightness temperatures showed a pronounced warming between 1992–1994 and 2016–2018 across the study region, with a 0.1–2.9 °C increase among individual LCZs. Our results indicate that future applications of LCZs towards urban change analyses should develop a stronger understanding of LCZ microclimate sensitivity to changes in size and configuration of urban neighborhoods and regions. Furthermore, while OBIA is promising for capturing the heterogeneous and multi-scale nature of LCZs, its applications could be strengthened by adopting more generalizable approaches for LCZ-relevant segmentation and validation, and by incorporating active remote sensing data to account for the 3D complexity of urban areas.

scholarly journals Impact of Urban Canopy Parameters on a Megacity’s Modelled Thermal Environment

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1349
Author(s):  
Mikhail Varentsov ◽  
Timofey Samsonov ◽  
Matthias Demuzere
Keyword(s):  
Urban Areas ◽  
Climate Model ◽  
Thermal Environment ◽  
Weather Prediction ◽  
Urban Canopy ◽  
Anthropogenic Heat ◽  
Climate Modelling ◽  
Local Climate ◽  
Anthropogenic Heat Flux ◽  
Local Climate Zones

Urban canopy parameters (UCPs) are essential in order to accurately model the complex interplay between urban areas and their environment. This study compares three different approaches to define the UCPs for Moscow (Russia), using the COSMO numerical weather prediction and climate model coupled to TERRA_URB urban parameterization. In addition to the default urban description based on the global datasets and hard-coded constants (1), we present a protocol to define the required UCPs based on Local Climate Zones (LCZs) (2) and further compare it with a reference UCP dataset, assembled from OpenStreetMap data, recent global land cover data and other satellite imagery (3). The test simulations are conducted for contrasting summer and winter conditions and are evaluated against a dense network of in-situ observations. For the summer period, advanced approaches (2) and (3) show almost similar performance and provide noticeable improvements with respect to default urban description (1). Additional improvements are obtained when using spatially varying urban thermal parameters instead of the hard-coded constants. The LCZ-based approach worsens model performance for winter however, due to the underestimation of the anthropogenic heat flux (AHF). These results confirm the potential of LCZs in providing internationally consistent urban data for weather and climate modelling applications, as well as supplementing more comprehensive approaches. Yet our results also underline the continued need to improve the description of built-up and impervious areas and the AHF in urban parameterizations.

Geospatial Analysis of Agriculture Land Loss in Qena City, Upper Egypt

2021 ◽  
Vol 12 (4) ◽  
pp. 40-57
Author(s):  
Mostafa Kamal Kamel Mosleh ◽  
Khaled Mohmmad Amin Hazaymeh
Keyword(s):  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Support Vector ◽  
Land Loss ◽  
Landsat Images ◽  
Index Set ◽  
Upper Egypt ◽  
Agriculture Land ◽  
Local Decision

Although urbanization presents opportunities for new urban developments, it may have serious problems on environment and land use/cover patterns. The present study aims to evaluate the performance of built‑up delineation index set (BDIS) for mapping agricultural land loss in Upper Egypt. Three Landsat images were obtained for the years 1986, 2000, and 2016 and utilized as inputs to calculate the BDIS variables. Then a supervised classification technique (i.e., support vector machine) was used to classify the images. The findings showed that urban areas have witnessed a dramatic expansion at a growing rate of 44.1% during the 30 years. As a result, the loss of the agricultural land was found to be approximately 64.83 ha, which represents -4%, during the same period because of the urban expansion and the illegal construction of settlements. These findings would support the local decision makers in urban and agriculture land management authorities to develop sustainable development plans that control the spatiotemporal urban expansion and agricultural land loss.

Mapping the local climate zones of urban areas by GIS-based and WUDAPT methods: A case study of Hong Kong

Urban Climate ◽  
2018 ◽  
Vol 24 ◽  
pp. 567-576 ◽  
Author(s):  
Ran Wang ◽  
Chao Ren ◽  
Yong Xu ◽  
Kevin Ka-Lun Lau ◽  
Yuan Shi
Keyword(s):  
Hong Kong ◽  
Urban Areas ◽  
Climate Zones ◽  
Local Climate ◽  
Local Climate Zones
Sign in / Sign up

Export Citation Format

Share Document