Remote Sensing of Urban Environments

Aims

to provide background knowledge on urban physics and on the special characteristics of the urban surface and its interactions with atmosphere. to provide background knowledge on the parameters that define the urban landscape and the urban climate and on how these can be monitored by remote sensing. to explain the role of remote sensing in understanding and monitoring the function of cities from physical and socio-economic perspectives. to provide an introduction to the recent developments in urban remote sensing. to discuss the contribution of remote sensing in applications related to urban dynamics, urban planning, urban metabolism, urban climate and urban energy fluxes.

Prerequisites

Remote sensing
  

Learning Outcomes

Enhance understanding of the role of remote sensing in assessment of urban dynamics, as well as in supporting urban studies related to thermal comfort, energy efficiency, public health and environmental security. Providing students with new research directions and stimulation to perform more in depth work and analysis on the applications of remote sensing to urban environments. Be able to contribute to bridging the gap that exists between the research-focus results offered by the urban remote sensing community and the application of these data and methods by the local authorities in cities. Communicate their knowledge and experiences to specialists within a multi-disciplinary research working group, focusing on urban sustainability. Communicate their knowledge to a non-expert audience.

Syllabus

This component examines how different urban land cover types are spatially and spectrally discriminated and which sensor configurations are most useful to map urban areas, demonstrating the potential of new remote sensing technologies to improve capabilities to map urban areas in high spatial and thematic detail; and to estimate urban surface parameters.  It focuses on urban remote sensing characteristics, scale issues related to urban landscapes, satellite data processing methods and algorithms for SAR, optical, hyper-spectral and thermal infrared observations, urban surface models development, urban land cover mapping and change detection, urban surface imperviousness, albedo, emissivity and temperature estimation, as well as on urban particulate matter estimation from satellite observations. Emphasis is placed on the contribution of Earth Observation in urban applications related to human activities, such as urban planning and management, analysis and mapping of human settlements, urban energy balance, thermal comfort and air quality, urban climate, urban metabolism and management of natural hazards in urban and peri-urban areas. Results from recent (FP7 and H2020) urban research projects led by FORTH will be discussed, aiming at understanding the bio-physical properties, patterns and processes of urban landscapes.  

Content Delivery

The component shall be delivered through oral lectures by the tutor (75%) and projects (25%). The lectures will focus on: Principles & Data      - Introduction      - Landsat time series analysis      - VHR optical sensing      - Hyperspectral data analysis      - VHR SAR data analysis      - Lidar data exploitation      - Future trends Methods & Products      - Urban surface morphology      - Urban surface cover      - Urban surface albedo      - Urban surface emissivity and temperature      - Urban PM estimation   Applications      - Global urban observation      - Urban planning      - Urban environmental security      - Urban energy budget      - Urban climate, Local Climate Zones, Nature Based Solutions      - Urban metabolism The projects concern practical research exercises for groups of students, mainly focusing on the following issues (VHR data and products to be provided by the tutor):       - Urban Land Cover estimation from VHR optical imagery using GEOBIA.       - Urban sprawl estimation using time series of HR imagery.       - Urban green infrastructure indicators evaluation based on SMA.       - Estimation of surface roughness (z0, zd, SVF) based on 3D morphological analysis.       - Local scale urban surface temperature estimation.  

Coursework And Assignment Details

Student’s performance will be evaluated through: a. Student’s participation during lectures. b. Student’s performance in projects, based on projects’ reports.   Evaluation conditions: a. = 20% of the end note. b. Projects’ reports = 80% of the end note.