What are Urban Heat Islands?

• a difference in temperature of urban and suburban areas compared to their outlying rural surroundings
  • E.g. the annual mean air temperature of a city with one million or more people can be 1 to 3°C warmer than its surroundings,
  • Usually the difference decreases as city size decreases,
  • We can differ surface and atmospheric urban heat islands.

Atmospheric UHI

• Canopy layer urban heat islands exist in the layer of air where people live, from the ground to below the tops of trees and roofs.
• Boundary layer urban heat islands start from the rooftop and treetop level and extend up to the point where urban landscapes no longer influence the atmosphere. This region typically extends no more than 1.5 km from the surface

Why UHI are important?

Most impacts (if not all) of the UHI are negative:

• Increased energy consumption
• Elevated emissions of air pollutants and greenhouse gases
• Compromised human health and comfort
• Impaired water quality

Details on the UHI and the mitigating strategies presented here are defined and shown in:

Akbari, H., Bell, R., Brazel, T., Cole, D., Estes, M., Heisler, G., ... & Oke, T. (2008). Reducing Urban Heat Islands: Compendium of Strategies Urban Heat Island Basics. Environmental Protection Agency: Washington, DC, USA, 1-22.

Remote sensing

• the acquisition of information about an object or phenomenon without making physical contact
• used in numerous fields, including geography, land surveying and most Earth science disciplines (for example, hydrology, ecology, meteorology, oceanography, glaciology, geology); it also has military, intelligence, commercial, economic, planning, and humanitarian applications.
• repeating satellite orbits enable continuous monitoring of the object, phenomenon or area in general, i.e. gathering EO time series

EO time series

• Efficiently provides information on
  • Land cover change
  • Vertical and horizontal land movements
  • Sea level change
  • Ice sheet balance change
  • Temperature change over the season or different time
  • Specific events such as fires, floods, landslides, earthquakes, etc.
  • Enable global and local analysis (such as detecting and monitoring Urban Heat Islands, UHIs)

EO time series for UHI monitoring

Satellite sensors can observe surface temperature using thermal sensors Sensors available at different satellites

EO time series for UHI monitoring

• Different sensing instruments
  • ASTER
    • Advanced Spaceborne Thermal Emission and Reflection Radiometer
    • Imaging device on the TERRA satellite
  • ETM+
    • Enhanced Thematic Mapper
    • Sensor platform on the LANDSAT satellite
  • ESA Copernicus Sentinel-3A (focus of this presentation)
    • Carried out by EUMETSAT
    • Toolbox available

ASTER and LANDSAT

• Thermal infrared band
  • ASTER
    • Bands 10-14, 8.125-11.65 µm
    • Spatial resolution 90x90 m
  • LANDSAT
    • Band 6, 10.4-12.5 µm
    • Spatial resolution 60x60 m

Following slides on ASTER and LANDSAT data and procedures are designed based on the Christopher S Martin’s (csmartin@buffalo.edu) presentation: Remote Sensing of the Urban Heat Island Effect

ASTER and LANDSAT

• Procedure (1)
  • Calibration
    • L = 0.0370588 ΣDN + 3.2
  • Convert to BT
    • BT = K2 / {ln [(k1 / L) + 1]}
  • Calculate LST (Land Surface Temperature) 
    • Ts = BT / {1 + [(λ * BT / ρ) * ln ε]}

ASTER and LANDSAT

• Procedure (2)
  • Separation of sources
    • Computed LST contains
      • Radiation
      • Anthropogenic sources
    • Different methods
      • E.g. Kato and Yamaguchi (2005)
        • Rn = G + LE + H vs Rn + A = G + LE + H
        • Estimate the values of each variable
          • G varies with material
          • H – total heat flux (final result)
        • Has = H – Hn
          • Hn – heat from „natural causes”, i.e. radiant heat flux

Vegetation – UHI relationship

• More the vegetation less the UHI effect
• Methods on quantifying this
  • NDVI (Normalized Difference Vegetation Index)
  • Vegetation fraction
  • LAI (Leaf Area Index)
  • Other methods
    • Handheld temperature sensors
    • Aerial temperature measurements

UHI detection from Sentinel-3A with the case study

• Methods and details presented here are taken from RUS (Research and User Support for Sentinel Core products), the service used to promote the uptake of Copernicus data and to support the scaling up of R&D activities with Copernicus data
• Video webinar on UHI detection available at: https://youtu.be/5UHghHjP6ZQ
• Details on webinar available at: https://rus-training.eu/training/urban-heat-island-with-sentinel-3
• Some of the figures are screenshotted from the webinar
• Instead of the following slides, you are advised to take a look at the webinar

Sentinel-3 SLSTR - hands on

• Data access: Copernicus Open Access Hub (registration required and free)

Sentinel-3 SLSTR - hands on

• Data acquisition

Sentinel-3 SLSTR - hands on

• Data acquisition (SL_2_LST – level 2 data)

Sentinel-3 SLSTR - hands on

• Data acquisition – data download – each scene up to 2GB

Sentinel-3 SLSTR - hands on

• Data processing – SNAP (Sentinel Application Platform) software
  • Published under GPL-3 licence
  • Common architecture for all Toolboxes
  • Very fast image display and navigation even of giga-pixel images
  • Graph Processing Framework (GPF): for creating user-defined processing chains
  • Advanced layer management allows adding and manipulation of new overlays such as images of other bands, images from WMS servers or ESRI shapefiles
  • Rich region-of-interest definitions for statistics and various plots
  • Easy bitmask definition and overlay
  • Flexible band arithmetic using arbitrary mathematical expressions
  • Accurate reprojection and ortho-rectification to common map projections,
  • Geo-coding and rectification using ground control points
  • Automatic SRTM DEM download and tile selection
  • Product library for scanning and cataloguing large archives efficiently
  • Multithreading and Multi-core processor support
  • Integrated WorldWind visualisation

Sentinel-3 SLSTR - hands on

Data preview (select two downloaded images of the same scene)

Sentinel-3 SLSTR - hands on

Making a subset for both images of the same scene (Raster… Subset… Band subset – NDVI, LST, x_in, y_in, biome, lat, lon)

Sentinel-3 SLSTR - hands on

• Adapting the coordinate projection (Raster… Geometric Operation… Reprojection – select desired)
• Changing color ramp

Sentinel-3 SLSTR - hands on

• Changing color ramp – sensor detection of temperature in one image (°K) – day scene

Sentinel-3 SLSTR - hands on

• Changing color ramp – sensor detection of temperature in one image (°K) – night scene

Sentinel-3 SLSTR - hands on

• Changing color ramp – temperature day vs night comparison

Sentinel-3 SLSTR - hands on

• Overlapping the night temperature map with land cover map (Add Land Cover Band – automatic process in SNAP)

Sentinel-3 SLSTR - hands on

• Overlapping the night temperature map with land cover map (Add Land Cover Band – automatic process in SNAP)

Sentinel-3 SLSTR - hands on

• Within overlapping process select manually some of the pixels with known rural position (e.g. import .shp with points)

Sentinel-3 SLSTR - hands on

• Results

Sentinel-3 SLSTR - hands on

• Results presented in QGIS for London, 2018 (see RUS webinar for a whole procedure) – if processed for different dates – TIME SERIES ANALYSIS

Take-aways

• Time series serve different purposes one of which is monitoring of the UHIs
• UHI detection and monitoring is routine job that can be performed for local or global purposes
• Sentinel 3 satellite mission provides data freely available for such purposes
• On top of that EU and ESA are trying hard to get people use Sentinel data – beside the data, they provide software and trainings, sometimes even grants to pay off the Copernicus investments

Thanks!

Feel free to contact EO4GEO for more!