Multi-temporal Local Climate Zone mapping

• This is an experimental, no longer maintained nor developed multi-temporal LCZ mapping procedure.
• This workflow was first tested over Canadian cities (Demuzere et al., 2020), and has been developed further for Chinese megacities (Cai et al., 2022).
• In contrast to the other LCZ mapping efforts over Europe (Demuzere et al., 2019), the USA (Demuzere et al., 2020), and the world (Demuzere et al., 2022), this work only uses input from the Landsat sensor. As such, the quality of such a landsat-only map might be inferior to one produced by the LCZ-Generator (Demuzere et al., 2021), which also has access to more recent Sentinel-1, 2 and other earth observation products.
• No temporal filtering has been applied on the resulting LCZ maps. That means that LCZ labels might jump from one LCZ class to another in subsequent years, which might be artificial and due to the nature of the random forest classifier.

Requirements

• a working python environment: see below how to set this up.
• an account on Google Earth Engine. You can sign up here.
• Prior to using the Earth Engine Python client library, you need to authenticate (verify your identity) and use the resultant credentials to initialize the Python client. See here for more info.
• training area polygons representative for each year of interest. Ideally the TA sets across years share the same polygons (for areas that did not change over time), to increase consistency across the dynamic LCZ maps. Make sure to make the training area sets according to this template (https://www.wudapt.org/wp-content/uploads/2020/08/WUDAPT_L0_Training_template.kml) and these guidelines (https://www.wudapt.org/digitize-training-areas/)!

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LCZ mapping procedure

The dynamic LCZ mapping procedure in general follows the procedure of the LCZ-Generator. Yet keep in mind the following changes:

• a buffer of 15km is applied around the bounding box of all TAs (all years). This defines the mapped Region of Interest, being the same for all years.
• all other TA filters and LCZ mapping settings from the LCZ Generator are retained.
• Only TA set representative for year X is used for the production of the LCZ map of that year X. As such, the resulting LCZ maps might differ from year to year, even in areas that did not change.
• only Landsat input features are used: from Landsat 5, 7 (years prior to 2003 because of the scan line error) and 8
• this limited set of input features might result in classification errors, which might be most noticeable around edges of features, e.g. along coastlines / edges of water bodies
• all images with cloud cover < 70%, and for year +/- 180 days (e.g 01-07-2002 to 01-07-2004 for the year 2003) are used
• all images are masked for clouds
• selected bands:
  • blue, green, red, near infrared (nir), shortwave infrared (swir) 1/2 and thermal infrared (tirs),
  • band rations bci, ndbai, ndbi, ebbi, ndvi, ndwi
  • for all bands and all images, derive 10th, 50th and 90th percentile and standard deviation
  • add slope from ASTER DEM to account for topography
• LCZs are mapped at a resolution of 30m. Afterwards, the default Gaussian filter is applied, as described in Demuzere et al. (2020).
• resulting resolution is 100m, yet thematic resolution is slightly lower due to the Gaussian filter.

Please follow the steps below to create mult-temporal LCZ maps for your city of choice.

0. Set python environment

python3.9 -m venv venv
source venv/bin/activate

# Install packages first time
pip install -r requirements.txt

1. Prepare CITY config & Data folders

Create ./config/CITY.yaml file by making a copy from existing one:

• In this .yaml file name, replace CITY by the name of your city
• Fill in TA version (e.g. v1), year (e.g. 2003) and file names (...)
• Set Author name(s)
• Check if other settings need to be changed, e.g.:
  • CC: Cloud Cover (DEFAULT: 70% Cloud Cover allowed)
  • EXTRA_DAYS: Extra days outside the year of interest? (DEFAULT: 180 days = half a year before and after)
  • ADD_L7: include Landsat 7 sensor or not (bool as string) (DEFAULT: "True")
  • JD _START/_END: Should julian days be excluded, e.g. because of snow cover? (DEFAULT: 0 and 366 = all days included)

Create a CITY folder under data/:

• with sub-folders input/ and output/
• store .kml or .kmz files under proper filename, e.g.: input/XX, with XX referring to the version (same as in CITY.yaml, e.g. v1)

2. Create TA set

• This routine combines all years into one .shp and afterwards .zip.

python create_ta_shp.py CITY EE_ACCOUNT TA_VERSION

# E.g.:
python create_ta_shp.py Melbourne v1

3. Upload TA set

• Manually upload to EE, in your EE_IN_DIR defined in the .yaml file.

4. LCZ mapping

Create the LCZ map, including:

• processing of EO input features on the fly (store to asset option possible via EXPORT_TO_ASSET in .yaml namelist)
• store Landsat ID list as csv file
• 25 bootstraps to get the confusion matrices for the accuracy assessment.
• Final LCZ map using all polygons (band lcz)
• Gaussian filtered version added (band lczFilter)

python create_lcz_map.py CITY TA_VERSION

# E.g.:
python create_lcz_map.py Melbourne v1

5. Copy files to local output folder

Once Google Earth Engine has finished the processing, and the data is available in your Google Drive folder, then copy all files to its default output location, e.g. ./data/CITY/output/

6. Plotting

For a quick assessment of the results, create the following plots:

• Training Area polygon frequencies per year, in a stacked barchart
• Accuracy boxplots, one per year, in multipanel: (1 x len(years))
• lczFilter map, one per year, in multipanel: (1 x len(years))

python create_plots.py CITY TA_VERSION

# E.g.:
python create_plots.py Melbourne v1

7. LCZ map as kmz

Also store the LCZ GeoTIFF map as .kmz, that can be opened in Google Earth

python create_lcz_kmz.py CITY TA_VERSION

# E.g.:
python create_lcz_kmz.py Melbourne v1

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Outputs

All outputs are available in output/.

• The outputs are similar as those provided by the LCZ Generator (see here).

• The FILENAME reflects the settings used in the LCZ map production:

  • XX_: CM or IDs or LCZ (type of output, see below)
  • CITYNAME_
  • v1_: TA version (in case multiple iterations will be done)
  • YEAR_
  • CC_: Cloud Cover threshold
  • ED_: Extra Days before and after relevant year
  • JDs_: Julian days considered (0_366 = all year)
  • L7True: Landsat 7 info is used prior to 2003.

• Data output:

  • IDs_FILENAME_LS_IDs.csv: The IDs of the Landsat images that were used to create the Landsat mosaics as input to the random forest classifier.
  • CM_FILENAME.csv: Raw confusion matrix
  • CM_FILENAME_oa_df.csv: Overall accuracies and F1 metrics per bootstrap. This is the data used to make the plot_OA_BOXPLOT.jpg
  • LCZ_FILENAME.tif: Actual LCZ map, with two bands: "lcz" is raw map, "lczFilter" is Gaussian filtered map.
  • LCZ_FILENAME.kmz: LCZ map ("lczFilter"), in kmz format, which can be opened in Google Earth.

• Figures:

  • plot_TA_FREQ.jpg: stacked bar plot showing the number of TAs per class and year
  • plot_OA_BOXPLOT.jpg: accuracy boxplots per year
  • plot_LCZ_MAP.jpg: LCZ map (using lczFilter band) per year

Relevant References

• Demuzere M, Bechtel B, Mills G. Global transferability of local climate zone models. Urban Clim. 2019a;27:46-63. doi:10.1016/j.uclim.2018.11.001
• Demuzere M, Bechtel B, Middel A, Mills G. Mapping Europe into local climate zones. Mourshed M, ed. PLoS One. 2019b;14(4):e0214474. doi:10.1371/journal.pone.0214474
• Demuzere M, Hankey S, Mills G, Zhang W, Lu T, Bechtel B. Combining expert and crowd-sourced training data to map urban form and functions for the continental US. Sci Data. 2020a;7(1):264. doi:10.1038/s41597-020-00605-z
• Demuzere M, Mihara T, Redivo CP, Feddema J, Setton E. Multi-temporal LCZ maps for Canadian functional urban areas. OSF Prepr. December 2020b. doi:10.31219/osf.io/h5tm6
• Demuzere M, Kittner J, Bechtel B. LCZ Generator: A Web Application to Create Local Climate Zone Maps. Front Environ Sci. 2021;9. doi:10.3389/fenvs.2021.637455
• Demuzere M, Kittner J, Martilli A, et al. A global map of Local Climate Zones to support earth system modelling and urban scale environmental science. Earth Syst Sci Data, doi: 10.5194/essd-14-3835-2022
• Cai Z, Demuzere M, Tang Y, Wan Y. The characteristic and transformation of 3D urban morphology in three Chinese mega-cities. Cities. 2022;(July 2021):103988. doi:10.1016/j.cities.2022.103988

Disclaimer

Code Development:

The code provided is for educational and informational purposes only. It is not intended for production use without thorough testing and validation. The author makes no representations or warranties regarding the accuracy or completeness of the code. The author shall not be liable for any errors or omissions in the code or for any actions taken in reliance on the code. Use the code at your own risk.

Data:

The data provided is for reference and informational purposes only. The author makes no representations or warranties regarding the accuracy, completeness, or suitability of the data for any specific purpose. The author shall not be liable for any errors or omissions in the data or for any actions taken in reliance on the data. Use the data at your own risk.