Global Warming

• Not all temperature increases are created equal
• Pacific Northwest: up to 5.7°C temperature increase in 100 years (Mote & Salathé Jr.,2010)
• With over 50% of the world’s population living in cities (United Nations, 2015), we have a problem…

The Urban Heat Island Effect

• Intra-urban phenomena
• Spatially and temporally variable
• Cities are warmer than rural areas
  • Documented since early 1800’s (Howard, 1820)

Heat and Health

• Greatest natural hazard in post-industrial societies (Poumadère, Mays, Le Mer, & Blong, 2005)
• Marginalized populations are more exposed (Huang, Zhou, & Cadenasso, 2011)

Satellite- vs. Traverse-based UHI

• Satellite spatial resolution
  • We need fine spatial resolution imagery to describe urban phenomena (Sobrino et al., 2012)
• Thermal infrared imagery is inaccurate at human-level within cities (Song, Park, Song, & Park, 2014)

90m Grid (ASTER TIR spatial resolution) Example

Previous Work

Traverse-based UHI Modeling

• Gell out of style with the introduction of free satellite imagery.
• Models human-level temperature based on spatial configuration of land classifications.

City of Portland

• LiDAR-derived land descriptors
• Required massive resources (data, time, money, and computational power)

Previous modeling of UHI has been created with remote sensing-derived land cover data (Voelkel, Shandas, and Haggerty, 2016); the intent of this project is to both remove feature classification from the workflow, and leverage freely available satellite data

Question #1:

Can vehicle traverse-based in-situ temperature readings, coupled with non-Thermal Infrared satellite bands, accurately model the Urban Heat Island Effect?

Hypothesis #1:

By converting individual imagery bands into regional descriptions of their own internal variability/texture, it is possible to accurately predict urban heat. Using the spectral structure that informs classification should sidestep the need to classify land uses in the UHI model.

Question #2:

Can a UHI model created in Portland be transferred to another city?

Hypothesis #2:

A Portland UHI will have some relation to other nearby cities, however will not be as powerful of a predictor as in the area that in-situ measurements were collected.

Portland Metropolitan Area, Oregon, USA

Project Workflow

Overview:

• Create regional descriptions of raster variability in the form of:
  • textons
  • multi-distance means of textons
  • multi-distance means original bands
• Use these newly-created data sets as independent variables in a series of modeling exercises to predict the dependent variable: temperature.
• Test transferability of the UHI model to other cities (with Eugene, Oregon as a test case)

Sentinel-2 Bands

Data for Portland Metro and Eugene, Oregon taken on July 27, 2016. Acquired through Google Earth Engine.

Sentinel-2: 10m bands

Sentinel-2: 20m bands

Texton Filter Bank

Textons are simply the result in raster format of a moving window analysis wherein the kernel filter weights optimize the exaggeration of texture and variability. Here is a regular kernel window, which would calculate the mean within a 5 pixel window:

##            [,1]       [,2]       [,3]       [,4]       [,5]
## [1,] 0.00000000 0.00000000 0.07692308 0.00000000 0.00000000
## [2,] 0.00000000 0.07692308 0.07692308 0.07692308 0.00000000
## [3,] 0.07692308 0.07692308 0.07692308 0.07692308 0.07692308
## [4,] 0.00000000 0.07692308 0.07692308 0.07692308 0.00000000
## [5,] 0.00000000 0.00000000 0.07692308 0.00000000 0.00000000

sum of weights adds up to 1, therefore this will calculate the mean.

However, texton filters are slightly more complex. Here they are represented as rasters (for visualization purposes). All 12 filters are used in the analysis.

Filters mimic those found in the The Leung-Malik (LM) Filter Bank, which are commonly used in robotic vision. These are used to create new versions of imagery that represent a ‘vocabulary’ of structural information.

Leung, T. & Malik, J. International Journal of Computer Vision (2001) 43: 29. doi:10.1023/A:1011126920638

In-Situ Temperature

Temperature Collection

• 4 to 9 vehicles
• Measurements every 1s
• GPS synced with temperature measurement time
• 3 time periods: 6am - 7am, 3pm - 4pm, 7pm - 8pm
• Nighttime cooling is of greatest concern for human well being.
  • For now, only nighttime measurements will be used in modeling.

Buffering

Tobler’s First Law of Geography:

“Everything is related to everything else, but near things are more related than distant things.”

In addition to the textons, a moving window mean function was run on each band and texton raster (130 total rasters) to account for distance-based decay in correlation at 50m, 100m, 200m, 400m, and 800m.

Buffered Texton Rasters

Value Extraction

For all 650 rasters, cell values at each temperature measurement point are collected. This results in a table with 651 columns (including observed temperature) and a row for every temperature observation.

Modeling Workflow

• Cross-validation done with a 70/30 holdout
• Random Forest: top 10 most important variables selected
• Multiple Linear Regression: Random Forest variables, with VIF and p-value filtering
• Support Vector Machine: Iterative Model selection (variations in gamma and cost)

Random Forest

Top 10 Variables:

No texton rasters were highly significant!

Multiple Linear Regression

SVM

… still running!

• Started on 3/8/2017 - this slide will update if/when it ever finishes.
• Unlikely to be much better than Random Forest
  • Processing time alone makes it less feasible.

Transferability of RF

Hypothesis 1

Question #1:

Can vehicle traverse-based in-situ temperature readings, coupled with non-Thermal Infrared satellite bands, accurately model the Urban Heat Island Effect?

Hypothesis #1:

By converting individual imagery bands into regional descriptions of their own internal variability/texture, it is possible to accurately predict urban heat.

Yes! However, the complex texton rasters had little-to-nothing to do with the models. Simple spectral texture descriptions did well in predicting urban heat.

Hypothesis 2

Question #2:

Can a UHI model created in Portland be transferred to another city?

Hypothesis #2:

A Portland UHI will have some relation to other nearby cities, however will not be as powerful of a predictor as in the area that in-situ measurements were collected.

Yes, but not enough to create a satifactory UHI prediction map. The Portland UHI model did explain over 25% of the variation in heat in Eugene, however further analysis could yield a more transferable model.

Exposure

Within the Portland Metro region, we see temperatures vary between 78.7°F and 93.8°F! Further research should analyze demographic issues of disproportionate exposure to excessive heat.

Textons

Unnecessary! Literature suggests potential for classification (though is silent on regression).

Transferability

Currently, the model is not transferable; however, as more data is collect, I expect to see the intra-urban predictive power increase.

Future Work

• Test other time periods
• Collect more field data

Unlinked References

- Howard, L. (1820). The Climate of London: Deduced from Meteorological Observations Made at Different Places in the Neighbourhood of the Metropolis. In Two Volumes. W.Phillips.