Geospatial Technologies to Improve Urban Energy Efficiency
2. Study Area and Data Preprocessing
2.1. Study Site and TABI-320 Data
2.2. Emissivity Corrections
2.3. Geometric Corrections
3.1. Platform Design
3.2. The HEAT User Experience
3.2.1. HEAT Maps
3.2.2. Community HEAT Maps
|Community||No of Houses||Heated Area (105 sq.ft)||CO2e (T)||Reduced CO2e (T)||Cost/yr ($103 CAD)||Savings/yr ($103 CAD)||Histogram|
(house temp °C distribution)
|Brentwood (total of all communities)||368||4.41||1704.1||620.6||288.6||105.1|
3.2.3. Home HEAT Maps
3.2.4. The Fuel Table
3.2.5. The Annual Home Energy Use Model
3.3. Generating Hot Spots
3.4. The HEAT Score Algorithm
4. Discussion—Challenges, Lessons, Solutions
4.1. TABI Acquisition Limitations and Solutions
4.2. Object-Based Mosaicing (OBM)
4.2.1. OBM Pseudo Code
- In the case where cadastral polygons exist, each flight needs to be geometrically corrected to the corresponding roof-top polygons.
- In the case where only thermal data exists, object-based feature detection will be applied to each flight line (separately) to extract roof-top polygons.
- A suitable sized buffer around each roof [e.g., 2–3 times the reported geometric correction error (in pixels)] needs to be generated to compensate for unresolved local geometric errors between flight lines.
- For two adjacent flight lines, the overlap between each flight line will be defined, from which a linear center mosaic line will be defined (yellow line in Figure 9).
- The buffered roofs that are divided by the flight line will be identified.
- Based on the proximity of each bifurcated roof to the center of its flight line (so as to reduce radial displacement effects) the center mosaic line will be joined around defined roof buffers (red line Figure 9), and used to mosaic adjacent datasets together.
- This process is then applied to the next adjacent flight line and repeated until all flight lines are mosaiced.
4.3. Emissivity Modulation (EM)—Correcting for Emissivity
4.4. Thermal Urban Road Normalization (TURN)—Correcting for Local Climatic Variability
4.4.1 TURN Pseudo Code
- Begin by isolating roads of a common material type either from an available GIS road layer, or using GEOBIA methods to segment and define specific road types in the TIR image.
- A 1.5 m buffer will then be created around each side of the road center (thus 3.0 m total diameter) to remove sidewalks, parked vehicles, curb side drains etc., or in the case of segmented roads, the mathematical morphology function erosion can be applied until a 3.0 m road skeleton remains.
- The remaining road object will then be emissivity corrected for material types (i.e., concrete, gravel, tar) to generate a ‘kinetic’ or ‘true’ temperature ‘road’ object.
- A random sample will extract 20% of these ‘true’ road pixels for an accuracy assessment, while the mean of the remaining (80%) road pixels will be calculated.
- The mean temperature deviation per unit temperature for each ‘true-road’ pixel will then be calculated.
- The mean deviation will be interpolated over the entire image using three different interpolation methods: (i) Ordinary Kriging; (ii) Inverse Distance Weighting; and (ii) Splines.
- The temperature of all scene pixels will then be adjusted with the mean deviation values.
- An accuracy assessment will be performed for the three interpolation methods separately using the extracted (20%) road pixels. It is expected that the model will generate average road temperatures for each road pixel. Any deviation from the average temperature will be considered as an error (due to microclimatic variation).
- The RMSE will then be calculated for each interpolation method, and their accuracies will be compared.
- The resulting normalized image with the lowest RMSE and most visually meaningful results will then be used for further analysis.
4.5 HEAT and Carbon Scores
4.6. Updating Cadastral Errors with GEOBIA and Thermal Imagery
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© 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
Hay, G.J.; Kyle, C.; Hemachandran, B.; Chen, G.; Rahman, M.M.; Fung, T.S.; Arvai, J.L. Geospatial Technologies to Improve Urban Energy Efficiency. Remote Sens. 2011, 3, 1380-1405. https://doi.org/10.3390/rs3071380
Hay GJ, Kyle C, Hemachandran B, Chen G, Rahman MM, Fung TS, Arvai JL. Geospatial Technologies to Improve Urban Energy Efficiency. Remote Sensing. 2011; 3(7):1380-1405. https://doi.org/10.3390/rs3071380Chicago/Turabian Style
Hay, Geoffrey J., Christopher Kyle, Bharanidharan Hemachandran, Gang Chen, Mir Mustafizur Rahman, Tak S. Fung, and Joseph L. Arvai. 2011. "Geospatial Technologies to Improve Urban Energy Efficiency" Remote Sensing 3, no. 7: 1380-1405. https://doi.org/10.3390/rs3071380