1. University of Wisconsin-Milwaukee Geography 403 Guest Lecture: Urban Remote Sensing Rama Prasada Mohapatra PhD Candidate Department of Geography Spring 2010

2. University of Wisconsin-Milwaukee Outline 1.Traditional Remote Sensing Applications 2.Urban Remote Sensing: New Challenges 3.Urban Land Use Classification 4.The Vegetation-Impervious Surface-Soil Model 5.Population Estimation 6. Urban Growth Monitoring 7. Urban Growth Modeling

3. University of Wisconsin-Milwaukee 1.Traditional Remote Sensing Applications Vegetation (bio-geography) Vegetation index Biomass estimation Leaf area index (LAI) estimation Yield prediction Geology Oil inventory Soil science Climate studies Etc.

4. University of Wisconsin-Milwaukee Field spectrometry Truck-mounted imaging radar Vegetation (bio-geography) 1.Traditional Remote Sensing Applications

5. University of Wisconsin-Milwaukee Wildfires (ASTER data) San Bernardino Mountains, California, October 28, 2003 1.Traditional Remote Sensing Applications

6. University of Wisconsin-Milwaukee Wildfires (MODIS) Los Angeles, California, October 27, 2003. 1.Traditional Remote Sensing Applications

7. University of Wisconsin-Milwaukee Landsat 5 TM EO-1 Hyperion Landsat 7 ETM+ EO-1 ALI Green Vegetation Senescent vegetation Bare soil Band 2 Band 3 Band 4 Band 5Band 7 Band 1 2. Urban Remote Sensing: New Challenges - Spectral issues

8. University of Wisconsin-Milwaukee 40 km The study of the Earth requires many different levels of detail. Global forecast simulations use resolutions in the 40 to 200 kilometer range. 2. Urban Remote Sensing: New Challenges - Scale issues

9. University of Wisconsin-Milwaukee 10 kilometer resolution is characteristic of some atmospheric measurements from geosynchronous orbit. 10 km 2. Urban Remote Sensing: New Challenges - Scale issues

10. University of Wisconsin-Milwaukee 1 kilometer resolution is characteristic of weather satellite Earth images from geosynchronous orbit. 1 km 2. Urban Remote Sensing: New Challenges - Scale issues

11. University of Wisconsin-Milwaukee 30 m 30 meters is the resolution of a Landsat image. 2. Urban Remote Sensing: New Challenges - Scale issues

12. University of Wisconsin-Milwaukee 2. Scale Issues 10 m - Scale issues

13. University of Wisconsin-Milwaukee 4 m 2. Urban Remote Sensing: New Challenges - Scale issues

14. University of Wisconsin-Milwaukee 2 m 2. Urban Remote Sensing: New Challenges - Scale issues

15. University of Wisconsin-Milwaukee 1 m Some recent low Earth orbit commercial and Earth resource satellites have resolutions approaching 1 meter. 2. Urban Remote Sensing: New Challenges - Scale issues

16. University of Wisconsin-Milwaukee one-meter resolution (sharpened 4 meter) satellite image 11:46 a.m. EDT Sept. 12, 2001 2. Urban Remote Sensing: New Challenges - Scale issues

17. University of Wisconsin-Milwaukee 2. Urban Remote Sensing: New Challenges 1.Urban landscapes are composed of a diverse assemblage of materials (concrete, asphalt, metal, plastic, glass, water, etc.) 2.The goal of urban construction is to improve quality of life. 3.Urbanization is taking place at a dramatic rate, with or without planned development 4.Sustainable development (congestion, pollution, urban heat island, commuting time issue) - Urban issues

18. University of Wisconsin-Milwaukee - Remote sensing 1.Urban is a heterogenous region, with different kinds of manmade materials (impervious surface), such as asphalt, concrete, glass, etc. 2.Urban objects are small comparing to natural objects (e.g. forests, agriculture, geological structure, etc.) 3.Remote sensing data are typically in a medium resolution (e.g. Landsat Thematic Mapper 30 meter) 2. Urban Remote Sensing: New Challenges

19. University of Wisconsin-Milwaukee 3. Urban Land Use Classification 1)American Planning Association “Land-Based Classification standard” for urban/suburban land use. 2)U.S. Geological Survey “Land-Use/Land-Cover Classification System” was originally designed to be resource-oriented. Developed by Anderson (1976) in U.S.G.S. Driven primarily by the interpretation of remote sensing data Most land use classifications are based on this system (e.g. LULC data in 1990)

20. University of Wisconsin-Milwaukee Anderson Level II 1: Urban 11: Residential 12: Commercial and services 13: Industrial 14: Transportation, communications, and utilities 15: Industrial and commercial complexes 16: Mixed urban and built-up land 17: Other urban and built-up land 2: Agriculture 3: Rangeland 4: Forest land … 3. Urban Land Use Classification

21. University of Wisconsin-Milwaukee Can be downloaded from United State Geological Survey (USGS) website (http://landcover.usgs.gov/ftpdownload.asp). Created from a satellite data, Thematic Mapper (TM), with 30 meter spatial resolution Year 1992 - classification based on Anderson Level II (9 major classes with subclasses) Year 2001- In addition to classification, impervious surface information and tree canopy coverage are available (not public available yet) 3. Urban Land Use Classification

22. University of Wisconsin-Milwaukee Land use (1992) Commercial High residential Low residential 3. Urban Land Use Classification

23. University of Wisconsin-Milwaukee - Problems and current research Problems: The average urban land use classification accuracy is about 80- 85% (not adequate for urban growth monitoring and modeling) Research: 1) Spectral analysis (Sub-pixel classification) 2) Spatial analysis (texture analysis, wavelet analysis, etc.) 3) Con-textural analysis ( with localized knowledge) 4) Knowledge based analysis (Neural network, fuzzy classification, decision tree analysis) 3. Urban Land Use Classification

24. University of Wisconsin-Milwaukee 4. The Vegetation-Impervious Surface-Soil Model Assumptions 1)A remote sensing pixel includes more than one land cover types 2)Three basic compositions (vegetation, impervious surface, and soil) can represent the heterogeneous urban landscape. 3)The fractions of each composition can be calculated using mathematical techniques.

25. University of Wisconsin-Milwaukee 4. The Vegetation-Impervious Surface-Soil Model 1.Important indicator of urbanization - a major component of urban infrastructure - an indicator of human activities 2.Essential environmental index - model run-off volume - monitor water quality Impervious surface

26. University of Wisconsin-Milwaukee 4. The Vegetation-Impervious Surface-Soil Model

27. University of Wisconsin-Milwaukee 5. The Vegetation-Impervious Surface-Soil Model -ANN Classification  Artificial Neural Network (ANN?  Relatively crude electronic models based on the neural structure of the brain  Most widely used multi layer perceptron  Three layer perceptron with back propagation algorithm provide better alternatives than statistical  “Neuralnet back propagation classifier” tool in IDRISI are capable of creating activation level maps

28. University of Wisconsin-Milwaukee 4. The Vegetation-Impervious Surface-Soil Model  Input layer nodes: the number of input bands is four for the base model  Output layer nodes: number of desired classes (3)

29. University of Wisconsin-Milwaukee 5. The Vegetation-Impervious Surface-Soil Model

30. University of Wisconsin-Milwaukee 5. Population Estimation - House Count 1.Count the number of houses 2.Survey the average persons per house 3.Population = #house * #person/house

31. University of Wisconsin-Milwaukee 5. Population Estimation - House Count The imagery must have sufficient spatial resolution to allow identification of individual structures Some estimation of the average number of persons per dwelling unit must be available Some estimation of the number of homeless, seasonal, and migratory workers required It is assumed all dwellings are occupied

32. University of Wisconsin-Milwaukee 6. Population Estimation - House Count Advantages: Accurate Disadvantages: Manual counts, labor intensive and time consuming Cannot be applied in large urban areas

33. University of Wisconsin-Milwaukee 5. Population Estimation - Regression models 1.Regression with residential land use areas Census data available Residential land use classification 2.Regression with spectral reflectance and its transformations Census data available

34. University of Wisconsin-Milwaukee 5. Population Estimation - Regression models 3. Regression with impervious surface in residential areas I L : impervious surface in low density residential areas I H : impervious surface in high density residential areas

35. University of Wisconsin-Milwaukee Population Estimation: Landscan Project Input factors 1)Road 2)Slope 3)Land cover 4)Population places 5)Nighttime Lights 6)Urban density 7)Coastlines 5. Population Estimation

36. University of Wisconsin-Milwaukee 5. Population Estimation Defense Meteorological Satellite Program

37. University of Wisconsin-Milwaukee 6. Urban Growth Monitoring

38. University of Wisconsin-Milwaukee  City of Roses: Guadalajara, Mexico 6. Urban Growth Monitoring

39. University of Wisconsin-Milwaukee 6. Urban Growth Monitoring - Methods 1.Classify remote sensing images of two dates, and compare the results 2.Image regression 3.Image differentiation 4.Normalized Vegetation Differential Index (NDVI) comparison 5.Impervious surface fraction comparison

40. University of Wisconsin-Milwaukee 6. Urban Growth Monitoring - Problems Image classification accuracy is not adequate for urban Growth monitoring study Seasonality changes of urban spectra

41. University of Wisconsin-Milwaukee 7. Urban Growth Modeling From http://mcmcweb.er.usgs.gov/phil/modeling.html 1980 – 2025

42. University of Wisconsin-Milwaukee 7. Urban Growth Modeling 1.Ecometric model (Land-bid theory) Population CBD Sub-urban

43. University of Wisconsin-Milwaukee 7. Urban Growth Modeling 2. Land use allocation model Locate a land which minimizes certain criteria 1)Transportation costs (commuting time) 2)Congestion 3)Pollution

44. University of Wisconsin-Milwaukee 7. Urban Growth Modeling 3. Cellular Automata (IDRISI software) 1.Bottom-up approach 2.Four components 1) An action space 2) a set of states 3) the rules of neighborhood definition 4) a set of state transition rules 3. Simulate and calibrate using existing data

45. University of Wisconsin-Milwaukee 7. Urban Growth Modeling 4. Multi-agent models 1.Bottom-up approach 2.Multiple agents control urban growth process 1) Urban planners 2) Stakeholders 3) Communities 4) ……

46. University of Wisconsin-Milwaukee 7. Urban Growth Modeling Problems: Model calibrations 1)Although many models have been developed, few of them have been calibrated and compared (not conclusive). 2)Many models fail to explain the undergoing forces of urban growth or sprawl.

47. University of Wisconsin-Milwaukee END Geography 750: Urban Remote Sensing