COUNTY LEVEL POPULATION ESTIMATION USING KNOWLEDGE-BASED IMAGE CLASSIFICATION AND REGRESSION MODELS. Anjeev Nepali, B.S.

Transcription

1 COUNTY LEVEL POPULATION ESTIMATION USING KNOWLEDGE-BASED IMAGE CLASSIFICATION AND REGRESSION MODELS Anjeev Nepali, B.S. Thesis Prepared for the Degree of MASTER OF SCIENCE UNIVERSITY OF NORTH TEXAS August 2010 APPROVED: Pinliang Dong, Major Professor Paul Hudak, Committee Member and Chair of the Department of Geography Chetan Tiwari, Committee Member James D. Meernik, Acting Dean of the Robert B. Toulouse School of Graduate Studies

2 Nepali, Anjeev. County level population estimation using knowledgebased image classification and regression models. Master of Science (Applied Geography), August 2010, 65 pp., 11 tables, 24 illustrations, references, 38 titles. This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.

3 Copyright 2010 by Anjeev Nepali ii

4 ACKNOWLEDGEMENTS I would like to take this opportunity to express my appreciation towards Dr. Pinliang Dong for his full support and supervision throughout this project. I also like to acknowledge my committee member Dr. Paul Hudak and Dr. Chetan Tiwari for their support and suggestion to prepare this thesis work and Dr. Bruce Hunter for providing software application support used in this thesis work. I also would like to thank my friends Naresh Kanaujiya, Sanjay Gurung, Nick Enwright and Aldo Avina for their comments and suggestions in improving my thesis work. iii

5 TABLE OF CONTENTS Page ACKNOWLEDGEMENTS... iii LIST OF FIGURES...vi LIST OF TABLES... viii CHAPTER 1 INTRODUCTION... 1 Why Estimate Population?... 1 Why Use Remote Sensing for Population Estimation?... 2 Current Practice of Population Estimation Using Remote Sensing... 4 Research Objectives... 9 CHAPTER 2 STUDY AREA AND DATA Study Area Datasets CHAPTER 3 METHODOLOGY Image Calibration Impervious Dataset Calculation of Indices Knowledge-Based Classification Development of the Knowledge-Based Classification Model for Denton County Accuracy Assessment of Classified Images Input Data for Regression Models Regression Modeling Geographically Weighted Regression Model Accuracy Assessment of Population Estimation Relative Error (RE): iv

6 CHAPTER 4 RESULTS AND DISCUSSION Results from Maximum Likelihood Classification (MLC) Results from Knowledge-Based Classification of Landsat TM and Impervious Surface Data Results from Knowledge-Based Classification Using Landsat TM Data Alone 33 Regression Models Linear Regression Models Geographically Weighted Regression Discussion CHAPTER 5 CONCLUSION REFERENCES v

7 LIST OF FIGURES Page 1. Study area Flowchart of methodology Flowchart of knowledge-based classification model Indices data image from generated from TM image Classification indices value of various land use type vs. residential land use type Classification indices value of various land use type vs. residential land use type Spectral response of band 4 and band 7 in residential build-up area Knowledge-based classification model developed for land use classification using Landsat 7 ETM+ image classification rules and conditions Classified image of Landsat TM produced from MLC classification Classified image of Landsat TM after processing using impervious surface data Results from knowledge-based classification using Landsat TM data alone Linear regression models derived from sampling Denton County blockgroup level Linear regression models derived from sampling Denton County blockgroup level Scatter diagrams of relative population estimation error vs. population density at the census block-group level for Denton County Scatter diagrams of relative population estimation error vs. population density at the census block-group level for Denton County vi

8 16. Scatter diagrams of relative population estimation error vs. population density at the census block-group level generated from general regression for Denton County (03/04) Linear regression models derived from sampling Collin County blockgroup level Scatter diagrams of relative population estimation error vs. population density at the census block-group level for Collin County Scatter diagrams of relative population estimation error vs. population density at the census block-group level generated by general linear regression Collin County (03/04/2000) Scatter diagram of relative population estimation error vs. population density for GWR model (Denton County) Scatter diagram of relative population estimation error vs. population density for GWR model (Denton and Collin County) Scatter Diagram of relative population estimation error Vs. Population Density for GWR Model (Denton and Collin County) Sparsely populated region on aerial, Landsat TM and classified image Lake shore (sandy beach) and residential built-up on aerial, TM, and classified image vii

9 LIST OF TABLES Page 1. Population estimates from July 1, 2006 to July 1, Error matrix for maximum likelihood classification (MLC) Error matrix for impervious surface data Error matrix for spectral response alone Summary of linear regression model results -Denton County Summary of population estimates produced by general regression model for Denton County Summary of linear regression model results -Collin County Summary of population estimates produced by general regression model for Collin County Summary of linear regression model results Denton and Collin County Combine Summary of population estimates produced by general regression model (Denton & Collin County combined) Summary of geographically weighted regression model results viii

10 CHAPTER 1 INTRODUCTION Why Estimate Population? Half of the world s human population now lives in urban settlement with a rapid growth rate (UNCHS, 2001). According to the United Nation Human Settlements Program (UN-Habitat), nearly 60% of the world population will be urban dwellers by This rapid population growth has a direct impact on all aspects of human development such as social behavior, health, education, gender equality, economic development, job opportunities, and environment. Population growth impacts the sustainability of natural resources through processes of environmental deterioration, including deforestation and loss of biodiversity. Therefore, there is an urgent need to develop methods that can accurately estimate the spatial distribution of populations. This will allow decision makers/planners and environmental planners to develop a better understanding of the complex relationships between population growth, social/economic impact, environmental condition, and decision making process (Lu, Weng, & Li, 2006). Traditional population estimation is based on census which provides extensive information on demographic parameters but, in the mean time, it is also very labor intensive, time consuming and costly. Furthermore, in the United States, census data is only collected once every ten years, which is inadequate for modeling the population dynamics for rapidly changing urban environment 1

11 (Lee & Goldsmith, 1982). Therefore, the usefulness of decennial census dataset is becoming less representative for those urban settlements which are developing rapidly. For example, Denton County and Collin County were listed as one of the top 25 of U.S. Counties which received the largest numeric increase in population in one year (July 1, 2006 to July 1, 2007) by US Census Bureau 2008 report ( Recent demographic data (population estimations and projections) has become more important source of information for developing various applications including decision making processes for marketing, planning, government, and businesses. However, the resources available to collect up-to-date demographic information for rapidly growing regions are still inadequate. Why Use Remote Sensing for Population Estimation? Many researchers have used remotely sensed data such as aerial photographs for estimating population in urban settlement since 1950s. For example, Green (1956) used aerial images to count the number of dwelling units and dwelling type to conduct his demographic analysis in Birmingham, Alabama. Similarly, Collins and El-Beik (1971) used high spatial resolution image from Leeds, England to investigate the co-relation between dwelling type and resident population. Lo (1986a) applied the dwelling method (dwelling count and average household size using 1:20000 spatial resolution aerial Image) to estimate the population of Athens, Georgia successfully. The outcome of Collins and El-Beik 2

12 (1971) and Lo (1986b) indicates that remote sensing techniques can be used to estimate population of small areas with high accuracy. Early methods based on manual interpretation of remotely sensed data were highly time consuming, tedious, labor intensive and not feasible to use for large areas (e.g. metropolitan or county level); They also require high spatial resolution images, hence, are not suitable for images with resolution courser than 1 meter. In addition, consistency of the result might be an issue because the result is highly subjective to the image analyst (Zha, Gao, & Ni, 2003). As a result, image classification methods were employed to overcome the shortcomings of manual methods. Image classification can provide researchers with additional information (such as land use type, transportation network, and impervious surface) related to urban settlements that play crucial role in population estimation (Hardin, Jackson, & Shumway, 2007; J. T. Harvey, 2002a; S. Wu, Qiu, & Wang, 2005). Furthermore, readily available images from different space-borne and airborne sensors with various spatial resolutions are making it feasible to delineate ancillary information to assist population estimation. To sum up, remote sensing methods have been developed for population estimation because (1) remotely sensed images can provide spatial and spectral information for residential areas; (2) remotely sensed images can cover large geographic areas to support population estimation at different scales with less cost; and (3) computer-based digital image analysis methods greatly facilitate information extraction from remotely sensed images. 3

13 Current Practice of Population Estimation Using Remote Sensing In the United States, small area generally indicates counties and their subdivisions. However, some prefer the term small area to the land masses comprised of census tracts, block groups and blocks, as well. Remotely sensed images with various spatial resolutions (high, medium, low) have been used for estimating small area population. For example, high spatial resolution aerial images were used by Lo and Welch (1977) and Lo (1986a) for their research. Harvey (2002b; 2003), Lo (2003), Li and Weng (2005) used medium spatial resolution Landsat Thematic Mapper (TM), and Sutton et al. (1997; 2001) did their research using low spatial resolution data such as defense meteorological satellite program operational linescan system (DMSP OLS). However, these different resolutions have their own complications. For example, images with very high spatial resolution such as aerial photographs and IKONOS images can create processing problem because of their massive data content and possible spatial distortions while working on large areas; likewise, low spatial resolution data, such as DMSP OLS, is unable to provide significant information for population estimation at the regional and local levels. Because of their relatively rich spectral information for land cover mapping and intermediate spatial resolution to cover large areas, medium spatial resolution images, such as Landsat TM/Enhanced TM (ETM+) images, have become the main image source for population estimation (J. T. Harvey, 2002b; 2003; Li & Weng, 2005; Lo, 1995; 2003). 4

14 Different methods have been used for residential population estimation based on remotely sensed data. Lo (1986b) summarized four distinguished approaches that are mainly used in remote sensing literature. They are based on: 1. Counting individual dwelling units on high spatial resolution imagery 2. Extracting the size of the urban settlement from medium or high spatial resolution images 3. Using land-use type classification for extracting urban settlement 4. Using automated digital image classification based on spectral features of satellite imagery The first three approaches have been previously used for visual interpretation and analysis. However, the fourth technique has emerged as a different methodology; it can be applied to any remotely sensed data using particular spectral information and spatial resolution provided by the image (J. T. Harvey, 2002b). Under Lo s fourth approach, researchers have invested considerable effort on modeling automated digital image analysis techniques using various computer assisted methods. Among the various digital image analysis techniques that are used for population estimation, supervised maximum likelihood classification (MLC) is most commonly used in the remote sensing literature. The basic MLC principle relies on decision rules that classify image pixels to particular classes based on probabilities. This classification method is faster 5

15 and less labor intensive compared with traditional census approaches. However, automatic classification of remotely sensed data for extracting urban settlement is a difficult task to achieve at high levels of accuracy. This is due to diverse range of land cover type associated with the urban environment (Zha et al., 2003). The majority of the automated image classification (including MLC), in some extent, requires training samples to run the algorithms. The size, location, and representativeness of training samples also play a pivotal role on the reliability of the output of these classification methods. As a result, conventional supervised classification method is fairly time consuming and labor intensive (Zha et al., 2003) Langford et al. (1991) used land use classification to estimate the population of northern Leicestershire based on supervised classification. They used regression analysis that takes the number of pixels in each land use category as explanatory variables. Likewise, Lo (1995) also used a regression model to estimate population and number of dwelling units based on reflectance and pixel counts as explanatory variables. Qiu et al. (2003) tested the regression analysis approach using geographic information system (GIS) derived transportation networks (roads network) to perform population estimates. Similarly, dasymetric model is another of the renowned technique that uses ancillary information from satellite imagery to perform population estimates. Harvey (2000) adopted dasymetric model in his study and argued that the method significantly improved the efficiency of the land use classification in 6

16 determining the residential population estimates. Later, his method was supported by Wu et al. (2005) who argued that the dasymetric method does indeed produce more accurate estimation with remotely sensed ancillary information compared to those without the information. In addition, more remote sensing attributes, such as texture and temperature, were included as ancillary information in remote sensing population research. Wu and Murray (2005) and Lu et al. (2006) explored the possibility of using impervious surface (any surface where water cannot infiltrate is termed as impervious surface) as a remote sensing attribute for population estimation. Impervious surfaces are important ancillary information as they are associated with roads, buildings, and other built-up areas that are relatively stable. Lu et al. (2006) also used regression analysis model to estimate population. Their approach produced an overall population estimation error of -0.97% for the study area. Scientists are finding ways to extract residential features more quickly and precisely in order to develop a base for understanding complexity of urban ecosystems. To overcome the existing shortcomings of the available approaches, Ridd (1995) proposed a pixel based classification method that utilizes spectral properties of green vegetation, impervious surface material, and surface soil to delineate urban pixels as these attributes are major component urban ecosystem. Ridd (1995) argues that the developed vegetation-impervious surface-soil (V-I-S) model produced using spectral properties of vegetation, 7

17 impervious and soil attributes can discriminate urban built-up with high accuracy. Qiao et al. (2009) also developed a pixel based unified conceptual model for discriminating urban area more precisely. This model was based on Ridd s V-I-S model that uses spectral information such as spectral indices and texture of the remotely sensed data to perform image classification. Qiao et al. (2009) used hierarchical classification method that defines the specific rules for classifying land use classes based on spectral properties of the features. Regression modeling techniques on remotely sensed data have been widely used for population estimation. Wu et al. (2005) argues that because of its unbiased model accuracy test through statistical significance, regression analysis is widely used methods in remote sensing literature of population estimation. In order to simplify the process of outlining different land cover classifications using automated image analysis, researchers have developed techniques such as using various indices derived from remotely sensed data. Normalized difference vegetation index (NDVI) is one of the commonly used indices for delineating vegetation. In addition, normalized difference water index (NDWI) is used for mapping open water bodies from remotely sensed data. Similarly, normalized difference built-up index (NDBI) and normalized difference blue band built-up index (NDBBBI) are two other indices developed for mapping urban settlements using satellite image data (Baraldi et al., (2006); Zha et al. (2003)). 8

18 Research Objectives The objectives of the research are (1) To develop automated knowledgebased classification models for extracting residential areas from Landsat Thematic Mapper (TM) imagery; and (2) to develop linear regression and geographically weighted regression (GWR) models using classified images and census data to estimate population for Denton County and Collin County, Texas, United States. 9

19 CHAPTER 2 STUDY AREA AND DATA Study Area Denton County and Collin County in north Texas were selected as the study area for this research. According to U.S. Census Bureau, both counties were listed among the top 25 counties that had the largest numeric population influx within a year (July 1, 2006 to July 1, 2007). According to Census 2000, Denton County had 189 block groups, and Collin County had 282 block groups. Figure 1. Study area. 10

20 Table 1 Population Estimates from July 1, 2006 to July 1, 2007 Rank Change, 2006 to Geographic Area Population Estimates 2007 July 1, July 1, County State Number Percent Maricopa County Arizona 3,880,181 3,778, , Riverside County California 2,073,571 2,007,206 66, Harris County Texas 3,935,855 3,876,306 59, Clark County Nevada 1,836,333 1,777,168 59, Tarrant County Texas 1,717,435 1,668,541 48, Bexar County Texas 1,594,493 1,555,192 39, Wake County North Carolina 832, ,129 38, Collin County Texas 730, ,383 34, Travis County Texas 974, ,577 32, Mecklenburg County North Carolina 867, ,328 31, Pinal County Arizona 299, ,316 30, Orleans Parish Louisiana 239, ,198 28, Dallas County Texas 2,366,511 2,337,956 28, Santa Clara County California 1,748,976 1,720,839 28, Fulton County Georgia 992, ,649 27, Gwinnett County Georgia 776, ,836 26, San Diego 17 County California 2,974,859 2,948,362 26, Denton County Texas 612, ,582 25, King County Washington 1,859,284 1,834,194 25, Fort Bend County Texas 509, ,482 24, Williamson County Texas 373, ,879 22, Hidalgo County Texas 710, ,494 21, Lee County Florida 590, ,089 20, San Bernardino County California 2,007,800 1,987,505 20, Montgomery County Texas 412, ,233 19, Source: Population Division, U.S. Census Bureau Release Date: March

21 Datasets The datasets acquired for this project are: 1. Landsat TM images: Three TM images acquired in 2000 (2000/03/04 and 2000/03/12) were downloaded from the United States Geological Survey (USGS) for this particular research purpose. They were used for retrieving ancillary information (residential area) of the study area. 2. Impervious surface image: The impervious surface image (2001) used in this research is downloaded from the United States Geological Survey (USGS). 3. Census shapefiles: Census shapefile created by US Census Bureau for 2000 was used to build regression models to estimate population. 4. Census population data: US Census Bureau population data (2000) was used to build regression models and test population estimation accuracy. 5. Aerial image: Aerial image of Denton County acquired in 2000 was used as reference for image classification accuracy assessment. Software packages used in this research include ERDAS IMAGINE 9.3 for image processing and ArcGIS 9.3 for GIS data analysis. 12

22 CHAPTER 3 METHODOLOGY Image Calibration Chander and Markham (2003) developed methods and parameters to overcome radiometric calibration error generated by the degraded sensor s internal calibrator due to long term use. According to Chander and Markham (2003), the calibration process helped to improve the attributes of remotely sensed data such as spectral radiance, reflectance, and temperature estimates, providing better base for comparing images acquired in different dates and/or by different sensors. The methods and procedures suggested by Chander and Markham (2003) for post calibration of image are: a. Conversion from digital number (DN) to radiance: Where, L λ = spectral radiance at sensor s aperture in W/(m 2 *sr*μm); Q cal = quantized calibrated pixel value in DNs; Q calmin = minimum quantized calibrated pixel value (DN = 0) corresponding to LMIN λ. Q calmax = maximum quantized calibrated pixel value (DN = 255) corresponding to LMAX λ ; 13

23 LMAX λ = spectral radiance that is scaled to Q calmax in ; LMIN λ = spectral radiance that is scaled to Q calmin in ; b. Radiance to reflectance Where, ρp = unitless planetary reflectance; L λ = spectral radiance at sensor s aperture; d= earth-sun distance in astronomical units; ESUN λ = mean solar exatmospheric units θ s = solar zenith angle in degree. Impervious Dataset Surfaces or features that prevent water from infiltrating into the soil are defined as impervious surfaces. These are the major component of urban infrastructure, thus considered as an important indicator of urban settlement in remotely sensed dataset (C. Wu & Murray, 2005). Impervious images represent the percentage of impervious surface in a pixel. They are produced using remotely sensed data such as ETM+ and Terra ASTER, and tend to preserve more spectral information that can be useful for urban land use classification (Ji & Jensen, 1999; Li & Weng, 2005). The impervious image used in this research was prepared by USGS using spectral information from ETM+ dataset. 14

24 Calculation of Indices Normalized difference vegetation index (NDVI): NDVI is widely used for predicting vegetation characteristics from remote sensing image. Vegetation has low reflectance on red (R) band and has a high reflectance on near infrared (NIR) band on reflectance curve. These different bands obtained from vegetations were used by NDVI to detect vegetations. Where, NIR = reflectance value of near-infrared band R = reflectance value of red band Modified normalized difference water index (MNDVI): This index is generally used in identifying individual water bodies from satellite imagery. MNDWI uses the relation of green (G) band and mid-infrared (MIR) band to delineate water pixels from other spectral pixels because water has a higher and lower reflectance in green (G) band and MIR band respectively. Where, MIR = reflectance value of mid-infrared band G = reflectance value of green band 15

25 Normalized difference built-up index (NDBI): This index was proposed by Zha et al. (2003) for mapping the urban builtup instead of using NDVI and MNDWI indices. They concluded that urban settlement has higher reflectance in mid-infrared; and the use of mid infrared and near-infrared to define the index is more appropriate. Where, MIR = reflectance value of mid-infrared band NITR = reflectance value of near-infrared band Normalized difference blue band built-up index (NDBBBI): This index was discussed by Baraldi et al. (2006) as a suitable index for detecting urban pixels. This expression exploits a relation of mid-infrared and blue (B) band to delineate urban pixel from remotely sensed data. Where, MIR = reflectance value of mid-infrared band B = reflectance value of blue band Wetness index (WI): Spectral properties of soil depend on various soil attributes such as soil type, texture, moisture, and organic matter content. On the other hand satellite imagery obtained from remotely sensed imagery can produce varying spectral behavior depending on soil property and classification. Hence the use of satellite 16

26 imagery is a challenge in determining wetness index of soil. However, moist soil, in general reflects similar spectral property which is useful for delineating it from other classes such as vegetation, residential, and commercial. In general, wet soil exhibits low reflectance values in all TM band and the soil wetness index (WI) information obtained from tasseled cap indices (explained by Crist et al.(1986)) can assist in delineating it from other land cover types such as vegetation, industrial and residential classes from remotely sensed data (Crist et al., 1986). The wetness index (WI) can be defined as (Todd, Hoffer, & Milchunas, 1998): Knowledge-Based Classification This classification method is also termed as expert classification or rulebased classification. This classification method integrates and processes information available in multiple knowledge layers (e.g. spectral, temporal) from remotely sensed data to produce a single classified image. In terms of land use classification, by using knowledge-based classifier, a user can specify or design the required attributes or class based on user s knowledge, land use characteristics, as well as classification rules based on multi-spectral and multitemporal remotely sensed data. This classification approach follows a hierarchical expert decision tree method to classify defined variables. 17

27 Different knowledge based layers such as impervious, NDVI, MNDWI, NDBI, and NDBBI were used within hierarchical classification tree to produce classified image required for this research. All process performed using the knowledge engineer tool available in ERDAS IMAGINE 9.3 (eardas, 2008) Figure 2. Flowchart of methodology. 18

28 Figure 3. Flowchart of knowledge-based classification model. 19

29 Development of the Knowledge-Based Classification Model for Denton County The images produced by the indices (NDVI, MNDWI, NDBI, NDBBI and WI) of TM image showed that specific land-use (features) type has notably different index values as compared to other land-use types. For example, NDVI image showed notable high index value for vegetation areas; water is represented by highest index value on the MNDWI image; and NDBI image showed higher index values in residential built-up areas as compared to vegetated area. These results helped define threshold values in the hierarchical knowledge-based classification model from TM image for residential pixel extraction. (a) (b) 20

30 (c) (d) (e) (f) Figure 4. Indices data image from generated from TM image. (a) Landsat Image; (b) NDVI Index; (c) MNDWI Index; (d) NDBI Index; (e) Wetness Index; and (f) NDBBBI Index 21

31 (NDVI) Vegetation Residential Samples (a) Vegetation vs. residential MNDWI Water Residential Samples (b) Water vs. residential NDVI CIT Residential Samples (c) Commercial/Industrial/Transportation (CIT) vs. residential Figure 5. Classification indices value of various land use type vs. residential land use type. 22

32 NDBI Soils Residential Samples (a) Soils vs. residential NDBBBI Soils/Const Residential Samples (b) Soils/construction sites vs. residential 0.15 Wet_Soil Residential Wetness Index (WI) Samples (c) Wetland vs. residential Figure 6. Classification indices value of various land use type vs. residential land use type. 23

33 Reflectance Band 4 Band Samples Figure 7. Spectral response of band 4 and band 7 in residential build-up area. The graphs in Figure 5, 6, and 7 clearly show that the usefulness of various spectral indices in discriminating residential land use from other land use types. For example; normalized difference vegetation index (NDVI) graphs show that vegetation, commercial and industrial pixels can be distinguished from urban pixels (figures 5a, 5c). Similarly, the spectral indices like normalized difference built-up index (NDBI) and normalized difference blue band built-up index (NDBBBI) illustrate that they can be used for delineating urban built-up from its surrounding features such as bare soil and construction materials/sites (figures 6a, 6b). Figure 7 shows that the spectral response band 4 (NIR) and band 7 (TM7) in residential land use type can be used in defining additional rules for the knowledge-based model for extracting residential built-up pixels. 24

34 Figure 8. Knowledge-based classification model developed for land use classification using Landsat 7 ETM+ image classification rules and conditions. Accuracy Assessment of Classified Images Accuracy assessment for remote sensing image classification is the process involved in understanding the quality of the image produced by discovering and evaluating classification error. Therefore, accuracy assessment of the image is very important as it defines the reliability of the information provided by the image which, in turn, can be used for various decision making processes (Congalton & Green, 1999). Since the study concentrates on classifying urban settlements, the classified image produced from knowledge based model is re-classified into two 25

35 groups; residential and non-residential. In the next step, Accuracy assessment, the tool readily available in ERDAS IMAGINE 9.3 is used to test accuracy of re-classified images. Random samples plots were allocated around the study area (Denton County) and a 3 3 pixel cluster was selected as a minimal area for defining pixel classification. Furthermore, aerial image of Denton County acquired in 2000 was used as reference to define classification of allocated sample pixels manually. Input Data for Regression Models Census block group level is used to generate the regression model for producing population estimates. In this step, the total number of residential pixels that lie within individual census block groups is calculated using ArcGIS. This tool calculates statistics (e.g. sum, mean, median, standard deviation etc) on values of a raster within the zones of another dataset (raster or vector) and reports the results to table (ArcGIS, ESRI). Regression Modeling Regression is a statistical technique used for investigating relationships between the given (i.e. depended) variable and one or more other (i.e. independent) variables. Previous work in the area of population estimation has used regression methods to determine correlations between spectral reflectance value pixel and population density. For example, Lusaka and Hegedus (1982) used spectral reflectance of bands 4, 5 and 7 as input to develop a regression model to estimate population distribution in Tokyo, Japan. Similarly, Harvey 26

36 (2002a) used different spectral characteristic such as indices, band sum, and band difference as variables for regression analysis to estimate population. The two different regression analysis methods, linear regression and geographically weighted regression (GWR), are used in this study. Linear regression model: Linear regression model can be defined as follows: Where, P e = population estimates a = regression intercept b = slope x = area of residential pixels in a block group Geographically Weighted Regression Model In spatial datasets, the relationships between the dependent and independent variables are different across geographic space; i.e. the same attribute can have a different effect on the model in different parts of the study region (Fotheringham, Brunsdon, & Charlton, 2002). However, global regression models such as linear regression examines the relationship between the dependent and independent variables without explicitly considering the variations that may occur due to their spatial context. Therefore, there is a need for developing modeling techniques that define the relationship between variables 27

37 locally, i.e. the results produced from such models should be location dependent (Charlton, Fotheringham, & Brunsdon, 02/02/2009). Geographically weighted regression (GWR) is a local spatial statistical technique that defines and analyzes the relationship between various attributes that vary across geographic space (Fotheringham et al., 2002). Unlike traditional global model, the GWR model allows the explanatory variable to vary in terms of location, thus providing detail information on understanding and analyzing geographic data. A GWR model also takes into account the spatial weighting function which allows to define relationship among neighborhood according to spatial variation throughout the study area (Fotheringham et al., 2002), and due to its ability to incorporate spatial attribute for research, GWR analysis technique is widely used in many studies such as geography, remote sensing, and environmental science (Mennis, 2006). A GWR model can be expressed as (Lo, 2008): Y i n = ai + aik xik + e 0 i (i = 1, 2,, n) (9) k= 1 where Y i and x ik are the dependent and independent variables at i, k = 1, 2,, n, e i are normally distributed error terms (with zero mean and constant variance at point i), and a ik is the value of the k-th parameter at location i. 28

38 Accuracy Assessment of Population Estimation Accuracy assessment is an important procedure to test the developed regression model in population estimation research. The three error measures for accuracy assessment as suggested by (Lu et al., 2006) are: Relative Error (RE): Relative error compares the result produced by the developed model with the census measurement to test the goodness of the model. Where, P e = estimated population calculated from the regression model P r = reference population (for this research: block group population) Mean relative error (MRE): In the same way, MRE can be used test the overall performance of the model over the study area. Where, RE = relative error; n= total number of census block group used in study area Median relative error (MdRE): This measure is used to reduce the effect of extreme values to the overall result. 29

39 CHAPTER 4 RESULTS AND DISCUSSION Results from Maximum Likelihood Classification (MLC) The Landsat Thematic Mapper (TM) image of Denton County was classified into several land use classes such as vegetation, water, commercialindustrial- transportation area (CIT), soils, and residential classes based on training samples defined by using aerial Image of Denton County; furthermore, the classified image was reclassified into two major classes: residential and nonresidential for extracting residential built-up. (a) (b) Figure 9. Classified image of Landsat TM produced from MLC classification. (a) TM classified Image (MLC); (b) Reclassified Image from a. Black pixels are classified residential areas. 30

40 Table 2 Error Matrix for Maximum Likelihood Classification (MLC) TM Image True Data Total User Nonresidential Residential Accuracy (%) Non-residential Residential Total Producer Over All Accuracy (%) Accuracy assessment was performed using visual interpretation method by taking high resolution aerial image as a reference data. Random samples were selected throughout Denton County; despite the overall accuracy of near 95% was achieved for overall classification, the produced classified image was only able to achieve 67.85% and 79.16% user and producer s accuracy respectively in extracting residential area. Results from Knowledge-Based Classification of Landsat TM and Impervious Surface Data Knowledge-based classification model was developed using rules based on spectral indices and impervious surface layer characteristics. The TM image is classified by using developed knowledge-based model, further; it is reclassified into two major classes; residential and non-residential. 31

41 (a) (b) Figure 10. Classified image of Landsat TM after processing using impervious surface data. (a) Classified image using Landsat TM and impervious surface layer. (b) Reclassified residential and non-residential areas based on results in a. Table 3 Error Matrix for Impervious Surface Data TM Image True Data Total User Nonresidential Residential Accuracy (%) Non-residential Residential Total Producer Over All Accuracy (%) Table 3 summarizes the accuracy assessment result for classified image produced from knowledge-based model. This classification produce an overall all 32

42 accuracy of over 97% with improved user and producer accuracy for the residential built-up as compared to MLC classification result. Results from Knowledge-Based Classification Using Landsat TM Data Alone In the same way, knowledge-based model was developed using Landsat TM spectral properties alone. The image classification was broken into number of land use classes such as vegetation, water, CIT, soils, and residential classes based on spectral property described by characteristics of spectral indices. The image is then reclassified into two major classes: residential and non-residential, and subjected to accuracy test. (a) (b) Figure 11. Results from knowledge-based classification using Landsat TM data alone. (a) TM Classified image using spectral attributes. (b) TM reclassified image using spectral attribute. 33

43 Table 4 Error Matrix for Spectral Response Alone TM Image True Data Total User Nonresidential Residenti al Accuracy (%) Non-residential Residential % Total Producer 96.84% 90.48% Over All Accuracy (%) 95.73% Table 4 summarizes the accuracy assessment result produced from the classified. The over-all accuracy of over 95% is achieved for the produced classified image. The error matrix on table 4 also showed the improvement on user and producer s accuracy on delineating residential pixels. In comparison to the MLC and knowledge base model using impervious layer, the knowledge-based classification model produced using only TM spectral attributes was the most effective method for delineating residential areas. In addition to its effectiveness in delineating residential areas, the knowledge-based classification model does not need impervious surface data for a study area, thereby facilitating the use of the model in other study areas. Regression Models Based on the images classification results described in the previous section, the classified images produced from the knowledge based model using Landsat data alone were used as base images for regression modeling. Random samples from census block-group dataset are selected to generate 34

44 linear regression models. The developed models are then applied to the entire study area to make population estimates of the region. Relative errors at blockgroup level are calculated for model accuracies in estimating population. Linear Regression Models Denton County (03/04/2000 Image) Table 5 summarizes 12 linear regression models produced by various census block-group samples using block-group population as a dependent variable and block-group residential pixel area as explanatory variables. In addition, a new regression model is derived from incorporating multiple models generated from different group of samples. A total of 42 random block-groups were selected for generating each linear regression model. The high R 2 value for sample block-group suggested that the size residential area is highly correlated with population of the region at block-group level. 35

45 Table 5 Summary of Linear Regression Model Results -Denton County Linear Regression (Denton County) Samples R 2 Total Error Mean Error Median Model (%) (%) Error (%) y = x y = x y = x y = x y = x y = x y = x y = x y = x y = x y = x y = x General Linear Equation Model Y= X Selected regression models are shown in Figures 12 and 13. Scatter diagrams are produced for analyzing and understanding the relationship between the relative population estimation errors and population density at each census block-group of the Denton County (Figures 14 and 15). 36

46 Population y = x R² = Area (sq. m) (a) Population Population y = x R² = y = x R² = Area(sq. m) (b) Area(sq. m) (c) Figure 12. Linear regression models derived from sampling Denton County blockgroup level. 37

47 Population y = x R² = Area(sq. m) Population Population y = x R² = (a) y = x R² = Area(sq. m) (b) Area(sq. m) (c) Figure 13. Linear regression models derived from sampling Denton County block-group level. 38

48 Relative Error (%) Population Density (Persons per Sq. Km) (a) 500 Relative Error (%) Population Density (Person per Sq. Km) (b) 500 Relative Error (%) Population Density (Person per Sq. Km) (c) Figure 14. Scatter diagrams of relative population estimation error vs. population density at the census block-group level for Denton County. 39

49 500 Relative Error (%) Population Density (Person per Sq. Km) (a) Relative Error (%) Population Density (Person per Sq. Km) (b) Relative Error (%) Population Density (Person per Sq. Km) (c) Figure 15. Scatter diagrams of relative population estimation error vs. population density at the census block-group level for Denton County. Denton County (03/12/2000 image) Classified image generated from 03/12/2000 Landsat image is developed 40

50 from produced knowledge-based classification model. In the next step, the produced image was used estimate population using the general regression model produced for Denton County in table 5.The total population estimates and total error produced by the linear regression are summarized in table 6. Figure 16 summarizes the relationship between relative population estimates error and the block-group density for general regression model produced in table 5. Table 6 Summary of Population Estimates Produced by General Regression Model for Denton County General Linear equation(denton County): Y= X Image Total population Est. population Total Error (%) 03/04/ /12/ Relative Error (%) Population Density (Person per Sq. Km) Figure 16. Scatter diagrams of relative population estimation error vs. population density at the census block-group level generated from general regression for Denton County (03/04/2000) 41

51 Collin County (03/04/2000) In the same way, table 7 summarizes 12 Linear Regression models produced by selecting random census block-group samples of Collin County. Similar to previous methods, block-group population was selected as a dependent variable and block-group residential pixel area as explanatory variables. A new regression model is derived from incorporating multiple models generated from different sample groups. Table 7 Summary of Linear Regression Model Results -Collin County Linear Regression (Collin County) Samples R 2 Total Mean Error Median Error Model Error (%) (%) (%) y = x y = x y = x y = x y = x y = x y = x y = x y = x y = x y = x y = x General Linear Equation Model 2.10 Y= X A total of 60 random block-groups are selected as a sample size for generating each linear regression model presented in table 7. Figure 16 represent a graph generated by selected regression model from table 7. Due to 42

52 some anomalies present in relative population estimation error, the scatter diagram produced was condensed and became less representative because of those extreme values. For the purpose of better graphical representation, the extreme values were removed from the scatter plot (Figure 18), which only account for 1% of the data. 43

53 Population y = x R² = Area (Sq. m) (a) population y = x R² = Area (Sq. m) (b) population y = x R² = Area (Sq. m) (c) Figure 17. Linear regression models derived from sampling Collin County block-group level. 44

54 1000 Relative Error Population Density (Person/Sq. Km) (a) 1000 Relative Error Pop. Density (Person/Sq. Km) (b) Relative Error (%) Pop. Density (Person/Per sq. m) (c) Figure 18. Scatter diagrams of relative population estimation error vs. population density at the census block-group level for Collin County. 45

55 Collin County (03/12/2000 image) Again, a developed knowledge based classification model is used to generate classified image of Collin County dated 03/12/2000. The produced image was used to produce population estimates using the general regression model produced in Table 6. A summary of the results are presented in Table 8. Table 8 Summary of Population Estimates Produced by General Regression Model for Collin County General linear equation(collin County): Y= X Image Total population Est. population Total Error (%) 03/04/ /12/ Relative Error Pop. Density (Person/Sq. Km) Figure 19. Scatter diagrams of relative population estimation error vs. population density at the census block-group level generated by general linear regression Collin County (03/04/2000). 46

56 Denton & Collin County (03/04/2000 and 03/12/2000 image) Similarly, a combination of classified images of Denton and Collin County is used to generate general regression model for producing population estimates for both counties. Table 9 summarizes the results of the regression model produced from different samples groups selected from both counties. Table 9 Summary of Linear Regression Model Results Denton and Collin County Combine Linear regression (Denton and Collin County Combine) Samples R 2 Total Error Mean Error Median Model (%) (%) Error (%) y = X y = X y = X y = X y = X y = X y = X y = X y = X y = X General linear equation model 3.10 Y= X A total of 100 total random block-groups are selected from Denton and Collin County for generating each linear regression model. In the next step, the general regression model produced in table 9 is used to perform population estimates of Denton, Collin, and combine population estimates for both counties as well. Table 10 summarizes the results produced from the analysis. 47

57 Table 10 Summary of Population Estimates Produced by General Regression Model (Denton & Collin County Combined) General linear equation(denton & Collin County Combine): Y= X Image Total population Est. population Total Error (%) 03/04/2000 Denton /04/2000 Collin /04/2000 Combined /12/2000 Denton /12/2000 Collin /12/2000 Combined Geographically Weighted Regression Table 11 shows the errors calculated from the population estimates when geographically weighted regression (GWR) model is employed to the entire study area. GWR model was developed using 03/04/2000 classified data for Denton, Collin, and combine County separately. The GWR model based on 03/04/2000 image was used to perform population estimation from 03/12/2000 classified image. In this analysis, adaptive kernels are used, and the bandwidth is determined using cross validation (CV). Figure 18 illustrates the scatter diagrams obtained from GWR model and defines the relationships between relative population estimation error obtained from GWR models and population density. 48

58 Extreme values were removed from the scatter plot (figures 19, 20), which only account for one percent of the data for better graphical representation for the model. Table 11 Summary of Geographically Weighted Regression Model Results Geographically weighted regression Study area R 2 Total Error (%) Mean Error Median Error Denton (03/04) Local Models Denton (03/12) Local Models Collin (03/04) Local Models Collin (03/12) Local Models Combine (03/04) Local Models Combine (03/12) Local Models Denton Local (Combine-03/04) Models Collin Local (combine -03/04) Models Denton Local (Combine-03/12) Models Collin (Combine -03/12) Local Models

59 Relative Error (%) Population Density(Person per sq. km) (a) GWR County Denton(03/04) Relative Error (%) Population Density (Person per sq. km) (b) GWR Denton County (03/12) Figure 20 Scatter diagram of relative population estimation error vs. population density for GWR model (Denton County) Relative Error (%) Pop. Density (Person per sq. km) (a) GWR Collin County (03/04) Relative Error (%) Pop. Density (Person per sq. km) (b) GWR Collin County (03/12) Figure 21. Scatter diagram of relative population estimation error vs. population density for GWR model (Denton and Collin County). 50

60 Relative Error (%) Pop. Density (Person per sq. km) (a) GWR Denton and Collin County (03/04) Relative Error (%) Pop. Density (Person per sq. km) (b) GWR Denton and Collin County (03/04) Figure 22. Scatter Diagram of relative population estimation error Vs. Population Density for GWR Model (Denton and Collin County). Discussion A few observations and limitations faced while producing the above results are discussed below. 1. Knowledge-based vs. MLC: The MLC and knowledge-based model produced the higher overall accuracy for their respective image. However, the closer examination of the error matrix (tables 2, 3, 4) showed the knowledge-based model is able to discriminate residential pixel more accurately as compared to 51

61 MLC. In addition, MLC requires training samples for image classification whereas knowledge-based classification approach classifies image without using training samples; as the result, knowledge-based models facilitate the image classification compared with MLC. Impervious surface knowledge-based model: Impervious surface is an important attribute which is closely associated with urban ecosystems. This attribute can be very useful for developing the knowledge-based model. However, the impervious surface layer was not readily available for the temporal period of study (the impervious layer available through USGS for the research is from 2001). On the other hand, knowledge-based model developed only by using TM spectral attribute was able to produce similar accuracy results. The error matrix (tables 3, 4) showed that the knowledge-based model derived from spectral indices is able to discriminate residential pixels more precisely as compared to the knowledge-based model derived from original image bands and impervious data. 2. Spectral knowledge-based model: The model discriminated residential land use type with high accuracy from Landsat TM image. However, the model struggled to extract residential pixels from the sparsely populated neighborhood with high accuracy. Since the classification model is exclusively based on spectral response of TM bands, in thinly populated region, the spectral 52

62 response of the surrounding feature dominates the residential pixel s spectral response and causes errors in classification (figure 23). Another principal factor affecting the classification accuracy in sparsely populated areas is that the medium spatial resolution (30m 30m) of the Landsat TM which made it difficult to extract residential areas in low population density areas. (a) Aerial image (b) TM image (c) Classified TM image Figure 23. Sparsely populated region on aerial, Landsat TM and classified image. 3. The spectral response of TM band 4 (NIR) is affected by the moister content of the surrounding, and may affect the threshold value defined for the indices used in the models such as NDVI and NDBI. Likewise, Zha et al. (2003) also argue that the consistency of the NDBI for extracting residential built-up might be indirectly affected by the presence of other land use types that exhibit seasonal spectral response to TM bands, such as forests and soils. However, this setback may be overcome with the selection of the remotely sensed data captured during the time when the spectral discrimination between the surrounding features and residential built-up is higher. 53

63 4. The developed classification model only uses TM spectral properties to delineate land use type from each other. One of the limitations of this approach is: it is difficult to distinguish residential built-up from lake-shore or sandy beaches (where sand and silt concentration is high) because those features exhibit similar spectral response on all TM bands (Figure 24). (a) Aerial image (b) TM image (c) Classified TM image Figure 24. Lake shore (sandy beach) and residential built-up on aerial, TM, and classified image. 5. The majority of the old residential neighborhood of the study area is surrounded by tree canopies. Therefore, image captured during leaf-off season more likely to produce good classification results because residential area can be delineated more precisely as the few features are covered by tree canopies. In addition, the image acquired on 03/12/2000 was affected by jet trails in the sky and corrected by removing the errors manually. 6. The high R 2 values for both Linear model and GWR models suggest that population is strongly correlated with the residential built-up. 54