Report on Multiband Images,Colour Compositing, and Contrast Enhancement

Introduction

Image analysis by photo-interpretation is often facilitated when the radiometric nature of the image is enhanced to improve its visual impact. Specific differences in vegetation and soil types, for example, may be brought out by increasing the contrast of an image. Similarly the differences in brightness value can be highlighted either by contrast modification or by assigning quite different colors to those levels (Richards, 1989). This study presents a variety of image enhancement procedures often used with remote sensing image data. 

Objectives of study
i) To explore the basic capabilities of PCI Geomatica software,
ii)To introduce colour composites, histograms, and scatterplots as tools for exploring image data stored in database channels, and
iii)To learn about contrast enhancements, and the impact of the different enhancement types on raw imagery.

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Geographically Weighted Regression in Advanced Spatial Analysis and Modeling

The main purpose of this study is to run a Geographically Weighted Regression (GWR) on the City of Calgary Census Data using Geographical Weighted Regression. The data available for study is the Census Tract data set for the City of Calgary provided in a geodatabase format. The dataset is similar to the first and fifth Assignments. However, the previous assignments involved the calculation of a simple linear regression and spatial regression which outputs global parameter rather than local. Therefore, in this study, the objective is to fit a GWR model to the Census data using "Average Income" as the dependent variable so as to allow local parameter to estimate the model. The "Geographically Weighted Regression 3" software package is used to compute the global weighted regression model. This paper includes many statistical techniques applied to obtain the final model. It also compares the results obtained from the linear regression model and spatial regression model  with the Geographically Weighted Regression model.

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Spatial Regression in Advanced Spatial analysis and Modeling

Introduction

The main purpose of this study is to explore the process of model selection and spatial regression using S-Plus and its Spatial module. The data available for study is the Census Tract data set for the City of Calgary provided both in GeoDatabase and S-Plus data frame. The dataset is similar to our first Assignment but with an addition of two columns for X and Y to represent Easting and Northing respectively. The previous assignment involved the calculation of a simple linear regression without considering spatial autocorrelation into account. Therefore, in this study, we will fit a regression model to the Census data using ‘Average Income’ as the dependent variable by considering spatial autocorrelation in the regression model. The S-Plus command line was used to compute a Simultaneous Autoregressive (SAR) model so as to describe the relationship between ‘Average Income’ and the other independent variables. This paper includes many statistical techniques applied to obtain the spatial regression model. It also attempts to compare the results obtained from the simple linear regression (in Assignment 1) with the model obtained in this study.

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Variograms, Trend removal and Universal Kriging in Advanced Spatial Analysis and Modeling

Introduction

The main objectives of this study are i) to learn how to use ArcGis‟s GeoStatistical Analyst to examine spatial structure and to interpolate surfaces using GeoStatistical techniques, and ii) to explore GeoStatistical technique using S-Plus (variograms, trend removal, Universal Kriging). The data available for this study include a GeoDatabase that contains a random spot heights feature class for an area in the northwest of the City of Calgary and the City Limits for the City of Calgary. Additionally, a 25m Digital Elevation Model (DEM) for the same area is also included. In this study, we are interpolating the random spot heights surface so as to predict the elevation of the surrounding areas. The software packages used for interpolation are ArcGis and S-Plus. ArcGis is used for Ordinary Kriging interpolation and S-Plus for Universal Kriging. This study aims at evaluating the results performed by two software packages. It also highlights the strengths and weakness of the software packages used for the analysis. This study includes many maps, figures, tables and command lines created in Arcgis and S-Plus; however, the end product of this study is to match the Ordinary Kriging performed in ArcGis and Universal Kriging performed in S-Plus and to understand how different software packages perform the interpolation.

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Interpolation and Trend Surface Analysis in Advanced Spatial Analysis and Modeling

Introduction

'In the mathematical subfield of numerical analysis, interpolation is a method of constructing new data points within the range of a discrete set of known data points. Surface interpolation functions make predictions from sample measurements for all locations in a raster dataset whether or not a measurement has been taken at the location. There is a variety of ways to derive a prediction for each location; each method is referred to as a model. With each model, there are different assumptions made of the data, and certain models are more applicable for specific data. The Interpolation tools are
generally divided into deterministic and geostatistical methods' (ArcGis 2004). In this study IDW is used as the deterministic interpolator and Global Polynomial interpolation as geostatistical interpolator.

'Trend surface analysis is a method used for the analysis of change over space which attempts to decompose each observation on a spatially distributed variable into acomponent associated with any regional trends present in the data and a component associated with purely local effects. This separation into two components is accomplished by fitting a best-fit surface of a previously specified type using standard regression techniques' (Unwin, 1975). In this study, our main objective is to interpolate the average income for the City of Calgary using ArcGis and calculate a First and Second Order Trend Surface using S-Plus.

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