APPLICATIONS SENSING IN THE STUDY OF URBANIZATION OF REMOTE. Submitted by: Bryan Wong. Tonya Smith. Anna Yuill. Sebastian Kallos

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1 APPLICATIONS OF REMOTE SENSING IN THE STUDY OF URBANIZATION Image provided by Nasa Submitted by: Bryan Wong Tonya Smith Anna Yuill Sebastian Kallos FRST 443: Remote Sensing for Forestry and Agriculture April 2 nd, 2012

2 INTRODUCTION The application of remote sensing in mapping and understanding urban expansion has become an increasingly popular area of discussion. Mapping cities assists planning managers and governments in understanding trends that can be useful in areas such as urban planning, pollution mitigation and management, and urban ecology. Urban areas mapping and monitoring can be done on a number of scales using various remote sensing tools, and is a fast-growing area of the application of cutting edge sensing models and technologies. The purpose of this report is to provide an overview as to the various applications of remote sensing in mapping and monitoring urban areas, with some exploration into the different challenges and achievements in recent measuring techniques. URBANIZATION FROM ARCHIVAL LANDSAT IMAGERY Landsat TM is commonly selected as a data source for land cover information to characterize urban areas (Zhang et al 2010). Landsat is useful in that it has collected imaging data from the early 1980 s and is thus able to provide a temporal dimension to the data available on urbanization. Spatial data is also able to be acquired from Landsat satellites, including the 80m resolution multi-spectral scanner, the 30m resolution thermal mappers, and the 15m resolution of the panchromatic sensor of Landsat 7 (ibid). The ground coverage of Landsat, which is a scene approximately 180km x 180km in size, is also sufficiently large to capture connectivity between major cities in a single scene (ibid). Classes within urban areas may be additionally distinguished using spatial-spectral mapping methodologies to identify commercial-industrial, residential, transitional and open land regions, as well as a limited number of non-urban classes (Guindon

3 and Zhang 2010). To measure sustainability indicators however, the land cover provided from Landsat may be insufficient. Therefore, products like the Canadian GeoBase program may be used for the integration of information to the imagery. For example, an ortho-rectified Landsat imagery may cover some modern timeframe, and base land cover data may be acquired from other sources, such as imagery from Canadian archives, to collect data for a broader time series. In addition to Landsat TM and GeoBase data, urban areas in Canada maybe better understood with data from the National Topographic Data Base, which provides thematic layers that may not be reliably mapped from Landsat imagery, such as wetland areas, drainage and point features. These data sources, combined with census data, can be integrated to provide quantifiable information about urban form characterization (ibid). Urban form is commonly characterized using medium- and high- resolution optical and synthetic aperature radar (SAR) satellite data (Molch et al 2010). Very high resolution multispectral imagery is also increasingly available from censors such as IKONOS, QuickBird and GeoEye-1, allowing for a detailed picture of urban environments. Spectral similarity between land cover classes and high variation within land cover classes in urban environments due to the limited spectral resolution of very high resolution imagery creates issues when trying to use traditional pixel-based classification methods (Xu and Li 2010). Thus it has been found useful to classify objects in urban areas using spatial information and object-based classification methods (Pagot et al 2008). Object based image analysis uses spatially and spectrally similar groups of pixels in order to identify objects within an imaged scene. Examples of object based image analysis in an urban setting could be natural features of urban landscapes such as trees and lakes or man made features such as buildings or roads (Yang 2011). Two examples of object based analysis

4 procedures are the decision rule and nearest neighbor classifers which can be used to extract urban land cover types. When assessing SAR data, incidence angle may be important to understand the distortion in the image provided. A higher angle of incidence in SAR data may allow manmade buildings to become discernable from other objects around, but may also affect the detectability of nearby buildings (Xu and Li 2010). Lidar imagery is also used to give a detailed 3D view on urban landscapes, and associated technology is being developed for automatic building recognition (Samadzadegan et al 2010). IMPERVIOUS SURFACES Xian and Crane (2005) examined the influence that urbanization and its associated impervious surfaces on the landscape within individual drainage basins of the Tampa Bay watershed. Xian and Crane (2005) defined impervious surfaces as impermeable features such as rooftops, roads, and parking lots, and have proven to be key indicators for identifying the spatial extent of urbanization and urban sprawl. Using a method known as Sub-pixel Imperviousness Change Detection (SICD) Xian and Crane (2005) were able to quantify urban land cover and land use change spatially using remote sensing data. This SICD method uses high-resolution imagery as a source of training data for representing urban land-cover heterogeneity, and medium-resolution Landsat imagery to extrapolate impervious surfaces over a large spatial area (Xian and Crane 2005). Xian and Crane (2005) outlined 4 steps in the process of mapping impervious surfaces using SICD. The first step involved the development of training or validation data by using high-resolution digital orthophoto quarter quadrangles (DOQQs), followed by the second step of selecting predictive variables and initial regression tree modeling and assessment, the third step

5 included spatial modeling and mapping with Landsat TM/ETM+(Enhanced Thematic Mapper Plus) or other satellite imagery, and the fourth and final step was analyzing impervious surface change detection and interpretation. Some interesting findings from Xian and Crane (2005) were when using the Landsat imagery, high values of imperviousness were found in the suburban areas. However in some of these areas, vegetated land was miscalculated as impervious surface, to correct for this, they used a Normalized Difference Vegetation Index (NDVI) on the images. The modification produced an improvement in determining and separating vegetated land cover from urban land-cover. Xian and Crane (2005) then modelled into the future the extent to which impervious surfaces would change and predict objectively the extent and intensity of urban land cover and land use, and low density development (sprawl). URBAN SPRAWL The term urban sprawl can be synonymous with the undesired rapid urban growth that produces low density spatially wasteful development, usually associated with unplanned or uncoordinated growth. However, there is very little understanding of the determinants and structure associated with urban sprawl and any attempts to measure sprawl have focused largely on its social and economic costs rather than quantifying its characteristics. Torrens and Alberti (2000) have created a toolkit for assessing urban sprawl and identified 4 key indicators to measure the extent of urban sprawl that can be identified within remote sensing images. Their first indicator they suggest is density, this indicator can be measured by the housing per unit area or through density gradients that consider density as a function of distance from urban centers, where population per unit area declines with distance from urban centers. Their second indicator was the spatial geometry of built and open spaces; this has been adopted from ecological research, the idea of

6 Patch Theory in an urban setting where patches are defined as land use types such as residential, industrial, commercial, open-natural and open-agricultural. These spatially distinct patches have distinct qualities and parameters such as area, circumference, edge shape, and area circumference ratio. The distribution of patches across the landscape can be characterized and quantified with analyzing relative abundance, connectivity and degree of separation between similar patches. The composition and configuration of patches is dependent on the distribution of patches and provides a useful measure for comparison across landscapes and scales. Composition in detail can refer to how heterogeneous an area is with regards to the differing patch types and provides a comparison or establishes a relationship between different patches. Several measurements of patch composition are the contagion or aggregation of patches, and connectance and proximity between patches. Connectance and proximity between patches computes the functional closeness of patches of similar type and their results are interpreted such that the greater the dispersal of patches the greater the sprawl. Urban sprawl has the impact of poor accessibility followed by the large increase in the use of private vehicles. This leads to Torrens and Alberti (2000) third key indicator: accessibility between patches; they classify isochrones which are the measurements through which one counts the number of possible trip destinations in a given area. These accessibility measurements or isochrones can be analyzed through road length and area between residential, commercial, and industrial patches and their dependence on private vehicles in the absence of alternatives. Torrens and Alberti (2000) final indicator is the aesthetic measures of urban sprawl, since urban sprawl is uncoordinated there exists a retailscape or ribbon effect of commercial property alongside highways and highway exits that ribbon sprawls across the landscape. Although they

7 stated that this measurement is the least relevant to remote sensing experts, they considered this ribbon sprawl as quantifiable segments of developed land that are clumped together in aggregates but extend axially and leave the adjacent space undeveloped. In addition, the selection of scale is another critical issue when selecting appropriate techniques for measuring sprawl. Take for example patch contagion or aggregation of similar patches at the municipal scale might suggest large tracts of homogeneous land use whereas high contagion at the regional scale might suggest low amounts of homogeneous land use with the fragmentation of the landscape with built patches clustered and not fragmenting the open surrounding space. As a result, the sensitivity of spatial composition to scale should be considered before applying these indicator metrics to measuring urban sprawl. TRAFFIC AND DENSITY Traffic patterns in cities are a good indicator of the level of sustainable development that is happening in urbanization. Urban form mapping is often a useful indicator to understand traffic patterns and thus sustainable development within an area. Low density, sprawling urban areas will have a tendency to demonstrate higher vehicle usage by residents, as a large number of people who live in outlying regions of a city will depend more highly on vehicular travel to get to workplaces found in urban centers (Zhang et al 2010). Conversely, high density, mixed residential and employment growth in centers and corridors, which feature a mix of activities and pedestrian-friendly design, will tend to features less reliance on vehicular travel, and will therefore demonstrate lower carbon dioxide and exhaust emissions from commuters (Davis 1997). Additionally, corridors of high density residential buildings will allow for public transit systems to be successful in those areas, and high density residencies found along a transit

8 corridor leading in to a commercial or business urban center will additionally work to cut down on exhaust from vehicular commuting (ibid). As well as high density residential areas, it is found that land use mix, which involves residential areas being mixed with commercial-industrial areas, is useful for tracking urban efficiency, as a lower radius of commercial-industrial building surrounding a residential area will allow for less energy intensive forms of transportation, such as walking and biking, being selected over driving (Guindon and Zhang 2007). Travel modeling may be done with various spatial-interaction models in combination with remote sensing data such as Landsat TM, GeoBase or similar data and census data. One method of modelling transportation involves the following; to model, pixels in an image are identified by their type, such as residential or commercial-industrial etcetera (Zhang et al 2010). These pixels are then characterized as a trip origin location, or a trip destination location, and weighted by the amount of works per household in the trip origin location (ibid). The formula used includes variables like trip origin propulsion, destination attraction, trip deterrence, and expected flow of traffic from point A to B (ibid). These variables, along with distance between points, density of employment at a given location, and other aspects such as traffic congestion, may be combined to produce an indicator of inter-urban travel based off of the definition of urban form. IMPERVIOUS SURFACE DATA: SURROGATE TO WATER QUALITY AND QUANTITY Impervious surfaces can be defined as any material that prevents the infiltration of water into the soil (Arnold and Gibbons 1996). The impervious surfaces within an urban catchment could be associated with roads, rooftops, sidewalks, patios, compacted soil, and bedrock outcrops. Arnold and Gibbons (1996) distinguishes as development alters the natural landscape, the percentage of

9 the land covered by impervious surfaces will increase and a chain of events will occur that typically ends in degraded water resources. Arnold and Gibbons (1996) predict that this chain of events begins by altering the hydrologic cycle in the way that water is transported and stored, such that as impervious surfaces increase the velocity and volume of surface runoff increases resulting in a decrease in infiltration. This larger amount of runoff will increase the severity of flooding and contamination of water ways with point source pollution or polluted runoff. Arnold and Gibbons (1996) proceed to describe the use of impervious surface coverage as an environmental indicator that is measurable and a good predictor to water pollution at a community level and should be used in creating a framework for urban planners. Alternatively, Streutker (2002) identified the significant effects urbanisation had in which the temperatures of the central urban location was several degrees higher than those of nearby rural areas of similar elevation. Streutker (2002) quantified the urban heat island as a continuously varying surface through the use of AVHRR sensor data measuring surface radiance as an indicator of surface temperature. The results of this study found that heat island magnitudes varied up to a maximum value slightly over 4 C, and demonstrated the ability of remotely sensed temperature data is useful in performing comparative analysis on anthropogenic changes to surface temperatures that can aid in the studying and managing of urban heat islands and their impacts. LIGHT USE EFFICIENCY: PRIMARY PRODUCTION: CARBON SEQUESTRATION The accounting of urban carbon balance considers both vegetation carbon sinks and human energy uses. One of the challenges to carbon estimation is the availability of data on carbon exchange between vegetation and the atmosphere (Yang 2011). A Light use efficiency (LUE)

10 model addresses this carbon estimation challenge by integrating coarse resolution remote sensing imagery that correlates primary production from the function of incoming solar radiation on the types of vegetation modeled by vegetation biophysical parameters such as leaf area index, vegetation reflectance characteristics and carbon conversion efficiency (Yang 2011). Using a normalized difference vegetation index (NDVI) to derive red and near infrared reflectance, the impacts of high density and low density urban growth areas and rural areas on gross primary production (GPP) through a LUE approach found that high density areas reduced GPP by amounts of up to 50% in the South Atlantic United States and that areas of low density and rural were highest in GPP (Yang 2011). REMOTE SENSING METHODS IN URBAN BIODIVERSITY When compared to the amount of time and individuals needed to conduct annual field surveys on biodiversity affected by increasing urban growth, remote sensor data is a cost-effective source of information on biodiversity, due to its inexpensive ability to provide complete spatial coverage of environmental information over large areas of landscape. Yang X. (2011) identifies two different approaches in capturing remote sensing biodiversity components. The first approach is the direct approach; directly capturing individual species assemblages, identifying single species or classifications of vegetation types; all typically collected through high resolution imagery from airplanes or satellites such as Quickbird ( m), IKONOS (1-4m) and SPOT (2.5m). Improvements to the direct approach incorporated computerized approaches and the use of multilayer perception and neural networks and object based image segmentation moving away that move away from the pixel based approach (Yang 2011). New developments in retrieving biodiversity direct data are facilitated by the developments in radar and airborne lidar that has

11 been used to improve species distribution models by quantifying vegetation structure (Yang 2011). The second approach involves spatial ecological modeling, which explore the relationship between biodiversity components, ecological processes and predictive environmental variables derived from remote sensing. The response of these variables will predict future probability maps of spatial distributions of diversity and are based on such proxies such as land cover classes, climate, topography, connectivity, cost distance modeling, and heterogeneity in response to urban growth and land use change (Yang 2011). FUTURE OF REMOTE SENSING: USING HYPERSPECTRAL REMOTE SENSING The use of very high spatial resolution (VHR) satellite imagery gives its users high resolution less than 1m in resolution and is offered by many sensors: Quickbird, Worldview 1 and 2, Ikonos 2, and Earth Remote Observation Satellite (EROS). However, hyperspectral imagery resolves material-specific spectral reflection and absorption features making it highly more suitable for detailed mapping of urban surfaces (Yang 2011). Through a iterative procedure hyperspectral remote sensing is capable of analyzing urban structures at a subpixel level and provide fractional coverage information within each pixel (Yang 2011). Spatial inventories of natural and manmade surface materials allow for the mapping of imperviousness and characterizing thermal surface conditions, these inventories can then be processed with automated mapping of the whole image data set. Endmember detection is a distinct spectral signature of a surface material that produces pure reference image pixels needed in the automation process of unmixing hyperspectral image data (Yang 2011).

12 CONCLUSION Remote sensing data has facilitated environmentalists, planners, and land managers the ability to monitor the impacts of urban development has illuminated amount other things the extent to which urban development is growing at the cost of agricultural land/vegetation area in these areas. A key indicator of this rapid urban growth obtainable from remote sensed images is the continuous increase in impervious surface cover. This impervious cover is in itself a causation of environmental problems within urban centers such as decreased storage and increased surface water runoff which causes increased hydrologic response and increased pollutant accumulation. Urban sprawl has been defined as homogeneous development pattern identified by the lack of mixed land use at the neighborhood and city scale. Areas with a high rate of mixed land uses (heterogeneous) are regarded as compact and sustainable whereas a high percentage of residential land use is considered homogeneous and non-mixing and thus sprawling. When built areas are separated from one another by open space than the landscape is considered fragmented which is another sprawl characteristic

13 REFERENCES Arnold, C.L, Gibbons, C.J Impervious Surface Coverages: The Emergence of a Key Environmental Indicator. Journal of the American Planning Association. Vol 62. No. 2. P Bhatta, B., Saraswati, S., Bandyopadhyay, D Urban Sprawl Measurement from Remote Sensing Data. Applied Geography. Issue 30. P Davis, JS and S Seskin Impacts of urban form on travel behaviour. Urban Lawyer (29)2:, Guindon, B., and Zhang, Y Using satellite remote sensing to survey transportation-related urban sustainability. Part II: results of a Canadian urban assessment. Applied Earth Observation and Geoinformation (9): Molch, K et al Performance of built-up areas classification using high-resolution SAR data. Canadian Journal of Remote Sensing (36)3: Pagot, E., Pesaresi, M., Buda, D., and Ehrlich, D Development of an object oriented classification model using very high resolution satellite imagery for monitoring diamond mining activity. International Journal of Remote Sensing, Vol. 29, No. 2, pp Rogan, J. and Chen, D Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change. Progress in Planning. Issue 61 P Samadzadegan F. et al. (2010). An agent-based method for automatic building recognition from Lidar data. Canadian Journal of Remote Sensing 36(3): Streutker, D.R A Remote Sensing Study of the Urban Heat Island of Houston, Texas. Internation Journal of Remote Sensing. Vol. 23. No. 13. P Torrens, P. M. and Alberti, M Measuring Sprawl. Applied Spatial Analysis. Issue 1. P Ward, D., Phinn, S.R., Murray, A.T Monitoring Growth in Rapidly Urbanizing

14 Areas Using Remotely Sensed Data. Professional Geographer. Volume 52. Number 3. P Xian, G. and Crane, M Assessments of Urban Growth in The Tampa Bay Watershed Using Remote Sensed Data. Remote Sensing of Environment. Issue 97. P Xu, H and P. Li Urban land cover classification from very-high resolution imagery using spectral and invariant moment shape information.. Canadian Journal of Remote Sensing (36)3: Yang, X Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment. Chichester, West Sussex ; Hoboken, NJ : Wiley-Blackwell Zhang, Y et al Concepts and application of the Canadian Urban Land Use Survey. Canadian Journal of Remote Sensing (36)3: Zhang, L., Huang, X., Huang, B., and Li, P A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 10, pp Zhang, Q. Wang, J., Peng, X., Gong, P., Shi, P Urban Built-up Land Change Detection With Road Density and Spectral Information from Multi-temporal Landsat TM Data. International Journal Remote Sensing. Volume 23. Number 15. P

15 Annotated Bibliography Bhatta, B., S. Saraswati, and D. Bandyopadhyay Urban sprawl measurement from remote sensing data. Applied Geography 30: This report looks at urban sprawl and the measures and techniques that are used to quantify urban sprawl. It evaluates different techniques to quantify sprawl from remote sensing data, and attempts to merit or demerit these techniques for acceptance or rejection. Davis, J.S., and S. Seskin Impacts of urban form on travel behaviour. Urban Lawyer 29: This article looks at urban form and mixed use of urban spaces to indicate sustainable development. In particular, it looks at how transportation types are correlated to residential and commercial areas and their spatial context, with mixed dense urban associated with public transit and therefore fewer commuter hours and decreased vehicle emissions. Guindon, B., and Y. Zhang Using satellite remote sensing to survey transportation-related urban sustainability. Part II: results of a Canadian urban assessment. Applied Earth Observation and Geoinformation 9: This article discusses the application of remote sensing to quantify energy consumption associated with urban sprawl. Four indicators were analyzed quantify this energy use: population density, compactness, mode of travel, and probability of travel. Using these indicator cities across Canada were compared for travel efficiency. Haiqing, X., and L. Peijun Urban land cover classification from very high resolution imagery using spectral and invariant moment shape information. Canadian Journal of Remote Sensing 36: This article looks at in-depth detailed land cover classification using very high image resolution and three types of invariant moments to classify urban land cover imagery. This analysis found that it is extremely important to look at patterns and features of segmentation to improve urban classification. Molch, K., P. Gamba, and F. Kayitakire Performance of built-up areas classification using high-resolution SAR data. Canadian Journal of Remote Sensing 36: This article describes the use of unsupervised classification technology to analyze space borne radar imagery of urban areas for disaster planning and monitoring of urban sprawl in developing nations. The image processing procedure uses texture-based classification. Classification results of a user's accuracy of 78% and above were achieved without post-classification editing

16 Pagot, E., M. Pesaresi, D. Buda, and D. Ehrlich Development of an object-oriented classification model using very high resolution satellite imagery for monitoring diamond mining activity. International Journal of Remote Sensing 29: This article looks at classification of manmade structures using spatial data for an object-oriented approach, as opposed to the classic pixel-based classification method, for very high resolution spectral data. The study confirmed the usefulness of VHR satellite imagery for diamond mining with a bi-temporal dataset that allows information on the evolution of activity. Rogan, J., and D. Chen Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land- Use Change. Progress in Planning 61: This article outlines the history of urban remote sensing applications as well as the increasing demand and usefulness of remotely sensed data for urban development. Remote sensing technology has exceeded the majority of applicable users (city planners etc), causing a lag in the real world application of available uses for remote sensed urban data. Samadzadgan, F., F.T. Mahmoudi., and T. Schenk An agent-based method for automatic building recognition from lidar data. Canadian Journal of Remote Sensing 36: This article recognizes the limitations of light detection and ranging (LiDAR) and presents a multi-agent methodology for automatic building recognition based on the decision-level fusion of textural and spatial information extracted from LiDar range and intensity products. Decrease the conflicts in the field of automatic building recognition in complex urban areas Ward, D., Phinn, S.R., and A.T. Murray Monitoring growth in rapidly urbanizing areas using remotely sensed data. Professional Geographer 52: This article looks at historic urban growth between 1988 and 1995 to classify broad land cover types and how they have changed over time. It recognizes the importance that remote sensing has on evaluating the urban growth and the importance of classifying land cover within urban centers. Xian, G. and Crane, M Assessments of Urban Growth in The Tampa Bay Watershed Using Remote Sensed Data. Remote Sensing of Environment 97: This article discusses the use of landsat imagery to simulate past, present, and future urban development in watersheds. Urbanization causes decreased imperviousness of watershed land cover. This data is then used make predictions on the future behavior of urbanized watersheds.

17 Xu, H., and P. Li Urban land cover classification from very-high resolution imagery using spectral and invariant moment shape information. Canadian Journal of Remote Sensing 36: This paper looks at using spectral data and shape features, extracted from three types of invariant moments, to classify urban land cover types. It was found that usage of invariant moments is useful for identifying classes with similar spectral features but different spatial features, such as buildings, roads and other impervious features. The usage of invariant moments along with spectral data showed an overall significant improvement in accuracy. Zhang, Q., J. Wang, X. Peng, P. Gong, and P. Shi Urban built-up land change detection with road density and spectral information from multi-temporal landsat TM data. International Journal Remote Sensing 23: This paper proposes a new structural method based on road density combined with spectral data for change detection. Two classification methods, the spectral-structural post-classification comparison (SSPCC) and spectralstructural image differencing (SSID) methods were evaluated and compared. It was found that the SSPCC method was more accurate in classifying land cover types, while the SSID method improved the change detection results. Zhang, Y., B. Guindon., and K. Sun Concepts and application of the Canadian urban land use survey. Canadian Journal of Remote Sensing 36: This study used Landsat Thematic Matter to map the different types of urban land cover as it has been widely acknowledged that cities play a large role in consumption of resources and sources of pollution. This study was used to assist in understanding the relationship between urban areas and the type of transportation they require. Zhang, P., M.L. Imhoff., R.E. Wolfe., and L. Bounoua Characterizing urban heat islands of global settlements using MODIS and nighttime lights products. Canadian Journal of Remote Sensing 36: This article analyses the relationship between urban heat island signatures. It looks at the relationship between the intensity of the development, the size of the urban area and where it is located. This study was conducted using the remote sensing device MODIS for over 3000 urban areas worldwide.