Using remote sensing and GIS in developing indicators to support urban transport ecological footprint analysis

The growing concern for sustainable transport highlights the need for consistent information to support informed policy decision-making in this sector. Transport ecological footprint (TEF) is a straightforward tool that can help to easily communicate environmental impacts of urban transport. TEF measures and communicates in easily-understood manner the extent of ecological impacts of transport, using a single common measure – the area of productive land and sea needed to grow the necessary raw materials and/or to assimilate the relevant wastes. Expressed in area units (e.g., global hectares), TEF takes into account fuel use, materials used for manufacture and maintenance of vehicles, land occupied, and vehicle emissions. These components are related to travel patterns, which in turn are influenced by urban form elements and socio-economic attributes.

This study explores the extraction of urban form/land use information in developing indicators to support TEF analysis using Remote Sensing and GIS. Remotly sensed imagery provides a global information resource that when compared to traditional methods of data collection has the ability to provide data of in data scare environments. GIS supports handling of spatial data from remotely sensed imagery and integrate it with other images and ancillary data from different sources.

RS and GIS applications can handle various spatial analyses and other data manipulation techniques considered useful for data mining, such as indicator extraction and quantification. In this study, urban RS plays a key role in providing thematic classifications (i.e., residential, commercial, institutional, and industrial classes) based on IRS-P6 satellite imagery. Pixel-based classification methods, supervised and unsupervised, are explored in the extraction of urban form/land use information for indicator development. Texture based approaches in combination spatial metrics as well as hybrid approaches – involving already existing land use data for example – are used to improve the classification result. The utility of freely available high-resolution Google Earth images supported by global positioning systems (GPS) are also explored in the process. The utilization of RS and GIS applications is further illustrated in the extraction and quantification of TEF-related indicators, namely, density, proximity, trip distance estimate, and land-use mix.

Example results for a case study city of Ahmedabad City, India, provide preliminary insights into the challenges in deriving indicators from RS imagery for transport ecological footprint. This study shows that pixel based classification methods have limited capability in extracting residential, commercial, institutional, and industrial building classes, while the capability was improved by adding texture and spatial metrics measurements. Application of basic statistics, categorical analysis, and landscape metrics in quantifying RS-derived indicators show a weak relationship between urban form and TEF proxy, i.e. total number of trips.