Understanding the Drivers and Consequences of Global Urbanization using Emerging Remote Sensing Technologies

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By Fabio Dell’Acqua, Elizabeth A. Wentz, Soe W. Myint, Maik Netzband

Urban Remote Sensing Workshop and Forecasting Urban Land-Use Change Workshop logo

Urban Remote Sensing Workshop and Forecasting Urban Land-Use Change Workshop logo

Introduction

From April 1-3, 2011, two parallel, international workshops were held in Scottsdale, Arizona, devoted to Urban Remote Sensing (URS) and Forecasting Urban Growth (FORE). The URS workshop, funded by the National Science Foundation (NSF) to Arizona State University, was devoted to understanding the drivers and consequences of global urbanization using emerging remote sensing technologies. The organizers were Elizabeth Wentz and Soe Myint, both at the Arizona State University, and Maik Netzband from Ruhr-University Bochum, Germany (Figure 1).

Given the focus on urban areas and their dynamics, it was natural to co-locate the URS workshop with a complementary workshop, sponsored by National Aeronautics and Space Administration (NASA) and organized through Urbanization and Global Environmental Change (UGEC), on forecasting urban land use change. Karen Seto and Michail Fragkias led the FORE effort.

Further details on the two workshops and bios of each participant can be found online at the joint webpage.

1. Rationale

The rationale for focusing explicitly on remote sensing activities on urban areas stems from recent massive population changes in worldwide demographics. In the past, humans occupied predominantly rural areas subsiding on local economic opportunities.

Today, more employment opportunities exist in high density, large population centers, which leads to the relocation of people to these centers of activity. The ecological footprints of these cities — as well as the impact that the cities make themselves locally — require intensive observation, monitoring, and forecasting. Intensive examination of cities requires comprehensive understanding of the physical, political, social, and economic dynamics. One aspect that remote sensing technologies offer is that they can efficiently and objectively quantify the physical characteristics and growth of cities[1][2][3][4]. The physical characteristics can be anything from temperature [5][6], soil moisture [7], vegetation [8][9]and impervious surfaces [10][11], to albedo, evapotranspiration, pool, other water bodies[12], infrastructure, and building density[13]. City growth involves the conversion of land categories from rural uses such as agriculture or undeveloped land to urban uses such as industrial, residential, commercial and supporting infrastructure ( roads and utilities). These land-use and land-cover changes represent one of the most significant alterations that humankind has made to the surface of the Earth. Each land transformation impacts, to varying degrees, the local climatology, quality of air and water, energy, hydrology, geology, and biota that predate human settlement. The importance of remote sensing, both optical [14] and radar [15] in this context, has been referenced at many scientific conferences including JURSE[16].

Arizona State University campus2. Organization and major themes

The goal of the workshop was to share ideas on the needs, problems, expectations, consequences, and opportunities we face on the challenge of global urbanization. We aimed to identify the appropriate and necessary steps to move forward with this challenge. We had a combination of pre-workshop, during-workshop, and post-workshop activities.

Prior to the workshop, three participants were invited to write in-depth papers on three policy-based themes prior to the workshop: Data, scale, and applications. The goal with these white papers was to develop a common reference to improve group interaction. These themes were coincident with those in the workshop on FORE (three FORE participants also wrote theme papers). These background papers presented overviews of the current state of knowledge and served, whenever applicable, as the starting point for discussions. The remaining workshop participants received pre-workshop memos and were asked to submit comments and questions. The three white papers and participants memos were compiled and disseminated to all participants the week before the workshop commenced.

The starting points for discussions during the workshop were based on six themes:

‰Û¢ Theme 1: track urban area growth and change: speed, density, direction, structures, impervious surfaces, land consumed;

‰Û¢ Theme 2: assess the spatial arrangement of green/open space within cities and at the periphery: amount, distribution, connectivity;

‰Û¢ Theme 3: monitor changes in peri-urban regions: farmland conversions, wetland infringement, biodiversity threats;

‰Û¢ Theme 4: track land-cover and land-use changes that influence urban climatology and atmospheric deposition: impervious surfaces, vegetation cover, particulate matter, carbon release, haze, smoke;

‰Û¢ Theme 5: monitor urban growth as it intersects with areas of potential environmental hazards: earthquake, subsidence, mudslides, floods, tsunami;

‰Û¢ Theme 6: map environmental parameters (microclimate, heat island, access to open space, percent of impervious surface, percent of green space), assess the geographic differences within the region, and identify correlations with social, economic, and ethnic divisions.

The themes were examined from two different perspectives. This happened because roughly half of the participants were academic researchers engaged either in the use of new remote sensing technologies or in their application to solve problems associated with rapid urbanization, or even in formulating better management options for a sustainable built environment. The remaining workshop participants were local government planners, managers, and decision-makers who, on a daily basis, are confronted with problems and seek smart growth options where remote sensing technology may serve as a tool.

For example, a planner or local decision-maker may ask is if a tree planting and replacement program has been effective at reducing the urban heat island effect. Remotely sensed images taken before the program was established through to the present can help answer this question and justify the continuation of the program to budget planners and the general public.

3. Workshop outcomes

Similar to the pre-workshop documents, the discussions were also organized around three themes: Application, scale & date.

3.1 Application

Application refers to the use of remotely sensed imagery and software to solve a particular problem in cities. This topic emerged as a strong mismatch between stakeholder requests and remote sensing experts (RSEs). There were questions on both sides on what hinders a wider use of remotely sensed imagery in city planning or management. Parts of the problem we identified are:

‰Û¢ Software for handling remote sensing data is often expensive and difficult to use for the non-expert;

‰Û¢ Data are in ‰Û÷silos’ requiring knowledge on where to go and how to acquire them;

‰Û¢ There is the impression that Google-Earth-like systems ‰Û÷solve’ the problems of software and data;

‰Û¢ There is little understanding beyond experts on what interpretation of non-visible spectral bands offers (e.g., NDVI); the visible spectrum seen in Google-Earth-like images simply offers zero-level interpretation ( ‰ÛÏpeeking into your neighborhood backyards‰Û);

‰Û¢ Remote sensing data needs robust preparation to be effectively used, especially if we are talking about extracting quantitative information.

Two possible approaches were suggested, one that we might call ‰ÛÏdemocratization of data,‰Û and the other consisting of a tighter, probably also forced to some extent, interaction between RSEs and stakeholders. ‰ÛÏDemocratization of data‰Û can be referred to as transforming data and information into a form, and providing tools, that make it easy for non-experts to use them and access answers to questions they need to address.

This ‰ÛÏdemocratization‰Û of data and tools would potentially trigger interest and thus education of the potential users, similarly to the way Google Earth functions and the awareness about performances of very high-resolution optical satellites.

Figure showing that Scales depend on a number of aspects, and scales of any two processes may largely overlap.

Scales depend on a number of aspects, and scales of any two processes may largely overlap.

3.2 Scale

Scale turned out to be a very complex issue to address. Even the very concept of scale was questioned by arguing that a hierarchy of scales is not a correct representation of the urban reality and a system of interlaced and overlapping scales should be used instead. Some scales represented are physical and some are socially or politically based. For example, scales of a physical model on groundwater recharge and scales of decision-making processes generally do not overlap, although they have to fit with each other for a correct planning to be carried out; remotely sensed data works on other, different scales, which add even more complexity.

Consequently, a dedicated ontology should be developed. Other interesting themes that emerged included:

‰Û¢ There is a common misperception that ‰ÛÏfiner is more accurate‰Û in terms of spatial resolution; this is not the case because a higher resolution dataset simply implies more pixels and more detailed land cover features being observed in the imagery that can potentially lead to poor accuracy;

‰Û¢ More subtly, a wrong perception that finer and finer resolution in remote sensing data will eventually lead to potential identification of every details about the observed urban areas without realization that there will be numerous different land cover classes and features sharing the same spectral responses;

‰Û¢ With regards to fine resolution data, what remains missing is the contextual and perhaps social view of what is being observed (‰ÛÏSocialization of the Pixel‰Û);

‰Û¢ Another important issue to be considered for a fine resolution data is that it takes longer processing time or makes it impossible to perform a classification, especially when using an algorithm that requires extensive computation such as an object-based classifier;

‰Û¢ Linked to the above, convincing stakeholders of the usefulness of mid- and low-resolution remote sensing data for processes at the appropriate scale; this is vital because it provides cost-efficiency, processing efficiency, and larger area coverage.

3.3 Data

Data was the least controversial topic we encountered. The experts in the workshop were well aware of the abundantly available data. The use and selection of data generally depends on availability of budget, level of scale or spatial resolution required to generate land-use and land-cover classes or indices, spectral bands required to achieve the objectives, and expert knowledge of or familiarity with the type of remotely sensed data. Given our extensive knowledge, the conversation turned to:

‰Û¢ How to exploit the abundance of remote sensing data without being overwhelmed by the effort of locating the correct repository and finding the right dataset (e.g., data silos);

‰Û¢ How to translate remote sensing data into geospatial information at different scales (links to previous topics).

Various guidelines were proposed:

‰Û¢ Improving access and processing tools;

‰Û¢ Improving data comparability and compatibility;

‰Û¢ In general ‰ÛÏdemocratizing data‰Û, i.e. making it more easily accessible and processable.

3.4 Case studies, Outreach, Scenarios, Typologies

The second day of the workshop involved a deeper interaction between the two workshops, as a series of cross-cutting issues were discussed.

Case studies were discussed as a means to test and showcase models and techniques, and possible applications.

A strong need for outreach was recognized. Not many potentially relevant stakeholders and policy makers are convinced that remote sensing data can be useful for their work, and this is one of the biggest hindrances to routine use of remote sensing in urban planning and management for a sustainable future.

Urbanization scenarios are important for stakeholders to make the correct decisions, and the construction of scenarios should be started with the stakeholders’ engagement.

Defining city typologies is necessary to compare them across case studies. It is interesting to wonder what are the typologies that can be defined based only on remotely sensed data. Elements of the classification do not necessarily have a ‰ÛÏsnapshot‰Û character. Rather, changes in time also are considered.

4. Take-home Points

The workshop represents just one step of a continuing effort to understand a complex and diversified environment such as urban areas, and especially what remote sensing techniques and technologies can do to improve their growth, policy formulation, planning, and management. The workshop participants are currently engaged in several communication activities, including outreach, software development, organized sessions at meetings, and publications. We are creating a pilot website (J-Earth) to facilitate access to remotely sensed data and tools. In addition, manuscripts are being written for publication in a variety of peer-reviewed and outreach outlets. These summarize the workshop but more importantly, they identify what we believe are next steps and opportunities for the broader research community.

The overall impression is that, notwithstanding a great deal of work carried out in the past to bridge the gap between available technology and requests for information provision, there is still much to do. There seems to be a continuing mismatch between what remote sensing can offer and what stakeholders ask for; this is certainly, partly due to a lack of mutual understanding, given the different points of view and even different languages spoken by the two communities. Unrealistic expectations (including the cost of services) on one side and naÌøve offerings on the other side seems to be quite commonplace. This calls for more bridging actions in the future, such as this joint workshop.

References

[1] Wentz, Elizabeth A., David Nelson, Atiqur Rahman, William L. Stefanov, Shoursaseni Sen Roy 2008. Expert system classification of urban land use/cover for Delhi, India International Journal of Remote Sensing 29 (15): 4405-4427.

[2] Keys, Eric, Elizabeth A. Wentz, Charles Redman 2007. The spatial structure of land use from 1970-2000 in the Phoenix, Arizona metropolitan area. Professional Geographer 59(1): 131-147.

[3] Myint, S.W., Wang, L. 2006. Multi-criteria Decision Approach for Land Use Land Cover Change Using Markov Chain Analysis and Cellular Automata Approach. Canadian Journal of Remote Sensing 32(6):390-404.

[4] Myint, S.W., Jain, J., Lukinbeal, C., Lara-Valencia, F., 2010. Simulating urban growth on the U.S.-Mexico border: Nogales, Arizona and Nogales, Sonora, Canadian Journal of Remote Sensing , 36(3): 166-184.

[5] Weng, Q. : A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, vol. 22, issue 10, pp. 1999-2014, 2001.

[6] Myint, S.W., Brazel, A., Okin, G., Buyantuyev, A.. 2010. An interactive function of impervious and vegetation covers in relation to the urban heat island effect in a rapidly urbanizing desert city, GIScience and Remote Sensing 47: (3) 301-320.

[7] J.A Voogt, T.R Oke, Thermal remote sensing of urban climates, Remote Sensing of Environment, Volume 86, Issue 3, 15 August 2003, Pages 370-384, ISSN 0034-4257, 10.1016/S0034-4257(03)00079-8.

[8] Myint, S.W., 2006. Urban vegetation mapping using sub-pixel analysis and expert system rules: A critical approach, International Journal of Remote Sensing 27(12-14):2645-2665.

[9] Myint, S. W., Jyoti, J., Guhathakurta, S. 2010 Patterns and rates of land use change: a case study of Ambos Nogales (Arizona and Sonora), Journal of Latin American Geography 9(3): 246-274.

[10] Ridd, M.K.: Exploring a V-I-S (vegetaton-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International journal of remote sensing, 1995, vol. 16, no12, pp. 2165-2185 (2 p.1/2).

[11] Myint, S.W., and G.S. Okin, 2009. Modelling land-cover types using multiple endmember spectral mixture analysis in a desert city, International Journal of Remote Sensing, 30(9):2237 ‰ÛÒ 2257.

[12] Myint, S.W., Gober, P., Brazel, A, Grossman-Clarke, S., and Weng, Q., 2011. Per-pixel versus object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sensing of Environment 115(2011): 1145-1161.

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[14] Dell’Acqua, F.; Gamba, P.; Ferrari, A.; Palmason, J.A.; Benediktsson, J.A.; Arnason, K.; , “Exploiting spectral and spatial information in hyperspectral urban data with high resolution,” Geoscience and Remote Sensing Letters, IEEE , vol.1, no.4, pp. 322- 326, Oct. 2004. doi: 10.1109/LGRS.2004.837009

[15] Gamba, P.; Dell’Acqua, F.; Lisini, G.; , “Change Detection of Multitemporal SAR Data in Urban Areas Combining Feature-Based and Pixel-Based Techniques,” Geoscience and Remote Sensing, IEEE Transactions on , vol.44, no.10, pp.2820-2827, Oct. 2006. DOI: 10.1109/TGRS.2006.879498

[16] Stilla, U.; Gamba, P.; Juergens, C.; Maktav, D.; , “Preface,” Urban Remote Sensing Event (JURSE), 2011 Joint , vol., no., pp.V-VI, 11-13 April 2011 DOI: 10.1109/JURSE.2011.5764702