Remote Sensing – An Effective Data Source for Urban Monitoring

EarthzineArticles, Earth Observation, In-Depth, Original, Urban Monitoring Theme

3D city model of the coastal city of Padang, Indonesia. Source: DLR-DFD

H. Taubenböck and T. Esch
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling-Oberpfaffenhofen, Germany

Abstract — Urban monitoring implies multi-temporal observation and measurement of transformations or consistencies within cities. The urban context is highly complex, as cities consist of a large number of people living in close proximity and conditions of relative density and diversity in dynamically interrelated processes. Most of the conditions and processes are related to space. Thus, for measuring, analyzing and understanding the urban context, the dynamic interrelations and its permanent changes, spatial information are crucial. As one data source, remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their changes over time, at various spatial and temporal scales. This paper gives an overview and examples on current applications at DLR to demonstrate capabilities and limitations for monitoring in cities based on remotely sensed data.


Urbanization can basically be understood as the transition from rural to urban conditions. Thus, it implies the physical growth of a city or the built-up area in general. However, urbanization also is defined by the United Nations as movement of people from rural to urban areas [1]. The process of urbanization can basically be caused by three factors: Natural population increase, rural–urban migration, and annexation [2] [3].

Figure showing Multi-sensoral urban growth analysis – by the example of Manila, Philippines

Fig. 1: Multi-sensoral urban growth analysis – by the example of Manila, Philippines. Source: DLR-DFD

And this process of urbanization in the future will be, according to the latest United Nations’ projections [1], enormous: Virtually all of the world’s population growth over the next 30 years will be absorbed by urban areas.

The immense urbanization in recent history is a worldwide phenomenon, but not even two cities in the world became identical. The only consistent thing about cities is that they are always changing [4]. Urbanization may be linked with details of topography, transportation, land use, social structure and economic type, but is generally related to demography and economy in a city [5]. The most obvious consequence results in spatial expansion, often described as ‘urban sprawl.’ Drivers of urban development and urban sprawl are highly diverse: There are macro-economic factors (economic growth, globalization, etc.), micro-economic factors (rising living standards, price of land, availability of cheap agricultural land, competition between municipalities, etc.), demographic factors (population growth, increase in household, formation, etc.), housing preferences (more space per person, etc.), inner city problems (poor air quality, noise, small apartments, unsafe environments, social problems, lack of green open space, poor quality of schools, etc.), transportation (private car ownership, availability of roads, low cost of fuel, poor public transport, etc.), and regulatory frameworks (weak land use planning, poor enforcement of existing plans, lack of horizontal and vertical, coordination and collaboration, etc.) [6].

The causes and consequences of urbanization mostly have a reference to space. Thus, for most of the challenges related to urbanization, spatial knowledge is required of urban and spatial planning, politics, science, industry or the inhabitants.. Given that urbanization will continue to be one of the major global environmental changes in the foreseeable future, not only spatial information on the current status is of crucial importance, but also continuous knowledge on changes.

Unfortunately, conventional sources of information on urban areas are frequently inadequate. The necessary data are often generalized, outdated, unreliable, not in standard format, or in some cases simply unavailable.
As one data source, remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales [7]. Beyond this, Earth observation provides an independent data source.

This paper focuses on current applications at DLR to demonstrate capabilities and limitations of remote sensing to support urban monitoring. An interdisciplinary outreach underlines the need for research in this direction for a broader understanding and bigger picture of the situations in the complex and dynamically changing cities on our planet.


Since the launch of the internet platform “Google Earth,” Earth observation data became more or less common knowledge. On the one hand, the theoretical possibility to obtain spatial information globally on objects, structures or patterns of the land surface allows unimagined information and possibilities. However, the images are data, not information. The strength of remote sensing with its synoptic overview allows independent, fast, up-to-date, area-wide and relatively cost-effective transformation of data (or images) into information. Making use of a vast amount of methodologies – e.g. statistical-, neural-, fuzzy classifiers – for automatic information extraction for particular data sets, this transformation aims at application-driven products.

For multi-temporal monitoring of changes on the land surface, manifold techniques exist: Image differencing, vegetation index differencing, principal component analysis, direct multi-date unsupervised classification, post-classification change differencing and a combination of image enhancement and post-classification comparison [8] [9]. In recent years, spectral mixture analysis, artificial neural networks, and integration of geographical information system and remote sensing data have become important techniques for change detection applications [10]. Beyond this, [11] e. g. developed a curvelet-based change detection algorithm for radar images.

Long-time monitoring using remotely sensed data is of course limited to the available data source. Thus, monitoring for the past 20 or more years to date relies on medium resolution data from sensors such as Landsat, SPOT or IRS. With it, the limited geometric resolution does not allow for too much thematic detail, but provides enough information to analyze spatial urban growth on regional level, differentiating between urbanized and non-urbanized areas.

Figure 1 illustrates the result of spatial growth analysis from 1975 until 2010 at mega city Manila, Philippines, underpinning the immense spatial dimension of urban sprawl for the past 35 years in developing countries. The individual data sets from Landsat MSS in 1975 and Landsat TM in 1990 were classified using an object-oriented approach [12]. A pixel-based classification approach was applied to derive the urban footprint from TerraSAR-X data in 2010 [13].

Using a pixel-based post-classification change detection, spatial urban growth can be identified. It enables us to detect the spatial dimension of sprawl and the dynamics in dependence of time. Beyond that, processes such as re-densification, leap frog development as well as growth patterns such as axial, mono-or polycentric structures or satellite town evolution, can be identified and analyzed.

Figure Analyzing urban growth using multi-temporal high resolution Ikonos imagery from 2002 (a) and 2008 (b) in Warsaw, Poland; (c) Object-oriented classification result for 2002; (d) Object-oriented classification result for 2008; (e) Multi-temporal homogeneity differencing; (f) Multi-temporal differencing based on the NDVI. Source: DLR-DFD

Fig. 2: Analyzing urban growth using multi-temporal high resolution Ikonos imagery from 2002 (a) and 2008 (b) in Warsaw, Poland; (c) Object-oriented classification result for 2002; (d) Object-oriented classification result for 2008; (e) Multi-temporal homogeneity differencing; (f) Multi-temporal differencing based on the NDVI. Source: DLR-DFD

With the advent of high resolution satellite sensors in the last decade, urban monitoring approaches allow a higher thematic and geometric level [14]. With pixel-sizes becoming smaller than the imaged objects, object-oriented classification methodologies came into focus [15]. Segmentation techniques [16] enhance automatic classifications using not only spectral features, but also shape, texture, hierarchical and contextual information [17]. In recent literature, diverse object-based approaches have been presented for the classification of high resolution optical remote sensing images [18] [19] [20] [21].

Figure 2c) and d) show two individual object-oriented classification results based on the related multi-temporal Ikonos imageries from the years 2002 and 2008 at a test site in Warsaw, Poland presented in Figure 2a and b). Post-classification change detection allows identifying areas of spatial urbanization. However, accuracies of slightly above 80 % on average per classification lead to inherited errors in the post-classification change detection.

Figure 2e) and f) show a different approach on identification of changes. Here, an artificial chessboard segmentation is used to define a spatial reference. In this specific case, a grid pattern of 100m was used. The texture measure ‘homogeneity’ of the spectral values within this artificial box is calculated for 2002 and 2008. In the case of a significant difference, the multi-temporal comparison regarding the texture measure allows us to localize areas of high probability for changes (2e). Analogous, this multi-temporal comparison has been applied using the NDVI (Normalized Difference Vegetation Index) ratio. Both approaches identify and localize areas of high probability of multi-temporal change. However, the artificial chessboard also shows problems regarding marginal changes with respect to the grid size.

Although urban studies using remote sensing data receive more attention by researchers, it would be optimistic to assume that a single sensor can provide all the information required for the characterisation of the complex urban environment [22]. The derivation of the third dimension from multi-sensoral data sets is of crucial interest for modelling the complex urban environment. And it is also essential to monitor 3-Dimensional urban sprawl.

Digital surface models from LIDAR (Light detection and ranging) or stereo-imagery in combination with high resolution optical remote sensing images have been used widely to classify and model cities in 3D [23] [24] [25] amongst many others. Using hyperspectral data, even information on surface materials can be derived [26].

Figure 3 shows the thematic and geometric capabilities of an object-oriented approach to derive a 3D city model from multi-sensoral remotely sensed data. Although multi-temporal change detection has not been applied to this data set, it still shows the capability to monitor urbanization in 2D and 3D.

3D city model of the coastal city of Padang, Indonesia. Source: DLR-DFD

Fig. 3: 3D city model of the coastal city of Padang, Indonesia. Source: DLR-DFD

Based on such a 3D city model, higher ranking products can be derived, showing the potential for urban monitoring at the scale: Building parameters such as ground floor, height, roof type or number of houses, in addition to structural parameters such as average building sizes, building density, floor space index, percentage of impervious surfaces, vegetation fraction, and dominant roof materials. With this, the derivation of structural urban types or the mapping of urban biotopes becomes possible [27]. Figure 4 shows two parameters – average building size and built-up density per block – calculated from the 3D city model of Padang.

Beyond these block models (Fig. 3) airborne stereo-cameras to date even enable us to derive texturized 3D city models with a geometric resolution of up to 5cm [28]. With this, analysis is possible on very small urban objects such as parts of the roof, duct covers or road marking for monitoring and up-dating an urban cadastre..

With airborne sensors, traffic or parking lot monitoring enables real-time monitoring of urban mobility [29]. Real-time monitoring of crowd movements are applications especially relevant for monitoring of mass events [30].

figure showing Average building sizes and building density.

Fig. 4: Average building sizes and building density. Source: DLR-DFD

Remote sensing for cities is not limited to air- or space-borne sensors. Ground-based sensors are playing an essential role for surveying and monitoring in cities. Sensing the streets, e.g. from vehicles, adds sufficiently to the spectrum for applications such as analysis of thermal facade characteristics [31].

To date, remote sensing in urban areas is very much scientifically driven. However, a few projects such as the European Urban Atlas [32], MURBANDY / MOLAND, the ‘FTS Soil-Sealing’ product [33] and REFINA [34] extensively use remotely sensed data for precisely defined applications and coordinated user needs for cities.

The Urban Atlas is the first large-scale geodataset ever produced operationally for cities from satellite data. Production costs are in the order of a few Euros per km2. The new dataset must have been awaited because the announcement on Jan. 16, 2009, had caused several 100,000 Google references only a month later [35]. The value for the European level is clear: The Urban Atlas will provide a neutral and independent tool to monitor effects of structural measures, be they heading in the good or the bad direction. However, to date this approach is still mono-temporal.

Within the REFINA project a monitoring tool called ‘Flächenbarometer’ (land consumption barometer) was developed to evaluate sustainable urban development for Germany. With the tool, a set of sustainability indicators based on remote sensing can be derived. These indicators, e.g. imperviousness, can be calculated for various administrative units and combined with several statistical indicators. This enables several analyses, e.g. on the percentage of impervious surface per inhabitant. Comparison of the results with similar administrative units allows planners to evaluate the sustainability of land management activities. The tool was tested by several planners and found useful to estimate the need for construction sites, identification of empty building lots or as an information source for planning decisions [34].

These two examples clearly define the capability of urban remote sensing to integrate its products into an accepted decision-making process. The goal of mapping hundreds of cities with a consistent approach allows an objective, quantifiable and independent information set and thus proves the strength of Earth observation data and techniques.


Figure showing Population assessment and monitoring at different times of the day based on the 3-D city model presented in Fig. 3 (Visualization of Summary in Grids 100mx100m), integrated building usages and a ground survey presented in Fig. 3 (Visualization of Summary in Grids 100mx100m); the population flux shows areas gaining population during day time.

Figure 5: Population assessment and monitoring at different times of the day based on the 3-D city model presented in Fig. 3 (Visualization of Summary in Grids 100mx100m), integrated building usages and a ground survey presented in Fig. 3 (Visualization of Summary in Grids 100mx100m); the population flux shows areas gaining population during day time. Source: DLR-DFD

As defined above, urbanization is not solely defined as the physical growth of a city, but also as the movement of people from rural to urban areas. The integration of various scientific disciplines with remote sensing is promising to increase our understanding of urbanization. In the following, a few examples provide ideas on value-adding of remotely sensed products by other scientific fields.

The city, seen as a human product, is the physical and architectonic reflection of the society that created it [36]. Thus, the data and products indirectly contain additional information. Linking physical information on the urban environment with census data is a common interdisciplinary research field. Since the 1970s, remote sensing estimation of residential population has been applied more frequently, as an increasing amount of space-borne satellite data have become available [37] [38] [39] [40] [41] [42]. With this, multi-temporal approaches allow monitoring of population quantity and their spatial distribution on different geometric levels, from low resolution data to analysis on an individual building level.

For the case study of Padang, a bottom-up approach was used to assess time-dependent population distribution. Punctual information on population quantity per building at different times of the day has been surveyed for 1,000 buildings distributed around the city [43]. In this survey, various building usages – residential, commercial or mixed – were mapped and integrated in the 3D city model (cp. Fig. 3). With this, physical parameters from 3D city-like building sizes and heights, roof types, impervious surfaces, and building alignment were correlated to the surveyed building usages. In a further step, these characteristics were used to indirectly assess building usages from these parameters to the remaining non-surveyed building stock for extrapolation [44].

With this, the living space, calculated as ground floor multiplied by the number of floors, was used to derive average inhabitants per square meter at different times of the day with respect to building usage. Significant changes between morning and afternoon have been surveyed (e. g. for schools,offices, and market areas) between morning/,afternoon and night. Thus, multi-temporal population distribution at short time intervals by integrating the building usages allow for day- and nighttime population assessment or even an differentiation between morning, afternoon and night [45]. Accuracy assessment proved that the dynamic shift of population distribution could be mapped at building level in the correct dimension spanning from 65-90% accuracy.

Figure 5 presents the population assessment for the city of Padang, Indonesia. With this, short-time population monitoring becomes possible. For visualization, grids of 100x100m are used. The result shows the capability of remote sensing in combination with external data to provide value-adding.

Beyond this direct relation to urbanization, interdisciplinary research aims at increasing our knowledge and understanding of cities. With social sciences [46], civil engineering [47], urban planning [48], risk and vulnerability analysis [49], energy-relevant approaches [50] or urban climate analysis [51] and others, multi-disciplinary perspectives enable value-adding to remote sensing products for monitoring and understanding cities.

verlay of modelled tsunami extent and inundation height with classified buildings in Padang. Source: DLR-DFD

Figure 6: Overlay of modeled tsunami extent and inundation height with classified buildings in Padang. Source: DLR-DFD and Franzius-Institut für Wasserbau und Küsteningenieurwesen

Figure 6 exemplifies the multi-disciplinary combination of spatial extent and modelling of inundation from a potential tsunami [52] with the structural information on the city derived from remotely sensed data. Localization and quantification of affected structures, their physical type, distances to safe areas or assessment of time-dependent affected people are sample results to support urban planning for risk mitigation [53].


The short overview on methodologies and applications for urban monitoring using different remotely sensed data shows the capabilities of remote sensing for urban monitoring. With this, the presented new sources of spatial data, innovative techniques and broad thematic applications offer the potential to significantly improve the availability of data and the capability for analysis, understanding, and modelling of urban dynamics to add to the long research tradition in the fields of urban geography, urban planning and modelling.

However, limitations still exist: One major constraint is costs. The data costs are often too high for stakeholders. While satellite data are relatively low priced, some applications need high temporal repetition rates, where airborne or ground-based and thus cost-intensive remote sensing is required.

Furthermore, the investment in processing is still comparatively high due mostly to a lack of fully automated classification procedures. During processing, adjustments are needed due to different atmospheric conditions, land cover types or different user requirements. Algorithms also are still in experimental status.

Missing data standards or compatibility of software add to these problems [54]. Another constraint is the difference between requirements and capabilities regarding accuracy of the products: The synoptic overview of remote sensing in the previous chapter shows area-wide and spatially highly detailed information extraction, but the accuracy of cadastral data sets is not achieved. On the one hand, accuracies of 80-90 % and sometimes even higher provide an objective basis for decisions. On the other hand, these eEarth observation products are not established at the current legal foundation and now need to find juristic acceptance.

So does remote sensing have essential value to urban monitoring? In general the data and products are independent, up-to-date, and basically available from anywhere around the globe. The products also are reproducible and thus consistent and comparable. Especially in developing countries, remote sensing data often are the only data source.

The strength of remote sensing is the multidimensional perspective, allowing for spatial and quantitative statements from a physical, demographic, social, economic and environmental view. And the analysis is not restricted to administrative artificial boundaries, thus theoretically enabling the analysis on a regional, national or continental scale.

Beyond that, products can be produced on cross-community level. Thus, an advantage arises from comparisons between cities as a basis to learn from other examples and develop solutions not solely on the knowledge of a single city. In interdisciplinary projects. the strength arises through correlation of often punctual knowledge with the area-wide availability of remote sensing products, enabling the extrapolation of information.

In conclusion, the constantly increasing availability and accessibility of modern remote sensing technologies provides the unique capability to support decision-making with spatial, quantitative data and information products to open up new opportunities for urban monitoring.

However, for effective monitoring within the highly dynamic urban areas, remote sensing from air- or space-borne sensors can only be one data source. Data integration and harmonization from different sources such as ground-based sensors (e.g. cameras, microphones, thermostats), knowledge from the web or observations from humans, will allow for a more holistic picture of the complex urban environment. For this, the willingness for inter- and transdisciplinary approaches must be established. On these premises, we have a good chance to transfer the multidisciplinary knowledge into innovative ideas for liveable cities of the future.


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