The dynamic modification of the Earth's surface by humans has been identified to have relevant impact on future climate, the pollution of surface waters, biodiversity, and public health (e.g.Carpenter et al., 1998;Sala et al., 2000;DeFries et al., 2002;Patz et al., 2004;Foley et al., 2005). Despite all organisms modifying the environment they live in, the modification by humans is unique in a sense that no ecosystem on the Earth's surface is free of pervasive human influence (Vitousek, 1997). Over the past 50 years, anthropogenic ecosystem changes were more rapid and extensive than in any comparable period of time in history (MEA, 2005).
The concentration of humans in urban regions reflects the endpoint of landscape domestication. In cities every element of the environment has been consciously or unconsciously selected to accord with human desires and the flora and fauna are thus often quite different from those in rural settings (Kareiva et al., 2007). The process of urbanization – both as a social phenomenon and a physical transformation of landscapes – is one of the most powerful, irreversible, and visible anthropogenic forces on Earth (Sánchez-Rodríguez et al., 2005).
By the time this thesis was written, urban dwellers quite exactly made up one half of the world's population according to the United Nations' 2005 Revision of World Urbanization Prospects. 20 megacities, i.e. cities with a population greater 10 million, existed around the world. In 1950, for comparison, 29% of the world's population lived in cities and two megacities existed (UN, 2006). Against the background of the rapid increase of urban population, it is of no surprise that the 21st century is more and more referred to as "the first urban century" (e.g.Park, 1997;Dembrowski, 2004;Cadenasso et al., 2007).
Besides the pure quantity of people, it is the quality of the urbanization process that makes cities a good place to start, when considering broader implications of domesticated ecosystems (Kareiva et al., 2007): urbanization increases the per capita demand for energy, goods and services (Meyerson et al., 2007), and land conversions introduced by urban consumption patterns have regional consequences for the biophysical system that may lead to global consequences (Sánchez-Rodríguez et al., 2005). Thus, the total land area required to supply those resources can be used to quantify the cumulative demand of urban areas, a concept referred to as "ecological footprint" (Wackernagel and Rees, 1997). The increasing attention received by such concepts underpins the need to understand the impacts of cities as drivers of ecosystem change (e.g.York et al., 2003;Imhoff et al., 2004). With regards to population prospects, this need becomes even more important: by 2030, 60% of the world's population are expected to live in cities, a value that corresponds to 4.9 billion people; the number of megacities is predicted to grow to 22 until 2015 (UN, 2006).
Relevant differences in terms of urbanization exist between developed regions and less developed regions. In 2005, 74% of the population of developed regions lived in urban areas, compared to 43% in the less developed regions. Despite the lower level of urbanization, the absolute number of urban dwellers in less developed regions is two and a half times higher (UN, 2006). This disparity is of great importance. Firstly, urban planning often does not exist in less developed regions. Cities experience uncontrolled growth, which leads to problems with sanitation or fresh water supply and this way critical health conditions. Secondly, in these regions many people are concentrated in few places that are often subject to natural hazards (Sánchez-Rodríguez et al., 2005). Thus, feasible concepts for assessing and monitoring the structures and spatial patterns of urbanization are needed. Again, the expected population development underlines the importance of such concepts: until 2030, the urbanization rate in less developed regions will increase to 56% and the number of urban dwellers will be four times higher than in developed regions (UN, 2006).
In the context of global environmental change and urbanization, two broad categories of impact exist (Sánchez-Rodríguez et al., 2005): those originating in urban areas that have a negative effect on global environmental change and those caused by global environmental changes that negatively affect urban areas. With respect to the first category, the uncoordinated growth of cities contributes to most of the human-induced negative impacts on regional and global environment, e.g. the emission of pollutants or green-house gases (EPA, 2001;Marcotullio and Boyle, 2003;Svirejeva-Hopkins et al., 2004). Climate-related natural disasters such as floods and droughts or sea-level rise, on the other hand, affect cities and migration patterns as part of the second category. Changes in average and extreme temperatures influence economic life, the comfort of living, and public health (Sánchez-Rodríguez et al., 2005).
With regard to the global dimension of urbanization and its consequences, the need for spatially explicit information on the state of the Earth's surface is evident. Earth observation (EO) can provide information to better understand the drivers of processes and to more accurately quantify their consequences. It is thus critical for an ever-increasing number of applications related to the health and well-being of society (NRC, 2007).
"Natural and human-induced changes in Earth's interior, land surface, biosphere, atmosphere and oceans affect all aspects of life. Understanding these changes and their implications requires a foundation of integrated observation – taken from land-, sea-, air-, and space-based platforms – on which to build credible information products, forecast models, and other tools for making informed decisions (NRC, 2007)." With these lines the National Research Council's Committee on Earth Science and Applications from Space preludes its imperatives for future decades and this way emphasizes the emerging need for EO in the context of monitoring and assessing coupled natural and human systems.
Optical remote sensing is one main source of EO products. It delivers information on the impact of urbanization and global environmental change, for example by quantifying urban growth and its influences on the environment (Soegaard and Moller-Jensen, 2003;Seto and Fragkias, 2005) or by providing input for models on environmental quality and ecological performance of urban areas (Whitford et al., 2001;Nichol and Wong, 2005). Miller and Small (2003) mention the increased availability of high resolution imagery from EO satellites in combination with global connectivity and information technology as a means to identify, monitor, and apprehend a number of urban environmental problems.
Municipal administrative infrastructure in less developed regions of the world is often not capable of supporting the sustained and publicly available record keeping and data collection for urban environmental analysis and planning. The analysis and monitoring of urban areas is therefore especially important and challenging in those regions of the world with rapid and uncontrolled urbanization (Miller and Small, 2003;UN, 2006). Remotely sensed observations can provide spatially explicit information over very large areas, which would be very expensive or impossible to be directly measured in the field. For example, the issue of informal settlements that are often highly susceptible to natural hazards requires special attention in urban planning (Alder, 1995). Remote sensing offers means to provide spatially explicit information on such otherwise poorly documented and dynamically evolving phenomena (Weber and Puissant, 2003).
In the developed regions of the world, a need for detailed spatial information has been identified, for example in the context of urban environmental analysis, ecology, and planning, (e.g.Svensson and Eliasson, 2002;Alberti, 2005). Remote sensing has been used to monitor urban growth for several years (e.g.Ward et al., 2000;Stefanov et al., 2001). Since the advent of spaceborne sensors with high spatial resolution, the value of urban remote sensing is becoming more and more accepted (e.g.Mathieu et al., 2007;Thanapura et al., 2007). However, remote sensing of urban areas alone is often limited and cannot effectively compete with regulatory governmental and commercial sources or with census data (Miller and Small, 2003). In regions where additional data sources are available, remotely sensed information is often combined with such data, e.g. digital surface models (Nichol and Wong, 2005), census data (Mesev, 1998) or cadastre information (Lu et al., 2006). By combining the different data sources before, during or after analysis a surplus of information can be generated (Mesev, 1998). Nevertheless, spatial and spectral variation and compositional heterogeneity traditionally complicate the analysis of urban areas by EO (Mesev, 1998;Herold et al., 2003).
Collier (2006) concludes a study on the impact of urban areas on weather by saying that the use of remote sensing must play a major role in providing the required observations but care has to be taken to understand the true nature of what is actually measured. This understanding is needed to make sensible use of products from EO for detailed and accurate analysis and for its combination with additional data sources. The derivation of reliable and consistent end-user products must therefore be one of the main goals for application development in remote sensing (Schläpfer et al., 2007). New applications should take advantage of the characteristics of remote sensing data with regard to routine updating or the description, classification, and measurement of the desired surface properties (Miller and Small, 2003). Following these recommendations will help to better connect the so far only loosely connected three key elements of (1) information produced by raw observations, (2) analyses, forecasts, and models that provide timely and coherent syntheses of otherwise disparate information, and (3) the decision processes that produce actions with direct social benefits (NRC, 2007).
A better connection of these key elements is of great interest when regions with little or no additional data are observed and only single source EO data is available. It is then important to know what source best provides which information and how reliable and accurate this information is. Tests that are required to answer these questions are ideally performed in well known areas where plenty of documentation and additional data exists. Based on findings from such tests, the step into the less known and poorly documented regions of the world is possible and information urgently needed for planning and decision making can be derived with a good estimate of possible inaccuracies.
Undoubtly, urban areas are one of the most challenging environments for EO (Mesev, 1998). For the mapping of large areas or when only moderately resolved data is available, urban remote sensing makes use of metaindicators such as the distribution of nightlights (e.g.Small et al., 2005) or impervious surface coverage (e.g.Carlson and Arthur, 2000) in order to estimate urban extent or density. Impervious surface coverage has been identified a key environmental indicator for urbanization (Arnold and Gibbons, 1996). However, in most applications, impervious land cover is not directly mapped or not further differentiated into built-up and non built-up impervious areas. Instead, an aggregated degree of imperviousness is indirectly derived from vegetation cover as estimated by unmixing models (e.g.Wu, 2004;Yuan and Bauer, 2007) or vegetation indices (e.g.Morawitz et al., 2006). This is explained by the fact that impervious surface cannot be directly described by spectral properties in moderate resolution multispectral imagery (Small and Lu, 2006). For directly mapping the degree of imperviousness and patterns of impervious surfaces, EO data have to meet certain requirements, such as very high spatial resolution and spectral information that goes beyond that of multispectral systems (Jensen and Cowen, 1999;Herold et al., 2003;Gamba and Dell'Acqua, 2007).
Welch (1982) identifies varying spatial resolution requirements for different regions in the world, that range from 5 m in the case of China to up to 80 m in the case of the United States. Small (2003) assesses the characteristic spatial scale of urban reflectance for 14 cities around the world based on spatial autocorrelation in multispectral data at 4 m resolution. The values he derives are consistently between 10 and 20 m. Recent multispectral spaceborne imagery, as acquired by Quickbird or Ikonos, capture the spatial requirements for the detailed analysis of urban environments to a great extent (Ehlers, 2007). Still many mixed pixels can be expected at the 4 m resolution provided by Ikonos (Small, 2003). Traditional moderate resolution EO satellite data, e.g. from Landsat Thematic Mapper, lack the spatial resolution for detailed monitoring in cities but provide the area coverage to image entire urban agglomerations (Miller and Small, 2003). Most recent multispectral airborne line scanners yield even better spatial resolutions at 0.2 m and below (Ehlers et al., 2006). Thus, a trend in data resolution towards the Hresolution case according to Strahler et al. (1986) can be observed, which allows for resolving urban objects, and an increasing number of infrastructure elements and socio-economic attributes can be remotely sensed (Jensen and Cowen, 1999).
Jensen and Cowen (1999) state that "most image analysts would agree that, when extracting urban/suburban information from remotely sensed data, it is more important to have high spatial resolution than high spectral resolution." Gamba and Dell'Acqua (2007) compare the spatial and spectral very high resolution situation in a case study and conclude that spectral information is more important, once sufficient spatial resolution is achieved. Apparently, different opinions exist on the importance of spatial and spectral resolution. However, the importance of, for example, the information from the short-wave infrared (SWIR) region for detailed analysis has been stressed by several authors (e.g.Ridd, 1995;Jensen and Cowen, 1999). In a detailed study on the spectral resolution requirements of urban land cover mapping, Herold et al. (2003) discover drawbacks of multispectral sensors such as Quickbird and Ikonos to delineate urban surface types by statistical measures. This can be explained by the spectral ambiguity of materials from different land cover types. Spectral similarity is also identified by Gamba and Dell'Acqua (2007) who report increased differentiation capabilities by spectrally higher resolved data.
Airborne hyperspectral remote sensing data, also referred to as imaging spectrometry data, from sensors such as the Advanced Visible Near Infrared Imaging Spectrometer (AVIRIS) (Green et al., 1998) or the Hyperspectral Mapper (HyMap) (Cocks et al., 1998) provides very highly resolved spectral information. This quasi continuous spectral information covers spectral wavelengths that are not covered by the broader spectral bands of high spatial resolution multispectral instruments (Fig. I-1). Absorption features and the shape of spectral curves from imaging spectrometry data are frequently used for environmental and ecological applications (e.g.Johnson et al., 1994;McMorrow et al., 2004;Ustin et al., 2004) and have been shown to help differentiating urban surfaces (e.g.Ben-Dor et al., 2001;Segl et al., 2003;Herold et al., 2004;Heiden et al., 2007).
The spatial resolution of airborne hyperspectral data of as high as 4 m is similar to that of Ikonos (4 m multispectral) and Quickbird (2.5 m multispectral), while the spectral detail goes far beyond that of multispectral instruments (Fig. I-1). Thus, airborne hyperspectral data appears well suited to provide the information needed for detailed urban applications such as the direct mapping of impervious surface types.
|Figure I-1: Image data from Museumsinsel in Berlin-Mitte and spectral curves for six surface materials. The colored circles indicate the position of the sample spectra. The Quickbird data (bottom) has a slightly higher spatial resolution and shows more detail. The spectral resolution and wavelength coverage of the HyMap data (top) go far beyond that of Quickbird. Note: for matters of comparison the Quickbird spectra were resampled based on the HyMap spectra, since different acquisition dates, illumination conditions, and radiometric preprocessing do not allow direct comparison.|
Despite the very high spectral and spatial resolution of airborne hyperspectral data, image processing and analysis with methods developed for traditional, moderate resolution remote sensing data are not optimal. Instead, new approaches and workflows that make best use of the high information content are needed (e.g.Kuo and Landgrebe, 2004;Richards, 2005;Schläpfer et al., 2007). When this is given, information can be provided at a level of accuracy and detail that allows for a better understanding of the coupled natural and human system within the heterogeneous urban environment.
The challenges faced by urban remote sensing with airborne hyperspectral data affect several data processing steps. The radiometric preprocessing of airborne hyperspectral data differs from that of multispectral data, especially due to the influence of water vapor absorption. By incorporating physically based approaches that model the radiative transfer of the atmosphere and perform a spatially explicit estimation of water vapor, such influences are sufficiently eliminated (Richter and Schläpfer, 2002). Nevertheless, directional reflectance properties of the Earth's surface can lead to brightness gradients in the measured signal of instruments such as HyMap which acquire data at wide field-of-view (FOV) in order to cover large areas at low operating altitudes. Unlike the correction of atmospheric influences, the normalization of this effect requires further investigation (Beisl, 2001;Richter and Schläpfer, 2002;Schiefer et al., 2005). Various approaches have been suggested that include the use of semi-empirical BRDF models (Beisl, 2001) or the use of spectral libraries (Feingersh et al., 2005). Neither of these approaches appears feasible in heterogeneous urban environments, however. Directional reflectance properties of urban surface materials have been shown in the field (Meister et al., 2000) and their influence on spectral measurements at view-angles similar to those of the HyMap sensor has been demonstrated by the author of this thesis in Herold et al. (2006). During hyperspectral image analysis, directional reflectance increases intra-class variability (Lacherade et al., 2005) and negatively impacts results from frequently applied linear spectral unmixing or spectral angle mapper classifications (Langhans et al., 2007).
The complex geometric composition of the urban environment requires special attention during geometric preprocessing of airborne remote sensing data. Buildings are displaced as a function of sensor view-angle and their height – a phenomenon of great relevance in wide FOV data (Schläpfer and Richter, 2002;Schiefer et al., 2005). In the case of the Berliner Dom (Fig. I-1), for example, the high metal dome appears at different locations when viewed from different positions in HyMap and Quickbird data. In general, roofs appear further away from nadir than they actually are and also occlude lower objects. The orthorectification of displaced objects requires detailed information on their spatial position and vertical extent; additional image data from different viewing positions is needed to reconstruct occluded surfaces (Zhou et al., 2005). However, such information is not commonly available and the mentioned effects can often not be corrected. In general, insufficient digital elevation models (DEM) bear the greatest source of inaccuracy during parametric approaches for geometric correction of airborne hyperspectral data (Schläpfer and Richter, 2002). For urban areas, such inaccuracies will negatively impact to a great extent mapping accuracy of hyperspectral analyses.
For many applications in urban areas it is required to delineate the three major elements of urban heterogeneity – vegetation, built structures and surface materials (Cadenasso et al., 2007). Advanced image classifications are needed to produce accurate land cover maps of urban areas for two reasons. Firstly, urban land cover classes exhibit high intra-class variability. This is caused by the abundance of well-illuminated and shaded surfaces or reflectance anisotropy (e.g.Lacherade et al., 2005) as well as a great variety of spectrally different artificial surfaces materials, e.g. roofing tiles and metal roofs made from different materials or with various coatings and paint covers (Herold et al., 2004). Secondly, the separability of classes is low. Different land cover classes might include spectrally similar or identical materials such as tar roofs and asphalt roads (Herold et al., 2003). In addition, even at 4 m spatial resolution a high number of spectrally mixed pixels can be expected (Small, 2003). These are made up of two or more different surface materials and will be located in between clusters of purer pixels in the spectral feature space.
Thus, land cover and spectral reflectance do not describe a one-to-one relationship. Traditional parametric classifiers like the Gaussian maximum likelihood classifier cannot handle such many-to-one relationships (Seto and Liu, 2003). At the same time, mixed pixels cause problems during the discretization of the originally continuous spectral feature space inherent to image classification. Spectral mixture analysis (SMA) constitutes an alternative concept that avoids this discretization (Smith et al., 1990;Ridd, 1995;Small, 2001). It can provide a physically based framework for spectral characterization of urban reflectance, e.g. by representing urban surfaces as a linear combination of the three endmembers substrate, vegetation and dark surface (Small, 2004). However, the further differentiation of urban surfaces such as built-up and non built-up impervious area in a spectral mixing space is problematic: due to the spectral heterogeneity of the urban environment an enormous number of potential endmembers exists and endmembers that represent different surface types will be spectrally similar. Multi-step approaches that first identify pure seed pixels and then perform a locally optimized unmixing are promising (Roessner et al., 2001;Segl et al., 2003). Nevertheless, delineating different impervious surfaces types requires some way of classification.
The assessment of HyMap spectra from four land cover classes underlines the complexity of land cover classification in urban areas (Fig. I-2). In the feature space of the first four principal components (PC), no single clusters can be identified for all classes. Classes like built-up areas or soil show more than one cluster. All four classes exhibit great illumination differences that result in generally high variances for the first PC. Built-up and non built-up impervious areas are characterized by spectral similarity and therefore overlapping class distributions in PC feature space.
|Figure I-2: Distribution of sample spectra from four urban land cover classes in 2-dimensional representations of the PC feature space. HyMap spectra from vegetation (green), built-up (red) and non built-up impervious areas (yellow), and soil (cyan) are shown as scatter plots for PC 1 vs. PC 2 (left), PC 1 vs. PC 3 (center), and PC 2 vs. PC 4 (right). The white background shows the distribution of all pixels from a 512 by 7277 pixel HyMap image from a heterogeneous urban environment.|
Several authors try to overcome this spectral complexity by combining spectral information with additional sources of information before classification, e.g. texture (Baraldi and Parmiggiani, 1995;Benediktsson et al., 2005), segment features (Shackelford and Davis, 2003) or terrain elevation (Hodgson et al., 2003). In most of these approaches, the overall quality of results is increased. However, the need for additional data sources or the required additional processing steps make such approaches more complicated, harder to transfer, and sometimes infeasible. In the case of segment-based approaches, the definition of transferable aggregation levels for the image data is very time consuming (Schöpfer and Moeller, 2006). It is thus desirable to develop easy-to-perform single source land cover classifications that are based solely on spectral information. For the mentioned combined approaches it is also useful to make best use of the available spectral information.
According to Richards (2005) support vector machines (SVM) are perhaps the most interesting development in data classification. They were introduced in the remote sensing context almost 10 years ago (Gualtieri and Cromp, 1998) and are receiving increasing attention. In terms of accuracy, they outperformed other approaches under varying conditions in the very most cases or performed at least equally well (Huang et al., 2002;Foody and Mathur, 2004;Melgani and Bruzzone, 2004;Pal and Mather, 2006). In particular, SVM have been shown to be robust in terms of small training sample sizes (Melgani and Bruzzone, 2004;Pal and Mather, 2006). By exploiting a margin-based "geometrical" criterion rather than a purely "statistical" criterion, SVM are not affected by the so-called curse of dimensionality originally described in Hughes (1968) and they are capable of delineating linearly not separable classes directly in the hyperdimensional feature space (Melgani and Bruzzone, 2004).
Before the introduction of SVM, the progress in remote sensing image classification was characterized by the refinement of methods originally developed for multispectral imagery. In order to make statistical classifiers like the Gaussian maximum likelihood classifier applicable to hyperspectral imagery, feature extraction and selection techniques have been developed and integrated into the workflow (Fig. I-3) (e.g.Kuo and Landgrebe, 2004). Such approaches always lead to an unavoidable loss of information and processing times are often infeasible (Melgani and Bruzzone, 2004). In addition, spectral sub-classes have to be defined to avoid multi-modal class distributions – a very time intensive and almost infeasible task in urban areas. In order to simplify the classification process and make best use of the characteristics of airborne hyperspectral data, SVM-based approaches appear promising even for delineating spectrally complex urban classes.
|Figure I-3: Workflow for the use of parametric classifiers with hyperspectral data. Traditional classifiers that assume certain class distributions and rely on statistical parameters require the hyperspectral feature space to be modified and reduced. (Kuo and Landgrebe, 2004, modified)|
According to Miller and Han (2001) geography has changed from a data-poor and computation-poor to a data-rich and computation-rich environment, wherein traditional spatial methods cannot be used to discover hidden information from huge amounts of spatially related data sets. The development of Earth observation data products in optical remote sensing over the past three decades mirrors this development: data resolution in all information dimensions has increased and the user can choose from a great variety of image products depending on the scope of investigation; methods for the processing and analysis of these products exhibit constant improvement and extension (Richards, 2005). Nowadays, the decision on the appropriate data and the selection of the best suited methods for its processing pose one of the greatest challenges in application. For the optimal generation of different end-user products from hyperspectral data the full processing workflow requires harmonization, e.g. to reduce processing times or to avoid unnecessary resampling (Schläpfer et al., 2007).
The ongoing urbanization and its consequences, on the one hand, and the high EO data requirements due to the complex spatial and spectral situation of urban areas, on the other, create a special situation for application development. At the moment, the preprocessing of hyperspectral data from urban areas does not result in reliable and consistent products; this is a result of the inaccuracy introduced into geometric correction by the complex surface composition and the existence of brightness gradients caused by directional reflectance. In the context of hyperspectral image classification, sequential processing with feature extraction/selection and the definition of a multitude of sub-classes increases processing times while decreasing the amount of information actually used (Melgani and Bruzzone, 2004). In addition, feature extraction or classification approaches that assume certain class distributions are still very common although they are often not suited for urban areas. This can probably be explained by the complexity of more sophisticated, recent developments that deters many users from their application.
With regard to processing times and the amount of data to process for regular updating or large area application, airborne hyperspectral data require special attention. High spatial and spectral resolution lead to very large physical file sizes and data compression appears useful to make advanced processing techniques feasible. To maintain the high spectral information content, i.e. the main characteristic and advantage of hyperspectral data, a spatial generalization should be performed that conserves a sufficient degree of spatial detail. The issue of data compression is one so far neglected side effect of segment-based image processing that reduces the spectral information of groups of adjacent pixels to single mean values (Schiefer et al., 2005). However, especially in heterogeneous urban environments the decision for well suited levels of aggregation inherent to segment-based approaches constitutes another challenge as well as additional error source.
In terms of reliability and quality, a separate assessment of potential error sources is desirable to better understand results. Besides processing related and data specific errors, general drawbacks of remote sensing based analysis, e.g. the impact of surfaces obscured by tree crowns, still require detailed investigation and quantification. Such assessments are important against the background of data availability: so far, hyperspectral remote sensing data with sufficient radiometric quality is only available from airborne sensors. It is therefore limited to episodic campaigns, covers relatively small areas and comprises high acquisition costs. Thus, applications in less developed regions are for the moment not likely. Nevertheless, the number of airborne hyperspectral sensors is steadily increasing (e.g.Müller et al., 2005;Nieke et al., 2006); results from first experimental spaceborne sensors are promising (Guanter et al., 2005) and operational satellite missions are in advanced planning phases (Kaufmann et al., 2005). Advanced processing techniques and optimized workflows can help to achieve reliable end-user products and will this way also increase the acceptance of future hyperspectral data products. Therefore, comprehensive case studies are needed. In addition to the use for future missions, findings from case studies on the reliability of results are useful for the work with data from existing sensors, particularly when the increasing data availability might lead to more studies in so far poorly documented areas. Moreover, further knowledge on the processing of airborne hyperspectral data might be of value for other airborne sensors with similar characteristics (e.g. a wide FOV) or for the spectro-radiometric design of new sensors with fewer bands.
HyMap data acquired over the metropolitan area of Berlin, Germany, in 2003 and 2005 is used for experiments in this work. The history and structure of the city make it an ideal study area for the addressed research questions. The rise of the Prussian empire and the era of industrialization dominate the original structure of the city. After heavy destruction during World War II, a period of separation and parallel development under opposite political systems led to diverse new urban structures. Following the fall of the Berlin Wall and the closing down of industrial complexes from socialist times, many derelict sites exist and Berlin has recently been experiencing large scale development in very central areas. (Sukopp, 1990;SenStadt, 2007)
This situation is probably unique for a metropolitan area in the western hemisphere. Thus, a great variety of urban structure types exists within an area that can be covered by a single airborne hyperspectral data set. At the same time, the abundance of additional data sources in Berlin helps to evaluate the quality of results and sources of inaccuracy (SenStadt, 2007). This way, main findings from this case study are expected to be of value for applications in other urban areas as well as for airborne and/or hyperspectral analyses in general.
This work investigates the potential of airborne hyperspectral remote sensing data for the analysis of urban imperviousness. This analysis of imperviousness includes the estimation of the areal degree of impervious surface coverage and the attempt to delineate built-up and non built-up impervious surfaces. In doing so, a challenging application is performed in a complex environment and new insights on hyperspectral image processing are expected that might be important for a variety of other applications. The overarching goal is to optimize data processing with regard to the accuracy and reliability of results while assessing possible error sources. In previous sections, existing problems and remaining challenges of hyperspectral image processing and urban remote sensing have been identified. These include not only data preprocessing and the classification process but at the same time more general issues like the limits of airborne remote sensing or workflow optimization. Thus, individual processing steps as well as the entire workflow have to be considered for a comprehensive case study. In this work, a focus is put on processing steps that appear to require further improvement to make best use of the hyperspectral information. Concurrently, the entire workflow, i.e. the selection and sequence of processing steps, is assessed in consideration of efficient processing of large data sets.
The central part of this work is a land cover classification with SVM which includes the delineation of built-up and non built-up impervious surfaces. Based on this land cover information a map on impervious surface coverage is derived. Prior to the classification, the issue of across-track brightness gradients is looked at and a normalization procedure is suggested. The land cover map and the map on impervious surface coverage are evaluated for accuracy and potential error sources with a focus on the needs of potential end-users such as urban planners, ecologist or environmental modelers. In addition to the classification of the original image data, segmented data sets are classified and the potential of image segmentation to function as a mean of data compression is assessed against the background of the complete processing workflow.
In detail, the following four research questions will be addressed:
At several points an emphasis is put on the detailed accuracy assessment of results. This way, the potential of airborne hyperspectral data and remote sensing in general shall be evaluated for detailed analyses in the urban environment.
The four research questions and more specific objectives are addressed in Chapters II-V of this work. Chapter VI is a synthesis of the outcomes of the individual chapters. It draws more general conclusions with regard to the applicability of suggested approaches or expected data availability, and provides directions for future research. Chapters II, III, and V are stand alone manuscripts to be published in international peer-reviewed journals.
Chapter II, Correcting brightness gradients in hyperspectral data from urban areas, introduces an empirical approach for the class-wise correction of across-track brightness gradients. The approach adapts existing procedures that are solely based on the image data to the special needs of urban areas with high frequent changes of spectrally different surface materials.
Chapter III, Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines, examines the quality of a purely spectral classification of urban land cover classes. An additional focus is put on the influence of image segmentation on classification accuracy in different urban structure types.
Chapter IV, Processing large hyperspectral data sets from urban area mapping, provides the foundation for Chapter V and assesses the role of geometric correction and image segmentation for an efficient data processing workflow.
Chapter V, Mapping urban areas using airborne hyperspectral remote sensing data, evaluates the sources of inaccuracy in the maps on land cover and impervious surface coverage after geometric correction. The impact of different inaccuracies is quantified in consideration of the needs of different end-users.
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