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The Ehlers fusion method, which combines a standard intensity-hue-saturation (IHS) transform with fast Fourier transform filtering, is a high spectral characteristics preservation algorithm for multitemporal and multisensor data sets. However, for data sets of more than three bands, the fusion process is complicated, because only every three bands are fused repeatedly for multiple times until all bands are fused. The hyper-spherical colour sharpening (HCS) fusion method can fuse a data set with an arbitrary number of bands. The HCS approach uses a transform between an n-dimensional Cartesian space and an n-dimensional hyper-spherical space to get one single intensity component and n − 1 angles. Moreover, from a structural point of view, the hyper-spherical colour space is very similar to the IHS colour space. Hence, we propose to combine the Ehlers fusion with an HCS transform to fuse n-band data sets with high spectral information preservation, even hyper-spectral images. A WorldView-2 data set i...

The Wadden Sea is a large coastal transition area adjoining the southern North Sea uniting ecological key functions with an important role in coastal protection. The region is strictly protected by EU directives and national law and is a UNESCO World Heritage Site, requiring frequent quality assessments and regular monitoring. In 2014 an intertidal bedform area characterised by alternating crests and water-covered troughs on the tidal flats of the island of Norderney (German Wadden Sea sector) was chosen to test different remote sensing methods for habitat mapping: airborne lidar, satellite-based radar (TerraSAR-X) and electro-optical sensors (RapidEye). The results revealed that, although sensitive to different surface qualities, all sensors were able to image the bedforms. A digital terrain model generated from the lidar data shows crests and slopes of the bedforms with high geometric accuracy in the centimetre range, but high costs limit the operation area. TerraSAR-X data enabled identifying the positions of the bedforms reflecting the residual water in the troughs also with a high resolution of up to 1.1 m, but with larger footprints and much higher temporal availability. RapidEye data are sensitive to differences in sediment moisture employed to identify crest areas, slopes and troughs, with high spatial coverage but the lowest resolution (6.5 m). Monitoring concepts may differ in their remote sensing requirements regarding areal coverage, spatial and temporal resolution, sensitivity and geometric accuracy. Also financial budgets limit the selection of sensors. Thus, combining differing assets into an integrated concept of remote sensing contributes to solving these issues.

Satellite synthetic aperture radar (SAR) holds a high potential for remote sensing in intertidal areas. Geomorphic structures of the sediment surface generating patterns of water cover contrasting with exposed sediment surfaces can clearly be detected. This study explores intertidal bedforms on the upper flats bordering the island of Norderney in the German Wadden Sea using TerraSAR-X imagery from 2009 to 2015. Such bedforms are common in the Wadden Sea, forming crests alternating with water-covered troughs oriented in a north-easterly direction. In the western Norderney area, the crest-to-crest distance ranges from 50–130 m, and bedform length can reach 500 m. Maximum height differences between crests and troughs are 20 cm. A simple method is developed to extract the water-covered troughs from TerraSAR-X images for spatiotemporal analysis of bedform positions in a GIS. It is earmarked by unsupervised ISODATA classification of textural parameters, contrasting with various algorithm-based methods pursued in earlier studies of waterline detection. The high-frequency TerraSAR-X data reveal novel evidence of a bedform shift in an easterly direction during the study period. Height profiles measured with RTK-DGPS along defined transects support the findings from TerraSAR-X data. First investigations to characterise sediments and macrofauna show that benthic macrofauna community structure differs significantly between crests and troughs, comprising mainly fine sands. Evidently, bedform formation has implications for benthic faunal diversity in back-barrier settings of the Wadden Sea. SAR remote sensing provides pivotal data on bedform dynamics.

Remote sensing technology is increasingly being used for rapid detection and visualization of changes caused by catastrophic events. This paper presents a semi-automated feature-based approach to the identification of building conditions especially in affected areas using geographic information systems (GIS) and remote sensing information. For image analysis, a new “detected part of contour” (DPC) feature is developed for the assessment of building integrity. The DPC calculates a part of the building contour that can be detected in the remotely sensed image. Additional texture features provide information about the area inside the buildings. The effectiveness of the proposed method is proved by high overall classification accuracy for two different study cases. The results demonstrate that the “map-to-image” strategy enables extracting valuable information from the remotely sensed image for each individual vector object, thereby being a better choice for change detection within urban areas.

Urban sprawl and the related landscape fragmentation is a Europe-wide challenge in the context of sustainable urban planning. The URBan land recycling Information services for Sustainable cities (URBIS) project aims for the development, implementation, and validation of web-based information services for urban vacant land in European functional urban areas in order to provide end-users with site specific characteristics and to facilitate the identification and evaluation of potential development areas. The URBIS services are developed based on open geospatial data. In particular, the Copernicus Urban Atlas thematic layers serve as the main data source for an initial inventory of sites. In combination with remotely sensed data like SPOT5 images and ancillary datasets like OpenStreetMap, detailed site specific information is extracted. Services are defined for three main categories: i) baseline services, which comprise an initial inventory and typology of urban land, ii) update services, which provide a regular inventory update as well as an analysis of urban land use dynamics and changes, and iii) thematic services, which deliver specific information tailored to end-users' needs.

The tidal flats of the Wadden Sea are a highly dynamic and largely natural ecosystem with high economic and ecological value but which are also at risk due to climate change, rising sea levels, algae blooms, invasive species and marine pollution. There is a need for the detection of emerging changes and the potential loss of the natural or semi-natural ecosystems accompanied by a decrease in water quality. Accessibility both from sea and land is very poor, which makes the monitoring and mapping of tidal flat environments from in situ measurements very difficult. In this study, a multi-sensor concept for the classification of intertidal areas in the Wadden Sea is developed. The basis for this method is a combined analysis involving RapidEye (RE) and TerraSAR-X (TSX) satellite data combined with ancillary vector data about the distribution of vegetation, shellfish beds and sediments for the accuracy assessment. The overall methodology is based on a hierarchical decision tree. First, the water coverage is separated from the tidal flats by using the normalized difference water index (NDWI). Second, the shellfish beds are estimated with the textural features of the TSX data and morphologic filters (MFs). Third, the classification of vegetation (salt marsh, sea grass/algae) is based on the modified soil-adjusted vegetation index (MSAVI), object-based features and exclusionary criteria. The remaining area is then separated into different sediment types with an algorithm that uses a thresholding technique applied to radiometric values, the MSAVI and a majority filter. The results show that we are able to identify the location and shape of salt marsh and shellfish beds (a true positive rate of 0.63 and a precision of 0.55) by using multi-sensor remote sensing data. A detailed shellfish bed classification can only be done with radar sensors, like TSX. The extraction of the sea grass areas from the multi-sensor approach is difficult. Sea grass often grows very sparsely in the study area which, with respect to the spatial resolution of RE, leads to a mixture of the spectral signatures of sediment and sea grass. The classification of the sediment types in tidal flats is a challenge compared to vegetation and shellfish beds. An overall accuracy of 64.53%, 66.85%, 68.22% and 68.2% was achieved. The results emphasize that a sediment-type classification cannot be achieved with high accuracy using spectral properties alone due to their similarity, which is predominately caused by their water content.

The optimization of urban land use is a very important aspect of sustainable urban development, including recycling abandoned land and further developing in use areas. However, limited knowledge of these kinds of areas and their property have been restricting end users to explore and reuse them. URBIS (Urban land recycling information services for sustainable cities) is a European project aiming to identify urban areas which have potential to be further developed as well as to extract their land use information based on open spatial data. Urban Atlas, satellite images, and OpenStreetMap are chosen as main input data source. Through a first selection polygons which represent land without current use, green open spaces, gaps between buildings etc. are chosen and stored in a layer named as green layer, and polygons standing for brownfields are stored in another layer called grey layer. These two layers collect urban potential development areas (PDAs) which have high possibility to be considered by decision makers. To provide a comprehensive view about PDAs, physical properties (for example, location, size), vegetation coverage, transportation information etc. are also calculated and attached as attributes to the polygons. Furthermore, our results are presented through online interactive maps. Thus, end users cannot only view all these areas but also select their own areas of interest according to particular attributes. The URBIS strategy has been successfully implemented in three pilot cities, Osnabrueck (Germany), Amiens (France), and the Moravian-Silesian Region (Czech Republic). Our site selection and property calculation methods have been done in databases and all programs are open to the public, which means URBIS provides a methodology to be expanded to other European cities. DOI: https://doi.org/10.3846/enviro.2017.127

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and – for electro-optical sensors – in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

There exists a lack of detailed empirical studies to assess the relevance of WebMapping applications. Despite this fact, it is quite common to note that importance and influence of WebMapping applications have increased over the last years. This paper presents the result of an empirical study to analyze the importance of a Desktop-WebMapping application for a gastronomic web 2.0 portal for the city of Osnabruck. An exploratory focus is to evaluate how often geospatial information (in a broader information context) from this web portal is used and how important the WebMapping applications are for the users of the web portal. Furthermore, it is evaluated which WebMapping functions (e.g. routing, queries, printing) are of relevance to the users and what the order of importance is for the provided functions. To-date, just a few mobile WebMapping applications for handheld devices exist. Consequently, for OsnaGo we developed a mobile WebMapping component that can be used with mobile phones, PDAs and other handheld devices to access the web portal (http://www.osnago-mobil.de). Development and user based evaluation is presented in this paper.

Summary: The study is intended as a contribution to assessing the value of digital image data for semi-automatic analysis for classifying land cover and tree species and was carried out in the framework of the DGPF-project. Sensor specific strengths of ADS40-2 nd , Quattro DigiCAM, DMC, JAS-150, Ultracam-X, and RMK-Top15 cameras and weakness for classification purposes are presented and shortly discussed. The first approach is based on a maximum likelihood method in combination with a decision tree and produces 13 land cover classes. The second approach is based on logistic regression models and produces eight tree species classes. The classified images were visually assessed and quantitatively analyzed. The accuracy assessment reveals that in both approaches similar classification results are obtained by all sensors with overall Kappa coefficients between 0.6 and 0.9. However, a real sensor comparison was not possible since the image data was acquired at different dates. Thus, some variations in classification results are due to phenological differences and different illumination and atmospheric conditions. It is planned for the future that the classifications of the first approach will be adjusted to the characteristics of each sensor. In the second approach, further work is needed to improve distinguishing non-dominant, small and covered deciduous tree species. Zusammenfassung: Potenzial digitaler Sensoren zur Klassifizierung der Landbedeckung und Baumarten - eine Fallstudie im Rahmen des DGPF-Projektes. Anhand der Bilddaten aus den Kamerasystemen ADS40-2 nd , Quattro DigiCAM, DMC, JAS-150, Ultracam-X, und RMK-Top15 wurden zwei Klassifikationsverfahren (Maximum Likelihood und logistische Regression) getestet. Dabei wurden sensor-spezifische Eigenschaften erlautert, sowie die Starken und Schwachen der einzelnen Systeme aufgezeigt. Die Resultate wurden visuell und quantitativ bewertet. Direkte Sensorvergleiche erwiesen sich dabei als schwierig, da zum Aufnahmezeitpunkt der einzelnen Bilddaten sowohl eine unterschiedliche Vegetationsentwicklung wie auch Unterschiede in den Beleuchtungs- und atmospharischen Verhaltnissen vorherrschten. Quantitative Analysen zeigen, dass sich mit jedem Kamerasysteme sehr ahnlich gute Resultate erzielen liessen. Das erste Verfahren zeigt fur 13 Landnutzungsklassen Kappa Koeffizienten von gut 0.6 bei allen verwendeten Systemen. Allerdings unterscheidet sich die Genauigkeit der einzelnen spezifischen Klassen wie Mais oder Kartoffeln fur die unterschiedlichen Kameras. Hierzu soll in weiteren Analysen das Klassifikationsverfahren an die jeweiligen Kameras angepasst werden. Fur das zweite Verfahren liegt der Kappa Koeffizient fur 8 Baumarten zwischen 0.7 und 0.9. Bei diesem Verfahren soll in zukunftigen Analysen die Genauigkeit der Erkennung von nicht dominanten, kleinen und verdeckten Baumarten erhoht werden.

The main objective of this article is quality assessment of pansharpening fusion methods. Pansharpening is a fusion technique to combine a panchromatic image of high spatial resolution with multispectral image data of lower spatial resolution to obtain a high-resolution multispectral image. During this process, the significant spectral characteristics of the multispectral data should be preserved. For images acquired at the same time by the same sensor, most algorithms for pansharpening provide very good results, i.e. they retain the high spatial resolution of the panchromatic image and the spectral information from the multispectral image (single-sensor, single-date fusion). For multi-date, multi-sensor fusion, however, these techniques can still create spatially enhanced data sets, but usually at the expense of the spectral consistency. In this study, eight different methods are compared for image fusion to show their ability to fuse multitemporal and multi-sensor image data. A series of eight multitemp...

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1 : 10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1 : 10 to 1 : 30). However, even with a spatial resolution ratio as small as 1 : 30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1 : 30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

Image fusion is a technique that is used to combine the spatial structure of a high-resolution panchromatic image with the spectral information of a low- resolution multispectral image to produce a high-resolution multispectral image. Currently, image fusion techniques via color or statistical transforms such as the Intensity-Hue-Saturation (IHS) and principal component (PC) methods are still widely used. These methods create multispectral images of higher spatial resolution but usually at the cost of color distortions in the fused images. This is especially true if the wavelength range of the panchromatic image does not correspond to that of the employed multispectral bands or for multitemporal/multisensoral fusion. To overcome the color distortion problem, a number of new fusion methods have been developed over the last years. One of these is the Ehlers fusion algorithm, which is based on an IHS transform coupled with adaptive filtering in the Fourier domain. This method preserves the spectral character...

Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information.

For monitoring environmental changes, new digital remote sensors have become available that allow monitoring and change detection analyses at resolutions and scales that were deemed impossible just a few years ago. The advent of airborne stereo scanners of ultra high spatial resolution offers the possibility of a complete digital remote sensing processing system. Current sensors include the High-resolution Stereo Camera (HRSC), the ADS-40, and the Digital Mapping Camera (DMC). For automated analysis, however, the new sensors require also new processing techniques. This paper presents results of change monitoring analyses for areas along the shorelines of the Elbe and Weser rivers in North Germany using integrated HRSC and GIS datasets. An automated procedure for highly accurate mapping was developed which is based on a hierarchical stepwise approach integrating GIS methods and digital surface information in this process. This approach allows the production of GIS maps that are more detailed and accurate than those that were previously produced by conventional means. Within the GIS environment, the multitemporal analysis also allows the exact quantification and location of changes of the protected biotope types.

Modelling urban objects and phenomena puts extremely high demands on the acquisition and analysis of the necessary data. In this context, digital airborne camera systems offer very high spatial resolutions and the simultaneous acquisition of elevation data—either through stereo‐matching or by laser scanning. However, with these data the user community faces new problems in their analysis. In response, region‐based classification approaches have been developed, but these are not yet fully automatic, reliable and transferable. Hence, this paper proposes a novel hybrid and multi‐scale methodology which performs a classification on all segmented levels (called ‘Classification on multiple Segment levels’, ComS). First investigations validate the applicability as well as the accuracy of the proposed methodology compared with conventional approaches, such as a supervised, pixel‐based maximum likelihood classification.

The advent of very high-resolution satellite programs and digital airborne cameras with ultra high resolution offers new possibilities for very accurate mapping of the environment. With these sensors of improved spatial resolution, however, the user community faces a new problem in the analysis of this type of image data. Standard classification techniques have to be augmented with appropriate analysis procedures because the required homogeneity of landuse/landcover classes can no longer be achieved by the integration effect of large pixel sizes (e.g., 20–80 m). New intelligent techniques will have to be developed that make use of multisensor approaches, geographic information system (GIS) integration and context-based interpretation schemes. The ideal goal should be that GIS ‘intelligence’ (e.g., object and analysis models) should be used to automate the classification process. In return, GIS objects can be extracted from a remote sensing image to update the GIS database. This paper presents the development of an automated procedure for biotope type mapping from ultra high-resolution airborne scanner data (HRSC-A). The hierarchical procedure incorporates a priori GIS information, a digital surface model (DSM) and multispectral image data. The results of this study will serve as a basis for a continuous environmental monitoring process in the tidally influenced region of the Elbe River, Germany.

The term “real time” regarding airborne remote-sensing data can only seldom be found in recent literature on geospatial research. In the course of the German Aerospace Center (DLR) project VABENE, an airborne monitoring system has been developed which is able to pre-process remotely sensed images in real time by an on-board computing system. Afterwards, the data is directly transferred to a ground station via a microwave data transfer system installed on the aircraft. In contrast to remote sensing, the real-time aspect of other types of geo-sensors has been ubiquitous for several years. This article presents a novel concept for an information fusion infrastructure to fuse remote-sensing data and in-situ measurements for the integration in real-time applications via a spatial data infrastructure (SDI). Furthermore, an offline experiment will prove the concept by means of data sets that can already be provided by real-time systems. The study addresses the use case of agent-based modelling of people dynamics for security issues during major events to support avoiding tragedies.

This paper addresses the modelling of uncertainty in an integrated geographic information system (GIS), specifically focused on the fusion of activities between GIS and remote sensing. As data is abstracted from its ‘raw’ form to the higher representations used by GIS, it passes through a number of different conceptual data models via a series of transformations. Each model and each transformation process contributes to the overall uncertainty present within the data. The issues that this paper addresses are threefold. Firstly, a description of various models of geographic space is given in terms of the inherent uncertainty characteristics that apply; this is then worked into a simple formalism. Secondly, the various transformation processes that are used to form geographic classes or objects from image data are described, and their effects on the uncertainty properties of data are stated. Thirdly, using the formalism to describe the transformation processes, a framework for the propagation of uncertainty through an integrated GIS is derived. By way of a summary, a table describing sources of accumulated uncertainty across four underlying models of geographic space is derived.