On the Use of Airborne Imaging Spectroscopy Data for the Automatic Detection and Delineation of Surface Water Bodies

There is economical and ecological relevance for remote sensing applications of inland and coastal waters: The European Union Water Framework Directive (European Parliament and the Council of the European Union, 2000) for inland and coastal waters requires the EU member states to take actions in order to reach a good ecological status in inland and coastal waters by 2015. This involves characterization of the specific trophic state and the implementation of monitoring systems to verify the ecological status. Financial resources at the national and local level are insufficient to assess the water quality using conventional methods of regularly field and laboratory work only. While remote sensing cannot replace the assessment of all aquatic parameters in the field, it powerfully complements existing sampling programs and offers the base to extrapolate the sampled parameter information in time and in space. The delineation of surface water bodies is a prerequisite for any further remote sensing based analysis and even can by itself provide up-to-date information for water resource management, monitoring and modelling (Manavalan et al., 1993). It is further important in the monitoring of seasonally changing water reservoirs (e.g., Alesheikh et al., 2007) and of shortterm events like floods (Overton, 2005). Usually the detection and delineation of surface water bodies in optical remote sensing data is described as being an easy task. Since water absorbs most of the irradiation in the near-infrared (NIR) part of the electromagnetic spectrum water bodies appear very dark in NIR spectral bands and can be mapped by simply applying a maximum threshold on one of these bands (Swain & Davis, 1978: section 5-4). Many studies took advantage of this spectral behaviour of water and applied methods like single band density slicing (e.g., Work & Gilmer, 1976), spectral indices (McFeeters, 1996, Xu, 2006) or multispectral supervised classification (e.g., Frazier & Page, 2000, Lira, 2006). However, all of these methods have the drawback that they are not fully automated since the analyst has to select a scene-specific threshold (Ji et al., 2009) or training pixels. Moreover there are certain situations where these methods lead to misclassification. For instance, water constituents in turbid water as well as water bottom reflectance and sun glint can raise the reflectance spectrum of surface water even in the NIR spectral range up to a reflectance level which is typical for dark surfaces on land such as dark rocks (e.g., basalt, lava), bituminous roofing materials and in particular shadow regions. Consequently, Carleer & Wolff (2006) amongst others found the land cover classes water and shadow to be highly confused in image classifications. This problem especially occurs in environments where both, a high amount of shadow and water regions can exist, such as urban landscapes, mountainous landscapes or cliffy coasts as well as generally in images with water bodies and cloud shadows. In this investigation we focus on the development of a new surface water body detection algorithm that can be automatically applied without user knowledge and supplementary data on any hyperspectral image of the visible and near-infrared (VNIR) spectral range. The analysis is strictly focused on the VNIR part of the electromagnetic spectrum due to the growing number of VNIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential water pixels (section 4.1) and the removal of false positives from this mask (sections 4.2 and 4.3). In this context the separation between water bodies and shadowed surfaces is the most challenging task which is implemented by consecutive spectral and spatial processing steps (sections 4.3.1 and 4.3.2) resulting in very high detection accuracies

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data

Automatisierungspotenzial von Stadtbiotopkartierungen durch Methoden der Fernerkundung

Die Stadtbiotopkartierung hat sich in Deutschland als die Methode zur Schaffung einer ökologischen Datenbasis für den urbanen Raum etabliert. Sie dient der Untersuchung naturschutzfachlicher Fragen, der Vertretung der Belange des Naturschutzes in zahlreichen räumlichen Planungsverfahren und ganz allgemein einer ökologisch orientierten Stadtplanung. Auf diese Weise kommen die Städte ihrem gesetzlichen Auftrag nach, Natur und Landschaft zu schützen, zu pflegen und zu entwickeln (§ 1 BNatSchG), den es explizit auch innerhalb der besiedelten Fläche zu erfüllen gilt. Ein Großteil der heute bestehenden 228 Stadtbiotoptypenkarten ist in der Etablierungsphase der Methode in den 80er Jahren entstanden und wurde häufig durch Landesmittel gefördert. Der Anteil der Städte, die jemals eine Aktualisierung durchgeführt haben, wird jedoch auf unter fünf Prozent geschätzt. Dies hängt vor allem mit dem hohen Kosten- und Zeitaufwand der Datenerhebung zusammen, die durch visuelle Interpretation von CIR-Luftbildern und durch Feldkartierungen erfolgt. Um die Aktualisierung von Stadtbiotoptypenkarten zu vereinfachen, wird in der vorliegenden Arbeit das Automatisierungspotenzial von Stadtbiotopkartierungen durch Nutzung von Fernerkundungsdaten untersucht. Der Kern der Arbeit besteht in der Entwicklung einer Methode, die einen wichtigen Arbeitsschritt der Stadtbiotopkartierung automatisiert durchführt: Die Erkennung des Biotoptyps von Biotopen. Darüber hinaus zeigt die Arbeit das Automatisierungspotenzial bei der flächenhaften Erhebung von quantitativen Parametern und Indikatoren zur ökologischen Bewertung von Stadtbiotopen auf. Durch die automatische Biotoptypenerkennung kann die Überprüfung und Aktualisierung einer Biotoptypenkarte in weiten Teilen der Stadt automatisiert erfolgen, wodurch der Zeitaufwand reduziert wird. Das entwickelte Verfahren kann in den bestehenden Ablauf der Stadtbiotopkartierung integriert werden, indem zunächst die Kartierung ausgewählter Biotoptypen automatisch erfolgt und die verbleibenden Flächen der Stadt durch visuelle Luftbildinterpretation und Feldbegehung überprüft und zugeordnet werden. Die thematische Einteilung der Biotoptypen orientiert sich im urbanen Raum in erster Linie an der anthropogenen Nutzung, da diese den dominierenden Faktor für die biologische Ausstattung der Biotope darstellt. Die entwickelte Methode eignet sich vor allem zur Erkennung von baulich geprägten Biotopen, da die Nutzung - und dadurch der Biotoptyp einer Fläche - durch eine automatische Analyse der Geoobjekte innerhalb der Biotopfläche ermittelt werden kann. Die Geoobjekte wiederum können durch eine Klassifizierung von multisensoralen Fernerkundungsdaten (hyperspektrale Flugzeugscannerdaten und digitale Oberflächenmodelle) identifiziert werden. Die Analyse der Geoobjekte und der urbanen Oberflächenarten innerhalb der Biotopfläche erfolgt anhand von räumlichen, morphologischen und quantitativen Merkmalen. Auf Basis dieser Merkmale wurden zwei Varianten eines automatischen Biotopklassifizierers entwickelt, die unter Verwendung von Fuzzy Logik und eines neu entwickelten, paarweise arbeitenden Maximum Likelihood Klassifizierers (pMLK) implementiert wurden. Für die bisher implementierten 10 Biotoptypen, die zusammen etwa die Hälfte des Stadtgebiets abdecken, wurde eine Erkennungsgenauigkeit von über 80 % ermittelt. Der pMLK wurde erfolgreich in zwei Städten (Berlin, Dresden) erprobt, wodurch seine Übertragbarkeit nachgewiesen werden konnte

An automated and adaptable approach for characterizing and portioning cities into urban structure types

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomera-tions into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adapt-able approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with in UST classifica-tion. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability

Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

The modeling of flood damage is an important component for risk analyses, which are the basis for risk-oriented flood management, risk mapping, and financial appraisals. An automatic urban structure type mapping approach was applied on a land use/land cover classification generated from multispectral Ikonos data and LiDAR (Light Detection And Ranging) data in order to provide spatially detailed information about the building stock of the case study area of Dresden, Germany. The multi-parameter damage models FLEMOps (Flood Loss Estimation Model for the private sector) and regression-tree models have been adapted to the information derived from remote sensing data and were applied on the basis of the urban structure map. To evaluate this approach, which is suitable for risk analyses, as well as for post-disaster event analyses, an estimation of the flood losses caused by the Elbe flood in 2002 was undertaken. The urban structure mapping approach delivered a map with a good accuracy of 74% and on this basis modeled flood losses for the Elbe flood in 2002 in Dresden were in the same order of magnitude as official damage data. It has been shown that single-family houses suffered significantly higher damages than other urban structure types. Consequently, information on their specific location might significantly improve damage modeling, which indicates a high potential of remote sensing methods to further improve risk assessments

Feature-based identification of urban endmember spectra using hyperspectral HyMap data

Urban areas are among the most dynamic regions on earth, continuously and rapidly changing. For monitoring these changes, remote sensing has proven over the years to be a reliable source. Current airborne hyperspectral systems with spatial resolution of a few meters, combined with very high spectral resolution, facilitate the urban scene analysis by allowing to distinguish small details in the urban environment. This paper presents part of a project aiming to classify man-made objects using hyperspectral images and to investigate the complementarity between hyperspectral and SAR data. The intention is to develop methods that are able to quickly obtain an overview of the current situation and require as little human intervention as possible. This is very important for various applications related to disasters, e.g. emergency cartography, disaster monitoring, damage assessment, mission planning, etc.The paper describes a new method for classifying the main classes in an urban environment using hyperspectral data. The method is based on logistic regression (LR), which is a supervised multi-variate statistical tool that finds an optimal combination of the input channels for distinguishing one class from all the others. LR thus results in detection images per class that can then be combined into a classification image. The LR uses a step-wise method that implicitly performs a channel selection. The method is supervised in the sense that existing digital maps are used for learning. However, the method does not require the laboratory spectra or extensive ground truth. The method is applied on HyMAP data of an urban area in the South of Germany. The results of the proposed approach are compared to classical methods. Furthermore, a sensitivity analysis is presented, which investigates the robustness of the detection of the different classes against various influences and in particular the influence of channel width and pre-processing level. The classification results are better than those obtained by a classical method. The sensitivity analysis shows that the pre-processing level applied to the hyperspectral data does not influence the classification results significantly for this application. Furthermore, reducing the number of channels results in a drop of performance for some classes only when less the number of channels becomes inferior to 40

Potential of hyperspectral remote sensing for analyzing the urban environment

In contrast to widely used multispectral data, hyperspectral imagery resolves material-specific spectral reflection and absorption features making them especially suitable for detailed and comprehensive mapping of urban surface materials. However, this requires development of automated methods for efficient information extraction that take into account the special conditions in urban areas characterized by a big variety of materials and a large heterogeneity of small-sized urban structures. This chapter deals with the current state of methodological developments in urban hyperspectral remote sensing emphasizing the research of the authors towards the development of an automated system for comprehensive mapping of urban surface materials. This includes field- and image-based spectral investigations aiming at the automated derivation of robust quantitative spectral features. They serve as input information for the developed multi-step processing system allowing detailed mapping of urban surface materials. In this context, the iterative procedure is capable of analyzing urban structures at a sub-pixel level and provides area-wide information about the fractional coverage of surface materials for each pixel. Thus, the obtained results are characterized by a new level of thematic and spatial detail which significantly increases their suitability for subsequent modeling, evaluation and monitoring of the urban environment