Pierre Karrasch

Destriping of hyperspectral image data: an evaluation of different algorithms using EO-1 Hyperion data

Abstract Data from the Earth Observing-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensor-internal sources of errors. These include diffuse sensor noise, striping, smile-effect, keystone effect, and spatial misalignments between the detector arrays. For this research paper, the authors focus on the striping effect by comparing and evaluating different algorithms, methods, and configurations to correct striping errors. The correction of striping effects becomes necessary due to imprecise calibration of the detector array. This inaccuracy affects, especially, the first 12 visual and near-infrared bands and also a large number of bands in the short-wave infrared array. Altogether six destriping techniques were tested on the basis of a Hyperion dataset covering a test site in Central Europe. For the final evaluation, various analyses across all Hyperion channels were performed. The results show that some correction methods have almost no effect on the striping in the images. Other methods may eliminate the striping, but analyses show that these algorithms also alter pixel values in adjacent areas, which originally had not been disturbed by the striping effect. Being the first comprehensive comparison study of different destriping algorithms, this paper gives valuable recommendations on how to reach reliable results in further analyses of hyperspectral data.

Design and prototype of an interoperable online air quality information system

This paper focuses on the design and development of a Spatial Data Infrastructure (SDI)-compliant online system for air quality information retrieval, including support for real-time monitoring. This system assesses exposure to ambient air to mitigate potential health risks, which is crucial for susceptible individuals, health practitioners and decision makers. Particular attention is paid to the development of an interoperable, applicable and transferrable approach to the application of robust and flexible air quality modeling as required for early warning systems on the Web. Moreover, the design provides different access levels to system components for both non-expert and scientific users and supports extension with external standard compliant services. The developed Web-client Time2Maps enables the user to view, analyze and download requested air quality information and serves as a portal to the designed online system.

Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria

Abstract. Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R2 of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria.

Preprocessing of hyperspectral images: a comparative study of destriping algorithms for EO1-hyperion

In this study, data from the EO-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensorinternal sources of errors. These include the diffuse sensor noise, the striping effect, the smile effect, the keystone effect and the spatial misalignments between the detector arrays. For this research paper, the authors focus on the striping effect by comparing and evaluating different algorithms, methods and configurations to correct striping errors. The correction of striping effects becomes necessary due to the imprecise calibration of the detector array. This inaccuracy affects especially the first 12 visual and near infrared bands (VNIR) and also a large number of the bands in the short wave infrared array (SWIR). Altogether six destriping techniques were tested on the basis of a Hyperion dataset covering a test site in Central Europe. For the final evaluation, various analyses across all Hyperion channels were performed. The results show that some correction methods have almost no effect on the striping in the images. Other methods may eliminate the striping, but analyses show that these algorithms also alter pixel values in adjacent areas which originally had not been disturbed by the striping effect.

Determination of bank structures and river width variations using remote sensing data

The European Water Framework Directive commits the member states to achieve the good ecological status for all water bodies. For this purpose on the level of the national states monitoring programs are established with the aim to verify the actual status by means of regular surveys. Already in the past remote sensing data in conjunction with methods of geospatial data analysis revealed the added value in terms of monitoring strategies regarding the European Water Framework Directive. Depending on the type of data they can be used for example for the determination of several parameters of rivers and streams. The present analyses show how it is possible to determine the parameter of width variation of small and medium rivers based on digital orthophotos. Because this parameter strongly depends on the geometric quality of the riverbank line, its determination is given particular attention. It turns out that mainly riparian vegetation has a large impact on the visibility of the riverbank line. In a multi-stage process different methods for the identification of affected areas are developed with the aim to reconstruct the true riverbank line in a second step. Finally these data form the basis for the determination of river width variations.

Ad-hoc combination and analysis of heterogeneous and distributed spatial data for environmental monitoring – design and prototype of a web-based solution

ABSTRACT With regard to a multi-dimensional and multi-facetted implementation of the vision of a Digital Earth, capabilities to combine and analyze heterogeneous spatial data sources on the web are becoming increasingly important. In this article, an online system is conceptualized and implemented to facilitate spatial data analysis and decision making specifically for environmental applications. It supports a dynamic search and binding of suitable geoprocessing functionality with respect to the given input data and target description. Geoprocessing patterns are used to create an application-oriented abstraction layer on top of generic geoprocessing services available on the web. As an application scenario the determination and quality assessment for water body structures is taken. For this use case authoritative data, remote sensing imagery and citizen science data gets combined to gain a comprehensive picture of the various spatial, temporal and thematic aspects influencing the quality of inland waters. The prototypical implementation makes use of open standards to facilitate the integration with existing spatial data infrastructures.

Determination of water body structures for small rivers using remote sensing data

The diversity of habitats in water bodies like rivers is characterised by the status of morphological and hydrological conditions. The good ecological status of water bodies is claimed in the European Water Framework Directive. For the assessment of this status the hydro-morphology is one of the most important supporting components for the classification of the ecological status of water bodies. Therefore the periodical monitoring is a mandatory measure in the scope of the European Water Framework Directive. Regarding the so called overview-method of the LAWA (German Working Group on water issues of the Federal States and the Federal Government represented by the Federal Environment Ministry) the use of remote sensing data and remote sensing methodologies becomes more important. Therefore remote sensing data on different scales (satellite, aerial photographs) as well as other topographic information (ATKIS) and a high resolution DTM are merged into an integrative process of analysis using remote sensing and GIS methodology. The analyses are focused on two parameters. First, a detailed land use classification based on LANDSAT satellite data is performed for whole catchment of a small river. The results show significant increase of urban areas close to the river. The second analyses deals with the determination of river curvature and introduces the use of a quasi-continuously representation of the river. An additional challenge is the chosen study area of a low mountain range river. While large rivers are clear visible in remote sensing data, the usability and transformation of the well-established algorithms and work flows to small rivers need a further substantial research.

Modelling prehistoric terrain Models using LiDAR-data: a geomorphological approach

Terrain surfaces conserve human activities in terms of textures and structures. With reference to archaeological questions, the geological archive is investigated by means of models regarding anthropogenic traces. In doing so, the high-resolution digital terrain model is of inestimable value for the decoding of the archive. The evaluation of these terrain models and the reconstruction of historical surfaces is still a challenging issue. Due to the data collection by means of LiDAR systems (light detection and ranging) and despite their subsequent pre-processing and filtering, recently anthropogenic artefacts are still present in the digital terrain model. Analysis have shown that elements, such as contour lines and channels, can well be extracted from a high-resolution digital terrain model. This way, channels in settlement areas show a clear anthropogenic character. This fact can also be observed for contour lines. Some contour lines representing a possibly natural ground surface and avoid anthropogenic artefacts. Comparable to channels, noticeable patterns of contour lines become visible in areas with anthropogenic artefacts. The presented workflow uses functionalities of ArcGIS and the programming language R.1 The method starts with the extraction of contour lines from the digital terrain model. Through macroscopic analyses based on geomorphological expert knowledge, contour lines are selected representing the natural geomorphological character of the surface. In a first step, points are determined along each contour line in regular intervals. This points and the corresponding height information which is taken from an original digital terrain model is saved as a point cloud. Using the programme library gstat, a variographic analysis and the use of a Kriging-procedure based on this follow.2-4 The result is a digital terrain model filtered considering geomorphological expert knowledge showing no human degradation in terms of artefacts, preserving the landscape-genetic character and can be called a prehistoric terrain model.

Identification of long-term trends in vegetation dynamics in the Guinea savannah region of Nigeria

The availability of newly generated data from Advanced Very High Resolution Radiometer (AVHRR) covering the last three decades has broaden our understanding of vegetation dynamics (greening) from global to regional scale through quantitative analysis of seasonal trends in vegetation time series and climatic variability especially in the Guinea savannah region of Nigeria where greening trend is inconsistent. Due to the impact of changes in global climate and sustainability of means of human livelihood, increasing interest on vegetation productivity has become important. The aim of this study is to examine association between NDVI and rainfall using remotely sensed data, since vegetation dynamics (greening) has a high degree of association with weather parameters. This study therefore analyses trends in regional vegetation dynamics in Kogi state, Nigeria using bi-monthly AVHRR GIMMS 3g (Global Inventory Modelling and Mapping Studies) data and TAMSAT (Tropical Applications of Meteorology Satellite) monthly data both from 1983 to 2011 to identify changes in vegetation greenness over time. Analysis of changes in the seasonal variation of vegetation greenness and climatic drivers was conducted for selected locations to further understand the causes of observed interannual changes in vegetation dynamics. For this study, Mann-Kendall (MK) monotonic method was used to analyse long-term inter-annual trends of NDVI and climatic variable. The Theil-Sen median slope was used to calculate the rate of change in slopes between all pair wise combination and then assessing the median over time. Trends were also analysed using a linear model method, after seasonality had been removed from the original NDVI and rainfall data. The result of the linear model are statistically significant (p <0.01) in all the study location which can be interpreted as increase in vegetation trend over time (greening). Also the result of the NDVI trend analysis using Mann-Kendall test shows an increasing (i.e. positive) trend in the time series. The significance of the result was tested using Kendalls tau rank correlation coefficient and the results were significant. Finally the NDVI data and TAMSAT data were analysed together in order to describe the relationship between both values. Although, increase in rainfall over the last decades enhances vegetation greenness, other factors such as land use change and population density need to be investigated in order to better explain changing trends of vegetation greening for the study area in the future.

Evaluating Spatial Data Acquisition and Interpolation Strategies for River Bathymetries

The study implements a workflow to evaluate the effects of different data sampling methods and interpolation methods, when measuring and modelling a river bathymetry based on point data. Interpolation and sampling strategies are evaluated against a reference data set. The evaluation of the results includes critically discussing characteristics of the input data, the used methods and the transferability of the results. The results show that the decision for or against a particular sampling method and for a specific setting of the parameters can certainly have a great influence on the quality of the interpolation results. Further, some general guidelines for the acquisition of bathymetries are derived from the study results.