Dr. Christine Wessollek Profile

Dr. Christine Wessollek

at TU Dresden

SPIE Involvement:

Conference Program Committee | Author

Proceedings Article | 9 October 2018 Paper

Evaluation of MODIS NDVI based phenology indicators for the analysis of vegetation dynamics in the nature reserve Königsbrücker Heide

Pierre Karrasch, Christine Wessollek

Proceedings Volume 10790, 107900Y (2018) https://doi.org/10.1117/12.2325549

KEYWORDS: Vegetation, MODIS, Remote sensing, Temporal resolution, Scanning tunneling microscopy, Time series analysis, Sensing systems, Satellites, Earth observing sensors, Sensors

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Proceedings Article | 9 October 2018 Paper

Surface irradiance estimations on watercourses with remote sensing data

Marie-Luise Kautz, Pierre Karrasch, Christine Wessollek

Proceedings Volume 10790, 107900X (2018) https://doi.org/10.1117/12.2325373

KEYWORDS: Clouds, Solar energy, Solar radiation, Vegetation, Sun, Remote sensing, Atmospheric modeling, Satellites, Humidity, Visibility

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Proceedings Article | 2 November 2017 Paper

Evaluation of crop development stages with TerraSAR-X backscatter signatures (2010-12) by using Growing Degree Days

Atif Ishaq, René Pasternak, Christine Wessollek

Proceedings Volume 10421, 1042127 (2017) https://doi.org/10.1117/12.2278326

KEYWORDS: Backscatter, Synthetic aperture radar, Temperature metrology, Radar, Remote sensing, Soil science, Agriculture, Geographic information systems, Data acquisition, Data modeling

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Proceedings Article | 5 October 2017 Paper

Monitoring structural breaks in vegetation dynamics of the nature reserve Königsbrücker Heide

Christine Wessollek, Pierre Karrasch

Proceedings Volume 10428, 1042819 (2017) https://doi.org/10.1117/12.2278202

KEYWORDS: Vegetation, Time series analysis, Unmanned aerial vehicles, Mining, Remote sensing, Image processing, Satellite imaging, Earth observing sensors, Satellites, Photography

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Nowadays remote sensing is a well-established method and technique of providing data. The current development shows the availability of systems with very high geometric resolution for the monitoring of vegetation. At the same time, however, the value of temporally high-resolution data is underestimated, particularly in applications focusing on the detection of short-term changes. These can be natural processes like natural disasters as well as changes caused by anthropogenic interventions. These include economic activities such as forestry, agriculture or mining but also processes which are intended to convert previously used areas into natural or near-natural surfaces. The K¨onigsbr¨ucker Heide is a former military training site located about 30 km north of the Saxon state capitol Dresden. After the withdrawal of the Soviet forces in 1992 and after nearly 100 years of military use this site was declared as nature reserve in 1996. The management of the whole protection area is implemented in three different management zone. Based on MODIS-NDVI time series between 2000 and 2016 different developments are apparent in the nature development zone and the zone of controlled succession. Nevertheless, the analyses also show that short-term changes, so called breaks in the vegetation development cannot be described using linear trend models. The complete understanding of vegetation trends is only given if discontinuities in vegetation development are considered. Structural breaks in the NDVI time series can be found simultaneously in the whole study area. Hence it can be assumed that these breaks have a more natural character, caused for example by climatic conditions like temperature or precipitation. Otherwise, especially in the zone of controlled succession structural breaks can be detected which cannot be traced back to natural conditions. Final analyses of the spatial distribution of breakpoints as well as their frequency depending on the respective protection zone allow a detailed view to vegetation development in the K¨onigsbr¨ucker Heide.

Proceedings Article | 5 October 2017 Paper

Analyses of GIMMS NDVI Time Series in Kogi State, Nigeria

Jessica Palka, Christine Wessollek, Pierre Karrasch

Proceedings Volume 10428, 104281A (2017) https://doi.org/10.1117/12.2278216

KEYWORDS: Vegetation, Temporal resolution, Time series analysis, Remote sensing, Process modeling, Signal processing, Modeling, Agriculture, Knowledge management, Environmental monitoring

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The value of remote sensing data is particularly evident where an areal monitoring is needed to provide information on the earth’s surface development. The use of temporal high resolution time series data allows for detecting short-term changes. In Kogi State in Nigeria different vegetation types can be found. As the major population in this region is living in rural communities with crop farming the existing vegetation is slowly being altered. The expansion of agricultural land causes loss of natural vegetation, especially in the regions close to the rivers which are suitable for crop production. With regard to these facts, two questions can be dealt with covering different aspects of the development of vegetation in the Kogi state, the determination and evaluation of the general development of the vegetation in the study area (trend estimation) and analyses on a short-term behavior of vegetation conditions, which can provide information about seasonal effects in vegetation development. For this purpose, the GIMMS-NDVI data set, provided by the NOAA, provides information on the normalized difference vegetation index (NDVI) in a geometric resolution of approx. 8 km. The temporal resolution of 15 days allows the already described analyses. For the presented analysis data for the period 1981-2012 (31 years) were used. The implemented workflow mainly applies methods of time series analysis. The results show that in addition to the classical seasonal development, artefacts of different vegetation periods (several NDVI maxima) can be found in the data. The trend component of the time series shows a consistently positive development in the entire study area considering the full investigation period of 31 years. However, the results also show that this development has not been continuous and a simple linear modeling of the NDVI increase is only possible to a limited extent. For this reason, the trend modeling was extended by procedures for detecting structural breaks in the time series.

Proceedings Article | 25 October 2016 Paper

Evaluating the potential of image fusion of multispectral and radar remote sensing data for the assessment of water body structure

Sebastian Hunger, Pierre Karrasch, Christine Wessollek

Proceedings Volume 9998, 999814 (2016) https://doi.org/10.1117/12.2241264

KEYWORDS: Remote sensing, Radar, Image fusion, Data fusion, Multispectral imaging, Image processing, Wavelets, Agriculture, Data acquisition, Image sensors

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Proceedings Article | 18 October 2016 Paper

Monitoring of vegetation dynamics on the former military training area Königsbrücker Heide using remote sensing time series

Christine Wessollek, Pierre Karrasch

Proceedings Volume 10005, 100050Q (2016) https://doi.org/10.1117/12.2239944

KEYWORDS: Earth observing sensors, Vegetation, MODIS, Remote sensing, Time series analysis, Temporal resolution, Statistical analysis, Spatial resolution, Image resolution, Data analysis

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Proceedings Article | 18 October 2016 Presentation + Paper

Knowledge-based modelling of historical surfaces using lidar data

Veit Höfler, Christine Wessollek, Pierre Karrasch

Proceedings Volume 10005, 100050G (2016) https://doi.org/10.1117/12.2240388

KEYWORDS: Modeling, LIDAR, Data modeling, Process modeling, Data conversion, Statistical modeling, Data processing, Magnetism, Particles, Visualization

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Currently in archaeological studies digital elevation models are mainly used especially in terms of shaded reliefs for the prospection of archaeological sites. Hesse (2010) provides a supporting software tool for the determination of local relief models during the prospection using LiDAR scans. Furthermore the search for relicts from WW2 is also in the focus of his research. In James et al. (2006) the determined contour lines were used to reconstruct locations of archaeological artefacts such as buildings. This study is much more and presents an innovative workflow of determining historical high resolution terrain surfaces using recent high resolution terrain models and sedimentological expert knowledge. Based on archaeological field studies (Franconian Saale near Bad Neustadt in Germany) the sedimentological analyses shows that archaeological interesting horizon and geomorphological expert knowledge in combination with particle size analyses (Koehn, DIN ISO 11277) are useful components for reconstructing surfaces of the early Middle Ages. Furthermore the paper traces how it is possible to use additional information (extracted from a recent digital terrain model) to support the process of determination historical surfaces. Conceptual this research is based on methodology of geomorphometry and geo-statistics. The basic idea is that the working procedure is based on the different input data. One aims at tracking the quantitative data and the other aims at processing the qualitative data. Thus, the first quantitative data were available for further processing, which were later processed with the qualitative data to convert them to historical heights. In the final stage of the workflow all gathered information are stored in a large data matrix for spatial interpolation using the geostatistical method of Kriging. Besides the historical surface, the algorithm also provides a first estimation of accuracy of the modelling. The presented workflow is characterized by a high flexibility and the opportunity to include new available data in the process at any time.

Proceedings Article | 20 October 2015 Paper

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

Veit Höfler, Christine Wessollek, Pierre Karrasch

Proceedings Volume 9644, 96440B (2015) https://doi.org/10.1117/12.2194290

KEYWORDS: Data modeling, Systems modeling, LIDAR, Mathematical modeling, Computer programming languages, Computer programming, Algorithm development, Modeling, Data processing, Raster graphics

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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.

Proceedings Article | 20 October 2015 Paper

Introducing a rain-adjusted vegetation index (RAVI) for improvement of long-term trend analyses in vegetation dynamics

Christine Wessollek, Pierre Karrasch, Babatunde Osunmadewa

Proceedings Volume 9644, 96440M (2015) https://doi.org/10.1117/12.2192821

KEYWORDS: Remote sensing, Vegetation, Modeling, Statistical analysis, Near infrared, Mathematical modeling, Meteorology, Statistical modeling, Data modeling, Spatial resolution

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SPIE Journal Paper | 8 July 2015

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

Babatunde Osunmadewa, Christine Wessollek, Pierre Karrasch

JARS, Vol. 9, Issue 01, 096029, (July 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096029

KEYWORDS: Vegetation, Statistical analysis, Agriculture, Statistical modeling, Data modeling, Analytical research, Time series analysis, Image segmentation, Visualization, Process modeling

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Proceedings Article | 11 October 2014 Paper

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

Babatunde Osunmadewa, Christine Wessollek, Pierre Karrasch

Proceedings Volume 9239, 92390F (2014) https://doi.org/10.1117/12.2066876

KEYWORDS: Vegetation, Statistical analysis, Data modeling, Climatology, Climate change, Analytical research, Error analysis, Visualization, Satellites, Modeling

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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 Kendall's 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.

Showing 5 of 12 publications

Conference Committee Involvement (7)

Earth Resources and Environmental Remote Sensing/GIS Applications XV

16 September 2024 | Edinburgh, United Kingdom

Earth Resources and Environmental Remote Sensing/GIS Applications XIV

3 September 2023 | Amsterdam, Netherlands

Earth Resources and Environmental Remote Sensing/GIS Applications XIII

5 September 2022 | Berlin, Germany

Earth Resources and Environmental Remote Sensing/GIS Applications XII

13 September 2021 | Online Only, Spain

Earth Resources and Environmental Remote Sensing/GIS Applications XI

22 September 2020 | Online Only, United Kingdom

Showing 5 of 7 Conference Committees

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