Prof. Christopher Conrad Profile

Prof. Christopher Conrad

Assistant Professor of Geography Remote Sensing at Julius Maximilians Univ Wurzburg

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Publications (14)

Proceedings Article | 4 October 2017 Presentation + Paper

Semi-autonomous remote sensing time series generation tool

Dinesh Kumar Babu, Christof Kaufmann, Marco Schmidt, Thorsten Dahms, Christopher Conrad

Proceedings Volume 10427, 104270C (2017) https://doi.org/10.1117/12.2278213

KEYWORDS: Remote sensing, Data fusion, Satellites, Ecology, Data processing

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SPIE Journal Paper | 6 November 2014

Canopy cover estimation across semi-Mediterranean woodlands: application of high-resolution earth observation data

Hamed Naghavi, Asghar Fallah, Shaban Shataee, Hooman Latifi, Javad Soosani, Habib Ramezani, Christopher Conrad

JARS, Vol. 8, Issue 01, 083524, (November 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083524

KEYWORDS: Data modeling, Vegetation, Statistical modeling, Remote sensing, Statistical analysis, Feature selection, Error analysis, Reflectivity, Near infrared, Soil science

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Proceedings Article | 25 October 2012 Paper

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

Fabian Löw, Gunther Schorcht, Ulrich Michel, Stefan Dech, Christopher Conrad

Proceedings Volume 8538, 85380R (2012) https://doi.org/10.1117/12.974588

KEYWORDS: Personal protective equipment, Agriculture, Vegetation, Feature selection, Remote sensing, Satellites, Near infrared, Data modeling, Algorithm development, Satellite imaging

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Proceedings Article | 25 October 2012 Paper

Object-based cropland degradation identification: a case study in Uzbekistan

Olena Dubovyk, Gunter Menz, Christopher Conrad, Asia Khamzina

Proceedings Volume 8538, 85380W (2012) https://doi.org/10.1117/12.974647

KEYWORDS: Vegetation, Agriculture, Soil science, Image segmentation, Landsat, Shape memory alloys, Earth observing sensors, Satellites, Satellite imaging, Reflectivity

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Proceedings Article | 19 October 2012 Paper

A re-examination of perpendicular drought indices over central and southwest Asia

Alireza Shahabfar, Maximilian Reinwand, Christopher Conrad, Gunther Schorcht

Proceedings Volume 8531, 85311O (2012) https://doi.org/10.1117/12.974561

KEYWORDS: Vegetation, Meteorology, Climatology, Agriculture, MODIS, Soil science, Environmental sensing, Near infrared, Remote sensing, Statistical analysis

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Drought monitoring models and products assist decision makers in drought planning, preparation, and mitigation, all of which can play a role in reducing drought impacts. In this study, the performance of two newly developed remote sensing-based drought indices, the perpendicular drought index (PDI) and modified perpendicular drought index (MPDI), are further explored for regional drought monitoring in agricultural regions located in central and south western Asia. The study area covers regions from moderate and wet climatological zones with dense vegetation coverage to semi-arid and arid climatological conditions with moderate to poor vegetation coverage. The spatio-temporal patterns of surface drought derived by PDI and MPDI from 250m MODerate Resolution Imaging Spectroradiometer (MODIS) data in 8-day time steps are compared against two other drought indices: the Standardized Precipitation Index (SPI) as a meteorological drought index and the potential evapotranspiration (ET0) as an agro-meteorological drought index, which both were calculated based on field-measured precipitation and regional meteorological parameters. In addition, 8-day MODIS Normalized Difference Vegetation Index (NDVI) was calculated and its performance to detect drought occurrence and measuring of drought severity compared with the two perpendicular drought indices. Significant correlations were found between the PDI, the MPDI and precipitation and other applied meteorological and agrometeorological drought indices. The results confirm previous studies which has been analyzing the PDI and the MPDI over some study points in Iran. In this research, however, implementation of higher resolution data (MOD09Q1) in both spatial (250 m) and temporal (8-days) dimensions revealed a greater agreement between the drought information extracted by the MPDI, PDI and field meteorological measurements. It could be concluded that the applied perpendicular indices could be used as a drought early warning system over case study region and other regions with similar arid and semi-arid climatological conditions.

Proceedings Article | 19 October 2012 Paper

Crop classification at subfield level using RapidEye time series and graph theory algorithms

Gunther Schorcht, Fabian Löw, Sebastian Fritsch, Christopher Conrad

Proceedings Volume 8531, 85311G (2012) https://doi.org/10.1117/12.974670

KEYWORDS: Image segmentation, Satellites, Image processing algorithms and systems, Satellite imaging, Image classification, Remote sensing, Vegetation, Earth observing sensors, Agriculture, Personal protective equipment

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Accurate information about land use patterns is crucial for a sustainable and economical use of water in agricultural systems. Water demand estimation, yield modeling and agrarian policy are only a few applications addressed by land use classifications based on remote sensing imagery. In Central Asia, where fields are traditionally large and state order crops dominate the area, small units of fields are often separated for the additional cultivation of income crops for the farmers. Traditional object based land use classifications on multi-temporal satellite imagery using field boundaries show low classification accuracies on these separated fields, expressed by a high uncertainty of the final class labels. Although segmentation of smaller subfields was shown to be suitable for improving the classification result, the extraction of subfields is still a time-consuming and error-prone process. In this study, energy based Graph-Cut segmentation technique is used to enhance the segmentation process and finally to improve the classification result. The interactive segmentation technique was successfully adopted from bio-medical image analysis to fit remote sensing imagery in the spatial and in the temporal domain. A set of rules was developed to perform the image segmentation procedure on pixels of single satellite datasets and on objects representing time series of a vegetation index. An ensemble classifier based on Random Forest and Support Vector Machines was used to receive information about classification uncertainty before and after applying the segmentation. It is demonstrated that subfield extraction based on Graph Cuts outperforms traditional image segmentation approaches in simplicity and reduces the risk of under- and over-segmentation significantly. Classification uncertainty decreased using the derived subfields as object boundaries instead of original field boundaries. The segmentation technique performs well on several multi-temporal satellite images without changing parameters and may be used to refine object based land use classifications to subfield level.

Proceedings Article | 19 October 2012 Paper

Assessing irrigated cropland dynamics in central Asia between 2001 and 2010 based on MODIS time series

Christopher Conrad, Fabian Loew, Moritz Rudloff, Gunther Schorcht

Proceedings Volume 8531, 85310I (2012) https://doi.org/10.1117/12.974576

KEYWORDS: MODIS, Vegetation, Agriculture, Statistical analysis, Classification systems, Remote sensing, Spatial resolution, Accuracy assessment, Ecosystems, Climatology

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Monitoring of vegetation dynamics in extensive irrigated croplands is essential for improving land and water management, especially to understand the reaction of the system to water scarcity and degradation processes. This study focuses on the assessment of irrigated cropland dynamics in the western part of the Aral Sea Basin in Central Asia during the past decade. Extend of cropland and spatio-temporal cropping patters are analyzed based on phenological profiles extracted from 16day MODIS vegetation index time series at a spatial resolution of 250m. Knowledge-based classifications which needed to be adjusted for every single year were applied to distinguish between cropland and other major land cover types, the desert or sparsely vegetated steppes, settled areas, and water bodies. Interannual variability of the time series in the maximum cropland extend recorded between 2001 and 2010 was assessed by using Pearson’s cross correlation (PCC) coefficient. Shifts of maximum one month (+/-) were tested and the highest PCC coefficient was selected. Accuracy assessment using a multi-annual MODIS classification conducted for a representative irrigation system between 2004 and 2007 returned acceptable results for the cropland mask (<90%). Comparing the inter-annual cropland dynamics revealed using PCC with both, the MODIS classifications 2004-2007 and pure pixels of aggregated ASTER based maps showed that the PCC only permits differentiation between different modalities in the time series, i.e. years of a varying number of intra-annual crop cycles. However, simply overlaying the cropland extends 2001-2010 already exhibits areas of unreliable water supply. In this light, integration of both, PCC analysis of MODIS time series and annual maps of the cropland extent can be concluded as valuable next steps for better understanding the dynamics of the irrigated cropland at regional scale not only in the Aral Sea Basin of Central Asia, but also in other arid environments, where irrigation agriculture is essential for rural income generation and food security.

Proceedings Article | 27 October 2011 Paper

Development of a satellite-based multi-scale land use classification system for land and water management in Uzbekistan and Kazakhstan

Fabian Löw, Ulrich Michel, Stefan Dech, Christopher Conrad

Proceedings Volume 8181, 81811K (2011) https://doi.org/10.1117/12.898038

KEYWORDS: Agriculture, Spatial resolution, Remote sensing, Satellites, Classification systems, Image classification, Earth observing sensors, Satellite imaging, Temporal resolution, Sensors

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Proceedings Article | 7 October 2011 Paper

Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia

Christopher Conrad, Miriam Machwitz, Gunther Schorcht, Fabian Löw, Sebastian Fritsch, Stefan Dech

Proceedings Volume 8174, 817412 (2011) https://doi.org/10.1117/12.898345

KEYWORDS: Agriculture, Vegetation, Remote sensing, Data acquisition, Associative arrays, Classification systems, Composites, Spatial resolution, Algorithm development, Image classification

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Proceedings Article | 7 October 2011 Paper

Relationships between high resolution RapidEye based fPAR and MODIS vegetation indices in a heterogeneous agricultural region

Sebastian Fritsch, Miriam Machwitz, Christopher Conrad, Stefan Dech

Proceedings Volume 8174, 81740N (2011) https://doi.org/10.1117/12.898333

KEYWORDS: MODIS, Vegetation, Agriculture, Composites, Statistical analysis, Reflectivity, Remote sensing, Satellites, Sensors, Data modeling

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Proceedings Article | 22 October 2010 Paper

Mapping crop distribution in administrative districts of southwest Germany using multi-sensor remote sensing data

Christopher Conrad, Achim Goessl, Sylvia Lex, Annekatrin Metz, Thomas Esch, Christoph Konrad, Gerold Goettlicher, Stefan Dech

Proceedings Volume 7824, 78240C (2010) https://doi.org/10.1117/12.865113

KEYWORDS: Agriculture, Landsat, Remote sensing, Image segmentation, Data acquisition, Associative arrays, Satellites, Vegetation, Visualization, Earth observing sensors

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Proceedings Article | 22 October 2010 Paper

Mapping of large irrigated areas in Central Asia using MODIS time series

M. Machwitz, J. Bloethe, D. Klein, C. Conrad, S. Dech

Proceedings Volume 7824, 782403 (2010) https://doi.org/10.1117/12.865082

KEYWORDS: MODIS, Remote sensing, Agriculture, Ecosystems, Hydrology, Current controlled current source, Vegetation, Reflectivity, Image resolution

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Proceedings Article | 22 October 2010 Paper

Statistical derivation of fPAR and LAI for irrigated cotton and rice in arid Uzbekistan by combining multi-temporal RapidEye data and ground measurements

Andrea Ehammer, Sebastian Fritsch, Christopher Conrad, John Lamers, Stefan Dech

Proceedings Volume 7824, 782409 (2010) https://doi.org/10.1117/12.864796

KEYWORDS: Vegetation, Reflectivity, Satellites, Image segmentation, Sensors, Remote sensing, Satellite imaging, Biological research, Earth observing sensors, In situ metrology

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Proceedings Article | 26 October 2004 Paper

Modeling seasonal actual evapotranspiration with remote sensing and GIS in Khorezm region, Uzbekistan

Christopher Conrad, Gerd Rucker, Christian Schweitzer, Stefan Dech, Mohammad Hafeez

Proceedings Volume 5568, (2004) https://doi.org/10.1117/12.565611

KEYWORDS: Data modeling, MODIS, Atmospheric modeling, Remote sensing, Meteorology, Geographic information systems, Vegetation, Environmental sensing, Heat flux, Data centers

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Showing 5 of 14 publications

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