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Mewes T.,University of Bonn | Waske B.,University of Bonn | Franke J.,RSS Remote Sensing Solutions GmbH | Menz G.,University of Bonn
2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2010 - Workshop Program | Year: 2010

The benefits and limitations of crop stress detection by hyperspectral data analysis have been examined in detail. It could thereby be demonstrated that even a differentiation between healthy and fungal infected wheat stands is possible and profits by analyzing entire spectra or specifically selected spectral bands/ranges. For reasons of practicability in agriculture, spatial information about the health status of crop plants beyond a binary classification would be a major benefit. Thus, the potential of hyperspectral data for the derivation of several disease severity classes or moreover the derivation of continual disease severity has to be further examined. In the present study, a state-of-the-art regression approach using support vector machines (SVM) has been applied to hyperspectral AISA-Dual data to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat. Ground truth disease ratings were realized within an experimental field. A mean correlation coefficient of r=0.69 between severities and support vector regression predicted severities could be achieved using indepent training and test data. The results show that the SVR is generally suitable for the derivation of continual disease severity values, but the crucial point is the uncertainty in the reference severity data, which is used to train the regression. ©2010 IEEE.

Franke J.,RSS Remote Sensing Solutions GmbH | Navratil P.,RSS Remote Sensing Solutions GmbH | Keuck V.,RSS Remote Sensing Solutions GmbH | Peterson K.,RSS Remote Sensing Solutions GmbH | And 2 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2012

Climate change mitigation schemes, such as REDD and biodiversity conservation in tropical rainforests, necessitate remote sensing based forest monitoring capabilities with high spatial resolution and temporal coverage. Regular monitoring has to be capable of detecting rapid changes in forest extent, i.e. deforestation, and subtle changes to the forest cover caused by logging and/or fire, described as forest degradation. Particularly the early detection of illegal logging activities is important for the conservation of tropical forests. In the present study, a forest disturbance monitoring approach was developed and tested, which makes use of high resolution satellite imagery. A time series consisting of three images, acquired between May 2009 and June 2010, was analyzed covering a remote area of tropical peat swamp forest in Central Kalimantan, Indonesia. The forest area was assessed by an object-oriented classification. Logging activities and the impact of fire were detected by a pixel-based spectral mixture analysis. Forest, non-forest and logging trails could be differentiated with an overall classification accuracy of 91.5% (Kappa of 0.87). A high forest disturbance rate of 8.7% was found in the study area. Low impact logging could be detected reliably and the progress was tracked over time. The results show that the timely detection of forest disturbances is necessary because of the fast regrowth of vegetation. The study emphasises the importance of high resolution satellite imagery for tropical forest monitoring and for timely updating forest status assessments, which is important for the implementation of REDD. © 2008-2012 IEEE.

Mewes T.,University of Bonn | Franke J.,RSS Remote Sensing Solutions GmbH | Menz G.,University of Bonn
Precision Agriculture | Year: 2011

Remote sensing approaches are of increasing importance for agricultural applications, particularly for the support of selective agricultural measures that increase the productivity of crop stands. In contrast to multi-spectral image data, hyperspectral data has been shown to be highly suitable for the detection of crop growth anomalies, since they allow a detailed examination of stress-dependent changes in certain spectral ranges. However, the entire spectrum covered by hyperspectral data is probably not needed for discrimination between healthy and stressed plants. To define an optimal sensor-based system or a data product designed for crop stress detection, it is necessary to know which spectral wavelengths are significantly affected by stress factors and which spectral resolution is needed. In this study, a single airborne hyperspectral HyMap dataset was analyzed for its potential to detect plant stress symptoms in wheat stands induced by a pathogen infection. The Bhattacharyya distance (BD) with a forward feature search strategy was used to select relevant bands for the differentiation between healthy and fungal infected stands. Two classification algorithms, i.e. spectral angle mapper (SAM) and support vector machines (SVM) were used to classify the data covering an experimental field. Thus, the original dataset as well as datasets reduced to several band combinations as selected by the feature selection approach were classified. To analyze the influence of the spectral resolution on the detection accuracy, the original dataset was additionally stepwise spectrally resampled and a feature selection was carried out on each step. It is demonstrated that just a few phenomenon-specific spectral features are sufficient to detect wheat stands infected with powdery mildew. With original spectral resolution of HyMap, the highest classification accuracy could be obtained by using only 13 spectral bands with a Kappa coefficient of 0.59 in comparison to Kappa 0.57 using all spectral bands of the HyMap sensor. The results demonstrate that even a few hyperspectral bands as well as bands with lower spectral resolution still allow an adequate detection of fungal infections in wheat. By focusing on a few relevant bands, the detection accuracy could be enhanced and thus more reliable information could be extracted which may be helpful in agricultural practice. © 2011 Springer Science+Business Media, LLC.

Low F.,German Aerospace Center | Navratil P.,RSS Remote Sensing Solutions GmbH | Kotte K.,University of Heidelberg | Scholer H.F.,University of Heidelberg | Bubenzer O.,University of Cologne
Environmental Monitoring and Assessment | Year: 2013

With the recession of the Aral Sea in Central Asia, once the world's fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000-2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36 %. In parts of the Aralkum desalinization of soils was found to take place within 4-8 years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed. © 2013 Springer Science+Business Media Dordrecht.

Franke J.,RSS Remote Sensing Solutions GmbH | Keuck V.,RSS Remote Sensing Solutions GmbH | Siegert F.,RSS Remote Sensing Solutions GmbH | Siegert F.,Ludwig Maximilians University of Munich
Journal for Nature Conservation | Year: 2012

Grassland is a land cover in the area of conflict between agriculture and conservation, where intensification of land use is a major threat to grassland biodiversity. Grassland use intensity is a key factor for the conservation value of grassland, and detailed spatial data on grassland use intensity is needed to improve strategies for biodiversity conservation. A new remote sensing-based approach using multi-temporal high resolution RapidEye satellite data was developed in the present study that makes a large-scale assessment of grassland use intensity possible. RapidEye is a constellation of five satellites with 6.5. m spatial resolution, which allows frequent and timely image acquisition targeted at specific growing seasons. Semi-natural grassland, extensively used grassland, intensively used grassland and tilled grassland could be reliably differentiated at the management plot level in a study area in southern Germany. Various combinations of images from different observation dates have been tested as classification input and their overall classification accuracies were validated by field data. Best results were achieved using a combination of five multi-temporal scenes with an overall accuracy of 85.7%. A three-scene combination resulted in an overall accuracy of 82.2%. The analysis showed that seasonal aspects are very important when selecting adequate observation dates. Grassland use intensity was also assessed on peatlands using a peat soil map, since land use intensity significantly affects greenhouse gas emissions from peatlands. The results demonstrate the potential of targeted multi-spectral, high spatial resolution remote sensing for the large-scale monitoring of dynamic habitats, which is of vital importance to support various environmental conservation schemes through improved monitoring and reporting capabilities. © 2012 Elsevier GmbH.

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