Plant stresses, in particular fungal diseases, show a high variability in spatial and temporal dimension with respect to
their impact on the host. Recent "Precision Agriculture"-techniques allow for a spatially and temporally adjusted pest
control that might reduce the amount of cost-intensive and ecologically harmful agrochemicals. Conventional stressdetection
techniques such as random monitoring do not meet demands of such optimally placed management actions.
The prerequisite is an accurate sensor-based detection of stress symptoms. The present study focuses on a remotely
sensed detection of the fungal disease powdery mildew (Blumeria graminis) in wheat, Europe's main crop. In a field
experiment, the potential of hyperspectral data for an early detection of stress symptoms was tested. A sophisticated
endmember selection procedure was used and, additionally, a linear spectral mixture model was applied to a pixel
spectrum with known characteristics, in order to derive an endmember representing 100% powdery mildew-infected
wheat. Regression analyses of matched fraction estimates of this endmember and in-field-observed powdery mildew
severities showed promising results (r=0.82 and r2=0.67).
A fast and precise sensor-based identification of pathogen infestations in wheat stands is essential for the implementation
of site-specific fungicide applications. Several works have shown possibilities and limitations for the detection of plant
stress using spectral sensor data. Hyperspectral data provide the opportunity to collect spectral reflectance in contiguous
bands over a broad range of the electromagnetic spectrum. Individual phenomena like the light absorption of leaf
pigments can be examined in detail. The precise knowledge of stress-dependent shifting in certain spectral wavelengths
provides great advantages in detecting fungal infections. This study focuses on band selection techniques for
hyperspectral data to identify relevant and redundant information in spectra regarding a detection of plant stress caused
by pathogens. In a laboratory experiment, five 1 sqm boxes with wheat were multitemporarily measured by a ASD
Fieldspec® 3 FR spectroradiometer. Two stands were inoculated with Blumeria graminis - the pathogen causing
powdery mildew - and one stand was used to simulate the effect of water deficiency. Two stands were kept healthy as
control stands. Daily measurements of the spectral reflectance were taken over a 14-day period. Three ASD Pro Lamps
were used to illuminate the plots with constant light. By applying band selection techniques, the three types of different
wheat vitality could be accurately differentiated at certain stages. Hyperspectral data can provide precise information
about pathogen infestations. The reduction of the spectral dimension of sensor data by means of band selection
procedures is an appropriate method to speed up the data supply for precision agriculture.
Remote sensing-based vegetation indices are widely used for vegetation monitoring applications. The NDVI is the most
commonly used indicator for spatial and temporal vegetation dynamics. For long term or multitemporal observations,
the combined use of multisensoral NDVI data is necessary. However, due to different sensor characteristics NDVIvariations
occur. The sensor geometry, like viewing- and solar angle, atmospherical conditions, topography and spatial
or radiometric resolution influence the data. This study contributes to another important factor, the spectral
characteristics of different sensors, in particular the relative spectral response (RSR) functions. In order to analyze the
NDVI variations caused by different RSR functions, the multispectral bands of Landsat 5 TM, QuickBird, Aster and
SPOT 5 were simulated by the use of hyperspectral data of the airborne HyMap sensor. The observed NDVI differences
showed a non-linear but systematic NDVI offset between the sensors. Results indicate that the NDVI differences
decrease significantly after cross-calibration. A gradual cross-sensor calibration of NDVI taking first spectral
characteristics into account is essential. Residual factors could be calibrated in a second step. Such an inter-calibration is
desirable for multisensoral NDVI- analyses to ensure the comparability of achieved results.
The exact knowledge of the spatiotemporal dynamics of crop diseases for an implementation of a site-specific fungicide application is fundamental. Remote sensing is an appropriate tool to monitor the heterogeneity of fungal diseases within agricultural sites. However, the identification of an infection at an early growth stage is essential. This study assesses the potential of multispectral remote sensing for multitemporal analyses of crop diseases. Within an experimental test site near Bonn (Germany) a 6-ha sized plot with winter wheat was created, containing crops with each possible infection stage of three different pathogens. Two multispectral QuickBird images (04/22/2005 and 06/20/2005) and a spectrally resampled HyMap image (05/28/2005) were used to analyse the spatiotemporal dynamic of infection. The data preprocessing comprised a radiometric and a precise geometric correction by using DGPS-measurements that is an important requirement for Precision Agriculture applications. Ground truth data, in particular infection severity, growth stage/height, and spectroradiometer measurements were collected. A decision tree, using mixture tuned matched filtering results and a vegetation index was applied to classify the data (infected and non-infected areas). Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8% whereas the scene of 28 May (65.9%) and the scene of 20 June (88.6%) achieved considerably higher accuracies. The results showed that high-resolution multispectral data are generally suitable to detect in-field heterogeneities of vegetation vitality though they are only moderately suitable for early detection of stress factors.
In the context of precision agriculture, several recent studies have focused on detecting crop stress caused by pathogenic fungi. For this purpose, several sensor systems have been used to develop in-field-detection systems or to test possible applications of remote sensing. The objective of this research was to evaluate the potential of different sensor systems for multitemporal monitoring of leaf rust (puccinia recondita) infected wheat crops, with the aim of early detection of infected stands. A comparison between a hyperspectral (120 spectral bands) and a multispectral (3 spectral bands) imaging system shows the benefits and limitations of each approach. Reflectance data of leaf rust infected and fungicide treated control wheat stand boxes (1sqm each) were collected before and until 17 days after inoculation. Plants were grown under controlled conditions in the greenhouse and measurements were taken under consistent illumination conditions. The results of mixture tuned matched filtering analysis showed the suitability of hyperspectral data for early discrimination of leaf rust infected wheat crops due to their higher spectral sensitivity. Five days after inoculation leaf rust infected leaves were detected, although only slight visual symptoms appeared. A clear discrimination between infected and control stands was possible. Multispectral data showed a higher sensitivity to external factors like illumination conditions, causing poor classification accuracy. Nevertheless, if these factors could get under control, even multispectral data may serve a good indicator for infection severity.