Projects: Remote Sensing
2003
Application of orbital remote sensing data (AVHRR) to the detection and analysis of local sea surface temperature anomalies 

Several factors may cause local to large-scale anomalies of the sea surface temperature (SST). The small-scale anomalies induced by submarine freshwater springs in coastal areas are herein of special interest.
Negative SST-anomaly map of Elba Island, created with the AVLA81-filter (3-month averaged data, 2000). Strongest anomalies are coded violet.

AVLA81-map of Elba Island
In order to analyze the distribution of regional SST-anomalies over large areas with a medium spatial resolution a dedicated filter (AVLA – Absolute versus Local Average [SST]) was defined. This algorithm, a strongly modified multi-staged low-pass-convolution-filter is applied with a 9x9 kernel window (AVLA81) on weekly MCSST-maps provided by the orbital NOAA-AVHRR-scanner system (approx. 1 km spatial resolution). The statistical reliability of the filter was mathematically tested. It can be shown that a SST-difference of 1K calculated by using the AVLA81-algorithm is sufficient to determine a real „absolute“ SST-anomaly. Furthermore the calculated value interval of 0.2-1 K can be assumed as a reliable trend indicator. 
Possible influences to the accuracy and reliability of data were discussed, including geometry of cloud casting and coastal topography, surf zone effects, upwelling as well as the influence of size and geometry of the anomalies related to the calculating method.
It can be demonstrated that seasonality of overall SST values is a very important factor in the interpretation of AVLA values. This signal may well suppress possible non-seasonal variations of the anomaly signal in areas of dominant seasonal SST variations. At present, only the consistency of anomaly development at discrete seasons over several years can be compared objectively. This goal is supported by a long-term (3 month used in this work) AVLA-value averaging, targeting to an elimination of ephemeral short-time anomalies (as produced by eddies etc.) and pronouncing long-time anomaly zones (as produced by freshwater discharge) in the data record.
The potential practical application of the anomaly detection, esp. with the purpose of exploration for coastal submarine freshwater sources, were demonstrated by two examples (Elba Island, coastal regions of Tunisia). In these, the AVLA81 data have been supported by the use of LANDSAT-TM scenes.
Diploma-Thesis, completed 12/2003, Supervisors: B. Rein, F. Sirocko, D. Ortlam

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