Optimization of Water Sampling Locations Using Remote Sensing Data Analyzed by Neural Networks

Abstract

In water sampling it is very common to use human experience to determine sampling locations. We present results from a neural network analysis of multispectral imaging data from the Compact Airborne Spectrographic Imager (casi) to determine significant water sampling locations. In this study Lake Tegel in Berlin, Germany, was overflown on different days. The analysis of the remote sensing data results in a clustering of the overflown water body for each pass. The neural network clusters found for each pass have been related to each other. This procedure enables us to optimize the number and location of water sampling stations.