Assessing the performance of different classification methods to detect inland surface water extent

Abstract

In recent decades, political as well as environmental conflicts about the Earth’s water resources became a significant issue with constantly growing importance all over the world. These issues include flooding as well as drying shrinkage of seas and rivers. In order to estimate the dimension of these impacts, reliable frequent observation of the surface water over a long period of time is essential.

By making use of the special spectral reflectance properties of water, especially in higher spectral ranges, it is possible to distinguish between water and other surface materials and create thematic maps using different classification methods. These methods can be based on supervised algorithms which make use of training data to classify an image. On the other hand they can be automatized computing algorithms which assign pixels to a class without any prior knowledge. The latter are referred as unsupervised methods.

Assessing the performance of these various methods will be task of this thesis. Here in this study four satellite images of Landsat 7 are selected for our case study. The images show the region around the Po River in northern Italy. The valley alongside the river is one of the strongest economic and agricultural region in Italy but it also suffers from regular flooding, especially in the delta region around Ferrara close to the Adriatic Sea.

The different classification methods are implemented in the software ENVI which is commonly used by remote sensing professionals to process and analyse geospatial imagery.

To make a statement about the accuracy of the single classification methods every classifier undergoes a few essential steps: A binary water mask is created in which the river width is measured at two selected gauge spots. At these spots precise in-field measurements were applied regularly in recent years and the results serve as reference values for a comparison in between the classification methods.

In general the supervised methods performed better than the unsupervised methods. The best performance is approached by the mahalanobis distance classification which is based on probability statistics with consistent covariances. This way the pixels can be classified by calculating their minimum euclidean distance in spectral space.

The results of this study are limited by the low resolution of 30m which does not leave vast room for interpretation. More reliable results could be achieved by measuring effective widths alongside the river and apply an area-wise comparison. However approximate estimates about the performance of the different classification methods and good first impressions of the advantages and disadvantages are given.