**4. Material and methods**

In this investigation a knowledge-based algorithm for the automated mapping of water bodies was developed based on a spectral database from five airborne hyperspectral datasets from the two German cities Berlin (two datasets) and Potsdam, and the German island Helgoland (two datasets) (Tab. 2). Five independent datasets were used for validation (Tab. 2). The selected scenes comprise urban, rural and coastal landscapes as well as different sensors to prove the wide applicability of the developed approach. The AISA Eagle sensor is an airborne VNIR pushbroom scanner (400 – 970 nm) with 12 bit radiometric resolution and variable spatial and spectral binning options, the latter resulting in mean spectral sampling intervals between 1.25 nm and 9.2 nm (Spectral Imaging Ltd., 2011) and


\* Datasets analyzed during algorithm development

° Independent datasets for validation

Table 2. Dataset-specific characteristics

Fig. 4. Location of the test sites within Germany

In this investigation a knowledge-based algorithm for the automated mapping of water bodies was developed based on a spectral database from five airborne hyperspectral datasets from the two German cities Berlin (two datasets) and Potsdam, and the German island Helgoland (two datasets) (Tab. 2). Five independent datasets were used for validation (Tab. 2). The selected scenes comprise urban, rural and coastal landscapes as well as different sensors to prove the wide applicability of the developed approach. The AISA Eagle sensor is an airborne VNIR pushbroom scanner (400 – 970 nm) with 12 bit radiometric resolution and variable spatial and spectral binning options, the latter resulting in mean spectral sampling intervals between 1.25 nm and 9.2 nm (Spectral Imaging Ltd., 2011) and

Test site Sensor Acquisition date, time (UTC) Pixel size (rounded)

Ammersee

Döberitzer Heide

Rheinsberg

Mönchsgut

Dresden

Potsdam Berlin

Potsdam (*urban*) HyMap 07.07.2004, 10:29 \* 4 m

Rheinsberg (rural) HyMap 20.06.1999, 10:46 ° 10 m Dresden (*urban*) HyMap 07.07.2004, 09:39 ° 4 m Mönchsgut (*coastal*) HyMap 03.09.1998, 13:47 ° 6 m Döberitzer Heide (*rural*) AISA Eagle 19.08.2009, 11:42 ° 2 m

20.06.2005, 10:12 \*

09.05.2008, 08:32 \* 09.05.2008, 09:26 ° 09.05.2008, 09:41 \* 4 m 4 m

1 m 1 m 1 m

Berlin (*urban*) HyMap 20.06.2005, 09:38 \*

Helgoland

AISA Eagle HyMap simulated EnMAP

Sensor types urban coastal rural Test site types

Fig. 4. Location of the test sites within Germany

**4. Material and methods** 

Helgoland (*coastal*) AISA Eagle

\* Datasets analyzed during algorithm development

° Independent datasets for validation

Table 2. Dataset-specific characteristics

488 to 60 spectral bands, respectively. The mean spectral sampling interval of the analyzed datasets is 2.3 nm for "Döberitzer Heide" and 4.6 nm for "Helgoland". The HyMap sensor is an airborne VNIR-SWIR whiskbroom scanner with 16 bit radiometric resolution consisting of four detector modules with mean spectral sampling intervals of 15 nm (VIS and NIR), 13 nm (SWIR1) and 17 nm (SWIR2) (Cocks *et al.*, 1998). The 128 spectral bands cover the spectral region from 440 nm to 2500 nm.

Water detection is a trivial task as long as there are no other dark surfaces present in the image. Unfortunately, the most prominent spectral characteristic of water pixels – water pixels are very dark – also applies to a couple of other surfaces such as dark rocks (e.g., lava, basalt) or bituminous roofing materials and especially to pixels covered by shadow. To account for this, we developed a two-step approach that firstly masks low albedo pixels as potential water pixels (section 4.1) and secondly applies a process of elimination to consecutively remove false positives (sections 4.2 and 4.3).
