This project is aimed to test, 1) how well visual interpretation and digital classification from

aerial photographs fits inventoried field data, 2) if these interpretation of aerial photographs

could be used to map and monitor vegetation in shallow coastal areas and be used as a tool

assessing the state of shallow coastal areas. Based on our results a modified method for

mapping and monitoring shallow coastal areas by interpretation and classification of aerial

photographs is presented and the time demand of the method is discussed. Further we suggest

that this method will be a useful tool in mapping and assessing the state of shallow coastal

areas.

The visual interpretation aimed to investigate at what time in the growth season aerial

photographs preferably should be taken and to what depth visual interpretation, and digital

classification, of aerial photographs could be used in Baltic waters with comparably high

turbidity. It also aimed to describe the inventoried species from the aerial photographs

focusing on their colour, height, texture, zone-structure and discrepancy in cover between

estimated cover in field and estimated cover from photographs. The results from the visual

interpretation are descriptive and focus on synthesising the description of interpretable

species. A comparison between early (July) and late (August) photographs showed that the

cover of the vegetation was less legible on the early pictures then on the late. Large species

and perennials appeared clearer in the early pictures, due to lower abundance of covering

filamentous and sheet-like algae in July than in August. Plant cover, for all species, was

obviously lower in the early photographs than in the late and the transparency was slightly

better in the early photographs. The aerial photographs should preferably be taken in late July

or August when the submersed vegetation reaches maximum cover.

The accuracy of the digital classification was initially tested on different taxonomic levels to

find a level that visually would predict vegetation with an acceptable accuracy. As a result of

the digital classification the submerged macrophytes were first classified into 7 categories

(Level 1). The seven categories for the classification are composed of two types of bare

bottom, i.e. Bare bottom, sand and Bare bottom, mud,

≤ 25 % plant cover, and five types of

vegetated bottom, i.e. Dense filamentous algae,

≥ 50 % cover, Thin sheet-like algae, ≥ 50 %

cover,

Najas marina, ≥ 50 % cover, Mixed stands of phanerogams, ≥ 50 % cover and Fucus

vesiculosus

, ≥ 50 % cover. The overall classification accuracy at Level 1 was 72 %. The best

accuracy of the classification, in Level 1, had category 5, 3 and 6, i.e.

Najas marina, ≥ 50 %

cover, Dense filamentous algae,

≥ 50 % cover and Mixed stands of phanerogams, ≥ 50 %

cover.

To further improve the accuracy of the classification the classes in Level 1 was reduced, by

adding categories together, to three and two categories at Level 2 and Level 3. The seven

categories were reduced to three categories in Level 2, Bare bottom, sand and mud,

≤ 25 %

cover, Dense filamentous algae, thin sheet-like algae included,

≥ 50 % cover and Mixed

stands of phanerogams,

Fucus vesiculosus and Najas marina included, ≥ 50 % cover. The two

categories in Level 3 are, Bare bottom, sand and mud,

≤ 25 % (category 1) and Vegetated

areas,

≥ 50 %, category 2 and 3 in Level 2 included. The overall accuracy improved from 72

%, Level 1, to 85 % and 87 % in Level 2 and 3 respectively. At Level 2, both vegetated

categories have a producer's and a user's accuracy above 85 % while the combined mud and

sand category amount to 77 %, producer's accuracy, and have a user's accuracy of 81 %. At

Level 3, the category 2, Vegetated areas, 50 % cover, have a producer's accuracy of 96 % and

A combined analysis with both visual interpretation and digital classification would be

favourable but would also be more time consuming then a digital classification only. The

result shows that digital classification seems to be appropriate to use as monitoring-method

for low detailed information, i.e. when monitoring functional groups of vegetation, such as

mats of green alga or mixed stands of canopy forming species, but does not seem to be an

good method to monitor single species or specific species combinations. On the other hand,

calibration data have to be collected for the digital classification and the reference plots could

be more thoroughly inventoried than needed for the digital analysis. Thus, species abundance

data from the reference plots could, after the digital classification, be interpolated within the

classified categories, which make it possible to use aerial photographs as monitoring method

at species level.