En el cumplimiento de la presente Convención, las Partes actuarán conforme a

5.2.4.1 Unsupervised classification (Isoclust)

Unsupervised classification was chosen over supervised classification (see Section 5.1.2). This decision was based on two reasons: lack of known habitat types in the area; and because a trial of supervised classification produced spectral signatures that were overlapping and not clearly differentiated.

Unsupervised classification groups similar pixels together into clusters (Lillesand et al. 2004, Eastman 2006). These clusters are then labelled according to habitat types using habitat validation and user knowledge of the area. The habitat classification covered all of the study area described above and was chosen to match the area surveyed for dolphin groups. The unsupervised classification was performed using the software package ENVI (Figure 5.1, Part 2 Classification and validation). The depth-corrected images were imported into ENVI and the module Isoclust was used to calculate initial means of the habitat classes across the study area. Isoclust is an unsupervised classification technique that clusters pixels together into classes based on a rule of minimum distance between pixels. Iterations were used (n=10) to recalculate class

means and reclassify pixels using the new means. This process was used until the number of pixels in each class changed by less than the selected pixel change threshold or the maximum number of iterations was reached. No additional constraints on distance thresholds or standard deviations for pixel classification were used. The purpose of this habitat classification was to identify ecologically meaningful habitat types to be ultimately used in the dolphin habitat model.

5.2.4.2 Habitat validation and labelling

It was necessary to validate and label the habitat classes produced by the unsupervised habitat classification process (Figure 5.1, Part 2 Classification and validation) as habitat types. The habitat validation (Figure 5.1, Part 2 Classification and validation) was restricted to the coastal areas and did not include Koombana Bay or the Leschenault Estuary because turbidity precluded visibility of the benthic substrate and in most places the water was too shallow for the boat to enter. In these shallow areas the habitat classes were determined based on the desktop classification only and ground-truthing was not used. The points for habitat validation were selected to correspond with the transect lines from dolphin surveys (Figure 5.2). Points were chosen strategically to take into account shallow, mid and deep water. However, the points were sampled whilst surveying for dolphins along the transect lines and not in a random fashion and therefore fewer points were sampled than is recommended (Congalton & Green 2009). Therefore pre-determined points were created using mapping software and uploaded to a Global Positioning System (GPS).

Underwater photographs of the benthic habitat were collected using an underwater camera following the methods developed by Tyne et al. (2010). A camera was mounted on a metal frame suspended above a 1 x 1 metre quadrat. The camera was attached 15 cm below the centre point of the frame and two metres above the quadrat to counter distortion and magnification caused by the water. This allowed the entire quadrat to be within the camera field of view.

Figure 5-2 Habitat validation points overlaid with the transect lines used for dolphin surveys in the northern (upper) and southern (lower) sections of the study area. Quickbird satellite scenes used to classify the habitat types is visible as the background image.

The camera was lowered over the side of the boat and a photograph was captured immediately after the quadrat frame settled on the benthos. Taking into account the drift of the boat and the accuracy of the GPS the underwater photographs were within approximately 20 metres of the pre-determined validation point. The habitat validation photographs were reviewed and analysed in the laboratory to determine the predominant benthic habitat types in the quadrat. The predominant habitat type were analysed qualitatively with a predominant cover being used as a descriptor (>50 %) per point. If there was more than one habitat type this was recorded as a second descriptor (Table 5.2).

Table 5-2 Habitat types used to inform the class labels from the unsupervised classification.

Habitat composition Habitat types Habitat description

*HOMOGENOUS

Algae

>75% of one habitat type covering the 1m2 quadrat Seagrass

Sand Reef

MIXED

Mud/silt

1m2 quadrat covered by 50% of one habitat type and an equal or smaller proportion of a second habitat type Seagrass/sand

Seagrass/algae Reef/algae

*Homogenous is habitat considered spectrally the same and ecologically uniform

The habitat validation points (n=60) were overlaid with their descriptors on satellite scenes to check how well they matched the habitat classes from the unsupervised classification (Figure 5.2). The attributes of the habitat validation points were used in conjunction with the satellite images to inform the labels of the clusters.

5.2.4.3 Calculation of habitat areas

A lookup table of the habitat class labels was created in Microsoft Excel and joined on a common field in ArcGIS 9.2. The area of each polygon was calculated using XTools add-on in ArcGIS 9.2. Subsequently, these were summed to produce an overall percentage of each habitat type within the study area.

5.2.4.4 Accuracy assessment of classification

The accuracy of the habitat classification (Figure 5.1, Part 2 Classification and validation) was assessed by constructing an error matrix and evaluating the congruence between the computer-generated habitat class and the corresponding field habitat validation point (Table 5.4). The matrix consisted of each point of habitat validation and the corresponding classified habitat. This was carried out in two ways: by intersecting the habitat validation points with habitat classified vector file (produced from the unsupervised classification); and by manually checking each point for consistency by checking the pixel type when the point and vector files are overlaid in GIS. The following formulae were used in the accuracy assessment to calculate the accuracy of the habitat classification in two ways. The producer’s accuracy calculates the accuracy for the habitat classification procedure and the user’s accuracy is calculated based on the ground-truthing validation points, the two were then combined to give an overall accuracy.

Producer’s accuracy

Number of points for each habitat type from classification/ Total number of

classification points for habitat type *100 = %

User’s accuracy

Number of points for each habitat type from classification / Total number of

ground truth points for each habitat type *100 = %