Principios que sustentan el medio de enseñanza.

The CASI imagery was used to derive the land cover classification used to assess

vegetation influence on instability in the transport corridor. Careful consideration was given to the choice of the possible number of land cover classes, sample size, location and field collection of training data and the collection of validation data to be used for accuracy assessment Reconnaissance field visits and review of literature on past land cover studies of the study area and environs provided insights into the possible number of land cover classes that existed within the transport corridor. Nine main land cover types were identified during the reconnaissance field visit; pasture, managed pasture, shrubs, deciduous woodland, river, bare earth and man-made structures (roads, rail and buildings). An unsupervised

classification of the CASI image of the transport corridor was performed in Erdars Imagine using the ISODATA classifier into 50 spectral classes. A large number of clusters was chosen to ensure adequate separation of the various land cover classes. The spectral classes

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were matched with land cover information from a 5cm aerial photograph acquired

contemporaneously with the data of the transport corridor. The spectral classes representing a particular land cover type were merged to arrive at the earlier identified nine main land cover classes. The unsupervised classification of the transport corridor derived from the 8 band CASI image, alongside the 5cm aerial photograph provided a basis for the positioning of stratified random sampling points for the collection of training data used in the

supervised classification of a land cover map of the study area. The stratified random sampling technique was adopted to ensure adequate sampling of the various land cover classes (Congalton, 1991). A total of 438 validation points and 200 training points were randomly located throughout the study area. The training and validation sets for each of the classes were established using a field mapping exercise and from the 5cm aerial photograph of the transport corridor.

Supervised classification was carried out on the processed 8-band CASI image using the maximum likelihood classifier routine in Erdas Imagine software. The field work

established nine main land cover classes for the transport corridor, this includes pasture, managed pasture, shrubs, deciduous woodland, water, bare earth and man-made structures like roads, rail and buildings. Training sets were digitized using visual interpretation of the image in different band combinations alongside in-situ land cover information acquired during the field survey. Visual interpretation of sets of image characteristics such as shape, size, structure, texture, tone and pattern of objects was also employed in the identification of digitized boundaries for each of the training sets for the various land cover classes. The spectral signature for the various land cover classes were derived from the training sets and evaluated to assess their degree of separability using the transformed divergence algorithm in Erdas Imagine (Jensen, 2004). Using maximum likelihood supervised classification based on a total of 200 randomly distributed training sites, the various land cover within the transport corridor were classified into one of the nine categories. A fuzzy convolution filter was applied to the resulting classification to optimise the classification results (ERDAS, 2008). The fuzzy convolution filter creates a single classification band by removing isolated pixels and smoothening boundaries between classes (Jensen, 2004). This context- based classification reduces the speckle effect and improves overall interpretation (Jensen, 2004). The classification accuracy was then assessed at 438 validation points randomly located throughout the study area. An overall classification accuracy of 80.37%.% with a

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kappa statistic of 0.85 was obtained for the simple band selection CASI image. While the classification result may suggest a high overall accuracy, closer inspection revealed the existence of misclassified pixels between land cover classes that lack significant spectral distance between the mean vectors and covariance of class signatures, hence these classes were subsequently merged into a single class. For example, there was significant overlap of the ballast-lain rail bed, roads and building land cover classes. Consequently these classes were merged to form the manmade land cover class. The pasture, managed pasture and shrub land cover classes were also combined into a single land cover class (pasture) as a result of their similar hydrological characteristics and the misclassified pixels that existed between these classes. A final land cover classification that identified (1) mixture of pasture grassland, agricultural weeds, wild flowers and shrubs (2) bare earth, (3) woodland

comprised of a mixture of coniferous and deciduous woodland majorly aligning flow routes of tributaries or situated within woodland estates (4) water and (5) manmade with an

overall accuracy of 90.15% with a kappa statistics of 0.85 (Table 4.1) was obtained for the simple band selection CASI image of the transport corridor. The contingency matrices and separability table tables are presented and discussed in detail in chapter five.

For a comparative assessment, the minimum noise fraction transformation technique was also applied to the primary image (19 band CASI image) which was introduced in section 3.4.2.1. The Minimum Noise Fraction (MNF) transform algorithm employs two

consecutive data reduction operations (Green et al., 1988; Van der Meer et al., 2006). The first is based on an estimation of noise in the data as represented by a correlation matrix. This transformation decorrelates and rescales the noise in the data by variance to improve the overall signal from vegetation (Green et al., 1988). The second operation creates a cascade of principal components that contain weighted information about the variance across all bands in the raw dataset. (Van der Meer et al., 2006). The dominant components were selected and used in an inverse MNF transform to convert the data back to its original spectral space, resulting in the same number of transformed channels as the original image (19 CASI bands). The spectral subset chosen for the transport corridor data were bands one to eight, which were determined to contain over 99% of the total variance in the data. The MNF transform is usually better than the Principal Components (PC) transform at

compressing and ordering multispectral and hyperspectral images in terms of image quality (Berman et al., 2012). The MNF transform is also invariant to invertible (i.e. non-singular)

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linear transformations of multispectral or hyperspectral data, a property not shared by the PC transform (Berman et al., 2012).

A maximum likelihood supervised classification was also implemented on the spectral transformed (MNF) CASI), using the same training areas that were employed for the simple band selection approach. A classification accuracy of 74.43 % and with a kappa statistics of 0.68 was obtained for the MNF transform CASI image of nine land cover classes. The overall classification accuracy was improved by merging land cover classes with overlapping spectral signatures. Consequently the rail, road and building classes were combined to achieve the manmade class. A merged land cover class (pasture) made up pasture, managed pasture and shrub land cover classes was also derived. A final land cover classification that identified pasture, bare earth, woodland, water and manmade with an overall accuracy of 83.59 % with a kappa statistics of 0.76 (Table 4.2) was obtained for the MNF transform CASI image of the transport corridor.

The supervised classification map obtained from simple band selection was used as the land-cover map for the transport corridor as it provided a higher overall accuracy. Table 4.1 Contingency matrix showing the classification accuracy of the maximum

classification routine carried out on the CASI imagery derived from simple band selection.

Woodland Pasture Bare

earth Manmade Total

Producer’s accuracy (%) User’s accuracy (%) Woodland 41 20 0 2 64 100.0 65.08 Pasture 0 181 8 3 189 87.86 94.27 Bare earth 0 6 17 0 25 68.0 73.91 Manmade 0 0 0 118 118 95.93 100.0 Total 41 207 25 123

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Table 4.2: Contingency matrix showing the classification accuracy of the maximum classification routine carried out on the MNF transform CASI image.

Woodland Pasture Bare

earth Manmade Total

Producer’s accuracy (%) User’s accuracy (%) Woodland 39 13 0 3 55 95.12 70.91 Pasture 2 152 4 0 158 73.43 96.20 Bare earth 0 42 21 1 64 84.0 32.81 Manmade 0 0 0 119 119 96.75 100.0 Total 41 207 25 123

Overall accuracy = 83.59% Overall Kappa statistics = 0.76