IMAGE CLASSIFICATION AND CHANGE DETECTION

This section describes the processes whereby a selection of Landsat-5 and Landsat-7 imagery was used to develop three land-cover maps for the Berg River catchment. First it was necessary to devise an appropriate legend with which to inform the classification. An overview of the classification and change detection procedures employed in this research is presented in Figure 4.1.

4.3.1 Land-cover classification

An object-orientated nearest neighbour supervised classification was performed in eCognition Developer 8 to classify the Landsat-5 and Landsat-7 images. A bottom-up, region-growing segmentation approach was used to produce consistent results across the relatively large and heterogeneous study area. A multi-resolution segmentation scale parameter of 30, a shape parameter of 0.1 and a compactness parameter of 0.5 was found to produce highly homogenous image objects. Once the segmentation process had been completed a supervised classification algorithm was used to classify the segments in accordance with the legend presented in Section 4.2.

In excess of 20 training sites were used for each of the mapped classes. The classification was repeated, refining both the segmentation parameters and the training data, until a satisfactory classification was reached. The results of this process were exported as shapefiles and visually assessed against the raw images, national land-cover (NLC) maps and higher-resolution 2008 SPOT-5 images as reference.

While the analytically-generated land-cover maps were found to broadly represent true patterns of land cover, significant discrepancies were noticed. The maps were consequently manually edited to improve the overall accuracy of the land-cover maps and to differentiate between natural vegetation in a pristine and degraded state.

Figure 4.1: Image classification and change detection

Methods

and ETM+ images and modified LCCS Land-cover maps Visual assessment Post classification editing SPOT-5and NLC Edited land-cover maps Accuracy assessment Edited land-cover map, SPOT-5, NLC reference data and aerial photographs Land-cover change analysis Land-cover change data Integration of land- cover data and vegetation types data Edited land-cover

maps and Vegetation map of South Africa,

Lesotho and Swaziland

Remaining vegetation maps

Vegetation type change analysis Vegetation type change data Error matrix Land-cover maps, SPOT-5, NLC and C.A.P.E, reference and aerial photographs Image merging Landsat TM and ETM+ data Input Merged Landsat TM and EMT+ images

4.3.2 Discrepancies between maps

Roads were inconsistently identified by the spectral classification as they do not usually constitute the primary land cover in any given pixel. Shadows formed by mountains and hills were occasionally incorrectly identified as water owing to the comparable spectral properties of these areas. Forestry plantations and water were sometimes confused by the spectral classification.

4.3.3 Post-classification editing

Following the spectral classification, the resultant land-cover maps were manually edited to reduce discrepancies, weed out classification errors, counter seasonal variations evident in the maps and differentiate between natural indigenous vegetation and degraded or alien-dominated areas. Higher-resolution SPOT-5 imagery and various GIS data sets were used in this process.

Most of the polygons generated through the nearest-neighbour classification were checked against the original Landsat imagery and against a set of ancillary data sets. Visual interpretation was used to reclassify certain polygons and the boundaries defined by the segmentation process were modified when they were found to inaccurately represent the boundaries of land-cover features. Roads were merged into the surrounding land-cover class as they could not be delineated consistently.

These methods were applied systematically to all the land-cover maps. Once this was done the maps were compared with one another to further ensure that the land cover was classified in a uniform manner. Following these steps it is necessary to provide an assessment of the accuracy of the final land-cover maps.

4.3.4 Land-cover change

Once all land the cover-maps had been generated and edited, they were exported to IDRISI and analysed in the Land Change Modeler (LCM) for Ecological Sustainability. The LCM is an application, available in IDRISI and several other GIS, developed to enhance the capacity of GIS to analyse and predict land-cover change and subsequently put forward recommendations on habitat and biodiversity management. It was developed by Clark Labs in conjunction with the Internal Union for the Conservation of Nature (IUCN) to meet the specific needs of conservation planning (Clark Labs 2009).

All of the land-cover maps were projected to Hartebeesthoek94/Lo19. To remove smaller detected changes that often resulted from slight incongruencies between polygons of the same class and to facilitate the analysis, transformed areas of less than 9 ha were eliminated using a sliver removal technique. The minimum mapping size for features was informed by McDonald et al. (1984).

4.3.5 Integration with vegetation type data

The resultant land-cover change matrices were analysed using ESRI’s ArcMap software to display the results in an appropriate and aesthetically appealing format. Remnants of natural vegetation identified as natural vegetation from each of the land-cover maps were then integrated with Mucina & Rutherfords vegetation map data to create a new layer for each year: 1986/1987, 1999/2000 and 2007. Change analysis was performed both on the completed land-cover datasets, as well as on the integrated layers using IDRISI land change modeller (LCM).