CORINE Land Cover Classification Methodology

In this research project, originally the only available land use data found for North Albania were very coarse maps from the Internet and sketchy data. For this reason, CORINE, 1991, land cover data were acquired. CORINE is a classification system developed by the European Commission which is used to produce digital maps, which give a realistic representation of the land cover varieties of Europe, based on manual interpretation. The CORINE land cover map uses a 3-level hierarchical classification scheme with 44 classes shown in Table 4 .15. CORINE land use maps only contain parcels exceeding 25 hectares, which means that all cartographic detail is generalised to this level making it very difficult to compare it with data coming from different sources.

The first level (Level I, Table 4 .15) of CORINE classification consists of 5 classes, (Plate 4 . 19), the second level (Level II, Table 4 .15) of 15 classes, (Plate 4 .20), and the third level (Level III, Table 4 .15) of 44 classes, (Plate 4 .21). Plate 4 .22 shows the 15 land cover classes out of the 44 CORINE (for a third level classification) that are found in the River Fani Catchment.

CORINE land cover classes are very detailed and several studies have been conducted, on detecting them from satellite data automatically or semi-automatically. Wilkinson and Folving (1991) and Wilkinson et. al. (1992) concluded that a large number of the 44 CORINE land cover classes cannot be easily detected from satellite images by traditional image processing, mainly because either the definition of these classes are too vague or they are a mixture of surface conditions only interpreted by the human expert. Examples are the classes such as sport facilities or construction sites, which are too fine in detail to be detected by automatic image processing, and agricultural classes, which are a mixture of different spectral types.

Table 4.15: CORINE hierarchical Land use Nomenclature (CORINE (1994))

Level I Level II Level III

1. Artificial Surfaces 11 Urban fabric 111 Continuous urban fabric

112 Discontinuous urban fabric 12 Industrial, commercial and

transport units 121 Industrial or commercial units

122 Road and rail networks and associated land

2 Agricultural areas 21 Arable land 211 Non-irrigated arable land

212 Permanently irrigated land

24 Heterogeneous agricultural areas 241 Annual crops associated with permanent crops

semi-natural areas 31 Forest 311 Broad-Leaved forest

312 Coniferous forest 33 Open spaces with little or no

vegetation 331 Beaches, dunes, sands

332 Bare rocks

333 Sparsely vegetated areas 334 Burnt areas

335 Glaciers and perpetual snow

4 Wetlands 41 Inland wetlands 411 Inland marshes

412 Peat bogs

42 Maritime wetlands 421 Salt marshes

422 Salines 423 Intertidal flats

5 Water bodies 51 Inland waters 511 Water courses

512 Water bodies

52 Marine waters 521 Coastal lagoons

522 Estuaries 523 Sea and ocean

Plate 4.19: CORINE land cover first level classification (i.e. 5 classes) of North Albania.

Plate 4.20: CORINE land cover second level classification of North Albania. This level consists of 15 classes, which are listed in Table 4 .15 as well on the left side of the plate on the legend.

Adriatic Sea

North Albania

Legend:

Artificial surfaces Agricultural areas

Forests and semi-natural areas Wetlands

Water bodies

Urban fabric

Industrial, commercial and transport units Mine, dump and construction sites Artificial non-agricultural vegetated areas Arable land

Permanent crops Pastures

Heterogeneous agricultural areas Forests

Shrub and/or herbaceous vegetation associations Open spaces with little or no vegetation Inland wetlands

Coastal wetlands Inland waters Marine waters

Legend:

Plate 4.21: CORINE land cover third level classification of North Albania. This level consists of 44 classes, which are listed in Table 4 .15 as well on the legend.

Industrial or commercial units Inland marshes

Intertidal flats

Land principally occupied by agriculture, with significant areas of natural vegetation Mineral extraction sites

Road and rail networks and associated land Salines

Plate 4.22: CORINE land cover third level classification (15 classes) for the River Fani Catchment.

In this context, CORINE method was used for land cover classification. The third level classification (i.e. 15 classes) was considered but was narrowed down to 10 classes. This was due to the training areas (from the below 5 categories) being too small for accurate representative training areas to be collected as well as due to the classification number optimisation results that suggested a 10 class. The disregarded classes are: (1) complex cultivation patterns, (2) beaches dunes sands, (3) discontinuous urban fabric, (4) fruit trees and berry plantation and (5) industrial or commercial units. The final 10 broader classes selected are: (1) broad-leaved forest, (2) transitional woodland-shrub, (3) coniferous forest, (4) land principally occupied by agriculture,

Broad-leaved forest Trasitional woodland-shrub Coniferous forest

Land principally occupied by agrculture Mixed forest

Sclerophyllous vegetation Sparsely vegetated areas Natural grasslands Bare rock

Complex cultivation patterns Beaches, Dunes, Sands Discontinuous urban fabric Water courses

Fruit trees and berry plantations Industrial or commercial units Legend:

(5) mixed forest, (6) sclerophyllous vegetation, (7) sparsely vegetated areas, (8) natural grasslands, (9) bare rock and (10) water courses.

Several image processing techniques for the enhancement of classification accuracy were investigated, such as band ratioing and the injection of indices derived from the image analysis including tasselled cap transformations. The classification trials also considered using all available bands in an unsupervised and supervised manner and testing parallelepiped, minimum distance and maximum likelihood algorithms. These techniques did not markedly improve the classification accuracy when tested on their own. It was also found that the use of all available bands in a supervised classification introduced redundancy of the data information and the classification process was time consuming especially when using the maximum likelihood algorithm. Although maximum likelihood algorithm is the most commonly used, in this project, the minimum distance was more attractive as it is faster but the most important is the fact that it gave much better results compared to the other two algorithms. Thus, a methodology was developed that was found to give the most accurate classification results for the current research project and is given below as a sequence of operations.

1. Pre-processing (common projection system, common pixel size, subset to AOI, atmospheric and geometric correction)

2. Select land cover classes according to CORINE nomenclature method 3. Mask clouds (if exist)

4. Define the training sites

a. The NDVI layer was stack to imagery b. Extract four principal components

5. Extraction of signatures (create a signature (SIG) file for every information class)

6. Classification of the images using minimum distance algorithm

7. Verification of classification results. The resulting classified images were assessed using maps and CORINE land cover information as ground truth, as well as photos and knowledge of the area due to a prior field visit.

Thus, the classification methodology starts with the pre-processing, a basic step required for any analyses technique applied to the satellite images (see section 4.4). Then, the selected land cover classes based on CORINE nomenclature were selected (as described earlier). Although clouds were not a problem, the 2000 image had some that were masked. For accurate correlation of the land cover classes, the 1984 and 2000 images were masked for the same cloud area used on the 2000 image. In defining the training sites, the NDVI data image of 1984 TM, 1991 TM and 2000 ETM+ was stack to its corresponding year thus creating three 8 band multi-layered images. A PCA (Principal Component Analysis) technique was used, which is basically a rotational transformation that provides better spectral separation between classes and improved enhancement (Nirala and Venkatachalam (2000)) for image compression (Gonzalez and Wintz, (1977) refd. in Richards (1994)), and for digital change detection (Koutsias et. al. (2009)). PCA defines the dimensionality of the data set and identifies the principal axes of variability within the data which produces uncorrelated data sets enabling efficient extraction of significant information. In the case of Landsat 7 ETM + data (consisting of eight bands), the dimensionality of the data set is not necessarily the same as the number of spectral bands. For example, highly positively correlated data from two bands will have one dimensionality (forming an almost straight line) as shown in Figure 4 .14 and adjacent image bands will generally be well correlated (Mather (1999)). This multi-spectral inter-correlation is due to common illumination, natural spectral correlation, slope of the terrain, and an overlap of spectral sensitivities between adjacent bands (Schowengerdt (1983)), and implies redundancy in information.

Figure 4.14: Two variables band 1 and band 2 with effectively one dimensionality (some scatter).

A four band combination was opted and used after several trials on the number of principal components that retain and reflect the most useful information which result into better spectral segregation of land cover classes. Thus, a four PCA was applied to Landsat TM5 1984 and 1991 and ETM+7 2000 images. Then, training areas have been identified for each image and signature files have been created, which were used to classify the images.

It was found that by using PCA technique in land cover classification can have effective results.

The 8-band images were compressed into fewer bands and information became more apparent aiding the sampling procedure for the land cover classes. PCA also gave better results as opposed to using other techniques such as the NDVI layer to collect sample points but at the same time, the sequence followed for the derivation of land cover maps for the River Fani Catchment was proven more effective, compared to all other techniques tested.