5.1 Land Cover Data of
5.1.1 Land Cover Classifications
The data set for training neural network was collected from existing database of land cover regions in Cambodia that were expertly labeled into many classes.
Evergreen Forest: of either broad-leaf or needle-leaf species; no distinction is made for tropical rain forest, hill ever green-forest and dry evergreen forest;
Mixed Forest: of evergreen and deciduous species; bamboo forest is included in this category
Deciduous Forest: Forest dominated by deciduous species
Wood and Shrub land: Vegetation types where the dominant woody elements are shrubs with less than 5 meters height on maturity.
Flood Forest: Stand and fruit trees, etc. distributed in the areas which are assumed to be flooded during the rainy season
Grass/Scattered Trees: Grass land with an upper layer crown density of not more than 10% of farmland
Slash and burn: Residual site of felling and burning; current state of possibly grassland or shrub but not under cultivation.
Flood Area: Area assumed to be flooded during the dry season, excluding Flood Forest; becoming grassland (Flooded Grass), Paddy Field (Farmland) during the dry season.
Rice Fields: Paddy Field.
Rubber Plantation: Distribution areas are restricted by visual interpretation.
Orchards/Other trees: Fruit trees growing on a plain, trees near a settlement area.
Water Surfaces: Areas under water on observation dates.
The data of "Forest Register" and "Land Use and Forestry Type Map" are the results of " Wide area Tropical Resources Survey (in fiscal 1993~1995)" which were carried out by Japan Forest Technical Association (JAFTA) under project named: "Information Syste
m Development Project for the Management of Tropical Forest". The present classifications of Land Use Cover Map were made by digital processing using LANDSAT TM DATA, which data were selected from the image scenes with the least cloud coverage (Activity
Report of Wide Area Tropical Forest Resources Survey, 1993). All training areas for classification purposes are extracted from aerial photographs which are interpreted on the map in different colors. Area of each classification--items in each land cover--
compartment was measured by computer. These result were integrated in each Land Use and Forest Type Map (scale 1 : 250,000).
In order to classify the areas of land cover types, the whole Cambodia is divided into compartments. One compartment is an area surrounded by a 15 minute mesh along the northern latitude or eastern longitude and/or province boundary as shown in Figure
5.1. Each compartment is given a six digit number. The first two digits indicate the province while the next four digits indicate the mesh coordinates Y and X established by 15 minute mesh to cover entire Cambodia.
5.1.3 Representation of data vectors
The methodology of knowledge extraction using self-organizing unsupervised learning technique consists of three phases.
Representation of the data to obtain the input vector matrix
Training the self-organizing network and visualizing tools
Formation of rule extraction
Generally, the given data must be normalized i.e. it has to be represented and scaled before being applied to the training network. Actually, no fixed method has been defined for scaling. The database consists of common attribute types either nominal o
r ordinal. The attribute are all represented in a common or compatible unit type.
Table 5.1 Provincial Codes
|04||Phnom Penh||15||Siem Riap
|05||Kampong Cham||16||Kampong Thom
|06||Kampong Chhang||17||Preah Vihear
|10||Kampong Som||21||Ratanak Kiri
|11||Koh Kong||00||Tonle Sap
Table 5.2 Land Cover Type Codes
|A1||Evergreen Forest||A9||Flooded Area
|A2||Mixed Forest||A10||Grass/Scattered Trees
|A3||Deciduous Forest||A11||Rice Fields
|A4||Wood- and Shrub-land||A12||Rubber Plantation
|A5||Pine Forest||A13||Orchards/Other Trees
|A6||Mangrove Forest||A14||Slush and burn
|A8||Flooded Forest||A16||Water Surfaces
The patterns of provinces and the attributes of land cover classifications in each compartment are shown in Table 5.1 and Table 5.2, respectively. There are 454 data vectors corresponding to the number of all compartments that will be used to produce a
set of clusters on the semantic map. The attribute vectors are specified by the sixteen attribute column vectors of the table according to each specific kind of land cover types. The data vector have been used to train a 50x50 planar array of neurons. Th
e initial weights have been selected of random values so that no initial ordering was imposed.
For training neural network, each input vector must be normalized. The raw data of land cover used as input vectors are normalized between the ranges as shown in Table 5.3.
Table 5.3 The range of data normalization
The whole compartments and land cover classification register of each compartment are shown in Appendix 1.
The training is performed by applying the data vectors of land cover types. The whole data are shown in Appendix 2.
5.2 Training Results
The input data has been trained two ways. One is normalized data and the other one is non-normalized data. The results show that the training with normalized data gives on the map the quantization errors less than the training with the non-normalize d
ata. So, the input data should be normalized before supplying to the training neural network to avoid large values from dominating the mismatching errors.
When the data has been trained many times, the location of each output neuron always changes corresponding to the weights that are randomly generated during training process. However, the groups with similar attributes are maintained.
After training input vectors, the neighborhood sensitivity has been ascertained. The neurons' responses became consistently stronger or weaker in certain regions. The input vectors with similar attributes were grouped together and with the dissimilar a
ttributes were located far apart on the map.