Results

2.5.1 Topographic correction

Table 2.3 shows the empirically calculated k values for each band for the study area.

The estimated values can be used to describe the roughness of the surface. In our case, the estimated values of k ranged from 0.2402 to 0.5237. The greatest range of difference was observed between bands 3 (red) and 4 (near infrared). When comparing different bands, the band 7 (thermal infrared) had the highest value of k.

Table 2. 3: Estimated values of the Minnaert constant for each band

Band 1 2 3 4 5 7 Minnaert

constant 0.2402 0.2587 0.3682 0.5035 0.5083 0.5237

After the Minnaert constant k was derived, the topographic correction was performed.

A reduction of the topographic effects was visually apparent in the normalized image.

The topographically normalized image shows that the dark sides (shadowed areas) on the raw image become brighter whereas the solar facing slopes appear in a rather darker tone on the normalized image (Figure 2.6b).

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(a) Raw image

(b) Topographically normalized image

Figure 2. 6: Comparison between (a) raw and (b) topographically normalized images (Landsat TM 4:3:2).

MAPPING OF FOREST COVER TYPE

2.3.2 Classification

Figure 2.7 depicts the mean digital numbers (DNs) for the different forest classes and bands. At bands 4 and 5, the spectral differences among forest classes could easily be identified, whereas the differences at the other bands were not so obvious. In the case of the deciduous forest (H), the mean value was lowest at band 4 and highest at band 5.

Figure 2.8 illustrates the distribution of DNs by forest classes in bands 4 and 5. In the classification process, coniferous and deciduous forests can clearly be discriminated while the mixed forest (M) can hardly be distinguished from those.

25 35 45 55 65 75 85 95

B1 B2 B3 B4 B5 B7

Bands

Digital numbers

C H M

Figure 2. 7: The mean digital numbers for the different forest classes and bands (C: coniferous forest; H: deciduous forest; and M: mixed forest).

Figure 2. 8: Distribution of digital numbers by forest classes in bands 4 and 5.

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2.3.3 Evaluation

Digital forest map

The digital forest map was derived from the interpretation of aerial photos in which the forest was divided into the three forest classes with a minimum area of 1 ha. Table 2.4 presents the error matrix for assessing the classification accuracy of the digital forest map with field plot data. In the accuracy analysis, the coniferous (C) and deciduous forests (H) had higher accuracy than the mixed forest (M). The user and producer accuracies of the mixed forest were 26% and 36%, respectively. The overall accuracy was about 70%. User and producer accuracies ranged from 26 to 80% and from 36 to 93%, respectively. The value of kappa was estimated to be 0.58.

Table 2. 4: Error matrices for assessing the classification accuracy of the digital forest map and the classified images for both classifiers with field plot data

Field plot data

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Classified images

Classified images were produced using both classifiers (Figure 2.9). Classification results from the cross validation are presented in Table 2.4. The accuracy for the MLC was modest; the user and producer accuracies ranged between 16 and 74%, and between 31 and 70%, respectively. The accuracy for the NNC was greatly improved compared to that for the MLC; its user and producer accuracies ranged between 58 and 82%, and between 60 and 83%, respectively. Particularly in the case of the mixed forest (M), the accuracy for the NNC was appreciably improved. Overall accuracies for the MLC and the NNC were 50% and 78%, respectively. The estimated kappa value for the NNC (0.69) was about twice as large as for the MLC.

Digital map vs. classified image by the NNC

The accuracy of the classification result by the NNC was assessed using the digital forest map as a reference (Table 2.5). Here, the pixel size was a square grid of 25 m (0.0625 ha). Compared with other classes in the classification accuracy assessment, the accuracy of the mixed forest class was lowest. Within the classified image, most of the mixed forest class on the digital forest map was divided into the deciduous forest (about 43%) and the coniferous forest classes (about 36 %). On the contrary, the accuracy of the non-forest class was highest. As a result, the overall accuracy was modest (48%). User and producer accuracies ranged from 19 to 60% and from 12 to 63%, respectively. The estimated value of kappa was to be 0.28.

Figure 2.9 shows the digital forest map (a) and the classified images by the MLC (b) and the NNC (c) for the study area. Due to the relatively large minimum area that was defined, forest cover types on the digital forest map can more clearly be discerned than those within both classified images.

MAPPING OF FOREST COVER TYPE Table 2. 5: Error matrix for assessing the classification accuracy of the NNC classified image and digital forest map over the entire test area per pixel

Digital forest cover map Classification

C H M Non Total User

accuracy

C 401081 169179 135135 90358 795753 50%

H 240009 411964 158982 52129 863084 48%

M 86692 75142 42541 18889 223264 19%

Classified Image

Non 106590 48389 31267 275584 461830 60%

Total 834372 704674 367925 436990 2343931

Producer

The classification result of field plot data compared with the digital forest map and both classified images by the Chi-square goodness-of-fit test is presented in Table 2.6.

The goodness-of-fit test indicates that the digital forest map and the MLC classified image differ significantly from the classification result of the field plot data, but there is no statistically significant difference found between the field plot data and the NNC classified image.

Table 2. 6: The result of the chi-square test for field plot data with digital map and both classified images

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igure 2. 9: Comparison of forest cover maps for different approaches

al forest map (1986) (b) Maximum likelihood classification (c) Nearest neighbor classification : Comparison of the forest cover maps for different approaches in the study area; digital forest map (a), and MLC (b) and the NNC (c), respectively. The observation units differ from depending on data source: st map (1 ha) and classified images (0.0625 ha); thus, the forest cover types on the digital forest map are obviously hereas they are highly fragmented within the classified images.

C H M

C H MH M

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