Results and Discussion

In the course of the classification test runs, it turned out that the original classification scheme had to be modified. There were some land cover types (fern and burnt areas) which had not been defined beforehand but turned out to be spectrally distinct and were added to the informational classes. By contrast, the informational class ‘young coniferous plantations’ was not at all spectrally separable in the Landsat image on the basis of the available training data, so the class had to be dropped. Mixed forests with a high percentage of broadleaved species were not spectrally separable from other broadleaved forest, so only the mixed coniferous forest in the mountain areas (pine forest with less than 50 % of broadleaved trees) was retained as a separate class. Some of the informational classes were spectrally heterogeneous and were split for the purpose of classification.

The mountainous coniferous forests (both dense and open) had a spectral signature which was distinct from that of the coniferous forests of the lower elevations and had to be classified

separately. The informational class ‘other cultivated land’ was split into two spectral classes, the one including fields with a very dense green vegetation cover (training areas were chayote fields) and the other including fields with a larger proportion of visible soil (fields of beans and other crops). The informational class ‘coffee without shadow’ was similarly split into dense plantations on the one hand and plantations with visible bare soil between the rows on the other hand. These classes were recombined for the final map and for the accuracy assessment. The final classes are specified in table 7.

Table 7: Land cover classes in the UCRYN Landsat classification.

Class name Definition Dense coniferous forest

(PFd) Pine forest with over 60 % crown cover

Open coniferous forest (PFo) Pine forest with 25-60 % crown cover, herbaceous layer usually consisting of grasses and/or ferns

Broadleaved forest (BF) Forest with a majority of broadleaved tree species, may contain pine trees Mixed coniferous forest

(MF) Mixed coniferous-broadleaved forest with a majority of pine trees

Burnt areas (brn) Areas of recent forest fires or other burning (spectral signature controlled by ash, charcoal and reduced vegetation in the months following a fire)

Matorral (Mat) Areas with over 25 % shrub cover, besides ferns and grasses

Fern (Cal) Areas dominated by the fern Dicranopteris pectinata, sometimes with a proportion of other fern species

Agroforestry (AF) Coffee and other crops with shade trees

Coffee without shade (Cof) Coffee plantations without shade trees, or with single trees amounting to less than 25 % crown cover

Grassland (GL)

Herbaceous vegetation cover, mostly grasses, combined coverage of shrubs and trees below 30 %, used mostly for low-intensity pasture, partly intensive pasture/managed grassland

Other crops (Cr) Cultivated agricultural areas (except coffee) without shade trees Bare ground (BG) No or very sparse vegetation cover

Built-up areas (bu) Urban areas and smaller settlements where most of the ground is covered by buildings and paved streets

Water (W) Rivers and lakes

No information Land cover not classifiable because of clouds in the image

These classes could be separated sufficiently well to create a land cover map which provides information about the overall spatial distribution of the main land cover classes of the UCRYN (plate 43). However, the overall classification accuracy as calculated from the confusion matrix (table 8) is only 40 %, and the estimated Kappa coefficient is 0.343, representing poor agreement.

Table 8: Confusion matrix for the Landsat classification. For class abbreviations see table 7. RD: Reference Data, CD:

Classified Data, UA: User’s Accuracy, PA: Producer’s Accuracy, OA: Overall Accuracy.

RD CD

PFd PFo BF MF brn Mat Cal AF Cof GL Cr BG bu W Sum UA (%)

PFd 14 4 11 2 1 1 33 42.4

PFo 6 3 3 8 2 1 1 24 12.5

BF 2 13 10 2 1 1 2 31 41.9

MF 5 4 2 1 12 16.7

brn 1 12 6 19 31.6

Mat 2 2 8 1 1 1 15 53.3

Cal 1 1 3 7 1 13 53.8

AF 1 1 18 1 3 24 12.5

Cof 1 3 1 4 0 1 10 0.0

GL 5 5 3 1 23 2 1 40 57.5

Cr 1 1 9 3 2 4 10 2 32 31.3

BG 1 1 1 2 4 4 4 1 18 22.2

bu 1 1 11 13 84.6

W 2 1 17 20 85.0

Sum 30 30 70 14 7 33 11 8 2 32 17 9 19 22 304

PA

(%) 46.7 10.0 18.6 14.3 85.7 24.2 63.6 37.5 0.0 71.9 58.8 44.4 57.9 77.3 OA:

39.8

The classes with the best class-specific user’s and producer’s accuracies were ‘grassland’, ‘water’

and ‘built up area’. The forest classes had low class-specific accuracies. The most accurately classified forest class was ‘dense pine forest’ with a producer’s accuracy of 47 % and a user’s accuracy of 42 %. ‘Broadleaved forest’ was often misclassified as ‘agroforestry’ and also as ‘dense pine forest’. Many errors can be attributed to mixed pixel effects and some class signature similarities (figure 12). Using only Landsat multispectral data and post-classification sorting with elevation information, it was not possible to distinguish coffee without shade from matorral, agroforestry from broadleaved forest and open coniferous forest from matorral with any confidence. In some cases, part of the error might also originate from the reference data, which is based mostly on the visual interpretation of high resolution remotely sensed data where it can also be difficult to see the difference between agroforestry and broadleaved forest or between coffee without shade and matorral. An additional potential error source is the fact that the ground data and high resolution data used as source for the reference data set were, apart from three of the IKONOS images, not acquired simultaneously with the Landsat data but with a time lag of up to two and a half years. Some types of misclassification can be partly attributed to gradual transitions (spatial

and temporal) between classes. Examples are the classification of ‘open pine forest’ as ‘burnt area’, of ‘matorral’ as ‘open pine forest’ or of ‘mixed coniferous forest’ as ‘broadleaved forest’. These would not all be counted as completely wrong in a fuzzy classification or fuzzy accuracy assessment.

An inherent classification problem is the fact that there is no one-to-one relationship between informational classes and spectral classes. On the one hand, there is quite a large spectral variation within large classes like broadleaved forest: the spectral signature of broadleaved cloud forest is quite different from other types of broadleaved forest (montane/submontane rain forest and riparian forest) and more similar to pine forest. On the other hand, for example the classes ‘broadleaved forest’ and ‘agroforestry’ are spectrally very similar with a large overlap in feature space, but from a land management standpoint it would be very desirable to have separate information about these two classes with their different types of land use. The class ‘broadleaved forest’ could not be partitioned into a number of more spectrally homogeneous different broadleaved forest types because the Landsat training data was not sufficiently comprehensive to split it into training areas which would have included samples adequately representing each of the spectral-informational broadleaved forest types of the UCRYN.

Table 9: Reduction of classification detail through class aggregation.

14 class scheme 5 class (land use) scheme Dense coniferous forest (PFd)

Open coniferous forest (PFo) Broadleaved forest (BF) Mixed coniferous forest (MF) Burnt areas (brn)

Forest (including burnt areas, which are mostly burnt forest, but not agroforestry)

Matorral (Mat)

Fern (Cal) Shrub and herb dominated semi-natural vegetation Agroforestry (AF)

Coffee without shade (Cof) Grassland (GL)

Other crops (Cr)

Agricultural land use

Bare ground (BG)

Built up areas (bu) Land with no or sparse vegetation

Water (W) Water

The large diversity of forest formations and other types of land cover in the heterogeneous, mountainous study area is not conductive to high classification accuracies when using medium

resolution remotely sensed data. The overall accuracy of 40 % achieved here is similar to the results of Langford & Bell (1997) who mapped the land cover of a similarly heterogeneous tropical hillside (10 classes) with Landsat TM data. Reducing the detail (precision) of the map by aggregating the 14 classes into five groups after classification (table 9) leads to the more acceptable overall accuracy value of 61 %, but there are still many problems of confusion between classes which could not be joined in one meaningful informational class.

According to this Landsat ETM+ classification, the total forest cover of the study area was 48 % in the year 2000. This is significantly more than the estimate of 33 % given in the GWB/GFA-Agrar (1998) study. Also, the confusion matrix for the aggregated classes indicates that the total forest area in my map was somewhat underestimated rather than overestimated. (According to the reference sample, 65 % of the ‘true’ forest was classified as forest, while 82 % of the forest in the map really is forest.) 36 % of the forest in the classification consists of open pine forest. The gradual transitions between open pine forest and some non-forest-classes and the fact that forest definitions may differ with respect to the lower crown cover threshold may, together with temporal change and misclassifications or -estimations, explain some of the above mentioned discrepancy.

The results point out the limitations of standard classifications of Landsat data for land cover classifications in landscapes of the type found in the UCRYN. There is certainly room for improvement regarding the accuracy and the precision of the classification with the help of higher resolution data and more advanced data processing methods, especially if looking at limited areas (e.g. special interest subsets of the catchment area).

8 Methods for an Optimised Information Extraction from IKONOS Data for Forest and Land Cover Mapping

A sub-area of the UCRYN was used to intensively test and compare a number of image processing and classification methods involving the high resolution IKONOS data (see flowchart figure 15).

These operations were limited to a smaller test area in order to reduce calculation costs and to concentrate on an area of high data and ground truth density. This main test area is the eastern test area depicted in figure 9.

Figure 15: Sequence of processing operations for the classification of data sets involving IKONOS data.