The aim of this study was to find optimised methods for the automated digital classification of satellite images – including high resolution satellite data – of heterogeneous tropical mountain areas and to produce forest and land cover maps of areas for which there was a lack of detailed spatial land cover information.
Landsat ETM+ and IKONOS-2 data were used as examples of conventional medium resolution satellite data on the one hand and of data of the new generation of high spatial resolution satellites on the other hand. A Landsat ETM+ classification was conducted for the mountainous upper catchment area of the Río Yaque del Norte (785 km²) in the Cordillera Central of the Dominican Republic. IKONOS data were analysed and processed for smaller subsets of this region.
Most of the analyses regarding the processing of high resolution IKONOS data were conducted for one test area only, using a subset of one IKONOS image. The eastern test area was chosen as an example of a heterogeneous tropical mountain area, containing protected forest areas as well as semi-natural vegetation and small-scale farming. A one-to-one transfer of the data processing results to other IKONOS images of other areas will not be feasible, because of the varying (geometric) aquisition paramters of IKONOS data, among other things. On the other hand, the data of the eastern test area were analysed in great detail. The qualified results and conclusions drawn from the analysis of this image area contribute to the body of experience which exists about the use of high resolution satellite data for tropical forest and land cover mapping, and which is up to now quite limited.
The results of the Landsat and IKONOS classifications which were carried out indicate that, for mapping the heterogeneous land cover of tropical mountain areas, the use of high spatial resolution data does indeed lead to higher accuracies, compared to a conventional classification of medium resolution data (as stated in the central hypothesis). It can also be confirmed that the spatial characteristics of high resolution data have to be taken into account in order to make good use of such data.
For the eastern test area, a conventional per-pixel classification of the four multispectral IKONOS channels did not lead to a significantly higher overall accuracy than the classification of seven Landsat channels. As a source of multispectral data for land cover and particularly forest classifications, the spatial resolution of IKONOS data is unnecessarily high, so that some kind of spatial integration (decreasing the resolution, low pass filtering or image segmentation) is needed in order to achieve optimal classification results. Similar accuracy improvements can also be achieved when mode filtering is used as a technique for post-classification spatial integration.
The higher resolution information, particularly of the panchromatic channel, should also be exploited and can be used through the derivation of texture parameters. GLCM texture features, calculated from the panchromatic channel in 15 m × 15 m windows, proved to be a useful addition to the limited number of multispectral IKONOS channels, increasing class separabilities and classification accuracies. The inclusion of texture data increased the classification accuracies for all forest classes and was especially useful for the discrimination of palm dominated forest, secondary forest and open pine forest. Among the non-forest classes, matorral benefits a lot from the inclusion of texture, with better separabilites between open forest and its ‘background class’ matorral on the one hand and between matorral and the more ‘smooth’ grassland on the other hand.
The best IKONOS based classification results were achieved when the integration of textural features and multispectral channels to spectral-textural data sets was combined with spatial integration methods, e.g. 3×3 or 5×5 mean filtering of the multispectral channels and post-classification mode filtering. Similar results (overall accuracy around 70 %) were also achieved without pre-classification spatial integration of the 4 m multispectral data, when information about larger areas was only introduced through the texture data (calculated in 15 m × 15 m windows) and post-classification mode filtering. The best classification results before post-processing (mode filtering) were achieved with the combination of mean filtered multispectral data and three texture features. Classifications of the segmented data (with object-oriented standard nearest neighbour classification in eCognition and MLC as tested classification methods) were less successful with overall accuracies not exceeding 60 %.
A basic factor limiting the accuracy of automated classifications of high resolution remotely sensed data is the trade-off between using either high resolution data with few mixed pixels, but with pixels which are often not representative of their classes, or data incorporating spatial information, where the pixels are more representative of their classes but edge effects play a larger role. The eastern test area and most of the UCRYN are areas of high land cover heterogeneity and fragmentation, which is typical for partly farmed tropical mountain areas. This entails that the threshold of homogeneity and the threshold of heterogeneity (in the terminology of Puech 1994) for detailed target land cover classes are quite close to one another. In other words, when the pixels become large enough (or there is enough spatial integration in another form) to aggregate the class elements in a representative pixel, many of the pixels are already so large (or contain so much information from neighbouring areas) that they represent several target land cover classes. In areas where the land cover classes exhibit a high within-class heterogeneity (e.g. open forest classes consisting of many different elements) while at the same time the contiguous areas of the individual classes are very small, the thresholds of homogeneity and of heterogeneity of the target classes can become very close to each other. This may entail that information about the separate target classes in such areas cannot be extracted from the data in an automated per-pixel classification.
When using medium resolution (Landsat ETM+) satellite data to classify the eastern test area, the threshold of heterogeneity is already reached for parts of some of the target classes (e.g. narrow riparian forest, the smaller patches of calimetal and matorral), meaning they are integrated with their neighbouring classes in the spectral response, and the percentage of mixed pixels is very high.
The 4 m resolution of the IKONOS data, on the other hand, is below the threshold of homogeneity for many target classes, meaning that the elements of many of the target classes are still resolved separately. Spatial integration of this high resolution data leads to better classification results until the amount of mixed pixels and the detrimental edge effects become so large that they cannot be compensated by improvements through spatial integration any longer.
When Landsat and IKONOS data were integrated, the classification results were degraded compared to optimised classifications of IKONOS data alone. Even though a higher spectral resolution is provided with the Landsat data, their spatial resolution is too low to effectively improve the class separabilities in the heterogeneous test area compared to IKONOS data alone.
The fundamental trade-off between trying to reduce edge effects or trying to reduce the within-class variability in the choice of a spatial resolution (degree of spatial integration) does not seem to be avoidable in automated per-pixel classifications. It becomes more relevant with increasing land cover heterogeneity and increasing within-class heterogeneity. While for the purpose of detailed classifications of areas of high land cover heterogeneity, high resolution data are clearly preferable to medium resolution data, processing them in a way that uses their advantages but cancels their disadvantages does pose some challenges. The methods of spatial integration and use of texture tested in this study go some way in that direction, but some problems of misclassifications, caused among other things by edge effects in the texture data, remain. With this in mind, a more thorough look at image segmentation of high resolution data and especially at optimising the classification methods for segmented data might still be worthwhile.
The low resolution of the available DEM (50 m grid spacing) limited its usefulness in connection with the high resolution IKONOS data. The DEM was useful for a post-classification sorting of the Landsat classification of the whole UCRYN, but the integration of DEM-derived topographic variables in the IKONOS classifications of the eastern test area did not manage to enhance the classification results significantly.
The high resolution data sets were classified better (or at least equally well) with the maximum likelihood classification method than with the tested non-parametric classification techniques (k-nearest neighbour classification, artificial neural network classification and object-oriented (k-nearest neighbour classification). The MLC method, which is well proven for the classification of medium resolution multispectral data, was shown to be suitable for the classification of the spectral-textural IKONOS data sets, which were mostly Gaussian and exhibited only moderate violations of the
Gaussian assumption for a minority of the classes and channels. It would have to be replaced or complemented by other classification methods only if more non-Gaussian ancillary data were used as variables during the classification.
An overall classification accuracy of around 70 % was the maximum that could be achieved for a
‘hard’ classification of the Ebano Verde test area with its 13 classes, no matter how much information was extracted or added. Edge effects (mixed pixels) boosted by a high landscape heterogeneity, the many occurrences of gradual transitions between the defined classes and the (usual) lack of ‘absolutely true’ reference data limit the agreement which can be achieved between the automated classification of high resolution satellite data and the reference data (which represents essentially an independent classification). A consideration of the land cover as fuzzy helps to differentiate between serious errors and less serious disagreements between the map and the reference data. ‘Softening’ the output of a maximum likelihood classification yields complimentary information about the spatial distribution of the map reliability and about possible alternative class assignments.
In spite of the above mentioned uncertainties, IKONOS data offer good possibilities to differen-tiate a number of land cover classes in the heterogeneous study area. Cloud forest, which is a particularly important forest type from a biodiversity and conservation standpoint, could be differentiated reliably from other forest types with the IKONOS data. It was sometimes confused with secondary forest, which represents a successional stage on the way towards cloud forest. Apart from that, the only significant number of confusions occurred with dense pine forest, with which cloud forest shares a more similar spectral signature (with relatively low reflection in the NIR) than with other broadleaved forest classes. Satisfactory classification accuracies could also be achieved for the other forest classes except open pine forest. The classification of agroforestry remains challenging even with high resolution and textural data.
For the small test area, IKONOS was clearly a better data source for a detailed classification than Landsat. Having said that, the choice of data is of course also a question of the desired scale of the map output, the size of the area to be classified and of the costs. IKONOS has a swath width of just 11 km and single IKONOS images cover accordingly much smaller areas than e.g. Landsat images.
This and their high resolution make it more difficult to map larger regions.
For an extension of the areas mapped with IKONOS so far in this study, the next step would be to transfer the methods which were successful in the eastern test area (mean filtering of multispectral channels, inclusion of texture channels, mode filtering) to other areas of interest within the UCRYN, e.g. to the western area around Manabao. To classify the complete UCRYN with high resolution data and the appropriate methods would be very expensive and time-consuming. It would involve the pre-processing of about 12 separate IKONOS images (maybe more because of the cloud
cover in many images), most of which come with different acquisition geometry, different acquisition dates and varying atmospheric effects. A high resolution DEM would be needed to geometrically correct the images with lower collection angles and strong topographic effects. Even after thorough pre-processing, the images would probably still have to be classified separately because their different acquisition dates would not allow the use of training data derived from one image to represent the same class in an image acquired in a different season. This would require the collection of a sufficiently large amount of field data in every IKONOS-image-area of the catchment.
Classifying medium resolution data like Landsat may be the best solution for the production of a coarse base map, providing an overview of the spatial distribution of a limited number of broad land cover or land use classes for a larger region like a catchment area. However, the accuracy and / or detail of maps based on 30 m resolution satellite data can always be expected to be rather limited in heterogeneous tropical mountain areas. High resolution satellite data would be a suitable source of more detailed spatial information about smaller areas of special ecological or economical interest (e.g. protected areas).
The methods of high resolution data processing which were shown to be successful in this study could be implemented for example to monitor protected areas, or to gain information about the area and extent of threatened ecosystems in order to be able to make informed decisions about the boundaries for protected areas. The methods presented here do not produce information which could replace a detailed forest inventory as a basis for specific economic forest management decisions. It can be said, however, that maps produced for areas of interest with these methods, using high resolution satellite data, would certainly be more detailed and accurate than the land cover information which has been available for most parts of the Dominican Republic until now.