The decision tree described in section 5.1 was first tested on the fully inventoried additional field data. At this point, no RapidEye data set and no Light Detection And Ranging (LIDAR) intensity were available. Therefore, the classification was performed on the airborne RGB, CIR and LIDAR height data sets and the additional SPOT satellite data. The automatic threshold selection in section 5.1.2 was used to find initial parameters that were then adjusted empirically. The results are given in table 6.1 where the following abbreviations are used. Douglas fir is denoted as “Dougl.” in the tables below and classified is the number of samples classified as the species given in the first column of each row. The last column gives the user’s accuracies and the last row gives the producer’s accuracies. The number in the lower
right corner is the overall accuracy. All samples from the additional field data were used as test data.
Table 6.1: Manual decision tree performed on additional field data
oak beech larch spruce Dougl. classi-
fied user’s oak 59 122 26 2 34 243 24.28 % beech 48 770 76 26 0 920 83.7 % larch 20 112 184 66 29 411 44.77 % spruce 5 33 27 366 123 554 66.06 % Dougl. 3 42 9 30 408 492 82.93 % samples 135 1079 322 490 594 2620 producer’s 43.7 % 71.36 % 57.14 % 74.69 % 68.69 % 68.21 %
To compare the approaches, a support vector machine based decision tree (SVMDT) was trained on a training set extracted from the additional field data and tested on the full set of additional inventory data, which also contains the training samples. The same data set was used as before, consisting of airborne RGB and CIR images and LIDAR height data, excluding the RapidEye, SPOT and the LIDAR intensity data. The results are given in table 6.2. The difference of 26 samples in the two
Table 6.2: SVMDT performed on additional field data
oak beech larch spruce Dougl. classi-
fied user’s oak 98 108 16 5 0 227 43.17 % beech 20 838 34 22 0 914 91.68 % larch 7 85 263 35 2 392 67.09 % spruce 5 41 11 412 42 511 80.63 % Dougl. 7 10 0 24 561 602 93.19 % samples 137 1082 324 498 605 2646 producer’s 71.53 % 77.45 % 81.17 % 82.73 % 92.73 % 82.09 % confusion matrices is due to the fact that 26 samples were classified as non-forest in the decision tree and are therefore not included in the according confusion matrix.
However, this analysis is not reliable as both algorithms were trained on data also used for testing, but the SVMDT clearly shows a higher classification result.
The manual decision tree was also tested on a set generated from the sample in- ventory data and the additional inventory data. The accuracy dropped significantly for the manual decision tree as the overall accuracies and mean user’s accuracy in table 6.3 show. The mean value of the overall and user’s accuracies is given by µ and the standard deviation is given as σ in percentage points (pp). Due to the equal sample sizes in the test data set, the producer’s accuracy is equal to the overall accuracy and therefore not given explicitly. The SVMDT was tested on the same test sets as the manual decision tree and trained on the according training sets.
Table 6.3: Manual decision tree and SVMDT performed on ten test sets
manual DT SVMDT
overall user’s overall user’s 1 41.19 % 35.49 % 68.09 % 71.25 % 2 44.44 % 39.06 % 71.06 % 73.98 % 3 43.98 % 38.20 % 71.49 % 73.05 % 4 42.75 % 37.68 % 68.72 % 70.75 % 5 39.80 % 33.47 % 70.00 % 71.83 % 6 43.73 % 38.36 % 72.98 % 75.25 % 7 45.21 % 40.44 % 71.49 % 74.52 % 8 44.72 % 39.06 % 75.74 % 78.92 % 9 42.75 % 37.20 % 72.77 % 74.25 % 10 44.67 % 39.32 % 71.06 % 73.95 % µ 43.32 % 37.83 % 71.34 % 73.78 % σ 1.72 pp 2.04 pp 2.20 pp 2.34 pp
Based on the poor results of the manual decision tree, the SVMDT was developed to provide more sophisticated decision functions at each node of the decision tree as the simple thresholds used in the manual decision tree are unsuitable for the low inter-species variability and high intra-species variability.
Another observation on the classification result of the manual decision tree (DT) for an unsuited parameter set is given in Fig. 6.1, which shows a subset of the whole test area. It also indicates that airborne image data can be less homogeneous than
would be preferred for tree species classification. Spectral tree species characteristics are already highly variable regarding water content, site properties, time of the year, climate conditions and other factors. Additional variability in the image quality adds another variation to the spectral characteristics. It shows distinct strips, in which
Figure 6.1: Manual decision tree classification with unsuitable parameter set highlights strips in the airborne image data
forest areas are classified as Douglas fir or larch respectively. It is unlikely that the tree species are actually distributed like that and sample-wise inspection of old forest inventory data also indicates that no such strips exist in the forest. However, the airborne images were acquired in horizontal flight-strips, so that these strips are actually a result of the strip-wise inhomogeneity of the airborne image data set due different acquisition times and dates. The strips are also visible in the airborne images, but due to the reduced number of colors and the thresholding in the manual decision tree, the strips are more prominent in the badly classified image in Fig. 6.1. Fig. 6.2 shows a classification result of the manual decision tree for a better suited parameter set. The strips are still visible in this image, but they are less prominent. Nevertheless, a more sophisticated approach is needed to handle variations in the
Figure 6.2: Manual decision tree classification with better parameter set data source itself, as well as variations in the reflectance of the trees. The decision tree has some desirable properties but the decisions at each node need to be smarter than the thresholds on the difference and ratio bands that were used in the manual decision tree. As the tree had a binary structure, a powerful binary classifier like a support vector machine (SVM) is predestined to be used under these circumstances.