International cooperation and technology transfer

Fras, Mojca Kosmatin

Bibliographic data

Monograph

Persistent identifier:: 856490555

Author:: Fras, Mojca Kosmatin

Title:: International cooperation and technology transfer

Sub title:: Ljubljana, Slovenia, February 2 - 5, 2000 : proceedings of the workshop

Scope:: VI, 163 Seiten

Year of publication:: 2000

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856490555

Illustration:: Illustrationen, Diagramme

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: FOREST BORDER IDENTIFICATION BY RULE-BASED CLASSIFICATION OF LANDSAT TM AND GIS DATA. Andrej Kobler and Dr. Milan Hocevar, Slovenian Forestry Institute, Slovenia Dr. Saso Dzeroski, Jozef Stefan Institute, Slovenia

Document type:: Monograph

Structure type:: Chapter

97 computing the ratio between the true and classified forest border length in the independent reference sample area. RESULTS AND DISCUSSION For unsupervised classification, it was decided to limit the number of clusters to 119, because that is where a break occurs in the histogram of the initial clustered image. Table 5 shows the two decision trees that were successively applied to reclassify the result of unsupervised classification of the Landsat TM data. Figure 2 shows the final forest border map, which was obtained after the result of the second reclassification was thematically aggregated back into the 4 main classes and generalized with the sieve filter. A comparison of the land cover structure within the area of the reference aerial images (Table 3) shows the classified map to be closer to the true values than the CLC, especially for the "Forest" and "Shrub" classes. A site-specific comparison confirms the improvement in classification accuracy over the CLC database (Table 4). The thematic accuracy for the 4 main classes is estimated to 81,8% (Kappa 66,6%) for the rule-based classification and 75,3% (Kappa 57,3%) for the CLC database, while at the "Forest" / "Everything else" thematic level the accuracy increases to 91,2% (Kappa 81,3%) and 87,2% (Kappa 73,5%) respectively. It is evident from the error matrices (Table 4) that the accuracy is the lowest for the "Shrub" and "Abandoned pasture" classes. We attribute this to (1) their transitional character, making them difficult to consider in the decision trees and to (2) the lack of relevant information in the GIS layers. The forest border delineation in the classified map is slightly more accurate than the one in the CLC: the accuracy of the forest border delineation as estimated by the IREB value is ± 14 m for the classification and ± 15 m for the CLC. The minimum mapping unit is 0,25 ha for the classified map and 20 ha for the CLC. The classified map is therefore spatially more precise by definition. The improvement in precision is confirmed by the ratio of the classified to the true forest border length, which is 92,6% for the rule-based classification and 33,4% for the CLC database. It may come as a surprise that the rule-based classification performs so much better than the CLC, given that the rules/trees were learned by using the CLC as the target class. However, there is a logical explanation for this phenomenon, which has also been observed in other applications of machine learning and is known under the name of "clean-up effect" (Michie and Camacho 1994). The learning process employed (which in our case also takes into account domain knowledge) has an averaging effect implicit in generalization which abstracts away the individual errors made by humans and yields performance similar to that of the trained humans but more dependable. Compared to the photointerpretation work on the Slovenian CLC database project (Kobler et al. 1998), 70% less man-days were needed to complete the classification of the study area. Proportionally less time should be needed for larger areas. True value Classification CLC database Forest 62,1% 62,8% 57,6% Shrub 5,0% 6,6% 11,9% Abandoned pasture 9,8% 7,8% 9,9% Non-forest 23,2% 22,7% 20,5% Table 3: Structure of the land cover within the area of the reference aerial images Classification CLC database Forest Shrub Aband. pasture Non forest Forest Shrub Aband. pasture Non forest TOTAL Reference data Forest 29.072 987 536 495 26.779 2.326 504 1.481 31.090 Shrub 986 862 303 334 783 1.027 318 357 2.485 Ab. pasture 707 963 1.843 1.394 530 1.625 2.110 642 4.907 Non-forest 691 497 1.241 9.169 775 1.002 2.048 7.773 11.598 TOTAL 31.456 3.309 3.923 11.392 28.867 5.980 4.980 10.253 50.080 Overall accuracy: 81,8% Kappa index of agreement: 66,6% Overall accuracy: 75,3% Kappa index of agreement: 57,3% Classification CLC database Forest Everything else Forest Everything else TOTAL Ref. data Forest 29.072 2.018 26.779 4.311 31.090 Everything else 2.384 16.606 2.088 16.902 18.990 TOTAL 31.456 18.624 28.867 21.213 50.080 Overall accuracy: 91,3% Kappa index of agreement: 81,3% Overall accuracy: 87,2% Kappa index of agreement: 73,5% Table 4: Comparison of the rule-based classification vs. the CLC database - error matrices and thematic accuracy assessment

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