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

93 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 KEY WORDS: Rule-based classification, forest border, Landsat TM, GIS ABSTRACT The paper reports on a method for identification of the forest border and areas of spontaneous afforestation of abandoned farmland. Unsupervised and rule-based classification procedures are combined to classify Landsat TM and GIS data. The CORINE Land Cover database (CLC) database, one of the basic GIS layers in this study, lacks both spatial precision and thematic accuracy for environmental management applications at the regional to local scales. The study confirms the feasibility and efficiency of improving the precision and accuracy of the CLC forest border delineation by a sequence of automated and analyst-assisted classification procedures using the widely available data. Unsupervised classification is performed first, using CLC as reference in the labeling stage. A reclassification model is then developed combining domain expert knowledge with machine learning of decision trees, again using CLC as reference. Finally, the accuracy of the final map is estimated on an independent sample of photo-interpreted aerial photographs. Compared to CLC, the final reclassified map has slightly better accuracy and considerably higher spatial precision. This study is a part of a project aimed at developing an improved forest cover map of Slovenia as a foundation for a forestry information system. INTRODUCTION Slovenia has recently experienced an accelerated rate of infrastructural development and an increased rate of urban spread. Since the end of World War 2 Slovenia has also been undergoing a process of rapid deagrarization followed by a widespread spontaneous afforestation of abandoned farmland. Whereas there were only 36% of forests at the turn of the century (Zumer 1976), there are 54 % of forests today according to official statistics (MAFF 1997). The true percentage could be even as high as 63,5% according to the most recent study of Kobler and Hocevar (1999). There is obviously an urgent need for suitable information for sustainable development to be used by decision makers in physical planning and environmental assessment. Demand seems to be the highest for accurate, precise and up-to-date land cover data, including GIS information regarding the forest area, forest type, forest landscape pattern etc. In the last decades there have been several successful efforts to map forest cover. Whereas some of them, like the land cover maps of the Kocevje forest management unit (Hladnik 1998), the Pomurska statistical region (Tretjak 1999) or the lower Vipava valley (Pavlin 1996) only tackled individual regions, others covered the entire country (EEA 1998, SMAS 1995, Zonta and SMAS 1999). The rapid process of afforestation, however, is continually making the existing forest maps obsolete. None of the conventional methods of large scale forest mapping used so far in Slovenia has been able to follow the rate of forest expansion. Although the recent satellite based CORINE Land Cover Slovenia (CLC) mapping project provided us with an up- to-date land cover map of the whole country (EEA 1998, Kobler et al. 1998), this map is still too generalized for users at regional to local scales (e.g. 1:25.000). This is due to the CLC methodology (EC 1993), which employs manual photointerpretation of 1:100.000 scale Landsat TM color prints. In addition, the CLC nomenclature contains some mixed classes, which make unambiguous forest border delineation from the CLC impossible. Some possibilities of automated updating of the CLC database have already been examined (Wilkinson et al. 1992) and there is an initiative for upgrading the CLC database to the 4 th thematic level (EEA PTL/LC 1998). However, at the time of writing this paper we found no studies published regarding automated improvement of this database in the sense of spatial precision. The main objective of this study is to develop a cost- and time-efficient method of automated forest mapping, using a combination of classification techniques. At the same time we aim to improve the classification accuracy and spatial precision of the CORINE Land Cover database forest border, which is one of the basic layers in our analysis. To ensure the applicability of the method beyond the study area, the method is based on widely available satellite and GIS data. Both unsupervised classification (clustering) and supervised classification (machine learning of decision trees) are used. The latter are further combined with domain expert knowledge to improve performance. The presented study is one stage in a broader project aimed at updating the forests map of Slovenia. STUDY AREA The study area covers 159.800 ha in the southwest of Slovenia, centered around the town of Postojna (Figure 2). The area was chosen for its geographic and ecological diversity, thus raising issues of land cover classification representative for most of Slovenia. In the recent decades, the rural population has been mostly employed in the surrounding regional centers, abandoning farming and leaving farmland to spontaneous afforestation.

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