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

94 There are 4 main ecological regions: 1. the Low Karst in the west shows a widespread natural afforestation of abandoned farmland, 2. the Nanos and Javorniki mountains in the SE-NW direction are heavily forested, 3. the continental part in the northeast features fragmented forest cover and 2 large marshy depressions, 4. the tip of the Vipava valley in the northwest and the Brkini hills in the south represent an intensively cultivated sub-mediterranean landscape. THE DATA The continuous process of widespread natural afforestation renders the drawing of a clear line between forest and non-forest impossible. There are several transitional vegetation successions from managed farmland to full-grown forest, but not all of them can be identified from Landsat TM and GIS data, nor are they all interesting for most users. It was decided to distinguish the following 4 land cover classes in the final map: 1. Non-forest, 2. Abandoned pasture, 3. Shrub, 4. Forest. Because of the heterogeneity of the non-forested areas and in order to reduce the within-class variability in the satellite image, the "Non-forest" class was additionally partitioned into the "Unvegetated", "Water", "Marsh" and "Farmland" subclasses (see also Table 1). In the intermediate stages of our study we thus considered 7 classes (termed CLC_G classes). Only the final result of classification was reclassified back into the 4 main classes. Table 1: Aggregation of the original CLC classes (EC 1993) into the more general CLC_G classes of the reference data set Attribute / GIS layer Content CLC_G The generalized CLC classes for training (see Table 1) UNSUPERVISED_RESULT Result of the unsupervised classification FOREST81 Presence / absence of forest in 1981 (SMAS 1995b) DEM Digital elevation model, 100 m resolution (SMAS 1995a), interpolated to 25 m resolution SLOPE Slope in degrees, derived from DEM LAPLACE Surface roughness, derived by the Laplace filter from DEM INSOLATION Yearly global insolation in MJ/rn^ (Gabrovec 1996) POP_DENSITY Population density at a geographical subregion level (Perko et al. 1998) POP_AGE_INDEX Ratio of population aged under 20 to aged over 60 (adapted from Perko et al. 1998) PROX_SET_HW Proximity to the nearest settlement or highway in meters, based on a digized maps of settlements (SMAS 1995b); the highway was digitized on-screen from the Landsat TM image PROX_WATER Proximity to the nearest stream or lake, based on a digitized hydrology map (SMAS 1995b) PROX_LAKE Proximity to the nearest lake, based on a digitized hydrology map (SMAS 1995b) TM2 Ortorectified, topographicaly normalized image channels of Landsat TM, acquired July 26, 1995 (EC 1997, NLR 1997). TM1 was not used because of a considerable stripping. TM3 TM4 TM5 TM7 NDVI NDVI = (TM4 - TM4) / (TM4 + TM4); Normalized difference vegetation index was computed from the Landsat TM image channels prior to topographic normalization. Table 2: Attributes of each pixel

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