Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

Bibliographic data

Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: Volume

Collection:: Earth sciences

Chapter

Title:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Interpretation of classification results of a multiple data set. Helmut Beissmann, Manfred F. Buchroithner

Document type:: Multivolume work

Structure type:: Chapter

13 dependent on the /er and on the and microrelief, control the Hence, auto- i very rare, thonous geo-eco- ie area. So one itions Arabidetum sslerio-Caricetum Lcate limestone garnet muscovite 3 is due to the nation of these -bearing surface Lon of carbonate- 3. The above sales cause a r of the stati- 3. Therefore the lological units .fications, which -iations in the leless, it has to type and the ihe random sample influence the lis understanding ;t limitations in >te sensing for lis application itself as a test theories about ■eas where rocks ie classification 'ises up to some tly the similar if the various prevent better illy valid for ,e and rauhwacke. icks is studied show significant ieir weathered lly be classified ie lithological cover could be y than others ‘ormed part of an ons cannot be at 27,3 % of the i a 1" and 20,9 % were classified rhaeticite and to some extent, ial deformations p, which only by inaccuracies In general, the ping will rather itional than by o it could be ering conditions that in places gement of the and soil map the formalized ion 2) than from of distribution drawing of more or less ans, always be In the present boundaries in out using an an additional The aspects of n, which is erformed during Table 1. Classification results of airborne multispectral percents). scanner data for various lithological classes (in No. Material N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 Gabbro-Amphibolite 61 100 2 Prasinite, Garnet-Bearing, partly Eclogitic 1.477 45,8 0,3 33,0 14,5 1,9 0,1 0,6 0,1 3,4 0,1 0,2 3 Peridotlte, Serpentinite 6.743 0,7 2,7 67,9 0,0 5,8 1,4 1,0 0,0 14,3 0,3 0,2 0,0 2,7 0,3 1.9 0,6 4 Chlorite-Talcum-Hornblonde-Schist 72 100 5 Quartzite, Rhaeticite, Graphitic Schist 837 91,2 0,8 1,4 0,5 1,0 2,3 0,4 2,5 6 Calcareous Mica-Schist 1.390 2,1 6,1 4,7 79,2 0,7 0,4 0,1 2,5 0,2 0,1 2,9 0,4 0,4 0,2 7 Garnet-Bearing Muscovite-Schist and Phyllite 4.847 0,3 15,2 3,2 1,8 35,9 28,8 0,9 1.3 4,4 0,4 0,2 0,1 1.3 5,5 0,1 0,1 0,7 8 Dark Mica-Schist and Phyllite 9.626 0,6 2,5 3,7 0,8 0,5 8,3 7,7 20,8 4,1 20,1 0,4 1,1 0,0 5,5 3,4 1,3 15,9 0,8 2,4 9 Quartz-Rich Breccia 177 4,0 1,1 93,2 1,7 10 Quartzite 4.200 0,1 0,4 4,3 0,0 5,7 3,4 2,7 0,5 68,9 0,0 3,4 5,7 0,1 1,3 1,9 0,6 0,9 11 Marble 4.706 0,3 0,6 4,6 0,9 2,0 1,0 0,2 3,3 64,8 4,0 11,0 2,8 2,3 0,1 2,1 12 Dolomite 3.426 0,0 1.8 3,8 1,1 0,5 2,5 0,3 12,0 21,3 17,4 14,5 10,0 0,7 0,3 12,2 0,8 0,7 13 Rauhwacke 1.250 0,2 0,1 1,0 0,3 17,7 5,6 69,0 4,6 1,6 14 Moraines: Fernau, Egesen, Daun, Older 40.150 1,6 0,1 5,1 0,6 9,9 3,0 0,3 4,2 0,1 25,0 0,5 3,6 0,4 22,6 2,2 7,3 6,4 2,3 4,8 15 Moraine of 1850 1.378 0,7 2,0 0,3 2,6 0,1 5,0 88,9 0,5 16 Peat and Bog 527 20,9 3,2 0,4 1,3 62,4 1,5 0,2 10,1 17 Ancient Rock Fall 3.844 0,1 2,3 1,8 0,8 5,3 0,1 4,4 1,2 2,4 0,0 4,8 1,1 0,1 70,6 1,3 3,7 18 Talus Fans 22.624 0,3 3,0 9,7 0,1 0,8 8,8 7,0 6,5 0,9 17,1 4,6 10,3 2,9 5,6 8,5 1,4 5,5 6,2 0,8 19 Recent Alluvial Material 2.024 0,3 27,3 0,0 0,6 1,4 0,7 1.9 0,5 13,4 2,7 0,4 50,6 field mapping, have also to be taken into consideration. For the above mentioned example e.g., the misclassification is due to wrong geometric demarcation of the class "quartzite, rhaeticite and graphitic schists". The mutual misclassifications of the classes "peat and bog" and "recent alluvial material" can probably be explained by a certain generosity during field mapping as well as through a change in spatial extension of these two classes between the time of field mapping (around 1930) and the remote sensing data acquisition (1979). These changes during time are also valid for the Grossglockner-Hochalpenstrasse (pass road). The deviations in its course given in the lithological map are not only due to cartographic generalization but mainly a result of reconstructions performed in the meantime and slight changes in position related to these activities. If there had only been a cartographic generalization that effected all objects of the study area to the same extent, the classes "gab- bro-amphibolite, chlorite, talcum, hornblende-schists" could not have been located so precisely and reclassified with a 100 % accuracy. An additional problem represents the different empirical content of identical classes in different thematic maps. The class "talus fans" e.g., has been distinguished in all four maps used. The common areal coverage, however, only amounts to some 10 % of the total of all individual talus areas. It is a well known fact that in classical (uncovered) geological maps the term detritus (talus/scree) is differently used from geomorphological maps. Nevertheless it is surprising that in the study area there also exist considerable differences between the soil and the vegetation map (80 % of the detritus of the vegetation map are also distinguished as this class in the soil map; however, only 16 % of the detritus of the soil map are classified as such in the vegetation map). It could be clearly demonstrated, that the congelifraction zone in the soil map was mainly asigned through the class detritus, thus simply omitting the initial soil formation, whereas in the vegetation map these areas were asigned to various plant associations with a low degree of density. This example may point out, that prior to such classifications the theoretical background and the conceptual contents of the used class names have to be clarified. This aspect is at least as important as the selection of an adequate classification method. As shown in Table 1, due to its heterogenous spectral properties, the material described by the term detritus as used in uncovered geological maps can not or hardly be reclassified. A splitting of this category in at least three sub-classes (detritus below soil and vegetation cover, detritus of bright rocks, detritus of dark rocks) yields an improvement of the classification results. Rock detritus under soil and vegetation cover can only be sufficiently reclassified in those places, where it causes a differenciating effect in the ecosystem, e.g. a high rate of subterraneous run-off and therefore a dry habitat. A similar problem arose in the classification of genetically different soil types. Many soils in the test area are in the process of pseudogleying and are in the soil map correspondingly distinguished as pseudogley and pseudogleyed brown-earth respectively. Due to the effects of the macro- and microrelief this type which has been distinguished according to the scientific rules of the official Austrian soil mapping, can be split into ecologically different subtypes, which do not have an official character and are therefore not displayed in the soil map but can be excellently determined from the airborne scanner data. The attempt to reclassify the superior main soil type failed due to the heterogenous statistics of the training areas. The same problem with reversed premises occured in the reclassification of the vegetation associations. Here often associations are distinguished, which are only different from each other through the

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