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:: 4 Renewable resources in rural areas: Vegetation, forestry, agriculture, soil survey, land and water use. Chairman: J. Besenicar, Liaisons: M. Molenaar, Th. A. de Boer

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Experiences in application of multispectral scanner-data for forest damage inventory. A. Kadro & S. Kuntz

Document type:: Multivolume work

Structure type:: Chapter

472 Photo 6 Photo 7 2.2 From higher altitudes From 1000 m and 3000 m altitude only groups of trees or stands can be detected. With increasing pixel size one has more problems in finding satisfactory homo geneous training areas for the computer classifica tion,especially in forests in Germany. From 1000 m altitude individual tree^shadow, slades between trees, roads etc. can be distiguished with some acuracy, but from 3000 m altitude and from the higher satellite data the contrast of these features will decrease and so the quality of class seperation decreases. But this effect can also be an advantage. Dead trees which are normally salvaged rapidly in German forests do not appear in the commonly used statistics of forest damage inventories. But as more trees are removed from a stand ground vegetation or soil is increasingly detected. This causes a higher reflection and a higher standart deviation of the pixels to represent this stand and so more pixels will be classified in a higher damage class. This classi fication might give more accurate information than the ground inventories which do not count salvaged trees. In pict. 1 the results of photointerpretation and computer classification for the same test sites are shown. (Picture 1 ) In dense old stands good correspondence between CIR- Photointerpretation and the computer classification was found. Worse results were found in steep slopes of mountains and on stands of lower density. The following photos show the results of computer classifications from different altitudes on a testsite in Southemwest of Germany. 3 Conclusion After many computer classifications of daimaged fo rests one can say that it is possible to classify with good accuracy healthy and severely damaged fo rest stands from 1000 and 3000 m with an airborne scanner and with less accuracy from satellite data. From satellite data only two classes can be sepera- ted: healthy and severely damaged forests, and these only when larger areas are affected. Significant problems exist in the middle damaged class S2 (26 to 60% needleloss). The wide distribution of this class makes it difficult to define exact class boundaries, so quite often pixels will be classified to the neighbor classes SO-1 or S3. For better results additional information should be included in the computer classification process, for example texture, terrain models, stand density etc. to minimize the still existing misclassifications. In the continuing project it is planned to investi gate in this. ACKNOLEDGEMENT: The authors thank Mr. H.P. Kienzle, H. Schneider and R. Waltenspiel of the Department of Photointerpre tation and Remote Sensing at the University of Frei burg for developing special software. The authors also thank Mr. V. Amann from the DFVLR Oberpfaffenhofen for aquiring the airborne scanner data. LITERATURE: A. Kadro. Investigation of spectral signatures of differently damaged trees and forest stands using airborne multispectral data. Proceedings of IGARSS'84 Syrnp. , Strassburg, 27. - 30. Aug. 1984. A. Kadro, S. Kuntz, C. Kim. Entwicklung eines Ver fahrens zur Waldschadensinventur durch multispek trale Fernerkundung. 1. Statuskolloquium des PEF vom 5. - 7. Maerz 1985, Karlsruhe. A. Kadro. Investigation of Spectral Reflectance Pro perties of Forest Damage Using Multispectral Data. 3rd Int. Colloquium; Spectral Signatures of Objects in Remote Sensing, Les Arcs, 16. - 20. Dec. 1985. G. Hildebrandt, A. Kadro, S. Kuntz. Entwicklung ei nes Verfahrens zur Waldschadensinventur durch mul tispektrale Fernerkundung. Zwischenbericht fuer das 2. Statuskolloquium des PEF, 4. - 7. Maerz 1986 in Karlsruhe. A. Kadro. Determination of Spectral Signatures of Different Forest Damages from Varying Altitudes of Multispectral Scanner Data. Int. Symp. on Remote Sensing, 25. - 29. Aug. 1986, Enschede.

Access restriction

Copyright

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation