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Remote Sensing of Forest Decline in the Czech Republic

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Remote Sensing of Forest Decline in the Czech Republic ££ V807/5Z Remote Sensing of Forest Decline in theCzech Republic fiNBC6lVE0 m OST i ■■ '' Jonas Ardo MEDDELANDEN FRAN LUNDS UNIVERSITETS GEOGRAFISKAINSTITUTIONER avhandlingar 135 DISCLAIMER Portions of this document may be illegible electronic image products. Images are produced from the best available original document. MEDDELANDEN FRAN LUNDS UNIVERSITETS GEOGRAFISKA INSTITUTIONER Avhandlingar 135 Remote Sensing of Forest Decline in the Czech Republic Jonas Ardo 1998 LUND UNIVERSITY, SWEDEN DEPARTMENT OF PHYSICAL GEOGRAPHY Author's address: Department of Physical Geography Lund University Box 118, S-221 00 Lund Sweden Email: [email protected] URL: www.natgeo.lu.se Lund University Press Box 141 S-221 00 Lund Sweden Art nr 20532 ISSN 0346-6787 ISBN 91-79-66-528-4 © 1998 Jonas Ardo Printed by KFS AB, Lund, Sweden 1998. Remote Sensing of Forest Decline in the Czech Republic Jonas Ardo N aturgeografiska institutional! Matematisk-naturvetenskapligfakultet Lunds universitet, Lund Avhandling FOR FILOSOFIE DOKTORSEXAMEN som kommer att offentligen forsvaras i Geografiska institutionemas forelasningssal, 3:e vaningen, Solvegatan 13, Lund, onsdagen den 20 maj 1998, kl. 13.15 Fakultetsopponent: Professor Curtis Woodcock, Department of Geography, Boston University, Boston, USA Document name LUND UNIVERSITY DOCTORAL DISSERTATION Physical Geography Date of issue Remote Sensing and GIS 1998-04-27 CODEN: ISRN lu : OTU3/NBNG-96/ 1135-SE Autbor(s) Sponsoring organisation Jonas Ardo Swedish National Space Board Title and subtitie Remote Sensing of Forest Decline in the Czech Republic This thesis describes the localisation and quantification of deforestation and forest damage in Norway spruce forests in northern Czech Republic using Landsat data. Severe defoliation increases the spectral reflectance in all wavelength bands, especially in the mid infrared region. These spec­ tral differences allow the separation of three damage categories with an accuracy of 75% using TM data and regression-based relationships. Estimating the same categories using an artificial neural network, multitemporal TM data and topographic data yields slightly higher accuracy (78%). The methods are comparable when using identical input data, but the neural network more efficiently manage large input data sets without pre-processing. The estimated coniferous deforestation in northern Bohemia from 1972 to 1989 reveals especially affected areas between 600 and 1000 m.a.s.1. and on slopes facing south and southeast. The sector downwind a large source of sulphur dioxide was strongly deforested. Comparing regional forest damage statistics to three methods estimating harmful effects of sulphur dioxide on Norway spruce yielded significant relationships versus level of forest damage and accumulated salvage felling. Quantifying the effect of data uncertainties permit mapping the probabilities of areas to be significantly over or below thresholds for harmful effects on spruce forests. Satellite based estimation of coniferous forest health is a good complement to field surveys and aerial photography. Key words Remote sensing, Landsat, spectral characteristics, GIS, forest decline, deforestation, Norway spruce, air pollution, sulphur dioxide, neural networks, Czech Republic, Ore Mountains Classification system and/or index terms (if any) Supplementary bibliographical information Language English ISSN and key title ISBN 0346-6787, Meddelande fr&n Lunds Universitets Geografiska Institutioner, Avhandlingar 135 91-79-66-528-4 Recipient’s notes Number of pages 150 Security classification Distribution by.- Lund University Press, Box 141 S-221 00 Lund, Sweden I the undersigned, being the copyright owner of the above-mentioned dissertation, herby grant to aii reference sources permission to publish and disseminate the abstract of the above-mentioned dissertation. Signature Date April 27, 1998 Contents List of papers..........................................................................................................................................4 Abstract....................................................................................................................................................5 1. Introduction.................................................................................................................................. 6 2. Background.................................................................................................................................. 7 2.1. Forests and forest decline...............................................................................................7 2.2. Forest decline in Europe and the Czech Republic....................................................... 8 2.3. Causes of forest decline................................................................................................... 9 2.4. Czech Republic and North Bohemia............................................................................14 2.5. Assessment of forest decline.........................................................................................19 3. Remote sensing of forest decline............................................................................................20 3.1. Factors affecting the spectral reflectance from forests............................................ 20 3.2. Factors related to forest decline....................................................................................21 3.3. Previous satellite remote sensing studies of coniferous forest decline................... 24 4. Present investigation.................................................................................................................29 4.1. Introduction...................................................................................................................... 29 4.2. Objectives........................................................................................................................ 29 4.3. Volume quantification of coniferous forest compartments [I]..................................29 4.4. Influence from forest stand variables on vegetation indices used for coniferous forest damage assessment [II]....................................................................30 4.5. Spectral characterisation and regression based estimates of forest damage in the Czech Republic [III]................................................................................................ 31 4.6. Neural networks, multi temporal TM data and topographic data to classify forest damage [IV].......................................................................................................... 32 4.7. Forest cover changes in the Ore Mountains 1972-1989 [V].....................................33 4.8. Critical levels of S02 - uncertainty and to regional forest decline [VI].................. 34 5. Conclusion and future perspective..........................................................................................35 5.1. Conclusion....................................................................................................................... 35 5.2. Future perspective........................................................................................................... 35 Acknowledgement...............................................................................................................................36 References............................................................................................................................................ 37 Appendices: paper I-VI List of papers This thesis is based on the following papers, which are referred to in the text by their Roman numeral. I. Ardo, J. 1992. Volume quantification of coniferous forest compartments using Spectral Radiance recorded by Landsat Thematic Mapper. International Journal of Remote Sensing 13:1779-1786. II. Ardo, J. 1992. Influence from forest stand parameters on vegetation indices used for coniferous forest damage assessment. Proceedings of the ASPRS/ ACSM/RT Conference in Washington, USA, August 2-14, 1992, pp. 523- 531, (ISBN 0-944426-88-3). III. Lambert, N.J., Ardo, J., Rock, B.N. and Vogelmann, J.E. 1995. Spectral Characterization and regression-based estimates of forest damage in Norway Spruce stands in the Czech Republic using Landsat Thematic Mapper data. International Journal of Remote Sensing 16:1261-1287. IV. Ardo, J., Pilesjo, R and Skidmore, A. 1997. Neural networks, multi temporal TM data and topographic data to classify forest damage in the Czech Republic. Canadian Journal of Remote Sensing 23:217-229. V. Ardo, J., Lambert, N. J., Henzlik, V. and Rock, B. N. 1997. Satellite Based Estimations of Coniferous Forest Cover Changes: Krusne Hory, Czech Republic 1972-1989. Arnbio 26:158-166. VI. Ardo. J., Barkman, A. and Arvidsson, R 1998. Critical levels of S02 - uncertainty and relationship to regional forest decline in the Czech Republic. Submitted. Reproduced with permission from the publishers: Taylor & Francis [I, III], Canadian Remote Sensing Society, [IV] and the Royal Swedish Academy of Sciences [V], 4 Abstract This thesis describes the localisation and quantification of deforestation and forest damage in Norway spruce forests in northern Czech Republic using Landsat data. Severe defoliation increases the spectral reflectance in all wavelength bands, especially in the mid infrared region. These spectral differences allow the separation of three damage categories with an accuracy of 75% using TM data and regression- based
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