STRUCTURE AND DYNAMICS OF BOREAL ECOSYSTEMS: ANOTHER APPROACH TO LANDSAT IMAGERY CLASSIFICATION
https://doi.org/10.24057/2071-9388-2017-10-3-20-30
Abstract
An alternative approach to information extraction from Landsat TM/ETM+ imagery is proposed. It involves transformation the image space into visible 3D form and comparing location in this space the segments of the ecosystem types with expressed graphically typology of forest and mire cover (biogeocenotic scheme). The model is built in LC1-LC2-MSI axis (the two first principal components of the image matrix in logarithmic form and moisture stress index). Comparing to Tasseled Cap, this transformation is more suitable for study area (north taiga zone of Eastern Fennoscandia). The spectral segments of mature and old-growth forests line up from the ecological optimum (moraine hills) along two main environmental gradients: i) lack of water and nutrition (fluvioglacial sands bedrock) and ii) degree of paludication (lacustrine plains). Thus, the biogeocenotic complexes are identified. The succession trajectories of forest regeneration through spectral space are also associated with the type of Quaternary deposits. For mire ecosystems spectral classes accurately reflect the type of water and mineral nutrition (ombrotrophic or mesotrophic). Spectral space model created using measured by the scanner physical ecosystem characteristics can be the base for developing objective classification of boreal ecosystems, where one of the most significant clustering criterions is the position in the spectral space.
About the Author
P. Litinsky
Forest Research Institute, Karelian Research Centre of RAS
Russian Federation
Russian Federation
Senior scientist,
11 Pushkinskaya St., 185910 Petrozavodsk, Karelia
References
1. Biotic diversity of Karelia: conditions of formation, communities and species (2003). Petrozavodsk: Karelian Research Centre of RAS.
2. Cohen, W. , Spies,T. and Fiorella M. (1995). Estimating the age and structure of forests in a multi-ownership landscape of western Oregon. U.S.A. Int. J. Remote Sensing, Vol. 16 No 4, pp. 721-746.
3. Hirata, Y. and Takahashi, T. (2011) Image segmentation and classification of Landsat Thematic Mapper data using a sampling approach for forest cover assessment. Can. J. For. Res. 41(1): pp. 35-43. doi:10.1139/X10-130
4. Huang, C.; Yang, Wylie L.; Homer, Collin and Zylstra, G. (2002). Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance: USGS Staff -- Published Research. Paper 621. http://digitalcommons.unl.edu/usgsstaffpub/6
5. Kanellopoulos, I., Wilkinson, G.G., (1997). Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18(4), pp. 711-725.
6. Kauth, R . and Thomas. G. (1976) The Tasseled Cap a graphic description of the spectraltemporal development of agricultural crops as seen by Landsat. In: Proceedings on the Symposium on Machine Processing of Remotely Sensed Data, 4b: 41-51, 6 June 2 July 1976 (West Lafayette, Indiana: LARS, Purdue University).
7. Krankina, O. , Pflugmacher, D., Friedl, M., Cohen, W. B., Nelson, P., and Bacini, A. (2008) Meeting the challenge of mapping peatlands with remotely sensed data. Biogeosciences Discuss., 5, 2075-2101, doi:10.5194/bgd-5-2075-2008.
8. Kryshen A., Litinsky P. (2013) Sopostavleniye i vzaimnaya verifikatsiya geoinformatsionnoy i ekologo-dinamicheskoy modeley raznoobraziya lesnykh ekosistem [Comparison and mutual verification of the geoinformation and the ecological dynamics models of forest ecosystems diversity]. Trudy KarNTS RAN. № 2, pp. 86-91. (in Russian)
9. Litinsky P. (1997) Assessment of forest decline around Kostomuksha ore-dressing mill using satellite images In: Ecosystems, fauna and flora of the Finnish-Russian Nature Reserve Friendship. Finnish Environment Institute, Helsinki, pp. 341-346.
10. Litinsky P. (2007) Trekhmernoye modelirovaniye struktury i dinamiki tayezhnykh landshaftov [3D modeling of taiga landscapes structure and dynamics] Petrozavodsk: KarNTS RAN. 130 p. (in Russian)
11. Litinsky P. (2011) Klassifikatsiya skanernykh snimkov metodom modelirovaniya spektral’nogo prostranstva [Multispectral imagery classification method based on spectral space modeling]. Trudy KarNTS RAN RAN, № 5, pp. 45-54. (in Russian)
12. Litinsky P. (2012) Geoinformatsionnaya model’ nazemnykh ekosistem severotayezhnoy podzony vostochnoy Fennoskandii [Geoinformation model of Eastern Fennoscandia northern taiga ecosystems]. Trudy KarNTS RAN. № 1, pp. 3-15. (in Russian)
13. Pekkarinen, A., Reithmaier, L., and Strobl, P. (2009). Pan-European forest/nonforest mapping with Landsat ETM+ and CORINE Land Cover 2000 data. ISPRS J. Photogramm. Remote Sens. 64(2): 171183.
14. Pignatti S., Box E. , Fujiwara K. (2002). A new paradigm for the XXIth century // Ann. Bot. Vol. 2. P. 3057.
15. Söderman T. (1997). Biotopes Mapping of the Eastern Baltic. CORINE Biotopes 17 Project in the Baltic States and Northwestern Russia. Nord, 100 p.
16. Tomppo, E., and Czaplewski, R.L. (2002). Potential for a remotesensing-aided forest resource survey for the whole globe. Unasylva, 210: 1618.
17. Volkov A. , Gromtzev A. , Erukov G. et al. (1995) Ekosistemy landshaftov zapada severnoy taygi (struktura, dinamika) [Ecosystems of north-west taiga landscapes (structure, dynamics)]. Petrozavodsk, KarRC RAS, 194 p. (in Russian)
18. Wood, J. (1996). The Geomorphological characterisation of Digital Elevation Models. Diss., Department of Geography, University of Leicester, U. K. http://www.soi.city.ac.uk/˜jwo/phd
19. Zhou, L. and Yang, X. (2008). Use of neural networks for land cover classification from remotely sensed imagery, The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, vol. XXXVII, part B7, Beijing, pp. 575-578.
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For citations:
Litinsky P. STRUCTURE AND DYNAMICS OF BOREAL ECOSYSTEMS: ANOTHER APPROACH TO LANDSAT IMAGERY CLASSIFICATION. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2017;10(3):20-30. https://doi.org/10.24057/2071-9388-2017-10-3-20-30
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