Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

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Abstract

Remote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

Andersen, H. E., Breindenbach, J., 2007: Statistical Properties of Mean Stand Biomass Estimators in a Lidar- Based Double Sampling Forest Survey Design. Proc. ISPRS III/3, III/4, V/3 and VIII/11., “LaserScanning 2007 and SilviLaser 2007“, p. 8-14.

Baatz, M., Schäpe, M., 2000: Multiresolution segmentation - An optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G. (eds.): Angewandte Geographische Informations- Verarbeitung XII. Wichmann Verlag, Karlsruhe, p. 12-23.

Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., 2004: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58:239-258.

Blaschke, T., 2010: Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65:2-16.

Bucha, T., Vladovič, J., Juriš, M., Barka, I., 2010: Aplikácie diaľkového prieskumu Zeme využiteľné v prácach HÚL. In: Súčasnosť a budúcnosť hospodárskej úpravy lesov na Slovensku, 10 p.

Cleve, C., Kelly, M., Kearns, F. R., Moritz, M., 2008: Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems, 32:317-326.

Debella-Gilo, M., K. Bjørkelo, K., Breidenbach, J., Rahlf, J., 2013: Object-Based analysis of aerial photogrammetric point cloud and spectral data for land cover mapping. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40:63-67.

Drăguţ, L., Tiede, D., Levick, S. R., 2010: ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24:859-871.

Drăguţ, L., Csillik, O., Eisank, C., Tiede, D., 2014: Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88:119-127.

Dou, W., Ren, Y., Wu, Q., Ruan, S., Chen, Y., Bloyet, D., Constans, J-M., 2007: Fuzzy kappa for the agreement measure of fuzzy classifications, Neurocomputing, 70:726-734.

Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T. et al., 2016: Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186:64-87.

Halvoň, Ľ., 2011: Posúdenie presnosti vyhodnotenia leteckých meračských snímok metódami digitálnej fotogrametrie pri lesníckom mapovaní. In: Racionalizácia lesníckeho mapovania - zborník referátov, Zvolen, Technická univerzita vo Zvolene, p. 14-22.

Heinzel, J., Koch, B., 2012: Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. International Journal of Applied Earth Observation and Geoinformation, 18:101-110.

Holmgren, J., Persson, Å., Söderman, U., 2008: Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images. International Journal of Remote Sensing, 29:1537-1552.

Kardoš, M., Medveďová, A., Supek, Š., Škodová, M., 2013: Object-oriented classification of tree species in digital aerial photos of landslide area. Zprávy lesnického výzkumu, 58:195-205.

Kim, M., Madden., M., 2006: Determination of optimal scale parameter for alliance-level forest classification of multispectral IKONOS image. Proc. of First International Conference on Object-based Image Analysis (OBIA 2006), Salzburg, XXXVI-4/C42.

Kressler, F., Steinnocher, K., 2008: Object-orientedanalysis of image and LiDAR data and its potential for dasymetric mapping applications. In: Blaschke, T., Lang, S., Hay, G. J. (eds.): Object Based Image Analysis. Springer, Heidelberg, Berlin, p. 611-624.

Korpela, I., Tuomola, T., Välimäki, E., 2007: Mapping forest plots: An efficient method combining photogrammetry and field triangulation. Silva Fennica, 41:457-469.

Landis, J. R., Koch, G. G., 1977: The measurement of observer agreement for categorical data. Biometrics, 33:159-174.

Leppänen, V. J., Tokola, T., Maltamo, M., Mehtätalo, L., Pusa, T., Mustonen, J., 2008: Automatic delineation of forest stands from LiDAR data. Presented at GEOgraphic Object-Based Image Analysis for the 21st Century, Calgary, 6 p.

Majlingová, A., 2007: Digitálna obrazová analýza dát DPZ s vysokým priestorovým rozlíšením a jej využitie v lesníctve. In: Sympozium GIS Ostrava. Ostrava, VŠB TU, 16 p.

Machala, M., Zejdová, L., 2014: Forest mapping through Object-based image analysis of multispectral and LiDAR Aerial data. European Journal of Remote Sensing, 47:117-131.

Maltamo, M., Packalen, P., 2014: Species-specific management inventory in Finland. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds.): Forestry Applications of Air-borne Laser Scanning: Concepts and Case Studies. Springer, Dordrecht, p. 241-252.

Myint, S. W., Stow, D., 2011: An Object-Oriented Pattern Recognition Approach for Urban Classification. In: Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment, p. 129-140.

Ørka, H. O., Wulder, M. A., Gobakken, T., Næsset, E., 2012: Subalpine zone delineation using LiDAR and Landsat imagery. Remote Sensing of Environment, 119:11-20.

Pascual, C., García-Abril, A., García-Montero, L. G., Martín-Fernández, S., Cohen, W. B., 2008: Objectbased semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands. Forest Ecology and Management, 255:3677-3685.

Petr, M., Smith, M., Suaréz, J. C., 2010: Object-based approach for mapping complex forest structure phases using LiDAR data. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 6 p.

Pippuri, I., Suvanto, A., Maltamo, M., Korhonen, K.T., Pitkänen, J., Packalen, P., 2016: Classification of forest land attributes using multi-source remotely sensed data, International Journal of Applied Earth Observation and Geoinformation, 44:11-22.

Rittl, T., Cooper, M., Heck, R. J., Ballester, M. V. R., 2013: Object-Based method outperforms per-pixel method for land cover classification in a protected area of the brazilian atlantic rainforest region. Pedosphere, 23:290-297.

Sasaki, T., Imanishi, J., Ioki, K., Morimoto, Y., Kitada, K., 2012: Object-based classification of land cover and tree species by integrating airborne LiDAR and high spatial resolution imagery data. Landscape and Ecological Engineering, 8:157-171.

Tiede, D., Lang, S., Hoffmann, Ch., 2006: Supervised and forest type -specific multi-scale segmentation for a one-level-representation of single trees. In: Lang S. et al. (eds.): Bridging remote sensing. 1st international conference on object-based image analysis (OBIA 2006). Salzburg, 5 p.

Tomljenovic, I., Tiede, D., Blaschke, T., 2016: A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm, International Journal of Applied Earth Observation and Geoinformation, 52:137-148.

Wack, R., Schardt, M., Barrucho, L., Lohr, U., Oliveira, T., 2003: Forest inventory for eucalyptus plantations based on airborne laserscanner data. WG III/3 Workshop „3-D reconstruction from airborne laserscanner and InSAR data“. Dresden, 7 p.

Wang, Z., Boesch, R., 2007: Color- and texture-based image segmentation for improved forest delineation. IEEE Transactions on Geoscience and Remote Sensing, 45:3055-3062.

Wang, Z., Boesch, R., Ginzler, C., 2012: Forest delineation of aerial images with Gabor wavelets. International Journal of Remote Sensing, 33:2196-2213.

Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D., 2006: Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering & Remote Sensing, 72:799-811.

Zhang, C., Xie, Z., Selch, D., 2013: Fusing lidar and digital aerial photography for object-based forest mapping in the Florida Everglades, GIScience & Remote Sensing, 50:562-573.

Central European Forestry Journal

The Journal of National Forest Centre – Forest Research Institute Zvolen

Journal Information


CiteScore 2016: 0.56

SCImago Journal Rank (SJR) 2016: 0.230
Source Normalized Impact per Paper (SNIP) 2016: 0.454

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