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Estimation of standing wood volume and species composition in managed nemoral multi-layer mixed forests by using nearest neighbour classifier, multispectral satellite images and airborne lidar data / Puistute liigilise koosseisu ja tüvemahu hindamine k-lähima naabri meetodil mitmerindelistes majandatavates segametsades


Cite

Adermann, V. 2010. Development of Estonian National Forest Inventory. - Tomppo, E., Gschwantner, T., Lawrence, M., McRoberts, R.E. (eds.). National Forest Inventories. Heidelberg, Springer, 171-184.Search in Google Scholar

Arumäe, T., Lang, M. 2013. A simple model to estimate forest canopy base height from airborne lidar data. - Forestry Studies / Metsanduslikud Uurimused, 58, 46-56.Search in Google Scholar

Breidenbach, J., Nothdurfth, A., Kändler, G. 2010. Comparison of nearest neighbour approaches for small area estimation of tree species-specifi c forest inventory attributes in central Europe using airborne laser scanner data. - European Journal of Forest Research, 129, 833-846.Search in Google Scholar

Chirici, G., Barbati, A., Corona, P., Marchetti, M., Travaglini, D., Maselli, F., Bertini, R. 2008. Nonparametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems. - Remote Sensing of Environment, 112, 2686-2700.Search in Google Scholar

Fassnacht, F.E., Hartig, F., Latifi , H., Berger, C., Hernández, J., Corvalán, P., Koch, B. 2014. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. - Remote Sensing of Environment, 154, 102-114.Search in Google Scholar

Fazakas, Z., Nilsson, M., Olsson, H. 1999. Regional forest biomass and wood volume estimation using satellite data and ancillary data. - Agricultural and Forest Meteorology, 98/99, 417-425.Search in Google Scholar

Franco-Lopez, H., Ek, A., Bauer, M. 2001. Estimation and mapping of forest sand density, volume, and cover type using the k-nearest neighbors method. - Remote Sensing of Environment, 77, 251-274.Search in Google Scholar

Gilichinsky, M., Heiskanen, J., Barth, A., Wallerman, J., Egberth, M., Nilsson, M. 2012. Histogram matching for the calibration of kNN stem volume estimates. - International Journal of Remote Sensing, 33, 7117-7131.Search in Google Scholar

Holmgren, J., Joyce, S., Nilsson, M., Olsson, H. 2000. Estimating stem volume and basal area in forest compartments by combining satellite image data with fi eld data. - Scandinavian Journal of Forest Research, 15, 103-111.Search in Google Scholar

Howard, J.A. 1991, Remote sensing of forest resources. Chapman & Hall, London, UK. 420pp.Search in Google Scholar

Huiyan, G., Limin, D., Gang, W., Dong, X., Shunzhong, W., Hui, W. 2006. Estimation of forest volumes by integrating Landsat TM imagery and forest inventory data. - Science in China: Series E Technological Sciences, 49, 54-62.Search in Google Scholar

Kajisa, T., Murakami, T., Mizoue, N., Kitahara, F., Yoshida, S. 2008. Estimation of stand volume using k-nearest neighbors method in Kyushu, Japan. - Journal of Forest Research, 13, 249-254.Search in Google Scholar

Kangur, A., Sims, A., Jõgiste, K., Kiviste, A., Korjus, H., von Gadow, K. 2007. Comparative modeling of stand development in Scots pine dominated forests in Estonia. - Forest Ecology and Management, 250, 109-118.Search in Google Scholar

Katila, M., Tomppo, E., 2001. Selecting estimation parameters for the Finnish Multisource National Forest Inventory. - Remote Sensing of Environment, 76, 16-32.10.1016/S0034-4257(00)00188-7Search in Google Scholar

Kiviste, A., Hordo, M. 2002. Network of permanent forest growth plots in Estonia. - Metsanduslikud Uurimused / Metsanduslikud Uurimused, 37, 43-58.Search in Google Scholar

Krigul, T. 1972. Forest mensuration. (Metsatakseerimine). Valgus, Tallinn. 358 pp. (In Estonian).Search in Google Scholar

Lang, M. 2010. Estimation of crown and canopy cover from airborne lidar data. - Forestry Studies / Metsanduslikud Uurimused, 52, 5-17.Search in Google Scholar

Lang, M. Lükk, T., Rähn, A., Sims, A. 2005. Change detection on permanent forest growth sample plots using satellite images. - Forestry Studies / Metsanduslikud Uurimused, 43, 24-37.Search in Google Scholar

Lang, M., Arumäe, T., Anniste, J. 2012. Estimation of main forest inventory variables from spectral and airborne lidar data in Aegviidu test site, Estonia. - Forestry Studies/ Metsanduslikud Uurimused, 56, 27-41.Search in Google Scholar

Latifi , H., Koch, B. 2012. Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands. - International Journal of Remote Sensing, 33, 6668-6694.Search in Google Scholar

Lõhmus, E. 2004. Estonian forest site types. (Eesti metsakasvukohatüübid). Eesti loodusfoto, Tartu. 80 pp. (In Estonian).Search in Google Scholar

McGaughey, R.J. 2014. FUSION/LDV: Software for LIDAR Data Analysis and visualization. March 2014 - FUSION, Version 3.42. United States Department of Agriculture Forest Service Pacifi c Northwest Research Station.Search in Google Scholar

McInerney, D.O., Nieuwenhuis, M. 2009. A comparative analysis of kNN and decision tree methods for the Irish National Forest Inventory. - International Journal of Remote Sensing, 30, 4937-4955.Search in Google Scholar

McRoberts, R.E. 2008. Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories. - Remote Sensing of Environment, 112, 2212-2221.Search in Google Scholar

McRoberts, R.E. 2012. Estimating forest attribute parameters for small areas using nearest neighbour techniques. - Forest Ecology and Management, 272, 3-12.Search in Google Scholar

McRoberts, R.E., Tomppo, E.O. 2007. Remote sensing support for national forest inventories. - Remote Sensing of Environment, 110, 412-419.Search in Google Scholar

McRoberts, R.E., Næsset, E., Gobakken, T. 2015. Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data. - Remote Sensing of Environment, 163, 13-22.Search in Google Scholar

Moeur, M., Stage, A. R. 1995. Most similar Neighbor: An improved sampling inference procedure for natural resource planning. - Forest Science, 41, 337-359.Search in Google Scholar

Nilson, T., Peterson, U. 1994. Age dependence of forest refl ectance - analysis of main driving factors. - Remote Sensing of Environment, 48, 319-331.Search in Google Scholar

Packalén, P., Maltamo, M., 2007. The k-MSN method for the prediction of species specifi c stand attributes using airborne laser scanning and aerial photographs. - Remote Sensing of Environment, 109, 328-341.10.1016/j.rse.2007.01.005Search in Google Scholar

Poso, S., Karlsson, M., Pekkonen, T., Härmä, P. 1990. A system for combining data from remote sensing, maps and fi eld measurement for forest planning purposes. - University of Helsinki, Department of Forest Mensuration and Management. Research notes, 23, 40 pp.Search in Google Scholar

R Core Team. 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. [WWW document]. - URL http://www.Rproject.org [Accessed 10 October 2014].Search in Google Scholar

Raudsaar, M., Pärt, E., Adermann, V. 2014. Review of Estonian forest resources. Yearbook Forest 2013. Compiled by Keskkonnaagentuur. OÜ Paar, Tartu, p. 1-2. (In Estonian).Search in Google Scholar

Tamm, T., Remm, K. 2009. Estimating the parameters of forest inventory using machine learning and the reduction of remote sensing features. - International Journal of Applied Earth Observation and Geoinformation, 11, 290-297.Search in Google Scholar

Tomppo, E., Gagliano, C., De Natale, F., Katila, M., McRoberts, R.E. 2009. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. - Remote Sensing of Environment, 113, 500-517.Search in Google Scholar

Tomppo, E., Schadauer, K., McRoberts, R. E., Gschwantner, T., Gabler, K., Ståhl, G. 2010. History of NFIs. - Tomppo, E., Gschwantner, T., Lawrence, M., McRoberts, R.E. (eds.). National Forest Inventories. Heidelberg, Springer, 1-2.Search in Google Scholar

Zald, H.S.J., Ohmann, J.L., Roberts, H.M., Gregory, M.J., Henderson, E.B., McGaughey, R.J., Braaten, J. 2014. Infl uence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure. Remote Sensing of Environment, 143, 26-38. Search in Google Scholar

eISSN:
1736-8723
Language:
English
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Journal Subjects:
Life Sciences, Plant Science, Ecology, other