References
- 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.
- Arumäe, T., Lang, M. 2016. ALS-based wood volume models of forest stands and comparison with forest inventory data. – Forestry Studies / Metsanduslikud Uurimused, 64, 5–16.10.1515/fsmu-2016-0001
- Barrett, B., Raab, C., Cawkwell, F., Green, S. 2016. Upland vegetation mapping using Random Forests with optical and radar satellite data. – Remote Sensing in Ecology and Conservation, 2(4), 212–231.10.1002/rse2.32
- Breiman, L. 2001. Random forests. – Machine Learning, 45(1), 5–32.10.1023/A:1010933404324
- Duveneck, M.J, Thomson, J.R., Wilson, B.T. 2015. An imputed forest composition map for New England screened by species range boundaries. – Forest Ecology and Management, 347, 107–115.10.1016/j.foreco.2015.03.016
- Forest database. 2016. State register for accounting of forest resource (Metsaressursi arvestuse riikli ku registri põhimäärus). Riigi Teataja, RT I, 12.01.2016, 2. (In Estonian).
- GRASS Development Team. 2017. Geographic Resources Analysis Support System (GRASS) Software, Version 7.2.2. Open Source Geospatial Foundation. [WWW document]. – URL http://grass.osgeo.org. [Accessed 1 March 2018].
- Kiviste, A., Hordo, M., Kangur, A., Kardakov, A., Laarmann, D., Lilleleht, A., Metslaid, S., Sims, A., Korjus, H. 2015. Monitoring and modeling of forest ecosystems: the Estonian Network of Forest Research Plots. – Forestry Studies / Metsan duslikud Uurimused, 62, 26–38.10.1515/fsmu-2015-0003
- Kõlli, R., Asi, E., Köster, T. 2004. Organic carbon pools in Estonian forest soils. – Baltic Forestry, 10(1), 19–26.
- Korjus, H., Põllumäe, P., Kiviste, A., Kangur, A., Laarmann, D., Sirgmets, R., Lang, M. 2017. Online streaming public participation in forest management planning. – Forestry Studies / Metsanduslikud Uurimused, 66, 5–13.10.1515/fsmu-2017-0001
- Laarmann, D., Korjus, H., Sims, A., Stanturf, J.A., Kiviste, A., Köster, K. 2009. Analysis of forest naturalness and tree mortality patterns in Estonia. – Forest Ecology and Management, 258S, S187–S195.10.1016/j.foreco.2009.07.014
- Lang, M., Arumäe, T., Lükk, T., Sims, A. 2014. 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. – Forestry Studies / Metsanduslikud Uurimused, 61, 47–68.10.2478/fsmu-2014-0010
- Lang, M., Gulbe, L., Traškovs, A., Stepčenko, A. 2016. Assessment of different estimation algorithms and remote sensing data sources for regional level wood volume mapping in hemiboreal mixed forests. – Baltic Forestry, 22(2), 283–296.
- Lang, M., Kõlli, R., Nikopensius, M., Nilson, T., Neumann, M., Moreno, A. 2017. Assessment of MODIS NPP algorithm-based estimates using soil fertility and forest inventory data in mixed hemiboreal forests. – Forestry Studies / Metsanduslikud Uurimused, 66, 49–64.10.1515/fsmu-2017-0006
- McRoberts, R.E., Tomppo, E.O. 2007. Remote sensing support for national forest inventories. – Remote Sensing of Environment, 110, 412–419.10.1016/j.rse.2006.09.034
- McRoberts, R.E., Winter, S., Chirici, G., LaPoint, E. 2012. Assessing forest naturalness. – Forest Science, 58(3), 294–309.10.5849/forsci.10-075
- Metsakorralduse. 2017. Forest inventory act. (Metsa korraldamise juhend). – RT I, 22.02.2017, 11. (In Estonian).10.17851/1982-0739.22.2.312-314
- Mullakaardi. 2001. The fine-scale map of Estonian soils. (Vabariigi digitaalse suuremõõtkavalise mullastiku kaardi seletuskiri). Maa-amet, Tallinn. [WWW document]. – URL http://geoportaal.maaamet.ee/est/Andmed-ja-kaardid/Mullastikukaart-p33.html [Accessed 16 April 2016]. (In Estonian).
- Niculescu-Mizil, A., Caruana, R. 2005. Predicting good probabilities with supervised learning. – Proceedings of the 22nd International Conference on Machine Learning, August 7–11, 2005, Bonn, Germany, 625–632.10.1145/1102351.1102430
- Nilson, T., Peterson, U. 1994. Age dependence of forest reflectance – analysis of main driving factors. – Remote Sensing of Environment, 48, 319–331.10.1016/0034-4257(94)90006-X
- R Core Team. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [WWW document]. – URL https://www.R-project.org/ [Accessed 1 March 2018].
- Raudsaar, M., Pärt, E., Adermann, V. 2014. Forest resources. – Yearbook Forest 2013. Keskkonnaagentuur, Tartu. p. 37.
- Raudsaar, M., Sims, A., Timmusk, T., Pärt, E., Nikopensius, M. 2017. Forest resources. – Raudsaar, M., Siimon, K.-L., Valgepea, M. (eds.). Yearbook forest 2016. Keskkonnaagentuur, Tartu, 18–81.
- Spurr, S.H. 1948. Aerial photographs in forestry. New York, Ronald Press.
- SUHET. 2015. Sentinel-2 user handbook. ESA standard document. Issue 1, rev 2. 64 pp.
- 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, 290–297.10.1016/j.jag.2009.03.006
- 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, 1–2.10.1007/978-90-481-3233-1
- USGS. 2016. Landsat 8 (L8) data users’ handbook. Version 2.0. Department of interior, U.S Geological survey. 98 pp.
- Valgepea, M., Maamets, L. 2017. Forest ownership. – Raudsaar, M., Siimon, K-L., Valgepea, M. (eds.). Yearbook forest 2016. Keskkonnaagentuur, Tartu, 82–105.
- Wilson, B.T., Lister, A.J., Riemann, R.I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. – Forest Ecology and Management, 271, 182–198.10.1016/j.foreco.2012.02.002
- Yang, X., Rochdi, N., Zhang, J., Banting, J., Rolfson, D., King, C., Staenz, K., Patterson, S., Purdy, B. 2014. Mapping tree species in a boreal forest area using RapidEye and LiDAR data. 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, 2014, 69–71. DOI: 10.1109/IGARSS.2014.6946357.10.1109/IGARSS.2014.6946357
- Zhao, M., Running, S.W., Heinsch, F.A., Nemani, R.R. 2011. MODIS-derived terrestrial primary production. – Ramachandran, B., Justice, C.O., Abrams, M.J. (eds.). Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS. New York, Springer-Verlag, 635–660.10.1007/978-1-4419-6749-7_28