Construction of tree species composition map of Estonia using multispectral satellite images, soil map and a random forest algorithm

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. 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-2016-0001Search in Google Scholar

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/rse2.32Search in Google Scholar

Breiman, L. 2001. Random forests. – Machine Learning, 45(1), 5–32.10.1023/A:1010933404324Search in Google Scholar

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.2015.03.016Search in Google Scholar

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).Search in Google Scholar

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].Search in Google Scholar

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-2015-0003Search in Google Scholar

Kõlli, R., Asi, E., Köster, T. 2004. Organic carbon pools in Estonian forest soils. – Baltic Forestry, 10(1), 19–26.Search in Google Scholar

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-2017-0001Search in Google Scholar

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.2009.07.014Search in Google Scholar

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-2014-0010Search in Google Scholar

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.Search in Google Scholar

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-2017-0006Search in Google Scholar

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.2006.09.034Search in Google Scholar

McRoberts, R.E., Winter, S., Chirici, G., LaPoint, E. 2012. Assessing forest naturalness. – Forest Science, 58(3), 294–309.10.5849/forsci.10-075Search in Google Scholar

Metsakorralduse. 2017. Forest inventory act. (Metsa korraldamise juhend). – RT I, 22.02.2017, 11. (In Estonian).10.17851/1982-0739.22.2.312-314Search in Google Scholar

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).Search in Google Scholar

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, 625–632.10.1145/1102351.1102430Search in Google Scholar

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-XSearch in Google Scholar

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].Search in Google Scholar

Raudsaar, M., Pärt, E., Adermann, V. 2014. Forest resources. – Yearbook Forest 2013. Keskkonnaagentuur, Tartu. p. 37.Search in Google Scholar

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.Search in Google Scholar

Spurr, S.H. 1948. Aerial photographs in forestry. New York, Ronald Press.Search in Google Scholar

SUHET. 2015. Sentinel-2 user handbook. ESA standard document. Issue 1, rev 2. 64 pp.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, 290–297.10.1016/j.jag.2009.03.006Search 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, 1–2.10.1007/978-90-481-3233-1Search in Google Scholar

USGS. 2016. Landsat 8 (L8) data users’ handbook. Version 2.0. Department of interior, U.S Geological survey. 98 pp.Search in Google Scholar

Valgepea, M., Maamets, L. 2017. Forest ownership. – Raudsaar, M., Siimon, K-L., Valgepea, M. (eds.). Yearbook forest 2016. Keskkonnaagentuur, Tartu, 82–105.Search in Google Scholar

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.2012.02.002Search in Google Scholar

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.2014.6946357Open DOISearch in Google Scholar

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_28Search in Google Scholar