Adermann, V. 2010. Development of Estonian National Forest Inventory. – Tomppo, E., Gschwantner, T., Lawrence, M., McRoberts, R.E. (eds.). National Forest Inventories: Pathways for Common Reporting. Heidelberg, Springer, 171–184.
Arumäe, T. 2020. Estimating forest variables using airborne lidar measurements in hemi-boreal forests. – Doctoral thesis. Tartu, Estonian University of Life Sciences. 195 pp. http://dspace.emu.ee/xmlui/handle/10492/5764.
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. (In Estonian with English summary).
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. https://doi.org/10.1515/fsmu-2016-0001. (In Estonian with English summary).
Arumäe, T., Lang, M. 2018. Estimation of canopy cover in dense mixed-species forests using airborne lidar data. – European Journal of Remote Sensing, 51(1), 132–141. https://doi.org/10.1080/22797254.2017.1411169.
Arumäe, T., Lang, M., Laarmann, D. 2020. Thinning- and tree-growth-caused changes in canopy cover and stand height and their estimation using low-density bitemporal airborne lidar measurements – a case study in hemi-boreal forests. – European Journal of Remote Sensing, 53(1), 113–123. https://doi.org/10.1080/22797254.2020.1734969.
Ayrey, E., Hayes, D.J. 2018. The use of three-dimensional convolutional neural networks to interpret LiDAR for forest inventory. – Remote Sensing, 10, 649. https://doi.org/10.3390/rs10040649.
Balsi, M., Esposito, S., Fallavollita, P., Nardinocchi, C. 2018. Single-tree detection in high-density LiDAR data from UAV-based survey. – European Journal of Remote Sensing, 51, 679–692. https://doi.org/10.1080/22797254.2018.1474722.
Cosenza, D.N., Korhonen, L., Maltamo, M., Packalen, P., Strunk, J.L., Næsset, E., Gobakken, T., Soares, P., Tomé, M. 2020. Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock. – Forestry, 2020, 1–13. https://doi.org/10.1093/forestry/cpaa034.
Guerra-Hernández, J., Arellano-Pérez, S., González-Ferreiro, E., Pascual, A., Altelarrea, V.S., Ruiz-González, A.D., Álvarez-González, J.G. 2021. Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data. – Forest Ecology and Management, 481, 118690. https://doi.org/10.1016/j.foreco.2020.118690.
Jakubauskas, M., Price, K.P. 1997. Empirical relationships between structural and spectral factors of Yellowstone lodgepole pine forests. – Photogrammetric Engineering and Remote Sensing, 63, 1375–1381.
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 / Metsanduslikud Uurimused, 62, 26–38. https://doi.org/10.1515/fsmu-2015-0003.
Korpela, I., Ørka, H.O., Maltamo, M., Tokola, T., Hyyppä, J. 2010. Tree species classification using airborne LiDAR – effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. – Silva Fennica, 44(2), 319–339.
Kotivuori, E., Korhonen, L., Packalen, P. 2016. Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland. – Silva Fennica, 50, 1567. http://dx.doi.org/10.14214/sf.1567.
Kotivuori, E., Maltamo, M., Korhonen, L., Packalen, P. 2018. Calibration of nationwide airborne laser scanning based stem volume models. – Remote Sensing of Environment, 210, 179–192.
Laarmann, D., Korjus, H., Sims, A., Stanturf, J., Kiviste, A., Köster, K. 2009. Analysis of forest naturalness and tree mortality patterns in Estonia. – Forest Ecology and Management, 258, 187–195.
Lang, M., Arumäe, T. 2018. Assessment of forest thinning intensity using sparse point clouds from repeated airborne lidar measurements. – Forestry Studies / Metsanduslikud Uurimused, 68, 40–50. https://doi.org/10.2478/fsmu-2018-0004.
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. https://doi.org/10.2478/v10132-012-0003-7. (In Estonian with English summary).
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. https://doi.org/10.2478/fsmu-2014-0010.
Lang, M., Arumäe, T., Laarmann, D., Kiviste, A. 2017. Estimation of change in forest height growth. – Forestry Studies / Metsanduslikud Uurimused, 67, 5–16. https://doi.org/10.1515/fsmu-2017-0009. (In Estonian with English summary).
Lang, M., Kaha, M., Laarmann, D., Sims, A. 2018. Construction of tree species composition map of Estonia using multispectral satellite images, soil map and a random forest algorithm. – Forestry Studies / Metsanduslikud Uurimused, 68, 5–24. https://doi.org/10.2478/fsmu-2018-0001.
McRoberts, R.E., Tomppo, E.O. 2007. Remote sensing support for national forest inventories. – Remote Sensing of Environment, 110, 412–419. https://doi.org/10.1016/j.rse.2006.09.034.
Morsdorf, F., Kötz, B., Meier, E., Itten, K.I., Allgöwer, B. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. – Remote Sensing of Environment, 104, 50–61. https://doi.org/10.1016/j.rse.2006.04.019.
Næsset, E. 1997. Determination of mean tree height of forest stands using airborne laser scanner data. – ISPRS Journal of Photogrammetry and Remote Sensing, 52, 49–56.
Noordermeer, L., Bollandsås, O.M., Ørka, H.O., Næsset, E., Gobakken, T. 2019a. Comparing the accuracies of forest attributes predicted from airborne laser scanning and digital aerial photogrammetry in operational forest inventories. – Remote Sensing of Environment, 226, 26–37. https://doi.org/10.1016/j.rse.2019.03.027.
Noordermeer, L., Gobakken, T., Næsset, E., Bollandsås, O.M. 2020. Predicting and mapping site index in operational forest inventories using bitemporal airborne laser scanner data. – Forest Ecology and Management, 457, 117768. https://doi.org/10.1016/j.foreco.2019.117768.
Põldveer, E., Korjus, H., Kiviste, A., Kangur, A., Paluots, T., Laarmann, D. 2020. Assessment of spatial stand structure of hemiboreal conifer dominated forests according to different levels of naturalness. – Ecological Indicators, 110, 105944. https://doi.org/10.1016/j.ecolind.2019.105944.
Xu, Q., Li, B., Maltamo, M., Tokola, T., Hou, Z. 2019. Predicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning. – Forest Ecology and Management, 434, 205–212. https://doi.org/10.1016/j.foreco.2018.12.020.