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Abstract

The very dense floodplain vegetation on the artificially confined floodplains results in decreased flood conveyance, thus increase in flood levels and flood hazard. Therefore, proper floodplain management is needed, which must be supported by vegetation assessment studies. The aims of the paper are to introduce the method and the results of riparian vegetation classification of a floodplain area along the Lower Tisza (Hungary) based on automatized acquisition of airborne LiDAR survey. In the study area 15x15 m large training plots (voxels) were selected, and the statistical parameters of their LiDAR point clouds were determined. Applying an automatized parameter selection and 10-fold cross-validation he most suitable decision tree was selected, and following a series of classification steps the training plots were classified. Based on the decision tree all the pixels of the entire study area were analysed and their vegetation types were determined. The classification was validated by field survey. On the studied floodplain area the accuracy of the classification was 83%.

-vegetation-library-delivers-advanced-models-urban-design-gis-professionals . (accessed January 30, 2020). Petráš, R., Pajtík, J., 1991: Sústava česko-slovenských objemových tabuliek drevín. Lesnícky časopis, 37:49–56. Sačkov, I., Santopuoli, G., Bucha, T., Lasserre, B., Marchetti, M., 2016: Forest Inventory Attribute Prediction Using Lightweight Aerial Scanner Data in a Selected Type of Multilayered Deciduous Forest. Forests, 7:307. Sačkov, I., Sedliak, M., Kulla, L., Bucha, T., 2017a: Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach. Forests, 8

-canopy cave openings in the karst landscape around the Maya polity of Caracol using airborne LiDAR, Journal of Cave and Karst Studies, 73, 187-196. 36. Wilson T., Sleeter B., Sleeter R. and Soulard C., 2014 - Land-use threats and protected areas: a scenario-based, landscape level approach, Land, 3, 362-389. 37. ***, 1956 - Harta topografică a României, foaia: L-34-35-A-d, scara 1:25,000, R. S. România, Direcţia Topografică Militară, MApN Bucureşti, România. (in Romanian) 38. ***, 1992 - Atlasul cadastrului apelor din România, I și II, AQUAPROIECT, IGFCOT, Bucureşti, România

References Andersen, H.-E., Reutebuch, S.E., McGaughey, R.J., d’Oliveira, M.V.N., Keller, M. 2014. Monitoring selective logging in western Amazonia with repeat lidar flights. – Remote Sensing of Environment, 151, 157–165. Anniste, T., Viilup, Ü. 2010. Estimation of forest characteristics with laser scanning. (Metsa takseertunnuste määramisest laserskanneerimise abil). – Articles and Studies, Luua Forestry College, 10, 38–53. (In Estonian). Arumäe, T., Lang, M. 2013. A simple model to estimate forest canopy base height from airborne lidar data. – Forestry Studies

airborne lidar data). - Forestry Studies / Metsanduslikud Uurimused, 52, 5-17. (In Estonian with English summary). Leica. 2007. Leica ALS50-II. Airborne laser scanner product specifications (760344en-V.07-INT). Leica Geosystems AG, Heerbrugg, Switzerland. 12 p. Liang, S. 2004. Quantitative remote sensing of land surfaces. John Wiley & Sons, Inc. Hoboken, New Jersy. 543 pp. McGaughey, R.J. 2010. FUSION/LDV: Software for LIDAR Data Analysis and Visualization. July2010 - FUSION Version 2.90. United States Department of Agriculture Forest Service Pacific Northwest Research

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. 2013. A simple model to estimate forest canopy base height from airborne lidar data. - Forestry Studies / Metsanduslikud Uurimused, 58, 46-56. 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

Metsa katvuse ja liituse hindamine lennukilt laserskanneriga

Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.

the estimation of vertical canopy cover, angular canopy closure and leaf area index. - Remote Sensing of Environment, 115, 1065-1080. Kucharik, C.J., Norman, J.M, Gower, S.T. 1998. Measurements of branch area and adjusting leaf area index indirect measurements. - Agricultural and Forest Meteorology, 91(1-2), 69-88. Lang, M. 2006. The performance of foliage mass and crown radius models in forming the input of a forest reflectance model. - PhD thesis. ISBN-10: 9949-426-16-2. Eesti Maaülikool, 201 pp. Lang, M. 2010. Estimation of crown and canopy cover from airborne

References Apostol, B., Lorent, A., Petrila, M., Gancz, V., Badea, O., 2016: Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce ( Picea abies [L.] Karst.) Test Site in Romania. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 44:313–323. Awaya, Y., Takahashi, T., 2017: Evaluating the Differences in Modeling Biophysical Attributes between Deciduous Broadleaved and Evergreen Conifer Forests Using Low-Density Small-Footprint LiDAR Data. Remote Sensing, 9:572. Dalponte, M., Frizzera, L

scanner. - Remote Sensing of Environment, 90, 415-423. Howard, J.A. 1991. Remote Sensing of Forest Resources. Theory and application, London, Chapman & Hall. 420 pp. Jennings, S.B., Brown, N.D., Sheil, D. 1999. Assessing forest canopies and understory illumination: canopy closure, canopy cover and other measures. - Forestry, 72, 59−74. Kato, A., Moskal, L.M., Schiess, P., Swanson, M.E., Calhoun, D., Stuetzle, W. 2009. Capturing tree crown formation through implicit surface reconstruction using airborne lidar data. - Remote Sensing of Environment, 113, 1148-1162. Kiviste