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Uporaba lidarsko zajetih podatkov za zaznavanje razlik gostote uspevanja visokega rastlinstva
ID Adlešič, Maša (Author), ID Repe, Blaž (Mentor) More about this mentor... This link opens in a new window

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Abstract
V zaključni seminarski nalogi sta predstavljeni dve različni metodi zaznavanja gostote visoke vegetacije s pomočjo lidarskih podatkov. Postopek zaznavanja posameznih drevesnih krošenj smo izdelali v programskem orodju ArcMap 10.8.1. Izbrali smo si 1 km2 velika območja za 4 različne gozdne združbe v Sloveniji (gozdna združba dinarskega gorskega gozda jelke in bukve, gozdna združba acidofilnega borovega gozda, gozdna združba bukovega gozda z gradnom ter gozdna združba primorskega gozda gradna, puhastega hrasta in kraškega jesena) ter zanje izračunali število drevesnih krošenj. Metodo izdelave digitalnega modela krošenj ter metodo segmentacije smo na koncu med seboj primerjali, izločili neustrezne točke ter območja in tako dobili število zaznanih drevesnih krošenj na območjih, katerim smo določili, da se tam pojavlja gozd. Izkazalo se je, da sta metodi pri zaznavanju gostote delno uspešni, za večjo točnost rezultatov in njihovo preverjanje pa bi priporočili še terensko delo.

Language:Slovenian
Keywords:daljinsko zaznavanje, LiDAR, digitalni model krošenj, GIS
Work type:Bachelor thesis/paper
Organization:FF - Faculty of Arts
Year:2021
PID:20.500.12556/RUL-130282 This link opens in a new window
Publication date in RUL:13.09.2021
Views:700
Downloads:119
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Secondary language

Language:English
Title:The use of lidar data for detection of differences in high vegetation density
Abstract:
In the study, two different methods of detecting the density of high vegetation with the help of lidar data are presented. The procedure for detecting individual tree canopies was developed in the software tool ArcMap 10.8.1. We selected the areas of 1 km2 covered by 4 different forest associations (Orno-Quercetum pubescentis, Querco petraeae-Fagetum, Abieti-Fagetum dinaricum, Vaccinio myrtilli-Pinetum sylvestris) and calculated the number of tree canopies. At the end, we compared the method of making a digital canopy model and the method of watershed segmentation. We eliminated inadequate points and areas and thus obtained the number of detected tree canopies within areas where forest had previously been determined. The methods have proved to be partially successful at detecting tree density, and fieldwork would be recommended for verification and to increase the accuracy of the results.

Keywords:remote sensing, LiDAR, digital canopy model, GIS

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