diplomska naloga
Aleksander Šašo (Author), Mojca Kosmatin Fras (Mentor), Dušan Kogoj (Thesis defence commission member), Alma Zavodnik Lamovšek (Thesis defence commission member), Urška Kanjir (Co-mentor)

Abstract

Spremljanje stanja vegetacije na osnovi podatkov daljinskega zaznavanja

Keywords

geodezija;GIG;diplomska dela;daljinsko zaznavanje;biofizikalne spremenljivke;vegetacija;MERIS;

Data

Language: Slovenian
Year of publishing:
Source: Ljubljana
Typology: 2.11 - Undergraduate Thesis
Organization: UL FGG - Faculty of Civil and Geodetic Engineering
Publisher: [A. Šašo]
UDC: 528.7(043.2)
COBISS: 6368097 Link will open in a new window
Views: 2022
Downloads: 543
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Other data

Secondary language: English
Secondary title: Monitoring of vegetation condition from remote sensing data
Secondary abstract: In the graduation thesis different biophysical variables that can be derived from remotely sensed data are presented. The algorithms for their calculations, that have been tested on the medium resolution remotely sensed MERIS data, are given. Based on the linear regression the interdependence of individual biophysical variables has been determined. The visual comparison between our results and digital orthophoto as well as biophysical products, available on the Internet, has been made. We have found out that all biophysical variables are mutually dependent. All the algorithms, needed for calculation of biophysical variables, except the MERIS Terrestrial Chlorophyll Index (MTCI), were suitable for the purpose. At the algorithm MTCI in case when clouds and sea surfaces are present more significant mistakes may occur. Further on, we have realized that the medium resolution remotely sensed data are suitable for monitoring the condition of vegetation in larger areas.
Secondary keywords: graduation thesis;geodesy;GIG;remote sensing;biophysical variables;vegetation;MERIS;
File type: application/pdf
Type (COBISS): Bachelor thesis/paper
Thesis comment: Univ. v Ljubljani, Fak. za gradbeništvo in geodezijo
Pages: XII, 33 str., 11 pril.
Type (ePrints): thesis
Title (ePrints): Monitoring of vegetation condition from remote sensing data
Keywords (ePrints): daljinsko zaznavanje;biofizikalne spremenljivke;vegetacija;MERIS
Keywords (ePrints, secondary language): remote sensing;biophysical variables;vegetation;MERIS
Abstract (ePrints): V diplomski nalogi so predstavljene različne biofizikalne spremenljivke, ki jih lahko pridobimo s pomočjo posnetkov daljinskega zaznavanja. Podani so algoritmi za njihov izračun, kateri so bili preizkušeni na srednje ločljivih daljinsko zaznanih podatkih MERIS. Na osnovi linearne regresije smo ugotavljali medsebojno odvisnost posameznih biofizikalnih spremenljivk. Narejena je bila vizualna primerjava rezultatov z državnim letalskih ortofotom in izdelki, ki so nam na voljo na spletu. Ugotovili smo, da so vse biofizikalne spremenljivke med seboj odvisne. Vsi algoritmi za izračun biofizikalnih spremenljivk, razen MERIS kopenski indeks klorofila (MTCI, angl. MERIS Terrestrial Chlorophyll Index), so ustrezali namenu. Pri algoritmu MTCI v primeru prisotnosti oblakov in morskih površin prihaja do večjih napak. Ugotovili smo, da so srednje ločljivi podatki daljinskega zaznavanja primerni za spremljanje stanja vegetacije večjih območij.
Abstract (ePrints, secondary language): In the graduation thesis different biophysical variables that can be derived from remotely sensed data are presented. The algorithms for their calculations, that have been tested on the medium resolution remotely sensed MERIS data, are given. Based on the linear regression the interdependence of individual biophysical variables has been determined. The visual comparison between our results and digital orthophoto as well as biophysical products, available on the Internet, has been made. We have found out that all biophysical variables are mutually dependent. All the algorithms, needed for calculation of biophysical variables, except the MERIS Terrestrial Chlorophyll Index (MTCI), were suitable for the purpose. At the algorithm MTCI in case when clouds and sea surfaces are present more significant mistakes may occur. Further on, we have realized that the medium resolution remotely sensed data are suitable for monitoring the condition of vegetation in larger areas.
Keywords (ePrints, secondary language): remote sensing;biophysical variables;vegetation;MERIS
ID: 8312875