Povijesno kartiranje vegetacije korištenjem arhivskih Landsat snimaka

master's thesis

• Title (english): Testing possibilities of historical vegetation mapping using Landsat archive imagery
• Author: Barbara Špadina
• Mentor: Sven Jelaska (mentor)
• Committee member: Sven Jelaska (predsjednik povjerenstva)
• Committee member: Ivana Maguire (član povjerenstva)
• Committee member: Sofia Ana Blažević (član povjerenstva)
• Committee member: Tomislav Ivanković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Biology); Zagreb
• Defense date and country: 2019-05-28, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Biology

Abstract

Vegetacijske karte su topografski prikazi sastava i rasprostranjenosti biljnih zajednica nekog područja te kao takve predstavljaju značajan izvor informacija. Retrospektivnim kartiranjem omogućuje se uvid u prošlost i pružanje osnove na temelju koje je moguće odrediti tijek i intenzitet okolišnih promjena. S ciljem provjere mogućnosti dopune starih terenskih vegetacijskih karata iz druge polovice 20. stoljeća koje pokrivaju područja PP Medvednica, PP Žumberak-Samoborsko gorje i NP Krka, metodom kontrolirane klasifikacije u okviru geografskog informacijskog sustava analizirane su slobodno dostupne Landsat arhivske satelitske snimke. Na svim analiziranim područjima, algoritam maximum likelihood se pokazao pouzdanijim od algoritma minimum distance. Kontrola točnosti procesa klasifikacije pokazala je vrlo slabo podudaranje klasificiranih klasa i kontrolnih klasa karte staništa za područje PP Medvednica čije Kappa vrijednosti iznose 0,121 za maximum likelihood i 0,065 za minimum distance algoritam, dok se najpouzdanijim pokazalo područje PP Žumberak-Samoborsko gorje s Kappa vrijednostima od 0,716 za maximum likelihood i 0,616 za minimum distance algoritam. Za sjevernu satelitsku snimku područja NP Krka Kappa vrijednosti iznose 0,620 za maximum likelihood i 0,456 za minimum distance algoritam, a za južnu satelitsku snimku 0,511 za maximum likelihood i 0,245 za minimum distance algoritam. S povećanjem broja kartiranih klasa, točnost klasifikacije se smanjuje. Rezultati ovog istraživanja ukazuju na ograničenu mogućnost primjene Landsat arhivskih snimaka za potrebe retrospektivnog kartiranja.

Abstract (english)

Vegetation maps represent the composition and distribution of plant communities of an area and thereby are a significant source of information. Retrospective mapping can provide an insight into the past and provide a basic point according to which it is possible to determine the course and intensity of the environmental changes. This research examines the possibilities of supplementing the old field vegetation maps from the second half of the 20th century that cover the areas of Nature parks Medvednica and Žumberak-Samoborsko gorje and National park Krka, using the supervised classification of Landsat archive satellite images in a geographic information system environment. The results suggest that “maximum likelihood” algorithm was more reliable than the “minimum distance” algorithm in all analyzed areas. The accuracy analysis of the classification process showed very poor matching of classified classes and control classes of habitat maps for the area of Nature park Medvednica whose Kappa values are 0.121 for “maximum likelihood” and 0.065 for “minimum distance” algorithm, while the most reliable area was Nature park Žumberak-Samoborsko gorje with Kappa values of 0.716 for “maximum likelihood” and 0.616 for the “minimum distance”. For the northern satellite image of the National park Krka area, the Kappa values are 0.620 for “maximum likelihood” and 0.456 for the “minimum distance” and for the southern satellite image 0.511 for “maximum likelihood” and 0.245 for the “minimum distance” algorithm. With increase of mapped classes, obtained accurracy decreased. The results of this research indicate limited possibilities of using Landsat archive imagery for the purpose of retrospective mapping.

• Keywords: kartiranje vegetacije; retrospektivno kartiranje; daljinsko promatranje; GIS; Landsat TM
• Keywords (english): vegetation mapping; retrospective mapping; remote sensing; GIS; Landsat TM
• Language: croatian
• URN:NBN: urn:nbn:hr:217:192071
• Study programme: Title: Experimental Biology; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra eksperimentalne biologije (magistar/magistra eksperimentalne biologije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2019-12-12 08:25:42