Original scientific paper
https://doi.org/10.31298/sl.147.7-8.4
Assessment of forest resources based on Sentinel-2 images – case study Derventa, BiH (Cadastral municipality of Brezici)
Dragana Skorup
orcid.org/0009-0003-7067-5046
; Arhitektonsko-građevinsko-geodetski fakultet, Banja Luka, BiH
Miroslav Vujasinović
; Arhitektonsko-građevinsko-geodetski fakultet, Banja Luka, BiH
Goran Marinković
; Fakultet tehničkih nauka Novi Sad, Srbija
Ilija Grgić
; Državna geodetska uprava, Zagreb
Boban Miletić
; Poljoprivredni fakultet, Istočno Sarajevo, BiH
Abstract
Summary
The management of forest resources is complicated due to the complete lack of maintenance and disorganization of the land administration and survey that are decades old. Modern, unconventional monitoring systems are used with the aim of improving the existing records systems and creating a clearer insight into the state of forest resources. This study provides an example of the use of one such system, Sentinel-2. Using the R programming language, the multispectral Sentinel-2 images were classified by the Random Forest classification algorithm. Following the completion of the classifications, the accuracy of the classification was evaluated using the error matrix and the Kappa value. An analysis of forest resources for one cadastral municipality was accomplished using classified rasters and data from the Real Estate Cadastre Database. Based on the data analysis, major changes are visible in terms of the abandonment of agricultural land and its conversion into a certain form of forest vegetation. Furthermore, based on these data, the study demonstrates changes that can be monitored in shorter time intervals. Sentinel-2 images can be used to determine forest expansion, based on the aforementioned analyses, resulting in a clearer and better representation of existing forest resources that are unknown due to outdated and unreliable land administration systems.
Keywords
Sentinel-2; Real Estate Cadastre Database; Random Forest; forest
Hrčak ID:
307849
URI
Publication date:
31.8.2023.
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