Republika Srbija I Crna Gora – Analiza Bilateralnih Odnosa I Problema Nakon 2006. Godine

Đukanović, Dragan

doi:10.22182/spm.4932015.3

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most forest cover comprises deciduous species (beech, oak, and others), whereas coniferous woods (spruce, fir, and others) comprise a significantly minor portion of the land area. According to geomorphological properties, lowland plains are primarily used for agriculture, where cultivated arable crops such as wheat, maise, and others are planted [22]. Data preparation included several sub-procedures that were executed on Sentinel-2 bands.…”

Section: Satellite Imagery Processingmentioning

confidence: 99%

Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

et al. 2023

View full text Add to dashboard Cite

Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.

show abstract

Section: Satellite Imagery Processingmentioning

confidence: 99%