Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy

• Published in: Journal of imaging Vol. 11; no. 5; p. 153
• Main Author: Lemenkova, Polina
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.05.2025; MDPI
• Subjects: 20th century; Algorithms; Automation; Biodiversity; Biodiversity and Ecology; Case studies; Classification; Climate change; Clustering; Computer Science; Data analysis; Data processing; Earth Sciences; ecological conservation; Ecology, environment; ecosystem; Ecosystems; Environmental monitoring; Environmental Sciences; Environmental studies; Geographic information systems; geoinformatics; Health aspects; Humanities and Social Sciences; Image Processing; Land cover; Land use planning; Life Sciences; Machine learning; Mapping; Methods; Remote sensing; Reproducibility; satellite data; Satellite imagery; Sciences of the Universe; Signal and Image Processing; Software; Soil erosion; Spectral reflectance; Support systems; time series; Trends; Workflow; Italy; Apennines; environmental monitoring; GRASS GIS; ecosystem; multispectral image; geoinformatics; Landsat; ecological conservation; time series; satellite data; machine learning; Italy
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging11050153

This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018–2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python’s Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy.