An empirical analysis of changes in the Błędów Desert using machine learning methods

The aim of the study was to determine changes in the land cover of the Błędów Desert, which is a habitat for rare flora and fauna species protected under the Natura 2000 program. Invasive plants, which pose a threat to protected species, are present in this area. Additionally, human activities can have negative impacts on the desert ecosystem. Therefore, the land manager is obligated to carry out actions aimed at maintaining the appropriate size and character of the desert. The analysis was conducted using satellite imagery from the Sentinel-2 mission, which provides images with high temporal and spatial resolution. The study covered the years 2015–2022 and took into account seasonal variability due to the presence of green vegetation. Change detection methods based on data integration, including photointerpretation and machine learning classification, were used for land cover analysis. Five representative land cover classes were defined, enabling a quantitative presentation of changes in the Błędów Desert and a qualitative assessment of the classification performed. The results of the study indicate variability in land cover depending on the season, with an increasing number of protected plant species, including grasslands. Simultaneously, a slight increase in the desert area was noted, manifesting as an increase in sand in forested areas. The results obtained demonstrate the effective implementation of the Natura 2000 program objectives.

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Publication Details

Journal
Geology Geophysics and Environment
Published
2025-03-24
DOI
https://doi.org/10.7494/geol.2025.51.1.71
Primary Topic
Plant Ecology and Soil Science
Type
article
Field-Weighted Citation Impact
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article

An empirical analysis of changes in the Błędów Desert using machine learning methods

Natalia Borowiec, Urszula Marmol, Anna Czernik
Geology Geophysics and Environment
Plant Ecology and Soil Science
article

An empirical analysis of changes in the Błędów Desert using machine learning methods

Natalia Borowiec, Urszula Marmol, Anna Czernik
article en

Abstract

The aim of the study was to determine changes in the land cover of the Błędów Desert, which is a habitat for rare flora and fauna species protected under the Natura 2000 program. Invasive plants, which pose a threat to protected species, are present in this area. Additionally, human activities can have negative impacts on the desert ecosystem. Therefore, the land manager is obligated to carry out actions aimed at maintaining the appropriate size and character of the desert. The analysis was conducted using satellite imagery from the Sentinel-2 mission, which provides images with high temporal and spatial resolution. The study covered the years 2015–2022 and took into account seasonal variability due to the presence of green vegetation. Change detection methods based on data integration, including photointerpretation and machine learning classification, were used for land cover analysis. Five representative land cover classes were defined, enabling a quantitative presentation of changes in the Błędów Desert and a qualitative assessment of the classification performed. The results of the study indicate variability in land cover depending on the season, with an increasing number of protected plant species, including grasslands. Simultaneously, a slight increase in the desert area was noted, manifesting as an increase in sand in forested areas. The results obtained demonstrate the effective implementation of the Natura 2000 program objectives.

Geology Geophysics and EnvironmentVol. 51(1)
Jagiellonian University (PL), AGH University of Krakow (PL)
Openalex Percentile: Top 89%
Plant Ecology and Soil Science
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An empirical analysis of changes in the Błędów Desert using machine learning methods — Natalia Borowiec, Urszula Marmol, et al. · Geology Geophysics and Environment (2025) | TGRS Research Map | TGRS