ArticleScientific reports2019
Dynamics of Forest Fragmentation and Connectivity Using Particle and Fractal Analysis.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
13 citing papers in PubMed, 425 citations in OpenAlex.
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- Spatial Epidemiology of Pediatric Cancer in Romania: A Decade of Persistence, Continuity, and Localized Hotspots (Temporal Trend 2008-2017).Pediatric reports · 2025Article
- Understanding local connectivity and complexity in the skeleton of deforestation.Scientific reports · 2025Article
- Spatio-Temporal Pattern of Tuberculosis Distribution in Romania and Particulate Matter Pollution Associated With Risk of Infection.GeoHealth · 2024Article
- The Fractal Geometry of the Brain: AnOverview.Advances in neurobiology · 2024Article
- Point of Care Testing (POCT) in Psychopathology Using Fractal Analysis and Hilbert Huang Transform of Electroencephalogram (EEG).Advances in neurobiology · 2024Article
- Article
- Fractal algorithms and RGB image processing in scribal and ink identification on an 1819 secret initiation manuscript to the "Philike Hetaereia".Scientific reports · 2023Article
- ComsystanJ: A collection of Fiji/ImageJ2 plugins for nonlinear and complexity analysis in 1D, 2D and 3D.PloS one · 2023Article
- Kolmogorov compression complexity may differentiate different schools of Orthodox iconography.Scientific reports · 2022Article
- Local Fractal Connections to Characterize the Spatial Processes of Deforestation in the Ecuadorian Amazon.Entropy (Basel, Switzerland) · 2021Article
- Physiological fractals: visual and statistical evidence across timescales and experimental states.Journal of the Royal Society, Interface · 2020Article
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ever decreasing area of forests has lead to environmental and economical challenges and has brought with it a renewed interest in developing methodologies that quantify the extent of deforestation and reforestation. In this study we analyzed the deforested areas of the Apuseni Mountains, which has been under economic pressure in recent years and resulted in widespread deforestation as a means of income. Deforested surface dynamics modeling was based on images contained in the Global Forest Database, provided by the Department of Geographical Sciences at Maryland University between 2000 and 2014. The results of the image particle analysis and modelling were based on Total Area (ha), Count of patches and Average Size whereas deforested area distribution was based on the Local Connected Fractal Dimension, Fractal Fragmentation Index and Tug-of-War Lacunarity as indicators of forest fragmentation or heterogeneity. The major findings of the study indicated a reduction of the tree cover area by 3.8%, an increase in fragmentation of 17.7% and an increase in heterogeneity by 29%, while fractal connectivity decreased only by 0.1%. The fractal and particle analysis showed a clustering of forest loss areas with an average increase from 1.1 to 3.0 ha per loss site per year. In conclusion, the fractal and particle analysis provide a relevant methodological framework to further our understanding of the spatial effects of economic pressure on forestry.
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Registered trials
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