Dynamics of Forest Fragmentation and Connectivity Using Particle and Fractal Analysis.

Dynamics of Forest Fragmentation and Connectivity Using Particle and Fractal Analysis.

Andronache, Ion;Marin, Marian;Fischer, Rico;Ahammer, Helmut;Radulovic, Marko;Ciobotaru, Ana-Maria;Jelinek, Herbert F;Di Ieva, Antonio;Pintilii, Radu-Daniel;Drăghici, Cristian-Constantin;Herman, Grigore Vasile;Nicula, Alexandru-Sabin;Simion, Adrian-Gabriel;Loghin, Ioan-Vlad;Diaconu, Daniel-Constantin;Peptenatu, Daniel;
Scientific reports 2019 Vol. 9 pp. 12228
215
andronache2019dynamicsscientific

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.

Citation

ID: 26637
Ref Key: andronache2019dynamicsscientific
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
26637
Unique Identifier:
10.1038/s41598-019-48277-z
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet