Application of Shannon and Simpson indices for assessing landscape diversity: A case study of the Tashkent region, Uzbekistan
Résumé fourni par la source
The study of landscape diversity is essential for environmental protection, the rational use of natural resources, and the sustainable development of regions. The Shannon and Simpson indices are widely used to determine biological diversity, and the article analyses the use of these indices in geographical studies, in particular, in determining landscape diversity. According to the classification scheme developed by Nikolayev, the landscapes of the Tashkent region are classified into 2 classes, 6 types, 9 subtypes, 15 genera, and 50 species. The diversity of landscapes in the Tashkent region was assessed at the levels of type, subtype, and genus using the Shannon and Simpson indices. The study highlights the results of applying these indices to landscape diversity analysis, emphasising their similarities and differences, the advantages and disadvantages of their use, and their significance. When planning measures to preserve unique landscapes and the biological species inhabiting them, particular importance should be given to the Shannon index, as these landscapes serve as habitats for various plant and animal species and support essential ecological processes. The Simpson diversity index is particularly suitable for areas where a single landscape species dominates and is often preferred when designing reserves and national parks focused on the protection of a single plant or animal species.
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Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Application of Shannon and Simpson indices for assessing landscape diversity: A case study of the Tashkent region, Uzbekistan
- Date Crossref
- 27/04/2026
- Éditeur
- Bogucki Wydawnictwo Naukowe
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.