• Medientyp: E-Book; Hochschulschrift
  • Titel: Classification of main irrigated crop types towards sustainable development using Landsat and Sentinel data by GIS and remote sensing techniques in a semi-arid area in Tashkent Province, Uzbekistan
  • Beteiligte: Erdanaev, Elbek [VerfasserIn]; Kappas, Martin [AkademischeR BetreuerIn]; Gerold, Gerhard [AkademischeR BetreuerIn]
  • Erschienen: Göttingen, 2024
  • Umfang: 1 Online-Ressource; Illustrationen, Diagramme
  • Sprache: Englisch
  • Identifikator:
  • Schlagwörter: Land use land cover classification ; Landsat ; Sentinel ; machine learning algorithms ; crop types classification ; support-vector machine ; random forest ; maximum likelihood classification ; Hochschulschrift
  • Entstehung:
  • Hochschulschrift: Dissertation, Georg-August-Universität Göttingen, 2023
  • Anmerkungen:
  • Beschreibung: As the world population increases and cropland expansion occurs, there will be a high need in the food supply soon which will require higher agricultural yields. Crop yield estimation, management, and production assessments at the regional and country-level are very important in Uzbekistan which requires supplemental spatial data that provides timely information on crop type's spatial distribution, condition, and potential yields. Crop-type identification at the local and regional level is very important in agricultural regions in developing countries where it contributes the main share of ...
  • Zugangsstatus: Freier Zugang
  • Rechte-/Nutzungshinweise: Namensnennung (CC BY)