Spatial high-resolution modelling and uncertainty assessment of forest growing stock volume based on remote sensing and environmental covariates

Accurate forest growing stock (GSV) mapping is crucial for informed land management decisions, climate change mitigation, and sustainable resource utilization. This study aimed to digital mapping and uncertainty assessment of GSV levels in the Bashkiriya Nature Reserve, Russia. A random forest appro...

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Veröffentlicht in:	Forest ecology and management 2024-02, Vol.554, p.121676, Article 121676
Hauptverfasser:	Suleymanov, Azamat, Bogdan, Ekaterina, Gaysin, Ildar, Volkov, Alexander, Tuktarova, Iren, Belan, Larisa, Shagaliev, Ruslan
Format:	Artikel
Sprache:	eng
Schlagworte:	Climate climate change conservation areas decision making forest ecology Forest growing stock forests land management landscapes Machine learning prediction remote sensing Russia Sentinel Terrain uncertainty Uncertainty assessment
Online-Zugang:	Volltext
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