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Using historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests

dc.contributor.authorCatalina Munteanu
dc.contributor.authorCornelius Senf
dc.contributor.authorNita Mihai Daniel
dc.contributor.authorFrancesco Maria Sabatini
dc.contributor.authorJulian Oeser
dc.contributor.authorRupert Seidl
dc.contributor.authorTobias Kuemmerle
dc.date.accessioned2025-09-08T10:29:14Z
dc.date.issued2022-04-02
dc.description.abstractHigh-conservation-value forests (HCVFs) are critically important for biodiversity and ecosystem service provisioning, but they face many threats. Where systematic HCVF inventories are missing, such as in parts of Eastern Europe, these forests remain largely unacknowledged and therefore often unprotected. We devised a novel, transferable approach for detecting HCVFs based on integrating historical spy satellite images, contemporary remote sensing data (Landsat), and information on current potential anthropogenic pressures (e.g., road infrastructure, population density, demand for fire wood, terrain). We applied the method to the Romanian Carpathians, for which we mapped forest continuity (1955–2019), canopy structural complexity, and anthropogenic pressures. We identified 738,000 ha of HCVF. More than half of this area was identified as susceptible to current anthropogenic pressures and lacked formal protection. By providing a framework for broad-scale HCVF monitoring, our approach facilitates integration of HCVF into forest conservation and management. This is urgently needed to achieve the goals of the European Union's Biodiversity Strategy to maintain valuable forest ecosystems.
dc.identifier.citationMunteanu, C., Senf, C., Nita, M. D., Sabatini, F. M., Oeser, J., Seidl, R., & Kuemmerle, T. (2022). Using historical spy satellite photographs and recent remote sensing data to identify high‐conservation‐value forests. Conservation Biology, 36(2), e13820.
dc.identifier.urihttps://repository.unitbv.ro/handle/123456789/622
dc.language.isoen
dc.titleUsing historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests
dc.typeArticle
dspace.entity.typePublication

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