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dc.contributor.authorErlandsson, Rasmus Ingel
dc.contributor.authorBjerke, Jarle W.
dc.contributor.authorFinne, Eirik Aasmo
dc.contributor.authorMyneni, Ranga B.
dc.contributor.authorPiao, Shilong
dc.contributor.authorWang, Xuhui
dc.contributor.authorVirtanen, Tarmo
dc.contributor.authorRäsänen, Aleksi
dc.contributor.authorKumpula, Timo
dc.contributor.authorKolari, Tiina H.M.
dc.contributor.authorTahvanainen, Teemu
dc.contributor.authorTømmervik, Hans
dc.description.abstractAlthough generally given little attention in vegetation studies, ground-dwelling (terricolous) lichens are major contributors to overall carbon and nitrogen cycling, albedo, biodiversity and biomass in many high-latitude ecosystems. Changes in biomass of mat-forming pale lichens have the potential to affect vegetation, fauna, climate and human activities including reindeer husbandry. Lichens have a complex spectral signature and terricolous lichens have limited growth height, often growing in mixtures with taller vegetation. This has, so far, prevented the development of remote sensing techniques to accurately assess lichen biomass, which would be a powerful tool in ecosystem and ecological research and rangeland management. We present a Landsat based remote sensing model developed using deep neural networks, trained with 8914 field records of lichen volume collected for >20 years. In contrast to earlier proposed machine learning and regression methods for lichens, our model exploited the ability of neural networks to handle mixed spatial resolution input. We trained candidate models using input of 1 × 1 (30 × 30 m) and 3 × 3 Landsat pixels based on 7 reflective bands and 3 indices, combined with a 10 m spatial resolution digital elevation model. We normalised elevation data locally for each plot to remove the region-specific variation, while maintaining informative local variation in topography. The final model predicted lichen volume in an evaluation set (n = 159) reaching an R2 of 0.57. NDVI and elevation were the most important predictors, followed by the green band. Even with moderate tree cover density, the model was efficient, offering a considerable improvement compared to earlier methods based on specific reflectance. The model was in principle trained on data from Scandinavia, but when applied to sites in North America and Russia, the predictions of the model corresponded well with our visual interpretations of lichen abundance. We also accurately quantified a recent historic (35 years) change in lichen abundance in northern Norway. This new method enables further spatial and temporal studies of variation and changes in lichen biomass related to multiple research questions as well as rangeland management and economic and cultural ecosystem services. Combined with information on changes in drivers such as climate, land use and management, and air pollution, our model can be used to provide accurate estimates of ecosystem changes and to improve vegetation-climate models by including pale lichensen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectRemote sensingen_US
dc.subjectTerricolous lichensen_US
dc.subjectDeep neural networksen_US
dc.subjectArtificial intelligence cladoniaen_US
dc.subjectReindeer lichenen_US
dc.subjectLight lichensen_US
dc.subjectLight coloured lichensen_US
dc.subjectPale lichensen_US
dc.titleAn artificial intelligence approach to remotely assess pale lichen biomassen_US
dc.title.alternativeAn artificial intelligence approach to remotely assess pale lichen biomassen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Authorsen_US
dc.subject.nsiVDP::Zoologiske og botaniske fag: 480en_US
dc.subject.nsiVDP::Zoology and botany: 480en_US
dc.source.journalRemote Sensing of Environmenten_US
dc.relation.projectAndre: National Natural Science Foundation of China (41861134036)en_US
dc.relation.projectNorges forskningsråd: 294948en_US
dc.relation.projectEC/H2020/CHARTER (869471)en_US
dc.relation.projectFramsenteret: 369911en_US
dc.relation.projectNorges forskningsråd: 287402en_US
dc.relation.projectFramsenteret: 369910en_US

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