Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2021
Forfattere
Stefano Puliti Johannes Breidenbach Johannes Schumacher Marius Hauglin Torgeir Ferdinand Klingenberg Rasmus AstrupSammendrag
This study aimed at estimating total forest above-ground net change (ΔAGB; Gg) over five years (2014–2019) based on model-assisted estimation utilizing freely available satellite imagery. The study was conducted for a boreal forest area (approx. 1.4 Mha) in Norway where bi-temporal national forest inventory (NFI), Sentinel-2, and Landsat data were available. Biomass change was modelled based on a direct approach. The precision of estimates using only the NFI data in a basic expansion estimator was compared to four different alternative model-assisted estimates using 1) Sentinel-2 or Landsat data, and 2) using bi- or uni-temporal remotely sensed data. We found that spaceborne optical data improved the precision of the purely field-based estimates by a factor of up to three. The most precise estimates were found for the model-assisted estimation using bi-temporal Sentinel-2 (standard error; SE = 1.7 Gg). However, the decrease in precision when using Landsat data was small (SE = 1.92 Gg). We also found that ΔAGB could be precisely estimated when remotely sensed data were available only at the end of the monitoring period. We conclude that satellite optical data can considerably improve ΔAGB estimates, when repeated and coincident field data are available. The free availability, global coverage, frequent update, and long-term time horizon make data from programs such as Sentinel-2 and Landsat a valuable data source for consistent and durable monitoring of forest carbon dynamics.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Key message Large-scale forest resource maps based on national forest inventory (NFI) data and airborne laser scanning may facilitate synergies between NFIs and forest management inventories (FMIs). A comparison of models used in such a NFI-based map and a FMI indicate that NFI-based maps can directly be used in FMIs to estimate timber volume of mature spruce forests. Context Traditionally, FMIs and NFIs have been separate activities. The increasing availability of detailed NFI-based forest resource maps provides the possibility to eliminate or reduce the need of field sample plot measurements in FMIs if their accuracy is similar. Aims We aim to (1) compare a timber volume model used in a NFI-based map and models used in a FMI, and (2) evaluate utilizing additional local sample plots in the model of the NFI-based map. Methods Accuracies of timber volume estimates using models from an existing NFI-based map and a FMI were compared at plot and stand level. Results Estimates from the NFI-based map were similar to or more accurate than the FMI. The addition of local plots to the modeling data did not clearly improve the model of the NFI-based map. Conclusion The comparison indicates that NFI-based maps can directly be used in FMIs for timber volume estimation in mature spruce stands, leading to potentially large cost savings.
Sammendrag
Rapporten gir en oversikt over tilstand i norsk skog for referanseåret 2017, basert på data registrert på Landsskogtakseringens permanente prøveflater i perioden 2015-2019. Resultat vises for hele landet og regioner.
Sammendrag
Butt rot (BR) damage of a tree results from a decay caused by a pathogenic fungus. BR damages associated with Norway spruce (Picea abies [L.] Karst.) account for considerable economic losses in timber production across the northern hemisphere. While information on BR damages is critical for optimal decision-making in forest management, maps of BR damages are typically lacking in forest information systems. Timber volume damaged by BR was predicted at the stand-level in Norway using harvester information of 186,026 stems (clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain characteristics). This study utilized Random Forests models with two sets of predictor variables: (1) predictor variables available after harvest (theoretical case) and (2) predictor variables available prior to harvest (mapping case). Our findings showed that forest attributes characterizing the maturity of forest, such as remote sensing-based height, harvested timber volume and quadratic mean diameter at breast height, were among the most important predictor variables. Remotely sensed predictor variables obtained from airborne laser scanning data and Sentinel-2 imagery were more important than the environmental variables. The theoretical case with a leave-stand-out cross-validation resulted in an RMSE of 11.4 m3 · ha−1 (pseudo-R2: 0.66) whereas the mapping case resulted in a pseudo-R2 of 0.60. When spatially distinct clusters of harvested forest stands were used as units in the cross-validation, the RMSE value and pseudo-R2 associated with the mapping case were 15.6 m3 · ha−1 and 0.37, respectively. The findings associated with the different cross-validation schemes indicated that the knowledge about the BR status of spatially close stands is of high importance for obtaining satisfactory error rates in the mapping of BR damages.
Forfattere
Knut Ole VikenSammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes BreidenbachSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes BreidenbachSammendrag
Det er ikke registrert sammendrag