Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

The age of forest stands is critical information for many aspects of forest management and conservation but area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between 58{\deg} and 65{\deg} northern latitude in a 181,773 km2 study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root-mean-squared-errors (RMSE) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between -1 and 3 years. The models improved with increasing SI and the RMSE were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot-level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%). Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI, that could be considered for practical applications but see considerable potential for improvements, if better SI maps were available

Building a high-resolution site index map using boosted regression trees: The Norwegian case

Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present and evaluate models to predict site index (SI) and whether a site is productive (potential total stem volume production ≥ 1 m3·ha−1·year−1) in a wall-to-wall high-resolution (16 m × 16 m) SI map for Norway. We investigate whether remotely sensed data improve predictions. We also study the advantages and disadvantages of using boosted regression trees (BRT), a machine-learning algorithm, to create high-accuracy SI maps. We use climatic and topographical data, soil parent material, a land resource map, and depth to water, together with Sentinel-2 satellite images and airborne laser scanning metrics, as predictor variables. We use the SI observed at more than 10 000 National Forest Inventory (NFI) sample plots throughout Norway to fit BRT models and validate the models using 5822 independent temporary plots from the NFI. We benchmark our results against SI estimates from forest monitoring inventories. We find that the SI from BRT has root mean squared error (RMSE) ranging from 2.3 m (hardwoods) to 3.6 m (spruce) when tested against independent validation data from the NFI temporary plots. These RMSEs are similar or marginally better than an evaluation of SI estimates from operational forest management plans where SI normally stems from manual photo interpretation.publishedVersio

Anvendelse av aldersfri bonitet for skog i Norge

Aldersfri bonitering er en metode for estimering av bonitet uten bruk av alder på skogen. Metoden er utviklet ved NIBIO i seinere år, og omtalt i tidligere publikasjoner. Vi går her videre i arbeidet med å kvalitetssikre metoden, og vurderer hvilken potensiell anvendelse den kan ha i skogbruket. Samlet sett viser resultatene at aldersfri bonitet har et potensial for å brukes i skogbruk i Norge. Det kan brukes for det første som et alternativ til konvensjonell bonitering i skogbruksplanlegging og på det landsdekkende skogressurskartet SR16, og for det andre som et supplement til konvensjonell bonitet på Landsskogtakseringens felt for å overvåke endringer forårsaket av klimaendringer. I det første tilfellet er fordelen at metoden ikke krever alder som input. En generell fordel er at metoden kan fange opp endringer i bonitet som skyldes endringer i vekstvilkår grunnet for eksempel klimaendringer, og dermed i større grad enn konvensjonell bonitet representere dagens bonitet. Metoden har også den fordelen at den er velegnet for bruk med fjernmåling, og resultatene viser at både enkelttre- og areal-baserte metoder fungerer, og at både laserskanning og stereo flybilder kan brukes.publishedVersio

Ressursoversikt og prognoser for framtidig virkestilgang fra SR16

SR16 er et skogressurskart utviklet og publisert av NIBIO. Det er tenkt som et supplement til allerede eksisterende ressurskart i skogbruket med kvalitet og romlig oppløsning mellom tradisjonelle takster og regionale oversikter fra Landskogtakseringen. SR16 byr på noen interessante muligheter for aktører i skogbruket. Formålet med denne rapporten er å vurdere SR16s kvalitet og innhold opp mot skogbrukets ønskede bruk av SR16. Alle aktørene som har uttalt seg om SR16 fremhever behovet for å «fylle hull» der det mangler informasjon om skogtilstanden. Videre er det sterke ønsker om ressursanalyser med tanke på tilgjengelighet, for eksempel skogressurs sett opp mot veinett, både nå og fremover (prognoser). Private aktører er i tillegg opptatt av om SR16 kan utnyttes i forbindelse med forenklet registrering av miljøelementer (MiS), mens det offentlige ønsker å koble ressurskart med kartlegging og stedfesting av (alle) tiltak som gjennomføres i skogbruket. Det er ønskelig at kvaliteten på SR16 er på høyde med dagens skogbruksplaner.....publishedVersio

Accuracy assessment of the nationwide forest attribute map of Norway constructed by using airborne laser scanning data and field data from the national forest inventory

submittedVersio

Modelling and mapping the abundance of lingonberry (Vaccinium vitis-idaea L.) in Norway

Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests