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.
2018
Sammendrag
Rapporten beskriver, og gir resultater fra, «Overvåkingsprogrammet for skog i verneområder» som omfatter nasjonalparker og naturreservater som var etablert pr. 1.1.2016. Registreringene ble gjennomført av Landsskogtakseringen i perioden 2012-2016. Statistikk for arealtyper, skogtilstand og miljøverdier som er viktig for biologisk mangfold sammenstilles. [...] Denne rapporten er en revidert utgave av NIBIO rapport 3(142) 2017, M-921. Kapittel 4.5: Volum, og tilhørende tabeller i vedlegget, er oppdatert på grunn av endring i grunnlagsdata.
Sammendrag
I områdene øst for Glomma sør for Elverum har fylkesmennene takket nei til tilbud om frivillig vern av flere skogområder. Begrunnelsen for dette er at områdene ikke har hatt tilstrekkelig høy kvalitet ut fra den metodikken som legges til grunn for verdisetting av skog. Det er ønskelig å undersøke om data fra Landsskogtakseringen kan brukes til å beskrive hva som er potensialet for å finne arealer i dette området som kan vernes gjennom ordningen frivillig vern av skog. I tillegg vil det med bakgrunn i beskrivelsen av skogtilstandsvariablene bli vurdert om resultatene kan bidra til bedre vurderinger av hvilke arealer som bør prioriteres for vern i denne regionen.......
Sammendrag
Uganda designated 16% of its land as Protected Area (PA). The original goal was natural resources, habitat and biodiversity conservation. However, PAs also offer great potential for carbon conservation in the context of climate change mitigation. Drawing on a wall-to-wall map of forest carbon change for the entire Uganda, that was developed using two Digital Elevation Model (DEM) datasets for the period 2000–2012, we (1) quantified forest carbon gain and loss within 713 PAs and their external buffer zones, (2) tested variations in forest carbon change among management categories, and (3) evaluated the effectiveness of PAs and the prevalence of local leakage in terms of forest carbon. The net annual forest carbon gain in PAs of Uganda was 0.22 ± 1.36 t/ha, but a significant proportion (63%) of the PAs exhibited a net carbon loss. Further, carbon gain and loss varied significantly among management categories. About 37% of the PAs were “effective”, i.e., gained or at least maintained forest carbon during the period. Nevertheless, carbon losses in the external buffer zones of those effective PAs significantly contrast with carbon gains inside of the PA boundaries, providing evidence of leakage and thus, isolation. The combined carbon losses inside the boundaries of a large number of PAs, together with leakage in external buffer zones suggest that PAs, regardless of the management categories, are threatened by deforestation and forest degradation. If Uganda will have to benefit from carbon conservation from its large number of PAs through climate change mitigation mechanisms such as REDD+, there is an urgent need to look into some of the current PA management approaches, and design protection strategies that account for the surrounding landscapes and communities outside of the PAs.
Forfattere
Annika Kangas Rasmus Astrup Johannes Breidenbach Jonas Fridman Terje Gobakken Kari T. Korhonen Matti Maltamo Mats Nilsson Thomas Nord-Larsen Erik Næsset Håkan OlssonSammendrag
The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field.
Forfattere
Svein Solberg Johannes May Wiley Steven Bogren Johannes Breidenbach Torfinn Torp Belachew Gizachew ZelekeSammendrag
Monitoring changes in forest height, biomass and carbon stock is important for understanding the drivers of forest change, clarifying the geography and magnitude of the fluxes of the global carbon budget and for providing input data to REDD+. The objective of this study was to investigate the feasibility of covering these monitoring needs using InSAR DEM changes over time and associated estimates of forest biomass change and corresponding net CO2 emissions. A wall-to-wall map of net forest change for Uganda with its tropical forests was derived from two Digital Elevation Model (DEM) datasets, namely the SRTM acquired in 2000 and TanDEM-X acquired around 2012 based on Interferometric SAR (InSAR) and based on the height of the phase center. Errors in the form of bias, as well as parallel lines and belts having a certain height shift in the SRTM DEM were removed, and the penetration difference between X- and C-band SAR into the forest canopy was corrected. On average, we estimated X-band InSAR height to decrease by 7 cm during the period 2000–2012, corresponding to an estimated annual CO2 emission of 5 Mt for the entirety of Uganda. The uncertainty of this estimate given as a 95% confidence interval was 2.9–7.1 Mt. The presented method has a number of issues that require further research, including the particular SRTM biases and artifact errors; the penetration difference between the X- and C-band; the final height adjustment; and the validity of a linear conversion from InSAR height change to AGB change. However, the results corresponded well to other datasets on forest change and AGB stocks, concerning both their geographical variation and their aggregated values.
Forfattere
Signe Kynding Borgen Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Gunnhild Søgaard Aaron SmithSammendrag
Det er ikke registrert sammendrag
Forfattere
Signe Kynding Borgen Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Gunnhild Søgaard Aaron SmithSammendrag
Det er ikke registrert sammendrag
2017
Sammendrag
The use of digital aerial photogrammetry (DAP) for forest inventory purposes has been widely studied and can produce comparable accuracy compared with airborne laser scanning (ALS) in small, homogeneous areas. However, the accuracy of DAP for large scale applications with heterogeneous terrain and forest vegetation has not yet been reported. In this study we examined the accuracy of timber volume, biomass and basal area prediction models based on DAP and national forest inventory (NFI) data on a large area in central Norway. Two separate point clouds were derived from aerial image acquisitions of 2010 and 2013. Vegetation heights were extracted by subtracting terrain elevation derived from ALS. A large number of NFI sample plots (483) measured between 2010 and 2014 were used as reference data to fit linear models for timber volume, biomass and basal area with height metrics derived from the DAP data as explanatory variables. Variables describing the heterogeneous environmental and image acquisition conditions were calculated and their influence on the model accuracy was tested. The results showed that forest parameter prediction using DAP works well when applied to a large area. The model fits of the timber volume, biomass and basal area models were good with R2 of 0.80, 0.81, 0.81 and RMSEs of 41.43 m3 ha−1 (55% of the mean observed value), 32.49 t ha−1 (47%), 5.19 m2 ha−1 (41%), respectively. Only a small proportion of the variation could be attributed to the heterogeneous conditions. The inclusion of the relative sun inclination led to an improvement of the model RMSEs by 2% of the mean observed values. The relatively low cost and stability across large areas make DAP an attractive source of auxiliary information for large scale forest inventories.
Sammendrag
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Sammendrag
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