Endre Hofstad Hansen

• Hansen, Endre Hofstad; Rahlf, Johannes; Astrup, Rasmus Andreas & Gobakken, Terje (2023). Taper, volume, and bark thickness models for spruce, pine, and birch in Norway. Scandinavian Journal of Forest Research. ISSN 0282-7581. 38(6), s. 413–428. doi: 10.1080/02827581.2023.2243821. Fulltekst i vitenarkiv Vis sammendrag

  Taper models, which describe the shape of tree stems, are central to estimating stem volume. Literature provides both taper- and volume models for the three main species in Norway, Norway spruce, Scots pine, and birch. These models, however, were mainly developed using approaches established over 50 years ago, and without consistency between taper and volume. We tested eleven equations for taper and six equations for bark thickness. The models were fitted and evaluated using a large dataset covering all forested regions in Norway. The selected models were converted into volume functions using numerical integration, providing both with- and without-bark volumes and compared to the volume functions in operational use. Taper models resulted in root mean squared error (RMSE) of 7.2, 7.9, and 9.0 mm for spruce, pine, and birch respectively. Bark thickness models resulted in RMSE of 2.5, 6.1, and 4.1 mm, for spruce, pine, and birch respectively. Validation of volume models with bark resulted in RMSE of 12.7%, 13.0%, and 19.7% for spruce, pine, and birch respectively. Additional variables, tree age, site index, elevation, and live crown proportion, were tested without resulting in any strong increase in predictive power.

• Hansen, Endre Hofstad; Wold, Julius; Dalponte, Michele; Gobakken, Terje; Noordermeer, Lennart & Ørka, Hans Ole (2023). Estimation of the occurrence, severity, and volume of heartwood rot using airborne laser scanning and optical satellite data. European Journal of Remote Sensing. 56(1). doi: 10.1080/22797254.2023.2229501. Fulltekst i vitenarkiv
• Ørka, Hans Ole; Hansen, Endre Hofstad; Dalponte, Michele; Gobakken, Terje & Næsset, Erik (2021). Large-area inventory of species composition using airborne laser scanning and hyperspectral data. Silva Fennica. ISSN 0037-5330. 55(4). doi: 10.14214/sf.10244. Fulltekst i vitenarkiv
• Fischer, Carolin; Høibø, Olav Albert; Vestøl, Geir Isak; Hauglin, Marius; Hansen, Endre Hofstad & Gobakken, Terje (2018). Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory, airborne laser scanning and harvester-derived data. Scandinavian Journal of Forest Research. ISSN 0282-7581. 33(6), s. 603–612. doi: 10.1080/02827581.2018.1427790. Fulltekst i vitenarkiv
• Ørka, Hans Ole; Bollandsås, Ole Martin; Hansen, Endre Hofstad; Næsset, Erik & Gobakken, Terje (2018). Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes. Forestry (London). ISSN 0015-752X. 91(2), s. 225–237. doi: 10.1093/forestry/cpx058. Fulltekst i vitenarkiv
• Hauglin, Marius; Hansen, Endre Hofstad; Sørngård, Erik; Næsset, Erik & Gobakken, Terje (2018). Utilizing accurately positioned harvester data: modelling forest volume with airborne laser scanning. Canadian Journal of Forest Research. ISSN 0045-5067. 48(8), s. 913–922. doi: 10.1139/cjfr-2017-0467. Fulltekst i vitenarkiv
• Hansen, Endre Hofstad; Ene, Liviu Theodor; Mauya, Ernest William; Patocka, Zdenek; Mikita, Tomas & Gobakken, Terje [Vis alle 7 forfattere av denne artikkelen] (2017). Comparing empirical and semi-empirical approaches to forest biomass modelling in different biomes using airborne laser scanner data. Forests. 8(5). doi: 10.3390/f8050170. Fulltekst i vitenarkiv
• Hansen, Endre Hofstad; Ene, Liviu Theodor; Gobakken, Terje; Ørka, Hans Ole; Bollandsås, Ole Martin & Næsset, Erik (2017). Countering negative effects of terrain slope on airborne laser scanner data using procrustean transformation and histogram matching. Forests. 8(10). doi: 10.3390/f8100401. Fulltekst i vitenarkiv
• Hauglin, Marius; Hansen, Endre Hofstad; Næsset, Erik; Busterud, Bjørn Even; Gjevestad, Jon Glenn Omholt & Gobakken, Terje (2017). Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems. Scandinavian Journal of Forest Research. ISSN 0282-7581. 32(8), s. 774–781. doi: 10.1080/02827581.2017.1296967. Fulltekst i vitenarkiv
• Solberg, Svein; Hansen, Endre Hofstad; Gobakken, Terje; Næsset, Erik & Zahabu, Eliakimu (2017). Biomass and InSAR height relationship in a dense tropical forest. Remote Sensing of Environment. ISSN 0034-4257. 192, s. 166–175. doi: 10.1016/j.rse.2017.02.010. Fulltekst i vitenarkiv Vis sammendrag

  In the present study we applied X-band interferometric SAR (InSAR) data from the TanDEM-X mission, and investigated the relationship between InSAR height above ground and above-ground biomass (AGB) in a forest with very high biomass. We carried out this study in the East Usambara Mountains in Tanzania, with AGB ranging up to N1000 t/ha. Field inventory provided AGB data for 153 plots of 900 m2 in size. An airborne laser scanning (ALS) provided a DTM as well as AGB predictions for larger 8100 m2 cells over the entire study area. Three TanDEM-X acquisitions provided single-pass InSAR data, from which we generated a Digital Surface Models (DSM) and InSAR height by subtracting the ALS DTM. The results showed that proportionality may represent the relationship where AGB increased with 18.4 t/ha per m increase in InSAR height. The accuracy was low with RMSE = 203 t/ha (44%), which was partly attributable to small field plots and partly to a limited sensitivity of InSAR height to variations in basal area and stand density. An identical proportionality model, with less residual noise, was achieved by replacing the small field plots with the 8100 m2 cells having AGB predictions from ALS data.

• Næsset, Erik; Ørka, Hans Ole; Solberg, Svein; Bollandsås, Ole Martin; Hansen, Endre Hofstad & Mauya, Ernest William [Vis alle 11 forfattere av denne artikkelen] (2016). Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision. Remote Sensing of Environment. ISSN 0034-4257. 175, s. 282–300. doi: 10.1016/j.rse.2016.01.006. Fulltekst i vitenarkiv
• Hansen, Endre Hofstad; Gobakken, Terje; Solberg, Svein; Kangas, Annika; Ene, Liviu Theodor & Mauya, Ernest William [Vis alle 7 forfattere av denne artikkelen] (2015). Relative efficiency of ALS and InSAR for biomass estimation in a Tanzanian rainforest. Remote Sensing. 7(8), s. 9865–9885. doi: 10.3390/rs70809865. Fulltekst i vitenarkiv Vis sammendrag

  Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900, …, 1900 m2, sampled in a Tanzanian rainforest, were assessed in a model-based inference framework. Remotely sensed data from airborne laser scanning (ALS) and interferometric synthetic aperture radio detection and ranging (InSAR) were used as auxiliary information. The findings indicate that larger field plots are relatively more efficient for inventories supported by remotely sensed ALS and InSAR data. A simulation showed that a pure field-based inventory would have to comprise 3.5–6.0 times as many observations for plot sizes of 700–1900 m2 to achieve the same precision as an inventory supported by ALS data.

• Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Zahabu, Eliakimu & Næsset, Erik (2015). Modeling aboveground biomass in dense tropical submontane rainforest using airborne laser scanner data. Remote Sensing. 7, s. 788–807. doi: 10.3390/rs70100788. Fulltekst i vitenarkiv
• Hansen, Endre Hofstad; Gobakken, Terje & Næsset, Erik (2015). Effects of pulse density on digital terrain models and canopy metrics using airborne laser scanning in a tropical rainforest. Remote Sensing. 7(7), s. 8453–8468. doi: 10.3390/rs70708453. Fulltekst i vitenarkiv
• Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernerst & Næsset, Erik (2015). Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania. Carbon Balance and Management (CBM). 10. doi: 10.1186/s13021-015-0021-x. Fulltekst i vitenarkiv

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• Hansen, Endre Hofstad & Sjølie, Hanne Kathrine (2023). Importance of ownership type on forest management. Fulltekst i vitenarkiv
• Hansen, Endre Hofstad; Dalen, Lars Sandved; Päivinen, Risto & Käär, Liisa (2017). Forest research and practice interactions in Norway. doi: https:/tapio.fi/julkaisut-ja-raportit/from-forest-research-to-forestry-practice-approaches-in-leveraging-forest-research-in-northern-and-central-european-countries/. Fulltekst i vitenarkiv Vis sammendrag

  Forests and other wooded land cover about 14 million ha or 43% of the land area in Norway. Of this, approximately 8.6 million ha or 61% of this is classified as productive forest area, i.e. forest area which can produce more than one m3 wood per ha per year (Tomter and Dalen 2014). Norway is dominated by small forest properties (Figure 1). As of 2015, there were approximately 128 000 forest owners with a minimum of 2.5 ha of productive forest. 85% of the productive forest area is owned by private forest owners. The stateowned forest is managed by Statskog, a state-owned enterprise, and make up 7.2% of the total forest area and 3.4% of the productive forest. The average size of privately owned properties is around 45 ha (Statistics Norway 2016). The implementation of Norwegian forest policy is based on a variety of measures and instruments. These include legislation, tax policy, financial support schemes, research, and guidelines.

• Ørka, Hans Ole; Hansen, Endre Hofstad & Bollandsås, Ole Martin (2016). Lasertakst i Vestlandsterreng. Fulltekst i vitenarkiv
• Næsset, Erik; Hansen, Endre Hofstad; Mauya, Ernest; Gobakken, Terje & Zahabu, Eliakimu (2015). Modeling and mapping of forest biomass and tree species diversity. I Næsset, Erik (Red.), Forest monitoring with airborne laser scanning in Tanzania. Norges miljø- og biovitenskapelige universitet (NMBU). s. 7–14. Fulltekst i vitenarkiv Vis sammendrag

  The research presented in this report was conducted in three separate study areas in Tanzania. First, an area in Amani Nature Reserve located in the Eastern Arc Mountains was selected to analyze to what extent airborne laser scanning (ALS) could be used to estimate biomass in dense sub-montane rainforests. A number of other issues arose during the course of the work, such as influence of pulse density of ALS systems on the quality of the digital terrain model (DTM) derived from the ALS data. The DTM is of fundamental importance because ALS data for the tree canopies are usually calculated as heights relative to the derived DTM surface. Another important issue was the influence of field plot size for precision of biomass estimates based on ALS. Second, a large study area in Liwale (Lindi District) was selected to quantify how well ALS data could characterize biomass in tropical savanna. The Liwale area is mainly miombo woodlands. The study area had a size of around 16,000 km2. It was also used to analyze the potential of use of ALS to estimate change in biomass over short time periods (two years) and to assess the cost-efficiency of use of ALS to enhance biomass and biomass change estimates. Third, a smaller area of 365 km2 in miombo woodlands was used to quantify the contribution of different remote sensing technologies to improve estimates of biomass. The technologies were (i) ALS, (ii) interferometric synthetic aperture radar (InSAR) derived from the TanDEM-X satellites, (iii) RapidEye optical imagery, and global forest map products derived from (iv) Landsat and (v) ALOS PALSAR L-band radar imagery. The overall findings in the project were that biomass observed on ground plots could be modelled with ALS-derived metrics such as canopy height and canopy cover as explanatory variables with model performance similar to what has been reported in other tropical studies. That even holds true for the high-biomass situations in the rainforest with recorded biomass densities up to around 1000 Mg/ha. Small plot sizes will tend to result in poorer models and therefore larger uncertainty of the final biomass estimates. For high-biomass situations plots larger than 2000 m2 seem to be suitable for improved biomass estimates. We did not evaluate the cost-efficiency of larger plots. Even for dense forests, it seems sufficient to use ALS data with a pulse density of 1 pulse per meter square. Lower densities should be avoided to ensure accuracy of the DTM suitable for extraction of canopy properties from the ALS data. For large area surveys for which acquisition of ALS data with complete coverage is cost prohibitive, sampling with ALS carried out by acquisition of ALS data along strips that may be spaced many kilometers apart, can improve the precision of biomass estimates by an order of magnitude compared to pure field sampling, like for example the national forest inventory of Tanzania (NAFORMA). In this situation ALS can also be highly cost-efficient. For change in biomass the cost-efficiency of use of ALS to enhance estimates was low, and pure field inventory is probably more cost-efficient – at least if the same field plots are visited on both occasions for which an estimate of change is sought. The current design of NAFORMA strongly restricts the benefits of remotely sensed data to enhance estimates. Further, transaction costs caused by problems with flight permissions to be granted by public authorities hamper efficient use of ALS technology in an operation setting in Tanzania. Different remote sensing techniques will have very different contributions to improve the precision of the biomass estimates, but the cost-efficiency of using different types of remotely sensed data remains an unexplored issue. Stratification of the entire Tanzanian land area in a manner that is consistent with the current NAFORMA stratification may open up for improved cost-efficiency of remote sensing.

• Næsset, Erik; Hansen, Endre Hofstad; Mauya, Ernest; Gobakken, Terje & Zahabu, Eliakimu (2015). Technical issues related to improved performance of laser scanning for topographic terrain modelling and biomass estimation. I Næsset, Erik (Red.), Forest monitoring with airborne laser scanning in Tanzania. Norges miljø- og biovitenskapelige universitet (NMBU). s. 15–21. Fulltekst i vitenarkiv Vis sammendrag

  The research presented in this report was conducted in three separate study areas in Tanzania. First, an area in Amani Nature Reserve located in the Eastern Arc Mountains was selected to analyze to what extent airborne laser scanning (ALS) could be used to estimate biomass in dense sub-montane rainforests. A number of other issues arose during the course of the work, such as influence of pulse density of ALS systems on the quality of the digital terrain model (DTM) derived from the ALS data. The DTM is of fundamental importance because ALS data for the tree canopies are usually calculated as heights relative to the derived DTM surface. Another important issue was the influence of field plot size for precision of biomass estimates based on ALS. Second, a large study area in Liwale (Lindi District) was selected to quantify how well ALS data could characterize biomass in tropical savanna. The Liwale area is mainly miombo woodlands. The study area had a size of around 16,000 km2. It was also used to analyze the potential of use of ALS to estimate change in biomass over short time periods (two years) and to assess the cost-efficiency of use of ALS to enhance biomass and biomass change estimates. Third, a smaller area of 365 km2 in miombo woodlands was used to quantify the contribution of different remote sensing technologies to improve estimates of biomass. The technologies were (i) ALS, (ii) interferometric synthetic aperture radar (InSAR) derived from the TanDEM-X satellites, (iii) RapidEye optical imagery, and global forest map products derived from (iv) Landsat and (v) ALOS PALSAR L-band radar imagery. The overall findings in the project were that biomass observed on ground plots could be modelled with ALS-derived metrics such as canopy height and canopy cover as explanatory variables with model performance similar to what has been reported in other tropical studies. That even holds true for the high-biomass situations in the rainforest with recorded biomass densities up to around 1000 Mg/ha. Small plot sizes will tend to result in poorer models and therefore larger uncertainty of the final biomass estimates. For high-biomass situations plots larger than 2000 m2 seem to be suitable for improved biomass estimates. We did not evaluate the cost-efficiency of larger plots. Even for dense forests, it seems sufficient to use ALS data with a pulse density of 1 pulse per meter square. Lower densities should be avoided to ensure accuracy of the DTM suitable for extraction of canopy properties from the ALS data. For large area surveys for which acquisition of ALS data with complete coverage is cost prohibitive, sampling with ALS carried out by acquisition of ALS data along strips that may be spaced many kilometers apart, can improve the precision of biomass estimates by an order of magnitude compared to pure field sampling, like for example the national forest inventory of Tanzania (NAFORMA). In this situation ALS can also be highly cost-efficient. For change in biomass the cost-efficiency of use of ALS to enhance estimates was low, and pure field inventory is probably more cost-efficient – at least if the same field plots are visited on both occasions for which an estimate of change is sought. The current design of NAFORMA strongly restricts the benefits of remotely sensed data to enhance estimates. Further, transaction costs caused by problems with flight permissions to be granted by public authorities hamper efficient use of ALS technology in an operation setting in Tanzania. Different remote sensing techniques will have very different contributions to improve the precision of the biomass estimates, but the cost-efficiency of using different types of remotely sensed data remains an unexplored issue. Stratification of the entire Tanzanian land area in a manner that is consistent with the current NAFORMA stratification may open up for improved cost-efficiency of remote sensing.

• Saarela, Svetlana; Næsset, Erik; Gobakken, Terje; Ørka, Hans Ole; Hansen, Endre Hofstad & Mauya, Ernest William [Vis alle 8 forfattere av denne artikkelen] (2014). Model-dependent inference for biomass estimation in a LiDAR sample survey in Liwale, Tanzania. Fulltekst i vitenarkiv
• Næsset, Erik; Gobakken, Terje; Hansen, Endre Hofstad; Mauya, Ernest & Zahabu, Eliakimu (2014). Impact of plot size on precision of lidar-assisted estimation of aboveground biomass in a submontane rain forest in Tanzania. Fulltekst i vitenarkiv

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