Marius Hauglin
Forsker
Forfattere
Ana Maria De Lera Garrido Terje Gobakken Marius Hauglin Erik Næsset Ole Martin BollandsåsSammendrag
The aim of this study was to analyze the accuracy of predictions of dominant height, mean height, basal area, and volume from the nationwide forest attribute map (SR16). The analysis took advantage of field observations from 33 different forest inventory projects across Norway used for validation. Forest attributes for more than 5000 plots were predicted using non-stratified and stratified models of SR16 and the predictions were compared against corresponding ground reference values. Finally, the effect of different factors that might have influenced the prediction errors were analyzed using partial least squared regression (PLSR) to determine under which conditions the SR16 is less apt. The overall results across all plots were adequate (RMSE of 10%, MD of 2% for dominant and mean height; RMSE of 28%, MD of 4% for basal area; RMSE of 31%, MD of 5% for volume). However, when the accuracy was assessed locally for each inventory project, large differences in accuracy were observed. The MD% values for some inventory projects were substantial (>30% for basal area and volume). The results showed that stratification did not necessarily improve the results and that factors related to the forest structure had the greatest impact on the PLSR analysis.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
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.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Inger Martinussen Mathias Amundsen Aksel Granhus Antje Gonera Marius Hauglin Anne Linn Hykkerud Laura Jaakola Mikko Kurttila Jari Miina Rainer Peltola Gesine Schmidt Josefine Skaret Baoru Yang Kjersti AabySammendrag
Almost 95% of the area in Norway is wilderness and 38% of the land area is covered by woods. These areas are abundant in valuable renewable resources, including wild berries. In our neighbouring countries, Sweden and Finland, wild berries are already a big industry. At the same time, on the market the Norwegian wild berries are almost non-existent and berries are left unexploited. Lingonberry (Vaccinium vitis-idaea) is one of the most abundant and economically important wild berries in the Nordic countries. Nevertheless, lingonberry has a large untapped potential due to its unique health effects and potential for increased value creation. It is estimated that 111,500 t of lingonberry are produced in the Norwegian woods. Norway is a long and diverse country with a range of climatic conditions. Adaptations to different conditions can give differences in both yield and quality of wild berries. Yields vary enormously from year to year and among different locations. A steady supply, predictable volumes and high quality are vital for successful commercialization of wild berries. To increase the utilization of berries, there is a need for increased knowledge regarding availability and quality variation of the berries. In addition, the Norwegian market suffers from high labour costs and cannot compete in product price. Innovative solutions and new knowledge on quality aspects can open possibilities for value creation. Toward achieving this goal, we have created a project called “WildBerries”, the main objective of which is to produce research-based knowledge that will create the basis for increased commercial utilization of Norwegian wild berries.
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
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.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes Schumacher Håvard Toft Larsen Paul McLean Marius Hauglin Rasmus Astrup Johannes BreidenbachSammendrag
The number of people affected by snow avalanches during recreational activities has increased over the recent years. An instrument to reduce these numbers are improved terrain classification systems. One such system is the Avalanche Terrain Exposure Scale (ATES). Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to (i) map forest stem density and canopy-cover based on National Forest Inventory and remote sensing data and, (ii) use these forest attributes as input to the AutoATES model. We predicted stem density and directly calculated canopy-cover in a 20 Mha study area in Norway. The forest attributes were mapped for 16 m × 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy-cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski-touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%.
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.
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Sammendrag
I rapporten sammenlignes resultatene av to ulike MiS-registreringer gjennomført i henholdsvis 2009 og 2020 i Follsjåområdet i Notodden kommune. Resultatene viste betydelige forskjeller mellom takstene både når det gjaldt totalt areal av registrerte livsmiljøer, fordeling av areal på ulike typer livsmiljøer, og evnen til å fange opp viktige livsmiljøer for rødlistearter. Vi diskuterer årsaker til disse forskjellene, og foreslår også noen muligheter for forbedringer av registreringene.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Rapporten dokumentere metodikken bak kartlag over skogbrannpotensiale. I planlegging av eventuelle preventive forvaltningstiltak, beredskapstiltak og i arbeidet med å slukke skogbranner er det viktig å ha god oversikt over skogen og potensialet for at en skogbrann vil spre seg hvis det begynner å brenne. I prosjektet har vi beregnet skogbrannpotensiale i et raster med 16 m oppløsning, basert på et sett med grunnlagsdata som beskriver terreng- og vegetasjonsegenskaper. Grunnlagsdataene er generert med bruk av fjernmåling. For egenskapene treslag, bonitet, hogstklasse, terrenghelling og eksposisjon er det beregnet delindeksverdier. Disse er vektet sammen og gir en indeksverdi for skogbrannpotensiale på mellom 0 og 100. Det er i beregningen av den endelig indeksverdien også tatt med informasjon om markfuktighet og høyde over havet. Det er produsert et kartlag for skogområder på sør- og østlandet, tilgjengelig i DSB sin kartløsning. Kartlaget kan danne grunnlag for videre utvikling, for eksempel i kombinasjon med Meteorologisk institutts skogbrannfareindeks.
Sammendrag
Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.
Sammendrag
På grunnlag av data fra Landsskogtakseringens prøveflater og skogressurskartet SR16 er det gjort beregninger av produktivt skogareal og tilgjengelig virkesvolum innen foreslåtte verneområder på Helgeland, i fem Helgelandskommuner (Grane, Hattfjelldal, Vefsn, Hemnes og Rana) samt i Nordland fylke. Tilgjengelig virkesvolum er definert på bagrunn av dominerende treslag, hogstklasse, driftsveilengde og klassefisering i produktiv eller uproduktiv skog. Tilgjengelig virkesvolum i de foreslåtte verneområdene er beregnet til å utgjøre ca to prosent av det samlede tilgjengelige virkesvolumet i de fem Helgelandskommunene. Av de produktive bartredominerte skogarealet i de fem Helgelandskommunene ligger seks prosent innen eksisterende verneområder, og utover dette er fem prosent I områder definert som viktige naturtyper. Bortfall av virkesressursene i de foreslåtte verneområdene kan føre til lengre transportavstand for virke til treforedlingsbedriften Arbor i Hattfjelldal.
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
Marius HauglinSammendrag
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.
Forfattere
Stefano Puliti Johannes Breidenbach Johannes Schumacher Marius Hauglin Torgeir Ferdinand KlingenbergSammendrag
Det er ikke registrert sammendrag
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, 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 latitudes 58° and 65° N in an 18.2 Mha 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. Results 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 (RMSEs) 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 RMSEs 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%), respectively. Conclusions 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. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.
Forfattere
Stefano Puliti Marius Hauglin Johannes Breidenbach P. Montesano C.S.R. Neigh Johannes Rahlf Svein Solberg T. F. Klingenberg Rasmus AstrupSammendrag
Boreal forests constitute a large portion of the global forest area, yet they are undersampled through field surveys, and only a few remotely sensed data sources provide structural information wall-to-wall throughout the boreal domain. ArcticDEM is a collection of high-resolution (2 m) space-borne stereogrammetric digital surface models (DSM) covering the entire land area north of 60° of latitude. The free-availability of ArcticDEM data offers new possibilities for aboveground biomass mapping (AGB) across boreal forests, and thus it is necessary to evaluate the potential for these data to map AGB over alternative open-data sources (i.e., Sentinel-2). This study was performed over the entire land area of Norway north of 60° of latitude, and the Norwegian national forest inventory (NFI) was used as a source of field data composed of accurately geolocated field plots (n=7710) systematically distributed across the study area. Separate random forest models were fitted using NFI data, and corresponding remotely sensed data consisting of either: i) a canopy height model (ArcticCHM) obtained by subtracting a high-quality digital terrain model (DTM) from the ArcticDEM DSM height values, ii) Sentinel-2 (S2), or iii) a combination of the two (ArcticCHM+S2). Furthermore, we assessed the effect of the forest- and terrain-specific factors on the models’ predictive accuracy. The best model (,i.e., ArcticCHM+S2) explained nearly 60% of the variance of the training set, which translated in the largest accuracy in terms of root mean square error (RMSE=41.4 t ha−1 ). This result highlights the synergy between 3D and multispectral data in AGB modelling. Furthermore, this study showed that despite the importance of ArcticCHM variables, the S2 model performed slightly better than ArcticCHM model. This finding highlights some of the limitations of ArcticDEM, which, despite the unprecedented spatial resolution, is highly heterogeneous due to the blending of multiple acquisitions across different years and seasons. We found that both forest- and terrain-specific characteristics affected the uncertainty of the ArcticCHM+S2 model and concluded that the combined use of ArcticCHM and Sentinel-2 represents a viable solution for AGB mapping across boreal forests. The synergy between the two data sources allowed for a reduction of the saturation effects typical of multispectral data while ensuring the spatial consistency in the output predictions due to the removal of artifacts and data voids present in ArcticCHM data. While the main contribution of this study is to provide the first evidence of the best-case-scenario (i.e., availability of accurate terrain models) that ArcticDEM data can provide for large-scale AGB modelling, it remains critically important for other studies to investigate how ArcticDEM may be used in areas where no DTMs are available as is the case for large portions of the boreal zone.
Forfattere
Stefano Puliti Marius Hauglin Johannes Breidenbach Paul M. Montesano C.S.R. Neigh Johannes Rahlf Svein Solberg Torgeir Ferdinand Klingenberg Rasmus AstrupSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupSammendrag
Det er ikke registrert sammendrag
Forfattere
Johannes Breidenbach Lars T. Waser Misganu Debella-Gilo Johannes Schumacher Johannes Rahlf Marius Hauglin Stefano Puliti Rasmus AstrupSammendrag
Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) plot data for modelling and subsequent mapping of spruce-, pine-, and deciduous-dominated forest in Norway at a 16 m × 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42 000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by ∼10%. The produced map was subsequently used to generate model-assisted (MA) and poststratified (PS) estimates of species-specific forest area. At the national level, efficiencies of the estimates increased by 20% to 50% for MA and up to 90% for PS. Greater minimum numbers of observations constrained the use of PS. For MA estimates of municipalities, efficiencies improved by up to a factor of 8 but were sometimes also less than 1. PS estimates were always equally as or more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.
Forfattere
Marius HauglinSammendrag
Det er ikke registrert sammendrag
Sammendrag
Accurately positioned single-tree data obtained from a cut-to-length harvester were used as training harvester plot data for k-nearest neighbor (k-nn) stem diameter distribution modelling applying airborne laser scanning (ALS) information as predictor variables. Part of the same harvester data were also used for stand-level validation where the validation units were stands including all the harvester plots on a systematic grid located within each individual stand. In the validation all harvester plots within a stand and also the neighboring stands located closer than 200 m were excluded from the training data when predicting for plots of a particular stand. We further compared different training harvester plot sizes, namely 200 m2, 400 m2, 900 m2 and 1600 m2. Due to this setup the number of considered stands and the areas within the stands varied between the different harvester plot sizes. Our data were from final fellings in Akershus County in Norway and consisted of altogether 47 stands dominated by Norway spruce. We also had ALS data from the area. We concentrated on estimating characteristics of Norway spruce but due to the k-nn approach, species-wise estimates and stand totals as a sum over species were considered as well. The results showed that in the most accurate cases stand-level merchantable total volume could be estimated with RMSE values smaller than 9% of the mean. This value can be considered as highly accurate. Also the fit of the stem diameter distribution assessed by a variant of Reynold’s error index showed values smaller than 0.2 which are superior to those found in the previous studies. The differences between harvester plot sizes were generally small, showing most accurate results for the training harvester plot sizes 200 m2 and 400 m2.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
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.....
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.
Sammendrag
The use of forest biomass for bioenergy purposes, directly or through refinement processes, has increased in the last decade. One example of such use is the utilization of logging residues. Branch biomass constitutes typically a considerable part of the logging residues, and should be quantified and included in future forest inventories. Airborne laser scanning (ALS) is widely used when collecting data for forest inventories, and even methods to derive information at the single-tree level has been described. Procedures for estimation of single-tree branch biomass of Norway spruce using features derived from ALS data are proposed in the present study. As field reference data the dry weight branch biomass of 50 trees were obtained through destructive sampling. Variables were further derived from the ALS echoes from each tree, including crown volume calculated from an interpolated crown surface constructed with a radial basis function. Spatial information derived from the pulse vectors were also incorporated when calculating the crown volume. Regression models with branch biomass as response variable were fit to the data, and the prediction accuracy assessed through a cross-validation procedure. Random forest regression models were compared to stepwise and simple linear least squares models. In the present study branch biomass was estimated with a higher accuracy by the best ALS-based models than by existing allometric biomass equations based on field measurements. An improved prediction accuracy was observed when incorporating information from the laser pulse vectors into the calculation of the crown volume variable, and a linear model with the crown volume as a single predictor gave the best overall results with a root mean square error of 35% in the validation.
Forfattere
Jari Vauhkonen Liviu Theodor Ene Sandeep Gupta Johannes Heinzel Johan Holmgren Juho Pitkänen Svein Solberg Yunsheng Wang Holger Weinacker Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Barbara Koch Erik Næsset Timo Tokola Matti MaltamoSammendrag
Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree detection was found to be dependent on tree density and clustering. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different forest types and may be of great value for future refinement of the single-tree detection algorithms.
Forfattere
Jari Vauhkonen Liviu Ene Svein Solberg Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Erik Næsset Timo Tokola Matti MaltamoSammendrag
The aim of this study was to validate and compare single-tree detection algorithms under different forest conditions. Field data and corresponding airborne laser scanning (ALS) data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany, and pulpwood plantations in Brazil. The data represented a variety of forest types from pure Eucalyptus stands with known ages and planting densities to conifer-dominated Scandinavian forests and more complex deciduous canopies in Central Europe. ALS data were acquired using different sensors with pulse densities varying between the data sets. Field data in varying extent were associated with each ALS data set for training purposes. Treetop positions were extracted using altogether six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different forest conditions were analyzed. The results showed that forest structure and density strongly affected the performance of all algorithms. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different conditions and may be of great value for future refinement of the single-tree detection algorithms.
Sammendrag
Vi har utviklet og testet en metodikk for direkte tolking av egnethet for selektive hogster i digitale flybilder. Resultatet fra et tolkingsforsøk med seks personer i 37 testbestand ble sammenlignet med indeksverdier beregnet på bakgrunn av intensive prøveflatetakster. Totalt ble 65 % av testbestandene tolket riktig. Andelen riktige klassifiserte bestand varierte fra 39 % til 79 % for de seks tolkerne. Det ble i deler av forsøket brukt maler i form av digitale stereobilder som støtte under tolkingen. Malene viser eksempler på bestand med ulik grad av egnethet for selektiv hogst. Med bruk av disse malene ble 58 % av testbestandene klassifisert riktig, altså en lavere andel riktig klassifisert enn uten bruk av malene. Forsøket viste videre at fototolkerne hadde lavest andel riktig klassifisert i bestand som var vel egnet for selektive hogster. Nøyaktigheten i tolkningene var også marginalt dårligere i tette bestand sammenlignet med mindre tette. Sammenlignet med klassifisering basert på feltregistreringer gav fototolking noe lavere andel riktig klassifiserte bestand. Det er store individuelle forskjeller mellom tolkerne, og faren for systematiske feil er større ved fototolking enn ved metoder basert på feltregistreringer.