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Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.

A single a priori chosen linear regression model with two alternative error structures is proposed for model-assisted (MA) and model-dependent (MD) estimation of state and change in aboveground tree biomass (AGB, Mg x [ha.sup.-1]) in three forest strata in the Valer forest in southeastern Norway. Field data of tree height and stem diameter were collected in 145 permanent 200 [m.sup.2] circular plots. Concurrent LiDAR data were collected for the entire forest. The regression model includes two LiDAR- based explanatory variables: the mean of canopy height raised to a power of 1.5 and the standard deviation of canopy heights. A nearest-neighbour thinning of the 2010 LiDAR data to the density of the 1999 data was implemented to counter density effects in the explanatory variables. Estimates of change based on a single regression model were more accurate than estimating change from year-specific models (and no data thinning). A canopy height dependent correlated error structure was preferred over a partitioning of the error to temporary and 'permanent' plot effects. For point estimates of AGB in 1999 and 2010, MA and MD estimates of errors were numerically comparable, but MD errors of change were much smaller than corresponding MA errors. Key words: standard error, model error, aboveground biomass, nearest-neighbour thinning, model assisted, model dependent. Un seul modele de regression lineaire choisi a priori avec deux structures d'erreur alternatives est propose pour l'estimation assistee d'un modele (AM) et dependante d'un modele (DM) de l'etat et des changements de la biomasse aerienne des arbres (BA, Mg x [ha.sup.-1]) de trois strates forestieres de la foret de Valer dans le sud-est de la Norvege. Des donnees de terrain sur la hauteur des arbres et le diametre des tiges ont ete recoltees dans 145 placettes circulaires permanentes de 200 [m.sup.2]. Des donnees lidar ont ete recoltees pendant la meme periode sur toute la foret. Le modele de regression inclut deux variables explicatives associees au lidar: la hauteur moyenne du couvert elevee a la puissance 1,5 et l'ecart-type de la hauteur du couvert. Une eclaircie des plus proches voisins a ete appliquee aux donnees lidar de 2010 pour correspondre a la densite des donnees de 1999 de facon a contrer les effets de densite dans les variables explicatives. Les estimations du changement fondees sur un seul modele de regression etaient plus precises que celles tirees des modeles specifiques a l'annee (et sans eclaircie des donnees). Une structure d'erreur correlee et dependante de la hauteur du couvert a ete preferee a une repartition de l'erreur entre un effet de placette temporaire et de placette permanente. Dans le cas des estimations ponctuelles de la BA en 1999 et 2010, les estimations AM et DM des erreurs etaient numeriquement comparables, mais les erreurs DM de changement etaient beaucoup plus petites que les erreurs AM correspondantes. [Traduit par la Redaction] Mots-cles: erreur-type, erreur du modele, biomasse aerienne, eclaircie des plus proches voisins, assistance d'un modele, dependance d'un modele., Introduction Airborne laser scanner data (LiDAR) are now widely used in the form of auxiliary variable metrics (X) in support of forest inventories (Vauhkonen et al. 2014). Practical applications are [...]

Tree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D2H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D2H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D2H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D2H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D2H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D2H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D2H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D2H without the adverse effects of loss in biomass prediction accuracy. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine whether pre-classification (stratification) of training data according to main tree species and stand development stage could improve the accuracy of species-specific forest attribute estimates compared to estimates without stratification using k-nearest neighbors (k-NN) imputations. The study included training data of 509 training plots and 80 validation plots from a conifer forest area in southeastern Norway. The results showed that stratification carried out by interpretation of aerial images did not improve the accuracy of the species-specific estimates due to stratification errors. The training data can of course be correctly stratified using field observations, but in the application phase the stratification entirely relies on auxiliary information with complete coverage over the entire area of interest which cannot be corrected. We therefore tried to improve the stratification using canopy height information from airborne laser scanning to discriminate between young and mature stands. The results showed that this approach slightly improved the accuracy of the k-NN predictions, especially for the main tree species (2.6% for spruce volume). Furthermore, if metrics from aerial images were used to discriminate between pine and spruce dominance in the mature plots, the accuracy of volume of pine was improved by 73.2% in pine-dominated stands while for spruce an adverse effect of 12.6% was observed. [ABSTRACT FROM PUBLISHER]

Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

The aim of this paper was to examine different plot selection strategies of field training plots in forest inventory using airborne laser scanner (ALS) data. The applied plot selection strategies were random selection, random selection within pre-stratification according to forest type, selection of plots according to geographical location and selection of plots based on properties of the ALS data given as a priori information. The study was conducted by means of simulation utilizing existing and independent training and validation plot data and the performance was evaluated by assessing bias and the root mean square error (RMSE). The accuracy of simultaneously derived biophysical stand properties, i.e. volume, number of stems and Lorey’s mean height, was examined using non-parametric modelling. The use of ALS data as a priori information provided the most accurate results in the case of stand volume and number of stems the RMSE being less than 15 and 30 per cent, respectively. For the mean height, also the other selection strategies were as good but the most accurate alternative varied according to number of training plots used. In most cases, the RMSE values for the mean height were between 8 and 9 per cent. The bias of the different strategies followed the same patterns as the corresponding RMSE values. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand.

Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.

In long-term forest management, aspatial models for economic optimization of timber production have proven to be efficient means in the management planning process. The increased concern for preservation ofbiodiversity increases the complexity of the forest management planning and new tools are needed in order to accomplish planning comprising objectives related to biodiversity and economic timber production.These needs coincide with the development of geographical information systems (GIS). GIS may in several ways contribute to a further development of management planning routines and tools that incorporates biodiversity goals and constraints. In this article, how GIS can he used to search for sensitive areas that should be devoted to careful timber management practices, and how the technology could be used to model phenomena of ecological significance is discussed. Special attention is devoted to the integration of GIS with quantitative models forlong-term forest management planning and several current applications are reviewed. A methodology for forest management planning promoting the preservation of biodiversity is proposed and demonstrated. [ABSTRACT FROM AUTHOR]

The accuracy of determination of stand volume (m3 ha-1) using two practical survey methods, i.e. relascope survey and photo-interpretation, was tested on 333 forest stands of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), andmixed stands of spruce, pine, and deciduous tree species. The material was collected at 14 different test sites in southern Norway and between 1 and 19 individual surveyors participated at each site. Reference values for stand volume of each stand were obtained from intensive field measurements. On average, the relascope surveyors underestimated the reference volume by 2% to 6%. The average standard deviation for the differences between practically determined volume and reference volume was in the range 15-31%. For stand volume determined by photo-interpretation, an average underestimation of reference volume of 4% to 38% was found. The average standard deviation for the differences was in the range 13-33%. In the relascope surveys the practically determined stand volume was predicted from mean height, and basal area, On average, the effect on stand volume of bias in the stand heightmeasurements and in the basal area measurements seemed to be equallyserious. [ABSTRACT FROM AUTHOR]

The effects, in terms of economic value and harvest potential, of adopting restricted timber management practices promoting the preservation of biodiversity are analysed in a 5620 ha boreal forest landscapein southeast Norway. A forest management model based on ecological principles aiming at preservation of biodiversity, e.g. by adopting restricted silvicultural treatments imitating the effects of natural processes, such as forest fires, is applied. The forest area is dividedinto four separate classes reflecting the probabilities of forest fires, and different treatment options are assigned to each of the classes. These options comprise extended rotation periods and treatments such as clear-cutting with retention of seed trees and shelterwood cutting. A management problem specified as maximization of net present value subject to a requirement to preserve (medium term) about 10% ofthe forest area for a period of 70 yrs is solved by means of linear programming. Compared with current timber management practices, the net present value of the restricted management problem is reduced by 14% (15%) for a real rate of discount of 3% (4%) pro anno. The annual harvest flow is reduced by 11--33%. For a management problem comprising even-flow harvesting constraints in addition to biodiversity constraints, the net present value is reduced by 16% (19%). Key words. biodiversity, GIS, linear programming, strategic planning, treatment schedule simulation. [ABSTRACT FROM AUTHOR]

The increased concern for environmental values increases the complexity of long-term forest management planning. Environmental issues have to be dealt with over space and time. By creating links between existing spatial long-term forest management models and geographical information systems (GIS), environmental issues can be treated within the forest management planning process. In this article, a spatial decision support system (SDSS) developed by incorporating a forest management planning model into a GIS is presented. A case study is employedto illustrate the usefulness of the SDSS using real data. In order to preserve the water bodies in a forest area, the allowable treatments in areas falling within certain distances of lakes, streams, and swamps were restricted. Treatment schedules were simulated for all stands, and linear programming was utilized to maximize the net present value (NPV) subject to a non-declining felling path. The NPV was reduced by 6.9% due to the treatment restrictions, and the annual harvest flow was reduced by about 10%. Various thematic maps of future time periods may be produced for subjective evaluation of the results. [ABSTRACT FROM AUTHOR]

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Global commitments to mitigating climate change and halting biodiversity loss require reliable information about Earth's ecosystems. Increasingly, such information is obtained from multiple sources of remotely sensed data combined with data acquired in the field. This new wealth of data poses challenges regarding the combination of different data sources to derive the required information and assess uncertainties. In this article, we show how predictors and their variances can be derived when hierarchically nested models are applied. Previous studies have developed methods for cases involving two modeling steps, such as biomass prediction relying on tree-level allometric models and models linking plot-level field data with remotely sensed data. This study extends the analysis to cases involving three modeling steps to cover new important applications. The additional step might involve an intermediate model, linking field and remotely sensed data available from a small sample, for making predictions that are subsequently used for training a final prediction model based on remotely sensed data:•In cases where the data in the final step are available wall-to-wall, we denote the approach three-phase hierarchical model-based inference (3pHMB),•In cases where the data in the final step are available as a probability sample, we denote the approach three-phase hierarchical hybrid inference (3pHHY)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier B.V.)

Background: Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is "invisible" to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data., Results: ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km 2 ) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha -1  yr -1 . The biomass carbon change was 0.405 (0.13) Mg ha -1  yr -1 , the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha -1  yr -1 , and the change in SO carbon was - 0.01 (0.003) Mg ha -1  yr -1 ., Conclusions: Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach., (© 2023. The Author(s).)