Seasonal dynamics of the radial increment of Scots pine and Norway spruce in the southern and middle boreal zones in Finland

2009 ◽  
Vol 39 (3) ◽  
pp. 606-618 ◽  
Author(s):  
Helena M. Henttonen ◽  
Harri Mäkinen ◽  
Pekka Nöjd

In the Finnish National Forest Inventory thousands of trees are cored every year. Based on the increment cores we assessed the progress of the radial increment in Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies (L.) Karst.) during the growing season. Data for 18 and 15 years were available from the southern and middle boreal zones, respectively. No major differences were found in the progress of the radial increment between either tree species or vegetation zones. In both zones, the radial increment began at approximately the same time, in late May or early June. On average, half of the annual radial increment was completed in early July, and ceased in late July or early August. However, there was large variation among years in the progress of the radial increment. On average, the radial-increment period (10%–90% of the total increment) was 41–59 days depending on tree species and region. The increment period was slightly shorter in the middle boreal zone, as increment ended 5–11 days later in the southern boreal zone. The variation of annual radial-increment indices was related to the dates of increment onset and cessation. However, increment-period length was more closely related to the differences in increment indices.

2020 ◽  
Vol 73 (1) ◽  
pp. 77-97
Author(s):  
Mait Lang ◽  
Allan Sims ◽  
Kalev Pärna ◽  
Raul Kangro ◽  
Märt Möls ◽  
...  

Abstract Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.


Author(s):  
Janne Räty ◽  
Rasmus Astrup ◽  
Johannes Breidenbach

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed- effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95–1.47 and 0.96–1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%.


2010 ◽  
Vol 161 (5) ◽  
pp. 171-180 ◽  
Author(s):  
Franz Kroiher ◽  
Katja Oehmichen

Deadwood is an important part of the forest ecosystem. The quantity available depends on the rates of accumulation and of decomposition. A comprehensive pool of data regarding the deadwood stock for Germany is collected by the German national forest inventory. Moreover, the Projection Modelling of Forest Development and Timber Harvesting Potential (WEHAM) adds other important parameters such as growth rates and potential roundwood availability. Using this data, scenarios for the accumulation of deadwood were developed. For the calculation of deadwood decomposition, independent of tree species, a decay constant k = 0.054 was derived for the whole of Germany. The study shows that a long-term stop in timber harvesting in Germany, assuming the proportions of different tree species remained constant, would lead to a saturation of deadwood with a total of 184 m3/ha. If the German forest presented a natural composition of tree species, a deadwood stock of 150 m3/ha at most could be accumulated. Based on these scenarios, rates of accumulation of total dead-wood and of deadwood of large diameter can be calculated taking into account the deadwood stock levels desired and the time span involved. It has been shown that 7.3% of the WEHAM potential roundwood availability must remain in the forest per year if the quantity of deadwood is to be maintained at 11.5 m3/ha. If an increase in the accumulation of deadwood is to be aimed for, the annual input rate together with the desired deadwood stocks are increasingly influenced by the time span involved. Thus shorter time spans with greater stocks of deadwood to be achieved make it possible to approach the WEHAM potential roundwood availability. The results presented in this paper should assist in decision-making concerning stocks of deadwood to be aimed for in the forest and, in the future, serve as a basis for the selection, evaluation and discussion of quantities of dead-wood to be achieved.


2018 ◽  
Vol 15 (2) ◽  
pp. 399-412 ◽  
Author(s):  
Titta Majasalmi ◽  
Stephanie Eisner ◽  
Rasmus Astrup ◽  
Jonas Fridman ◽  
Ryan M. Bright

Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.


2013 ◽  
Vol 59 (No. 10) ◽  
pp. 398-404 ◽  
Author(s):  
V. Podrázský ◽  
R. Čermák ◽  
D. Zahradník ◽  
J. Kouba

This article summarizes basic estimates of productivity and trend analysis of one of the principal introduced forest tree species in the Czech Republic, i.e. Douglas-fir (Pseudotsuga menziesii [Mirbel] Franco). As a comparison, we also examine grand fir (Abies grandis [D. Don] Lindl), northern red oak (Quercus rubra L. syn. Quercus borealis Michx.) and black locust (Robinia pseudoacacia L). This paper presents estimates of forest land area, standing volume, annual and total increments, distribution of age classes, average ages and site indexes for the period 1979–2010. All data were obtained from the national forest inventory of the Czech Republic. Korf’s growth function was used for the assessment of current and mean annual increments (CAI, MAI) of Douglas-fir compared to other tree species. Our results suggest a decline in the annual area afforested by Douglas-fir, as influenced by the State administration management choices, a low rate of an increase in the forest land area, increasing average age of the forests. On the other hand, we observed a dramatic increase in the standing volume as well as high annual increments in volume. Douglas-fir is the most productive major tree species in the Czech Republic and there is a great potential to expand its use throughout the country.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Marius Hauglin ◽  
Johannes Rahlf ◽  
Johannes Schumacher ◽  
Rasmus Astrup ◽  
Johannes Breidenbach

Abstract 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.


2021 ◽  
Author(s):  
Marius Hauglin ◽  
Johannes Rahlf ◽  
Johannes Schumacher ◽  
Rasmus Astrup ◽  
Johannes Breidenbach

Abstract Background The Norwegian forest resource map SR16 combines national forest inventory (NFI) and airborne laser scanning (ALS) 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. Material and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and ⅔ 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) strata 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) a stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is 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. RSMEs 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.


2002 ◽  
Vol 153 (6) ◽  
pp. 219-227 ◽  
Author(s):  
Peter Brang ◽  
Philippe Duc

A be-value for tree regeneration cover was derived from a static model of a Norway spruce mountain selection forest and published data. If those assumptions that seem most realistic are adopted in the model, the be-value is 10%cover. According to data from the second Swiss National Forest Inventory, some 55% of the mountain forests in Switzerland with Norway spruce dominance do not meet this be-value. Due to the uncertainty of several assumptions in the model it is not possible to quantify precisely the lack of regeneration, but the extent of insufficient regeneration has been shown to be considerable.


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