Change in avian abundance predicted from regional forest inventory data

An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning. We sought to predict avian response to projections of future forest conditions that were developed from nationwide forest surveys within the Forest Inventory and Analysis (FIA) program. To accomplish this, we evaluated the historical relationship between silvicolous bird populations and FIA-derived forest conditions within 25 ecoregions that comprise the southeastern United States. We aggregated forest area by forest ownership, forest type, and tree size-class categories in county-based ecoregions for 5 time periods spanning 1963–2008. We assessed the relationship of forest data with contemporaneous indices of abundance for 24 silvicolous bird species that were obtained from Breeding Bird Surveys. Relationships between bird abundance and forest inventory data for 18 species were deemed sufficient as predictive models. We used these empirically derived relationships between regional forest conditions and bird populations to predict relative changes in abundance of these species within ecoregions that are anticipated to coincide with projected changes in forest variables through 2040. Predicted abundances of these 18 species are expected to remain relatively stable in over a quarter (27%) of the ecoregions. However, change in forest area and redistribution of forest types will likely result in changed abundance of some species within many ecosystems. For example, abundances of 11 species, including pine warbler (Dendroica pinus), brown-headed nuthatch (Sitta pusilla), and chuck-wills-widow (Caprimulgus carolinensis), are projected to increase within more ecoregions than ecoregions where they will decrease. For 6 other species, such as blue-winged warbler (Vermivora pinus), Carolina wren (Thryothorus ludovicianus), and indigo bunting (Passerina cyanea), we projected abundances will decrease within more ecoregions than ecoregions where they will increase.     

Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory,airborne laser scanning and harvester-derived data

Norway spruce structural timber is one of the most important products of the Norwegian sawmilling industry, and a high grade-yield of structural timber is therefore important for the economic yield. Presorting of logs suited for production of structural timber might be one option to increase the grade yield. In this study, dynamic modulus of elasticity (E_dyn) of structural timber was predicted based on forest inventory data at site level and single-tree data from airborne laser scanning (ALS) and harvester. The models were based on 611 boards from 4 sites in southeastern Norway. Important variables at site level were elevation, site index (SI), and mean stand age. However, when combining data from all information sources, mean stand age and site index were the only significant variables at site level. Tree height and variables describing the crown, like crown length and crown volume, were important vaiables extracted from ALS data. Stem diameter measures and tapering were important variables measured by the harvester. The combined model with variables from all three information sources reduced the variance the most, especially when using individual tree age instead of average stand age. However, combining all these data requires accurate positioning of the trees by the harvester.     

森林资源续档和各项林业调查规划数据兼容的研究

在详细分折森资源续档数据和各项林业调查规划数据存在不兼容的问题原因基础上,进行深入地研究并提出解决对策,为林业生产提供决策服务。     

Possibilities for harmonizing national forest inventory data for use in forest biodiversity assessments

Representatives of the national forest inventories (NFIs) of27 European countries and the USA evaluated possibilities fora common approach to forest biodiversity reporting. The projectwas conducted under the auspices of COST (European Cooperationin the field of Scientific and Technical Research) Action E43,‘Harmonisation of National Forest Inventories in Europe:Techniques for Common Reporting’. Based on the analysisof responses to an initial questionnaire regarding the mostecologically important and technically feasible variables formonitoring forest biodiversity using NFI data, 16 key variableswere selected. Responses to a second questionnaire consistingof six questions regarding assessment of the 16 key variablesrevealed that the NFIs of most responding countries alreadyassess a large proportion of these variables. Each of the 16variables is assessed in at least eight NFIs. However, the responsesregarding assessment methods and necessary field crew expertisevaried considerably. As a first example, although tree diametersat breast height (d.b.h.) are assessed by every country, thereis a wide range of minimum d.b.h. thresholds from 0 mm in Finlandto 12 cm in Cyprus and Switzerland. As a second example, eightcountries estimated that almost all inventory variables requirehighly skilled forest staff with specialized training, but mostcountries agree that a middle level of expertise is sufficient.Despite differences in current NFI methods, overall, the resultsof the study indicate positive possibilities for achieving amoderate level of European-wide harmonization.     

Combining harvest sample data with inventory data to estimate forest biomass

Forest biomass databases which go beyond stem volume are needed for carbon balance calculations and also for forest monitoring, forest damage inventories and the solution of other related problems. In this article, it is shown how recursive multiple regression analysis can combine forest inventory data with biomass harvest data. The stand‐level conversion factor derived for Pinus sylvestris L. branch biomass for the “Severka”; Forest Farm shows a reasonable goodness of fit, with a coefficient of determination of R² = 0.87. Statistically significant coefficients were achieved by including stand density and the quadratic mean diameter in the regression. Equations developed for Switzerland, using Burger's Picea abies (L.) Karst. branch and needle conversion factors for individual trees, show R² = 0.87 and R ² = 0.91, achieved by taking h _dom.50, elevation, diameter at breast height and age into account.     

利用森林资源连续清查数据编制地位级表

首先利用新疆阿勒泰山国有林管局森林资源连续清查中的279块华山松样地,通过对比选择了方程作为地位级导向曲线的最优模型;在此基础上采用等分法编制了地位级表,并对其精度进行了检验;结果表明其精度可靠,达到了林业生产和调查的要求。     

Organic carbon stocks and stock changes of forest biomass in Belgium derived from forest inventory data in a spatially explicit approach

? Carbon sequestration in forest ecosystems is an important though still uncertain process in the global greenhouse gas balance.

? We computed biomass organic carbon (BOC) stocks of spatially explicit forested landscape units (LSU) in Belgium based on data collected in the regional forest inventories of 1984 (Wallonia region only) and 2000 (Wallonia and Flanders). C stock changes between 1984 and 2000 were estimated for Wallonia.

? The total BOC pool stored in Belgian forests in 2000 amounts to 57.8 Mt C in 6222 km², or 10.0 kg C m^?2 in broadleaf, 9.5 kg C m^?2 in coniferous and 8.7 kg C m^?2 in mixed forest. Based on previous soil organic carbon (SOC) analysis for the same LSU, BOC and SOC stock per LSU appeared only weakly correlated. The total BOC sequestration between 1984 and 2000 equals 5.7 Mt C over an area of 5 107 km², resulting in a flux of 0.07 kg C m^?2 y^?1. The BOC content of broadleaf forest in Wallonia increased with 6%, of coniferous forest with 32% and of mixed forest with 11%.

? The observed regional differences in BOC stocks and in BOC sequestration rates are explained by the forest age-class distribution and site productivity. The strength of the spatially explicit approach lies in the fact that BOC and SOC data originating from diverse sampling strategies can be combined for spatial or temporal comparison of C stocks.

     

规划设计调查数据在林分生长模型中的应用初探

采用森林资源规划设计调查小班数据编制了硬阔叶林分的地位级指数表,并建立了硬阔叶林分的生长模型。检验表明,所建模型达到了一定的精度,基本能满足森林经营的要求。     

Comparing model-based approaches with bucking simulation-based approach in the prediction of timber assortment recovery

The aim of the study was to compare model-based approaches inthe prediction of timber assortment recovery with bucking simulationbased on detailed stem data. A correction function for the totallength of saw log fragments and two optional saw log reductionmodels, that is, the MELA96 version and MELA05 version, wereapplied. In the bucking simulation, the volumes by timber assortmentswere calculated using a bucking-to-value simulator. The predictionof saw log recoveries varied between the bucking simulationand different versions of saw log reduction models. The levelof the reduction from the MELA96 version was at the same levelas from the bucking simulation where defects were taken intoaccount, but the saw log reduction had a very low variance dueto a small amount of independent variables. The saw log reductionof the MELA05 version included more variation although the levelof the reductions was higher and the variation did not meetwith the bucking simulation. As a conclusion, the model-basedapproaches seem applicable at least for the prediction of sawlog recovery in the large area forest inventories where thevariance of the standwise timber assortment recoveries neednot be predicted.     

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory(FRI) data, and SPOT Vegetation(SPOT-VGT) normalized difference vegetation index(NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China(with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg(1 Pg = 10~(15) g), with an average of 77.2 Mg ha~(-1), during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fineresolution biomass map.     

森林资源档案管理系统中数据更新方法的探讨

森林资源档案管理系统主要对森林资源的档案信息进行管理,是林业信息管理中的重要内容。森林资源档案数据具有易变性和时效性,森林资源档案数据的及时更新将有助于林业主管部门对森林资源状况的掌握。本文综合了时空数据模型中基态修正模型、生命周期模型和面向对象模型的特点,建立了增量存储数据更新模型,提出了增量存储数据更新方法,并以甘肃省小陇山林业局党川林场的部分数据为基础,利用增量存储数据更新方法,对数据更新实现过程进行了探讨。     

Evaluation of tree and stand-level growth models using national forest inventory data

Two models, Carbware (CW) and Growfor (GF), of different resolution and based on different frameworks were evaluated in relation to stand-level forecasts of volume and basal area using Ireland’s National Forest Inventory (NFI) data. CW is a distance-independent single-tree model that is based on diameter increment. GF is a stand-level dynamic empirical model that uses the von Bertalanffy–Richards growth equation in a state-space framework. NFI data were used as input to the models, and each model’s projections were compared to NFI data at the next measurement cycle. The NFI is a permanent sampling system with the objective to assess the composition and extent of the forest estate. A subset of the NFI was used in the study, single-species even-aged plots comprising Sitka spruce and lodgepole pine. The accuracy and performance of the CW and GF models were analysed using residual analysis and standard statistical techniques. Results show that both models require improvement, though the study has raised concerns regarding the suitability of the NFI data for this type of investigation.     

Significant differences and curvilinearity in the self-thinning relationships of 11 temperate tree species assessed from forest inventory data

Introduction  

In pure and even-aged stands, the allometry between mean tree size and maximum stand density—or self-thinning relationship—has long been considered a constant among tree species. Although the self-thinning allometric coefficient has been shown to be species-dependent, estimates available for a given species also differ. Whether this coefficient truly varies across species thus remains an open issue. A potential cause of variation in the coefficient may lie in a departure from the allometric assumption in the self-thinning relationship.     

Modeling individual tree height growth of Norway spruce and Scots pine from national forest inventory data in Norway

We developed individual tree height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway based on national forest inventory data. Potential height growth is based on existing dominant height growth models and reduced due to competition by functions developed in this study. Three spatially explicit and two spatially non-explicit competition indices were tested. Distance effects and diameter ratio effects were estimated from the data simultaneously with parameters of the potential modifier functions. Large height measurement errors in the national forest inventory data caused large residual variation of the models. However, the effects of competition on height growth were significant and plausible. The potential modifier functions show that height growth of dominant trees is largely unaffected by competition. Only at higher levels of competition, height growth is reduced as a consequence of competition. However, Scots pine also reduced height growth at very low levels of competition. Distance effects in the spatially explicit competition indices indicated that the closest neighbors are most important for height growth. However, for Scots pine also competitors at larger distance affected height growth. The five competition indices tested in this study explained similar proportions of the variation in relative height growth. Given that unbiased predictions can only be expected for the same plot size, we recommend a spatially explicit index, which describes the distance function with a negative exponential, for use in growth simulators.     

Predicting natural forest regeneration: a statistical model based on inventory data

Regenerating forest stands through natural seedlings is a commonly accepted silvicultural strategy in Germany. To plan for and increase natural regeneration within a given stand, foresters must be able to estimate whether the stand in its current state can produce sufficient saplings to replace the overstory. In this paper, we present two approaches to building a model that can estimate the probability of natural regeneration occurring, based on variables that are typically readily available from forest inventories. To estimate model parameters we used the large database of the third National Forest Inventory, which covers forest stands and sites across the whole of Germany, as well as weather and soil data. We examined how these variables impact the emergence of natural regeneration, ultimately fitting a model that can predict the occurrence of natural regeneration in 72% of cases. The influence of the variables on the predicted occurrence of natural regeneration was mixed, with most stand variables contributing only minor impact and most likely influencing natural regeneration via complex interactions. The exception was vertical structure (number of stand layers), which accounted for a large proportion of the goodness-of-fit of the model. An important finding was that forest ownership structure is a key variable for the prediction of the presence of regeneration. Data from this study support the assumption that some forest owners manage their stands in a way that fosters natural regeneration.     

A probability model for root and butt rot in Picea abies derived from Norwegian national forest inventory data

Root rot in Norway spruce (Picea abies (L.) Karst.) causes substantial economic losses to the forestry sector. In this study, we developed a probability model for decay at breast height utilizing 18,141 increment cores sampled on temporary plots of the Norwegian National Forest Inventory. The final model showed a good fit to the data and retained significant relationships between decay and a suite of tree, stand and site variables, including diameter at breast height, stand age, altitude, growing season temperature sum (threshold 5°C), and vegetation type. By comparing model predictions with recorded decay at stump height in an independent data set, we estimated a proportionality function to adjust for the inherent underestimation of total rot that will be obtained by applying a probability model derived from increment cores sampled at breast height. We conclude that the developed model is appropriate for national and regional scenario analyses in Norway, and could also be useful as a tool for operational forestry planning. This would however require further testing on independent data, to assess how well the new model predicts decay at local scales.     

Thinning inventory technique and its application in forest resource inventory

IntroductionForestpIanningrequiresinformationabouttheCurrentstateoftheforestresource.Themostcom-monstock-takingactivitiesarestandbasedinvento-riesscubascompartmentsamplingandsystematicsampIingschemesforforeststratainvoIvingpIots.ThesecIassicaIstock-takingactivitiesarescheduIedtotakepIaceatperiodictimeintervaIs.WiththeabandonmentofcIearfelIing,standstructureandcomplexitiesinthevariablesusedtodescribeforeststatushaveaccordinglyincurredchanges,sonewfactorsandtechniquesmustbeintroducedandadde…     

Ingrowth model for pyrenean oak stands in north-western Spain using continuous forest inventory data

In this paper, a two stage ingrowth model is presented for predicting periodic, 10 years ingrowth for pyrenean oak (Quercus pyrenaica Willd.) grown in medium to fully stocked coppice stands in north-western Spain. Data from the Spanish National Forest Inventory was used to develop the model, extracting the information from two inventories taken in 222 permanent plots. The first stage of the model predicts the probability of ingrowth occurrence, and in the second stage, the number of recruits is predicted using a conditional model. Both models were biologically realistic and presented logical behaviour. The ingrowth occurrence probability model was dependent on quadratic mean diameter and average height. The recruitment quantification model included stand density and average diameter as explanatory variables. Although the occurrence probability of ingrowth was predicted correctly in 71.7% of cases, the predictions of the number of recruitment are poorer, yielding a coefficient of determination of 0.358. The evaluation criteria included qualitative and quantitative examinations and a testing with independent data from another region. The proposed ingrowth model is the first to be developed for mediterranean oak species in Spain and is an essential feature in any stand growth system.     

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

Abstract

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.