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When conducting a forest inventory, sometimes portions of plots cannot be measured due to inaccessibility. Two primary methods have been presented to account for partial nonresponse in the estimation phase: (i) use a ratio-to-size estimator or (ii) apply an adjustment factor to all plot observations in proportion to the missing area. Both approaches provide identical estimates of the population mean, but the estimates of variance differ when partial nonresponse is present. The performance of variance estimators was examined for a range of population forest area and partial nonresponse proportions in the sample. The ratio-to-size variance estimator performed unbiasedly with respect to simulation results, but the adjustment factor variance estimates were biased, with magnitude and direction dependent upon the forest area proportion and amount of partial nonresponse. The bias is relatively small when the partial nonresponse is small, which is often the case; however, the ratio-to-size method is preferred to ensure accurate variance estimation for a wide range of circumstances.

National Forest Inventories are important primary data sources for large-scale forest resource surveys, in which volume estimates of sampled trees are essential for quantitative analysis. Volume prediction models in natural forests are scarce in Brazil due to legal restrictions for cutting trees, especially in the Atlantic Forest. This study aimed to fit volume models for the main forest types and timber species of the Atlantic Forest in Rio de Janeiro state, considering two hypotheses: (I) generic volume models provide greater generalizability of estimates; however, (II) they may reduce the accuracy of forest type- and species-specific predictions. Four linear models with logarithmic transformation of variables were evaluated to fit volume models for generic and specific datasets, which correspond to the main forest types and timber species. Goodness-of-fit statistics were calculated to compare the accuracy and efficiency of the models, and selected models were validated through leave-one-out cross-validation procedures. The estimates obtained by generic and specific models were compared by non-parametric hypothesis tests. Generic models showed similar predictions to the specific models for forest types and timber species, with similar potential for stem and total volume predictions. Therefore, generic models can be used for Atlantic Forests in Rio de Janeiro state, while specific models are recommended to obtain more detailed local estimates.

Instituto de Ciencias Forestales (ICIFOR), National forest inventories, in which trees are often mapped within the plots, provide a tool for the quantification of large-scale forest structure since they cover all forest areas. Many National Forest Inventories follow a nested design in order to reduce the sampling effort for smaller trees. We propose and test a methodology that allows the spatial pattern of trees, species mingling and size differentiation to be characterized using the nearest neighbour indices and second-order moment functions from nested plot data. The nearest neighbour indices and second-order moment functions for the actual distribution are compared with simulations of the appropriate null model: spatial randomness for spatial pattern characterization or spatial independence for species mingling and size differentiation. The proposed method consists of constraining the null model to fit the nested plot design. For the purposes of the study, we simulated 120 plots and used 26 real plots located in pure and mixed stands in Central Spain, for which a complete census with detailed information about trees was available. The nested design used in the Spanish National Forest Inventory (SNFI) plots was simulated to test the performance, taking the complete census as reference. Despite of the limited accuracy for some structural measures, the proposed method based on nested design data performed better for most of the nearest neighbour indices and second-order moment functions than the strategy currently used in the SNFI for structure assessment in a subsample of SNFI plots, consisting of mapping the 20 trees closest to the plot centre. Nearest neighbour indices provided greater accuracy for species mingling assessment than second-order moment functions, whereas the opposite occurred when describing spatial pattern and size differentiation. The methodology proposed provides the first insight into the characterization of forest structure in nested designs although more evaluations are required for different forest types.

Planning a forest inventory comprises making decisions related to the sampling strategy: cluster configuration, sample size and sample allocation within the survey area. Cluster configurati...

The prediction of tree growth is key to further understand the carbon sink role of forests and the short-term forest capacity on climate change mitigation. In this work, we used large-scale data available from three consecutive forest inventories in a Euro-Mediterranean region and the Bertalanffy–Chapman–Richards equation to model up to a decade’s tree size variation in monospecific forests in the growing stages. We showed that a tree-level fitting with ordinary differential equations can be used to forecast tree diameter growth across time and space as function of environmental characteristics and initial size. This modelling approximation was applied at different aggregation levels to monospecific regions with forest inventories to predict trends in aboveground tree biomass stocks. Furthermore, we showed that this model accurately forecasts tree growth temporal dynamics as a function of size and environmental conditions. Further research to provide longer term prediction forest stock dynamics in a wide variety of forests should model regeneration and mortality processes and biotic interactions.

Background and Objectives: Significant progress in developing European national forest inventory (NFI) systems could ensure accurate evaluations of gross annual increment (GAI) and its components by employing direct measurements. However, the use of NFI data is insufficient for increasing the efficiency of forest management and the use of wood, as well as for meeting sustainable forestry needs. Specification of forest characteristics, such as GAI and its components, identification of the main factors that impact forest growth, accumulation of wood, and natural losses are among the key elements promoting the productivity of forest stands and possibilities of rational use of wood in large forest areas. The aims of this research were (a) to validate the quality of forest statistics provided by a standwise forest inventory (SFI) and (b) to reveal the potential benefits of rational wood use at the country level through the analysis of forest management results, which are based on GAI, including its components derived from the NFI. Materials and Methods: SFI and NFI data from 1998&ndash, 2017 were collected from 5600 permanent sample plots and used to evaluate the main forest characteristics. Potential wood use was estimated based on the assumption that 50&ndash, 70% of the total GAI is accumulated for final forest use. Results: Mean growing stock volume (GSV) is underestimated by 7&ndash, 14% on average in the course of SFI. Therefore, continuous monitoring of the yield changes in forest stands, detection of factors negatively affecting yield and its accumulation, and regulation of these processes by silviculture measures could increase potential forest use in Lithuania. Conclusions: Implementation of sample-based NFI resulted in an improvement of forest characteristics and led to an increase in GSV and GAI. Continuously gathered data on GAI and its components are a prerequisite for efficient forest management and control of the use of wood.

National Forest Inventory and forest assessments are attracting increasing attention owing to their role in providing information related to manifold forest functions. There is a high demand for global information about forests and multiple services that these ecosystems provide. Of particular, current interest are forest assessment systems at national level by countries that wish to engage themselves in the REDD+ initiative. The discipline of Forest Inventory has developed a versatile toolbox of techniques and methods useful for national-level forest assessments. This chapter presents a brief overview of the National Forest Inventory (NFI) in India vis-a-vis some other developed countries and highlights the proposed changes in plot design while revising NFI in India. Some new initiatives by Forest Survey of India have also been highlighted. With an increase in requirement of information and new technological developments, a constant adaptation of the NFI framework and introduction of the new NFI design for India is essential. The new NFI for India has been designed to cope with changing contexts, new technical developments and the unique socio-ecological conditions in India. The details of the new design with a focus on terrestrial assessments using field plots of different sizes and shapes are presented. It is shown why concentric circular plots, which are widely used in the Northern Hemisphere, are found unsuitable for the species-rich forests of India. The particular structure of the new Indian NFI is also briefly discussed and compared with other large-scale NFI’s as well as global Big Data initiatives. A unique feature of the new Indian NFI is an integrated system of temporary and permanent field plots. Permanent observational plots are designed to monitor forest change and complement the network of temporary field plots.

Centenary forest statistics informing major attributes of French forests were digitized, checked for consistency, and used to infer forest dynamics. Comparison to forest inventory data highlights increases in forest area and tree diversity, and substantial maturation of forests. Dataset access at https://doi.org/10.5281/zenodo.3739458 The history of European forest dynamic remains fragmental. In France, the Daubree statistics (1908) and agricultural statistics (1892, 1929) formed fundamental material to fill this gap. Release, test, and summarize the digitalized dataset. Analyze long-term forest changes in forest area, composition, and structure. Primary data on forest area across NUTS-3 geographic units, split by forest management and ownership categories and dominating tree species (Daubree), were digitized and cross-compared. Centennial changes in forest attributes were assessed from modern forest inventory data. Cross-comparison revealed: (1) strong temporal consistency in forest changes over time, (2) systematic and interpretable biases in ownership/management categories between Daubree and agricultural statistics. Strong shift from coppices to high forests, increased prevalence of private ownership, and constant proportion of broadleaf- and conifer-dominated forests were highlighted, with increased tree species diversity at country scale. Ancient statistics are shown to play a major role in retrospective land-use and forest policy analysis.

The National Forest Inventory of Finland (NFI) produces national- and regional-level statistics for sustainability assessment and strategical-level decision making. So far, the regional-level statistics are based on a systematic sampling design with geographical stratification. Auxiliary information such as remote sensing is not used for design or estimation at the regional level, but it is used at the small-area level, i.e., for municipality-level results. To improve the cost efficiency of the NFI, possibilities for using auxiliary data in both the design and estimation are of interest. We assessed the improvements obtainable by using an interpreted satellite image — the multisource NFI result from a previous NFI — as auxiliary information in the design phase. The results show that even though the multisource NFI map is not very accurate, significant improvements in efficiency can be obtained by using either the local pivotal method (LPM) or stratification. LPM improves efficiency by matching the sample distribution to population distribution. These results encourage us to further investigate (i) what would be the improvement with more accurate auxiliary information, for example, laser scanning data, and (ii) how LPM fits in a real-life situation where part of the plots are permanent and it would be used to select only the temporary plots.

Many countries have employed recently developed technologies, such as airborne lidar, to capture nationwide three-dimensional information over the past few years. In Spain, a huge volume of lidar i...

We used national scenario analyses to examine the effects of harvesting intensity on the development of forest resources, timber supply, carbon balance, and biodiversity indicators of Finnish forestry in nine 10-year simulation periods (90-year simulation period) under the current climate. Data from the 11th National Forest Inventory of Finland were used to develop five even-flow harvesting scenarios for non-protected forests with the annual harvest ranging from 40 to 100 million m3. The results show that the highest annual even-flow harvest level, which did not decrease the growing stock volume over the 90-year simulation period, was 73 million m3. The total 90-year timber production, consisting of harvested volume and change in growing stock volume, was maximized when the annual harvest was 60 million m3. Volume increment increased for several decades when harvested volume was less than the current volume increment. The total carbon balance of forestry was the highest with low volume of harvested wood. Low harvested volume increased the values of biodiversity indicators, namely volume of deciduous trees, amount of deadwood and area of old forest., final draft, peerReviewed

The Land and Property Register (LPR) of Poland contains information on land use for the entire country. Additionally, a sample-based National Forest Inventory (NFI) provides statistical data for forest areas and detailed information on numerous forest parameters. However, until 2014 NFI plots were established only on areas classified as forest in the LPR. In this article, we present results of an estimation of forest area by extending the NFI measurements on all theoretical points in a grid, and compare the results with LPR information for one province (the Podlaskie voivodeship). At each point, we assess land use with recent aerial photographs and verify the ambiguous points in the field. Forest area in Poland is increasing due to afforestation and natural expansion of forest. Delays in the updating process of the LPR, and unwillingness of the owners to agree to reclassification of their land, have led to an underestimation of overall forest area. Our results demonstrate that forest area estimates made by the improved NFI are higher than those based upon the LPR. The modified NFI may be an appropriate tool for monitoring forest area changes in Poland.

International audience; Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter.

Raw data can be freely accessed online for French National Forest Inventories (http://inventaire-forestier.ign.fr), Quercus petraea (https://arachne.pierroton.inra.fr/QuercusPortal) and Abies alba common gardens (online repository, in progress). Climate data used for this study are available online (http://gentree.data.inra.fr/climate). R codes used for data analyses can be obtained from the corresponding author upon request.; International audience; Aim To test whether intraspecific trait responses to climate among populations across species distribution ranges can be untangled using field observations, under the rationale that, in natural forest tree populations, long-term climate shapes population responses while recent climate change drives phenotypic plasticity. Location Europe. Time period 1901-2014. Taxa Silver fir (Abies alba Mill.) and sessile oak [Quercus petraea (Matt.) Liebl.]. Methods We estimated the variation of individual tree height as a function of long-term and short-term climates to tease apart provenance effects (variation among populations of different geographical origin), plasticity and their interaction, using mixed-effect models calibrated with national forest inventory data (in-situ models). To validate our approach, we tested the ability of in-situ models to predict independently tree height observations in common gardens experiments where provenance and plastic effects can be measured and separated. In-situ model predictions of tree height variation among provenances and among planting sites were compared to observations in common gardens and to predictions from a similar model calibrated using common garden data (ex-situ model). Results In Q. petraea, we found high correlations between in-situ and ex-situ model predictions of provenance and plasticity effects and their interaction for tree height (r > .80). We showed that the in-situ models significantly predicted tree height variation among provenances and sites for A. alba and Q. petraea. Spatial predictions of phenotypic plasticity across species distribution ranges indicate decreasing tree height in populations of warmer climates in response to recent anthropogenic climate warming. Main conclusions Our modelling approach using national forest inventory observations provides a new perspective for understanding patterns of intraspecific trait variation across species ranges. Its application is particularly interesting for species for which common garden experiments do not exist or do not cover the entire climatic range of the species.

Puerta-Pinero, A. 2016. Identification and evaluation of afforestations through the Spanish National Forest Inventory. Ecosistemas 25(3): 43-50. Doi.: 10.7818/ECOS.2016.25-3.05 Afforestation programs in Spain were performed mainly between 1930-1970 decades. The study of the state and functioning of these anthropogenic forests has a wide debate among scientists and stakeholders. Forest inventories represent a crucial source of data to evaluate the long-term functioning of these forests at broad spatial scales. This article shows several tools to identify and select plots from the third Spanish Forest Inventory composed by uniform afforestations. The functions intend to facilitate the work of scientists and technicians whose objective could be influenced by the presence or absence of these artificial forest type. Those functions are a first attempt to include or exclude plots that are composed by artificially afforested forests. I also present some preliminary results, and discuss strengths, weaknesses and future directions relative to the use of these functions.

This study aims to estimate the forest volumes of the economic forest in Gangwon Province of Republic of Korea (hereinafter referred to as Gangwon) through the synthetic estimation. To estimate the forest volume, Stratified systematic sampling method was used along with the forest type maps and the National Forest Inventory data. The synthetic estimation includes sample plots of the expanded areas as well as those of the target area, and the forest volume of economic forest in every city and county throughout Gangwon. Results show that the average forest volume calculated by synthetic estimation was in national economic forest and in private economic forest. The total forest volume of the national economic forest was approximately , which was higher than that of the private economic forest. On the other hands, the standard error of the national economic forest was approximately , which was lower than that of the private economic forest. The lowest standard errors was about in broad-leaved forest, followed by of mixed forest, and of coniferous forest.

Angle-count sampling (ACS) is an established method in forest mensuration and is implemented in different National Forest Inventories (NFI). However, due to the lack of fixed reference areas of the inventory plots, these ACS-based field data are seldom used as training data for wall-to-wall mapping applications at forest enterprise level. In this paper, we demonstrate an approach to overcome this shortcoming. For a study area in northern Bavaria, Germany, we used ACS-based NFI data for model training to generate wall-to-wall maps of growing stock for broadleaf, conifer and mixed forest stands. Both spectral and height information from the very high resolution WorldView-2 (WV2) satellite were used as auxiliary information and the non-parametric Random Forests (RF) algorithm was chosen as modeling approach. The growing stock predictions were validated using out-of-bag (OOB) samples and further verified at the plot and stand level using additional data. For validation, field plots from a Management Forest Inventory (MFI) and delineated forest stands were used. Compared to stand-level aggregations based on field plots from the MFI, our approach explained 56% of the variability in the growing stock ( R 2 ) with a relative RMSE of 15% at the stand level ( n = 252). As expected, the scatter was higher at the plot-level ( n = 3973). Nonetheless, the models still achieved acceptable performance measures ( R 2 = 0.44; RMSE = 34%).

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

A new strategic inventory across all publicly managed forests has been installed in Victoria. For the preceding two decades, Victoria’s forest inventory has been compiled from various data sources from across the state, an approach that doesn’t allow information about the nature and rate of changes to the resource to be estimated, or for projections or forecasts to be made. To address these limitations, the Victorian Forest Monitoring Program (VFMP) was established, to monitor Victoria’s progress in meeting a commitment towards sustainable forest management, and to satisfy requirements for state and national reporting. The purpose of the new program is to ‘assess and monitor the extent, state and sustainable development of Victoria’s public forests in a timely and accurate manner.’ The VFMP consists of a plot-based system of permanent observational units located on a state-wide grid. A combination of ground-based and aerial photo plots and remotely sensed data are used to capture a set of basic attributes that are used to derive indicators of sustainability. To meet the monitoring needs a remeasurement framework has been developed to guide the change estimation procedures. The program was designed and implemented between 2009 and 2015 and used to generate data and information for the most recent Victoria’s State of the Forest Report. The first annual change estimate is due to be completed in 2017. A summary of implementation and proposed maintenance is presented and discussed in this paper.Keywords: large-area monitoring, national forest inventory, operational implementation, sampling design, Victoria

International audience; AbstractKey messageThe carbon density was not different between natural and planted forests, while the biomass carbon density was greater in natural forests than in planted forests. The difference is due primarily to the larger carbon density in the standing trees in natural forests compared to planted forests (at an average age of 50.6 and 15.7 years, respectively).ContextAfforestation and reforestation programs might have noticeable effect on carbon stock. An integrated assessment of the forest carbon density in mountain regions is vital to evaluate the contribution of planted forests to carbon sequestration.AimsWe compared the carbon densities and carbon stocks between natural and planted forests in the Lüliang Mountains region where large-scale afforestation and reforestation programs have been implemented. The introduced peashrubs (Caragana spp.), poplars (Populus spp.), black locust (Robinia pseudoacacia), and native Chinese pine (Pinus tabulaeformis) were the four most common species in planted forests. In contrast, the deciduous oaks (Quercus spp.), Asia white birch (Betula platyphylla), wild poplar (Populus davidiana), and Chinese pine (Pinus tabulaeformis) dominated in natural forests.MethodsBased on the forest inventory data of 3768 sample plots, we estimated the values of carbon densities and carbon stocks of natural and planted forests, and analyzed the spatial patterns of carbon densities and the effects of various factors on carbon densities using semivariogram analysis and nested analysis of variance (nested ANOVA), respectively.ResultsThe carbon density was 123.7 and 119.7 Mg ha−1 for natural and planted forests respectively. Natural and planted forests accounted for 54.8% and 45.2% of the total carbon stock over the whole region, respectively. The biomass carbon density (the above- and belowground biomass plus dead wood and litter biomass carbon density) was greater in natural forests than in planted forests (22.5 versus 13.2 Mg ha−1). The higher (lower) spatial carbon density variability of natural (planted) forests was featured with a much smaller (larger) range value of 32.7 km (102.0 km) within which a strong (moderate) spatial autocorrelation could be observed. Stand age, stand density, annual mean temperature, and annual precipitation had statistically significant effects on the carbon density of all forests in the region.ConclusionNo significant difference was detected in the carbon densities between natural and planted forests, and planted forests have made a substantial contribution to the total carbon stock of the region due to the implementation of large-scale afforestation and reforestation programs. The spatial patterns of carbon densities were clearly different between natural and planted forests. Stand age, stand density, temperature, and precipitation were important factors influencing forest carbon density over the mountain region.

Forest inventory data, observational studies (mensurative and manipulative), and planted experiments provide the main data sources for the study of mixed forests. Demonstrative experiments such as marteloscopes are also basic to both research and training. Under the umbrella of the EuMIXFOR project, several research efforts have been conducted to further our knowledge as regards the functioning and development of mixed forests. Within this project, information from different forest research experiments at European level focusing on mixed forests has been compiled in order to identify the current state of the art. In the case of the European beech and Scots pine mixture, a gradient of triplets of monospecific and pure plots of these species has been established across Europe. Common establishment and measurement protocols have been developed in order to harmonize comparisons and analyses. Data sharing is also an objective; therefore, data from the triplet study of the European beech and Scots pine mixture are now available. A common dataset with the National Forest Inventory data from some European countries has been used for the study of mixtures.

Sustainable forest management (SFM) is many things to many people – yet a common thread is the production of forest goods and services for the present and future generations. The promise of sustainability is rooted in the two premises; first that ecosystems have the potential to renew themselves and second that economic activities and social perceptions or values that define human interaction with the environment are choices that can be modified to ensure the long term productivity and health of the ecosystem. SFM addresses a great challenge in matching the increasing demands of a growing human population while maintaining ecological functions of healthy forest ecosystems. This paper does not seek to define SFM, but rather provides analyses of key indicators for the national-scale enabling environment to gain a global insight into progress in implementing enabling and implementing SFM at the national and operational levels. Analyses of the Global Forest Resources Assessment 2015 (FRA) country report data are used to provide insights into the current state of progress in implementing the enabling conditions for SFM. Over 2.17 billion ha of the world’s forest area are predicted by governments to remain in permanent forest land use, of which some 1.1 billion ha are covered by all of the SFM tools investigated in FRA 2015. At the global scale, SFM-related policies and regulations are reported to be in place on 97% of global forest area. While the number of countries with national forest inventories has increased over that past ten years from 48 to 112, only 37% of forests in low income countries are covered by forest inventories. Forest management planning and monitoring of plans has increased substantially as has forest management certification, which exceeded a total of over 430 million ha in 2014. However, 90% of internationally verified certification is in the boreal and temperate climatic domains – only 6% of permanent forests in the tropical domain have been certified as of 2014. Results show that more work is needed to expand the extent and depth of work on establishing the enabling conditions that support SFM over the long term and suggests where those needs are greatest.

A comparative analysis of National Forest Inventories (NFI) of many countries, including Serbia was made in the manuscript, in terms of methodology and in terms of the definition of different information collected about forest ecosystems. Further development of the NFI Serbia was also analyzed. It was determined that there are differences in national methodologies, but that they are not substantial and do not constitute an obstacle to the creation of uniform databases of forests at the regional and global levels as a prerequisite for political and professional action aimed at protecting and ensuring sustainability of all forest functions. Contrary to that, differences between national definitions for information that are collected by inventory are numerous, distinct and as such represent a major obstacle to the mentioned aspirations. The solution of the problem is standardization, i.e. in the far more acceptable harmonization process (adaptation) NFI, whereby such frameworks of that activity are imposed by the FAO (globally) and COST Action E43 (mainly in the European region). NFI Serbia, on the threshold of second realization, is burdened with a numerous institutional, organizational and methodological problems and dilemmas, inherited from the first measurement of Serbian forests. Therefore, an urgent recruitment of relevant experts is necessary to resolve and overcome problems, in order to ensure all the prerequisites for a successful implementation and obtaining reliable data of the second NFI, so that the future NFI can be alleviated of the conditions in which it is currently located. [Projekat Ministartsva nauke Republike Srbije, br. 37008: Održivo gazdovanje ukupnim potencijalima šuma u Republici Srbiji]

Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km. Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.2

A spatially explicit map of aboveground carbon stored in Mexico’s forests was generated from empirical modeling on forest inventory and spaceborne optical and radar data. Between 2004 and 2007, the Mexican National Forestry Commission (CONAFOR) established a network of ~26,000 permanent inventory plots in the frame of their national inventory program, the Inventario Nacional Forestal y de Suelos (INFyS). INFyS data served as model response for spatially extending the field-based estimates of carbon stored in the aboveground live dry biomass to a wall-to-wall map, with 30 × 30 m2 pixel posting using canopy density estimates derived from Landsat, L-Band radar data from ALOS PALSAR, as well as elevation information derived from the Shuttle Radar Topography Mission (SRTM) data set. Validation against an independent set of INFyS plots resulted in a coefficient of determination (R2) of 0.5 with a root mean square error (RMSE) of 14 t∙C/ha in the case of flat terrain. The validation for different forest types showed a consistently low estimation bias (15°) with an R2 of 0.34. A comparison of the average carbon stocks computed from: (a) the map; and (b) statistical estimates from INFyS, at the scale of ~650 km2 large hexagons (R2 of 0.78, RMSE of 5 t∙C/ha) and Mexican states (R2 of 0.98, RMSE of 1.4 t∙C/ha), showed strong agreement.

Forest ecosystem carbon (C) stocks and stock change in the United States (US) have been documented using Intergovernmental Panel on Climate Change (IPCC) procedures and guidance with 1990 as a baseline reference for all United Nations Framework Convention on Climate Change reports. In the US, estimates of forest C stocks and stock change are obtained from data collected and maintained by the Forest Inventory and Analysis (FIA) program of the US Forest Service. Over the course of the IPCC monitoring period, the FIA program made a transition from state-by-state multiyear periodic inventories selected on a rotating basis – with reporting standards largely tailored to regional requirements – to nationally consistent, annual inventories (where a proportion of plots is measured in each state each year) designed for large-scale strategic requirements. Lack of measurements on all forest land during the periodic inventories, along with plot access difficulties and misidentification of forest plots as nonforest due to poor aerial imagery, have resulted in missing data (i.e., nonresponse) throughout the FIA database. Nonresponse, which in some US states is greater than 20%, may lead to differences in estimates of forest C stock change due to the procedural transition from periodic to annual inventories. As an initial step towards rectifying the differences in estimates, we examined several strategies to compensate for missing observations using the most recent annual inventory data from the Lake States region of the US. Results varied by state in the study but given the annual reporting cycle and requirements to compile national estimates of forest C, it was deemed that techniques, where non-observed samples are removed from estimation procedures, provided the optimal combination of statistical performance and efficiency. While the initial analysis focused on the Lake States region, several compensation strategies described may be useful in bridging the gap between national C flux estimates from periodic and annual forest inventories.

Extensive monitoring of Italian forests: integration of the National Forest Inventory with the European Network of Level I. Forest monitoring is important for enhancing our knowledge on such ecosystems and their changes in order to enable actions helpful for their protection. At the European level, several monitoring programmes are carried out at present to pursue a better knowledge of specific characteristics of forests. Among those, national forest inventories are carried out to give a picture of their status (forest area, forest types, timber volume, growth and others), whereas health status of forests has been monitored over twenty years thanks to a specific programme, the ICP Forests. Under LIFE+ programme 2007, the FutMon project funded country-level studies aiming at evaluating the possibility to merge national forest inventory and Level I sample plots in a unique set of plots for forest monitoring and survey protocol. The underlying idea is that a common set of plots would enhance efficiency either in terms of costs or in terms of scientific results. However, an important issue is to preserve the validity of the twenty-years-long existing data series. For this reason in Italy, in 2009 and 2010, an experiment was carried out by measuring a common set of variables both in the Level I plots and in a subset of the inventory plots, in order to evaluate differences and similarities of surveying variables in the two different plot samples. More specifically, the integration between the two programmes would imply a change in the crown-condition sample-trees selection criteria and the shift of the Level I network to the inventory one. On the basis of the results, from 2012 the National Forest Inventory protocol for sample-trees selection has already been adopted on the Level I plots, whereas the feasibility of shifting the Level I plots from the ICP network to the National Forest Inventory one is still under evaluation.

This paper summarizes the output of an imputation model that simultaneously estimates multiple operational-scale forest inventory attributes in the Laurentian mixed forest type of the United States. The model was constrained by national forest inventory privacy protocols and temporal uncertainties in feature and reference data. Estimates were most accurate at the county level and more variable across smaller spatial extents. Model development and validation highlighted that performance and reliability were influenced by our approach of using publicly available remote sensing predictors and ground reference data in model building. Comprehensive validation included diagnostics of the chosen model and leveraged multiscale independent data for analysis of lack-of-fit spatially and by individual feature variables. Relatively poor performance in some forest types pointed to an impact of temporal mismatch in the estimation of forest stocking in typically even-aged stands dominated by fast-growing speci...

Emerging markets for small-diameter roundwood along with a renewed interest in forest biomass for energy have created a need for estimates of merchantable biomass above the minimum sawlog top diameter for timber species in the national forest inventory of the United States. The Forest Inventory and Analysis (FIA) program of the USDA Forest Service recently adopted the component ratio method for estimation of aboveground live tree component biomass, which incorporates regionally specific volume models by species and species group. The merchantable bole component is the biomass of sound wood from a 1.0-ft stump height to a minimum 4.0-in. top diameter. Minimum sawlog top diameters for softwoods and hardwoods in the FIA program are substantially larger than the merchantable bole top diameter, leaving additional bole biomass potentially available for utilization. This article describes a method for estimating merchantable bole biomass for the sawlog component and the component above the minimum sawlog top diameter for timber species in the FIA program.

tThe estimation of past and future forest carbon (C) dynamics in European countries is a challengingtask due to complex and varying silvicultural systems, including uneven-aged forest management, andincomplete inventory data time series. In this study, we tested the use of the Carbon Budget Model ofthe Canadian Forest Sector (CBM-CFS3) in Italy, a country exemplifying most of these challenges. Our objective was to develop estimates of forest carbon budgets of the Forest Management area (including allforests existing in 1990) for the period 1995–2009, and to simulate alternative scenarios of natural dis-turbance (fire) and harvest rates to 2020. A number of methodological challenges required modifications to the default model implementation. Based on National Forest Inventory (NFI) data, we (i) developed a historic library of yield curves derived from standing volume and age data, reflecting the effect of past silvicultural activities and natural disturbances, and a current library of yield curves derived from thecurrent net annual increment; (ii) reconstructed the age structure for a period antecedent to the refer-ence NFI year (2005), to compare the model results with data from other sources; and (iii) developeda novel approach for the simulation of uneven-aged forests. For the period 2000–2009, the model esti-mated an average annual sink of −23.7 Mt CO2yr−1excluding fires in Italy’s managed forests. Adding firesto the simulation reduced the sink to −20.5 Mt CO2yr−1. The projected sink (excluding all fires) for theyear 2020 was −23.4 Mt CO2yr−1assuming average (2000–2009) harvest rates. A 36% increase in harvestrates by 2020 reduced the sink to −17.3 Mt CO2yr−1. By comparing the model results with NFI data andother independent studies, we demonstrate the utility of the CBM-CFS3 both for estimating the currentforest sink in even-aged and more complex uneven-aged silvicultural systems in Italy, and for explor-ing the impact of different harvest and natural disturbances scenarios in managed forests. This studydemonstrates the utility of the CBM-CFS3 to national-scale estimation of past and future greenhousegas emissions and provides the foundation for the model’s future implementation to other European countries, JRC.H.3-Forest Resources and Climate