Search

Assessing forest biodiversity, naturalness and old-growth status (B-N-OG) is crucial for supporting sustainable forest planning, yet comprehensive monitoring networks specifically designed for such purposes are lacking in many countries. National Forest Inventories (NFIs) are the official source of statistics on status and trends of forests. While initially designed for wood production assessment, NFI data may be pivotal for ecological forest monitoring, thanks to their robust sampling protocols—enabling statistical inference—and regular field campaigns that ensure continuous information updates. As a consequence, in this study we explore the potential of NFIs for estimating B-N-OG indexes, aiming to establish compatible, scientifically relevant, and cost-effective indicators using existing NFI data at a European level. Based on data from the 2005 Italian NFI collected from 6563 plots, 18 indicators were selected following previous experiences and then used to estimate B-N-OG aggregated indexes. Relationships between the 18 indicators and the three indexes were investigated, along with comparisons of their relationships relative to forest type categories, management types and protected versus non-protected areas. Results confirm that NFI data offer valuable insights into specific B-N-OG indexes, especially concerning forest structure and deadwood. Moreover, the indexes contribute to developing meaningful relationships across geographic regions, forest categories and types of management. However, limitations in NFI field protocols are evident, as they are not explicitly designed for certain indicators. The study suggests the potential for NFIs to evolve into accessible, harmonized European reference networks for B-N-OG assessment for better supporting sustainable forest management, planning and conservation decisions related to forest ecosystems. [ABSTRACT FROM AUTHOR]

Model-based inference is an alternative to probability-based inference for small areas or remote areas for which probability sampling is difficult. Model-based mean square error estimators incorporate three components: prediction covariance, residual variance, and residual covariance. The latter two components are often considered negligible, particularly for large areas, but no thresholds that justify ignoring them have been reported. The objectives of the study were threefold: ( i) to compare analytical and bootstrap estimators of model parameter covariances as the primary factors affecting prediction covariance; ( ii) to estimate the contribution of residual variance to overall variance; and ( iii) to estimate thresholds for residual spatial correlation that justify ignoring this component. Five datasets were used, three from Europe, one from Africa, and one from North America. The dependent variable was either forest volume or biomass and the independent variables were either Landsat satellite image bands or airborne laser scanning metrics. Three conclusions were noteworthy: ( i) analytical estimators of the model parameter covariances tended to be biased; ( ii) the effects of residual variance were mostly negligible; and ( iii) the effects of spatial correlation on residual covariance vary by multiple factors but decrease with increasing study area size. For study areas greater than 75 km2 in size, residual covariance could generally be ignored. [ABSTRACT FROM AUTHOR]

Forest structural diversity plays a major role for forest management, conservation and restoration and is recognized as a fundamental aspect of forest biodiversity. The assessment, maintenance and restoration of a diversified forest structure have become major foci in the effort to preserve forest ecosystems from loss of biological diversity. However, the assessment of forest biodiversity is difficult because it involves multiple components and is characterized using multiple variables. The objective of the study was to develop a methodological approach for predicting, mapping, and constructing a statistical inference for a multiple-variable index of forest structural diversity. The method included three key components: (i) use of the k-Nearest Neighbors (k-NN) technique, field plot data, and airborne laser scanning metrics to predict multiple forest structural diversity variables simultaneously, (ii) incorporation of multiple diversity variable predictions into a single index, and (iii) construction of a statistically rigorous inference for the population mean of the index. Three structural diversity variables were selected to illustrate the method: growing stock volume and the standard deviations of tree diameter at breast-height and tree height. Optimization of the k-NN technique produced mean relative deviations less in absolute value than 0.04 for predictions for each of the three structural diversity variables, R 2 values between 0.50 and 0.66 which were in the range of values reported in the literature, and a confidence interval for the population mean of the index whose half-width was approximately 5% of the mean. Finally, the spatial pattern depicted in the resulting map of forest structural diversity for the study area contributed to validating the proposed method. [ABSTRACT FROM AUTHOR]

Almost all relevant data in forestry databases arise from either field measurement or model prediction. In either case, these values have some amount of uncertainty that is often overlooked when doing analyses. In this study, the uncertainty associated with both measured and predicted data was quantified for upper-stem diameter at 5.27 m. This uncertainty was propagated through a tree taper model into predictions of individual-tree volume. The effects of uncertainty on individual-tree volume predictions and population estimates of total volume were assessed. Generally, when little or no systematic measurement deviation was present, less uncertainty was associated with field-measured diameters compared to model predictions. However, diameters predicted from a model were preferred when systematic deviations in field measurement exceeded approximately 0.2 cm. Comparisons of results obtained from an alternative taper model showed that more precise estimates of population totals might be obtained without upper-stem diameter information. Upper-stem diameter information increases the prediction accuracy of individual-tree volume, and thus, models using this information may be preferable in applications such as timber sales containing high-value trees. Due to the various factors that influence measurement and modeling uncertainty, foresters are encouraged to make similar evaluations in the context of their specific activities. [ABSTRACT FROM AUTHOR]

For a study area in the Brazilian state of Santa Catarina, the utilities of local and global forest maps in combination with poststratified and model-assisted estimators for increasing the precision of estimates of forest area were compared. Auxiliary information was in the form of local maps, the recent Global Forest Change map, and combinations of these maps. The poststratified estimators produced estimates of greater precision than the model-assisted regression estimators for maps of categorical variables, but the model assisted estimators produced estimates of greater precision for maps of continuous variables. The Global Forest Change map was the least accurate of all the maps, but it produced estimates of forest area that were similar to those for the other maps and that were more precise than if the map had not been used. Thus, the Global Forest Change map may be an attractive option if local maps are not available or cannot be constructed. The primary contributions of the study are two-fold. First, this is one of the first case studies that rigorously assess the utility of global maps for national estimation. After accumulation of a few more such studies, broader generalizations should be forthcoming. Second, a statistical basis is provided for the previously unexplained greater precision for poststratified estimators than for model-assisted estimators. [ABSTRACT FROM AUTHOR]

Due to the availability of good and reasonably priced auxiliary data, the use of model-based regression-synthetic estimators for small area estimation is popular in operational settings. Examples are forest management inventories, where a linking model is used in combination with airborne laser scanning data to estimate stand-level forest parameters where no or too few observations are collected within the stand. This paper focuses on different approaches to estimating the variances of those estimates. We compared a variance estimator which is based on the estimation of superpopulation parameters with variance estimators which are based on predictions of finite population values. One of the latter variance estimators considered the spatial autocorrelation of the residuals whereas the other one did not. The estimators were applied using timber volume on stand level as the variable of interest and photogrammetric image matching data as auxiliary information. Norwegian National Forest Inventory (NFI) data were used for model calibration and independent data clustered within stands were used for validation. The empirical coverage proportion (ECP) of confidence intervals (CIs) of the variance estimators which are based on predictions of finite population values was considerably higher than the ECP of the CI of the variance estimator which is based on the estimation of superpopulation parameters. The ECP further increased when considering the spatial autocorrelation of the residuals. The study also explores the link between confidence intervals that are based on variance estimates as well as the well-known confidence and prediction intervals of regression models. [ABSTRACT FROM AUTHOR]

For purposes of greenhouse gas emissions (GHG) accounting, estimation of deforestation area in tropical countries often relies on satellite remote sensing in the absence of National Forest Inventories (NFI). Gabon has recently launched a National Climate Action Plan with the intent of establishing a National Forest Monitoring System that meets the Intergovernmental Panel on Climate Change (IPCC) 2006 guidelines for the Agriculture, Forestry and Other Land Use (AFOLU) sector. The assessment of areas of forest cover and forest cover change is essential to estimate activity data, defined as areas of various categories of land use change by the IPCC guidelines. An appropriately designed probability sample can be used to estimate forest cover and net change and their associated uncertainties and express them in the form of confidence intervals at selected probability thresholds as required in the IPCC 2006 guidelines and for reporting to the United Nations Framework Convention on Climate Change (UNFCCC). However, wall-to-wall mapping is often required to provide a comprehensive assessment of forest resources and as input to land use plans for management purposes, but wall-to-wall approaches are more expensive than a sample based approach based on visual interpretation and require specialized equipment and staff. The recent release of the University of Maryland (UMD) Global Forest Change (GFC) map products could be an alternative for tropical countries wishing to develop their own wall-to-wall forest map products but without the resources to do so. Therefore, the aim of this study is to assess the feasibility of replacing national wall-to-wall forest maps with forest maps obtained from the UMD GFC initiative. A model assisted regression (MAR) estimator was applied using the combination of reference data obtained from a probability sample and forest cover and forest cover change maps either (i) produced nationally or (ii) obtained from the UMD GFC data. The resulting activity data are potentially more accurate than the SRS estimate and provide an assessment of the precision of the estimate which is not available from map accuracy indices alone. Results obtained for 2000 and 2010 for both the national and UMD GFC datasets confirm the high level of forest cover in Gabon, more than 23.5 million ha representing approximately 88.5% of the country. Although the UMD GFC dataset provides a reliable means of producing area statistics at national level combined with appropriate sample reference data, thus offering an alternative to nationally produced datasets (i) the classification errors associated with the Global dataset have non-negligible effects on both the estimate and the precision which supports the more general statement that map data should not be used alone to produce area estimates, and (ii) the maps obtained from the UMD GFC dataset require specific calibration of the tree cover percentage representing a non-negligible effort requiring specialized staff and equipment. Guidelines on how to use and further improve UMD GFC maps for national reporting are suggested. However, this additional effort would still most likely be less than the production of national based maps. [ABSTRACT FROM AUTHOR]

National forest inventory programs are tasked to produce timely and accurate estimates for a wide range of forest resource variables for a variety of users and applications. Time, cost, and precision constraints cause these programs to seek technological innovations that contribute to measurement and estimation efficiencies and that facilitate the production and distribution of an increasing array of inventory data, estimates, and derived products. Many of the recent innovations have involved remotely sensed data and related statistical estimation techniques. Current applications of remote sensing in support of national forest inventories are reviewed for three areas: (1) observation or measurement, meaning using remotely sensed data in lieu of field observations or measurements; (2) estimation, meaning calculation of traditional inventory areal estimates such as forest area or volume per unit area; and (3) mapping. Future applications focus on two areas: augmenting field measurements with remotely sensed data obtained from lidar sensors and Internet accessible map-based estimation. [Copyright &y& Elsevier]

Sustainable management of natural resources requires informed decision flaking and post-decision assessments of the results of those decisions. Increasingly, both activities rely on analyses of spatial data in the forms of maps and digital data layers. Fortunately, a variety of supporting maps and data layers rapidly are becoming available. Unfortunately, however, user-friendly tools to assist decision makers and analysts in the use and interpretation of these data generally are not available. Such tools would properly be in the form of decision support systems that incorporate basic geographic information system (GIS) functionality. A spatial decision support system featuring basic GIS functionality was designed to illustrate how such systems may be used to support decision making and post-decision assessments. This utility is illustrated with four sustainable forest management examples. Decision making is the focus of three of the examples: (1) allocating funding for forest wildfire mitigation purposes, (2) identifying forested watersheds at risk of conversion to non-forest land uses, and (3) identifying lands in the Rocky Mountains with potential for management for water yield. An assessment of the results of previous decisions is the focus of the example: (4) evaluating the socio-economic effects of the allocation of wildfire mitigation funds. [ABSTRACT FROM AUTHOR]

Forest sustainability has emerged as a crucial component of all current issues related to forest management. The seven Montreal Process Criteria are well accepted as categories of processes for evaluating forest management with respect to sustainability, and data collected by the Forest and Inventory Analysis (FIA) program of the United States Forest Service are well suited for such evaluations. The FIA program focuses on the collection, analysis, and distribution of data for a core set of variables obtained using a plot configuration, a sampling design, and measurement protocols that all feature national consistency. Plot, subplot, and tree-level observations include traditional mensurational measurements such as forest area, tree species, diameter, and survival and a suite of non-tree measurements related to the health of the forest. FIA data are recognized for their completeness, geographic coverage, and accessibility to users via a user-friendly interface to a national database. Three examples for three different regions of the United States illustrate the relevance and utility of FIA data for environmental and ecological assessments in the context of the Montreal Process. Several conclusions may be drawn from the examples: (1) for the Southern region, the forest land base is stable, and growing stock volume is increasing; (2) for the Mid-Atlantic region, contributions to carbon accumulation are slightly greater than for storage, and storage increases from north to south; and (3) for the Central Hardwoods region, tree species richness increases from north to south and from west to east and is stable or slightly increasing. [ABSTRACT FROM AUTHOR]

USDA Forest Service Forest Inventory and Analysis plot information is widely used for timber inventories, forest health assessments, and environmental risk analyses. With few exceptions, true plot locations are not revealed; the plot coordinates are manipulated to obscure the location of field plots and thereby preserve plot integrity. The influence of perturbed plot locations on the development and accuracy of statistical models is unknown. We tested the hypothesis that the influence is related to the spatial structure of the data used in the models. For ordinary kriging we examined the difference in mean square error based on true and perturbed plot locations across a range of spatial autocorrelations. We also examined the difference in mean square error for regression models developed with true and perturbed plot locations across a range of spatial autocorrelations and spatial resolutions. Perturbing plot locations did not significantly influence the accuracy of kriging estimates, but in some situations linear regression model development and accuracy were significantly influenced. Unless the independent variable has high spatial autocorrelation, only coarse spatial resolution data should be used to develop linear regression models. [ABSTRACT FROM AUTHOR]

The achievement of international goals and national commitments related to forest conservation and management, climate change, and sustainable development requires credible, accurate, and reliable monitoring of stocks and changes in forest biomass and carbon. Most prominently, the Paris Agreement on Climate Change and the United Nations' Sustainable Development Goals in particular require data on biomass to monitor progress. Unprecedented opportunities to provide forest biomass data are created by a series of upcoming space-based missions, many of which provide open data targeted at large areas and better spatial resolution biomass monitoring than has previously been achieved. We assess various policy needs for biomass data and recommend a long-term collaborative effort among forest biomass data producers and users to meet these needs. A gap remains, however, between what can be achieved in the research domain and what is required to support policy making and meet reporting requirements. There is no single biomass dataset that serves all users in terms of definition and type of biomass measurement, geographic area, and uncertainty requirements, and whether there is need for the most recent up-to-date biomass estimate or a long-term biomass trend. The research and user communities should embrace the potential strength of the multitude of upcoming missions in combination to provide for these varying needs and to ensure continuity for long-term data provision which one-off research missions cannot provide. International coordination bodies such as Global Forest Observations Initiative (GFOI), Committee on Earth Observation Satellites (CEOS), and Global Observation of Forest Cover and Land Dynamics (GOFC‐GOLD) will be integral in addressing these issues in a way that fulfils these needs in a timely fashion. Further coordination work should particularly look into how space-based data can be better linked with field reference data sources such as forest plot networks, and there is also a need to ensure that reference data cover a range of forest types, management regimes, and disturbance regimes worldwide. [ABSTRACT FROM AUTHOR]

Highlights • Landsat data are used to estimate broadleaf encroachment in Irish forests. • A stratified estimator is used to estimate the area and CIs from the error matrix. • Total affected area of encroachment is 20,003 ha (±20%). • Results used by forest managers to revise operational forest management plans. Abstract In recent years, several critical issues have been identified concerning the performance of recently established spruce forest plantations in Ireland, in particular coniferous reforestation sites. More specifically, the reforestation of peatland areas in the midlands of Ireland have been subject to encroachment by broadleaf species, such as birch, and willow. These species regenerate naturally and compete with, and outgrow, the coniferous species (commonly Norway spruce) that were planted. In many cases, the growth of these trees is faster than the spruce, resulting in stands being completely encroached by approximately 15 years of age. Given the widespread nature of this problem coupled with the fragmented nature of the Irish forest estate, a project was established to use Earth observation data to predict the spatial distribution and species composition of the affected sites. The spatial distribution and associated area estimates of broadleaf encroachment within State owned spruce forests were assessed using multi-temporal Landsat satellite imagery. The overall accuracy of the encroachment map was 85.23% and a Kappa Index of Agreement of 0.58. The associated area of encroached or broadleaf dominated forests was 20,003 ha with a confidence interval of ±20% calculated using a sample-based estimator. [ABSTRACT FROM AUTHOR]

Information about forest cover and its characteristics are essential in national and international forest inventories, monitoring programs, and reporting activities. Two of the most common forest variables needed to support sustainable forest management practices are forest cover area and growing stock volume (GSV m3 ha−1). Nowadays, national forest inventories (NFI) are complemented by wall-to-wall maps of forest variables which rely on models and auxiliary data. The spatially explicit prediction of GSV is useful for small-scale estimation by aggregating individual pixel predictions in a model-assisted framework. Spatial knowledge of the area of forest land is an essential prerequisite. This information is contained in a forest mask (FM). The number of FMs is increasing exponentially thanks to the wide availability of free auxiliary data, creating doubts about which is best-suited for specific purposes such as forest area and GSV estimation. We compared five FMs available for the entire area of Italy to examine their effects on the estimation of GSV and to clarify which product is best-suited for this purpose. The FMs considered were a mosaic of local forest maps produced by the Italian regional forest authorities; the FM produced from the Copernicus Land Monitoring System; the JAXA global FM; the hybrid global FM produced by Schepaschencko et al., and the FM estimated from the Corine Land Cover 2006. We used the five FMs to mask out non-forest pixels from a national wall-to-wall GSV map constructed using inventory and remotely sensed data. The accuracies of the FMs were first evaluated against an independent dataset of 1,202,818 NFI plots using four accuracy metrics. For each of the five masked GSV maps, the pixel-level predictions for the masked GSV map were used to calculate national and regional-level model-assisted estimates. The masked GSV maps were compared with respect to the coefficient of correlation (ρ) between the estimates of GSV they produced (both in terms of mean and total of GSV predictions within the national and regional boundaries) and the official NFI estimates. At the national and regional levels, the model-assisted GSV estimates based on the GSV map masked by the FM constructed as a mosaic of local forest maps were closest to the official NFI estimates with ρ = 0.986 and ρ = 0.972, for total and mean GSV, respectively. We found a negative correlation between the accuracies of the FMs and the differences between the model-assisted GSV estimates and the NFI estimate, demonstrating that the choice of the FM plays an important role in GSV estimation when using the model-assisted estimator. [ABSTRACT FROM AUTHOR]

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer's Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User's Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy. [ABSTRACT FROM AUTHOR]