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• Global biomass map assessed for accuracy relative to independent reference data. • Design-based, model-based, and hybrid inferential frameworks used. • Two global maps validated for a sub-global validation site in the USA. Tropical countries without extensive ground sampling programs often use the gain-loss approach for greenhouse gas inventories. With this approach emissions are estimated as the products of estimates of areas of land use change characterized as activity data and estimates of emissions per unit area characterized as emission factors. For the special case of complete deforestation, the emission factor for the aboveground, live tree, biomass (AGB) pool can be estimated as mean biomass per unit area prior to the deforestation which, in turn, can be estimated using a single-date ground sample or a single-date biomass map. Multiple biomass maps at continental and global scales that can be considered for estimation of emissions factors have been forthcoming in recent years. However, regardless of the source of biomass data, estimates of emissions factors must still comply with the IPCC good practice guidelines for accuracy and uncertainty. For emission factors based on a biomass map, compliance requires validation of the map where validation means that independent reference data are used to ensure that the map is free from systematic error. The primary objective of the study was to illustrate methods for validating global AGB maps by assessing their conformity to reference data at the spatial scale of a sub-global validation site. For this study, validation entailed assessing the accuracy of map-based estimates of mean biomass per unit area for a sub-global, approximate 7600-km2 validation site in Minnesota, United States of America (USA). The accuracy assessment was in the form of a test of the hypothesis of whether the map-based estimate is or is not equivalent to an estimate based on independent reference data obtained from either a ground sample or a local biomass map of greater quality than the global map. Design-based, model-based, and hybrid inferential methods were used to conduct the test. The primary conclusions were threefold. First, no map exhibited systematic error; second, the model-based inferential approach produced the smallest standard errors; and third, operationally, differences between validation site estimates of mean biomass per unit area for two global biomass maps and estimates based on reference data were not statistically significantly different. [ABSTRACT FROM AUTHOR]

• The study aims at constructing wall-to-wall estimates of forest growing stock (GSV). • We combine NFI plot data, remotely sensed and auxiliary variables. • We applied the methodology in Mediterranean Forest. • We create a wall-to-wall GSV forest map in a large test area. • The GSV map was used to produce model-based estimates of GSV at small scale. Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy. For a study area of more than 48,657 km2 in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model. Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R2 = 0.69 and RMSE % = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI. [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]

The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters. Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using the echo-based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable results. Accuracies were greatest for dominant height (Hd) (R 2 = 0.91; RMSE% = 8.2%) and mean height weighted by basal area (R 2 = 0.83; RMSE% = 10.5%) when using the echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standard error of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m. [ABSTRACT FROM AUTHOR]