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Abstract Data uncertainty due to spatial gaps and heterogeneity is a fundamental problem in conservation and environmental planning. Thus, investigation of issues related to data uncertainty contributes to more efficient conservation plans. We evaluated the uncertainty of data related to forest diversity descriptors using a diffusion-based cartogram approach that visually displays how data information change in function with respect to degree of uncertainty. We used ground vegetation data for 3093 plots collected as part of the BioSoil project through the ICP Forests Level I network and stored in the LI-BioDiv database. For each plot, we assigned an uncertainty value based on the survey season and the mean monthly temperature for the survey period. The density-equalizing map or cartogram highlights that data collected in Spain, the United Kingdom and the German federal states of Berlin and Brandenburg have smaller values of species richness corresponding to larger values of uncertainty. We found that an awareness of the negative relationship between the survey period and species richness can lead to improved data handling and analysis. We demonstrated that cartograms are efficient tools for evaluating and managing uncertainty and can strengthen the results of data analysis by providing alternative perspectives and interpretations of spatial phenomena. Highlights • The assessment of data uncertainty is a crucial issue in vegetation science and is needed to avoid biased ecological inference • Density-equalizing maps are an increasingly used way to represent different aspects of spatial variation of several variables • We provide a solution to handle and compare data which contain uncertainty by a diffusion-based method • The cartograms show a geographic relationship between attributes and to highlight the effect of uncertainty on data pattern • Once highlighted data limitation, we provide a way for understanding how different survey may influence data analysis and statistical inference [ABSTRACT FROM AUTHOR]

The spatial prediction of growing stock volume is one of the most frequent application of remote sensing for supporting the sustainable management of forest ecosystems. For such a purpose data from active or passive sensors are used as predictor variables in combination with measures taken in the field in sampling plots. The Sentinel-2 (S2) satellites are equipped with a Multi Spectral Instrument (MSI) capable of acquiring 13 bands in the visible and infrared domains with a spatial resolution varying between 10 and 60 m. The present study aimed at evaluating the performance of the S2-MSI imagery for estimating the growing stock volume of forest ecosystems. To do so we used 240 plots measured in two study areas in Italy. The imputation was carried out with eight k-Nearest Neighbours (k-NN) methods available in the open source YaImpute R package. In order to evaluate the S2-MSI performance we repeated the experimental protocol also with two other sets of images acquired by two well-known satellites equipped with multi spectral instruments: Landsat 8 OLI and RapidEye scanner. We found that S2 worked better than Landsat in 37.5% of the cases and in 62.5% of the cases better than RapidEye. In one study area the best performance was obtained with Landsat OLI (RMSD = 6.84%) and in the other with S2 (RMSD = 22.94%), both with the k-NN system based on a distance matrix calculated with the Random Forest algorithm. The results confirmed that S2 images are suitable for predicting growing stock volume obtaining good performances (average RMSD for both the test areas of less than 19%). [ABSTRACT FROM AUTHOR]

Urban forests (UF) provide a range of important ecosystem services (ES) for human well-being. Relevant ES delivered by UF include urban temperature regulation, runoff mitigation, noise reduction, recreation, and air purification. In this study the potential of air pollution removal by UF in the city of Florence (Italy) was investigated. Two main air pollutants were considered – particulate matter (PM 10 ) and tropospheric ozone (O 3 ) – with the aim of providing a methodological framework for mapping air pollutant removal by UF and assessing the percent removal of air pollutant. The distribution of UF was mapped by high spatial resolution remote sensing data and classified into seven forest categories. The Leaf Area Index (LAI) was estimated spatially using a regression model between in-field LAI survey and Airborne Laser Scanning data and it was found to be in good linear agreement with estimates from ground-based measurements (R 2 = 0.88 and RMSE% = 11%). We applied pollution deposition equations by using pollution concentrations measured at urban monitoring stations and then estimated the pollutant removal potential of the UF: annual O 3 and PM 10 removal accounted for 77.9 t and 171.3 t, respectively. O 3 and PM 10 removal rates by evergreen broadleaves (16.1 and 27.3 g/m 2 ), conifers (10.9 and 28.5 g/m 2 ), and mixed evergreen species (15.8 and 31.7 g/m 2 ) were higher than by deciduous broadleaf stands (4.1 and 10 g/m 2 ). However, deciduous forests exhibited the largest total removal due to the high percentage of tree cover within the city. The present study confirms that UF play an important role in air purification in Mediterranean cities as they can remove monthly up to 5% of O 3 and 13% of PM 10 . [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]

We investigated the potential performance of air pollution removal by the green infrastructures and urban forests in the city of Florence, central Italy, with a focus on the two most detrimental pollutants for human health: particulate (PM 10 ) and ozone (O 3 ). The spatial distribution of green infrastructures was mapped using remote sensing data. A spatial modeling approach using vegetation indices, Leaf Area Index, and local pollution concentration data was applied to estimate PM 10 and O 3 removal. The results are discussed to highlight the role and potential of green infrastructures and urban forests in improving air quality in Southern European cities. [ABSTRACT FROM AUTHOR]

Highlights: [•] Current volume tables are unsuitable for large beech trees typical of old-growth forests. [•] The use of randomized branch sampling leads to imprecise estimations without repeating paths. [•] Measuring all canopy branches with modern tree climbing techniques was demonstrated as a valid alternative. [Copyright &y& Elsevier]

This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensing-based forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches – such as single tree detection and area-based modeling – have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems. [ABSTRACT FROM AUTHOR]

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Abstract: Statistically-designed inventories and biodiversity monitoring programs are gaining relevance for biological conservation and natural resources management. Mandated periodic surveys provide unique opportunities to identify and satisfy natural resources management information needs. However, this is not an end in itself but rather is the beginning of a process that should lead to sound decision-making in biodiversity conservation. Forest inventories are currently evolving towards multipurpose resource surveys and are broadening their scope in several directions: (i) expansion of the target population to include non-traditional attributes such as trees outside the forest and urban forests; (ii) forest carbon pools and carbon sequestration estimation; (iii) assessment of forest health; and (iv) inclusion of additional variables such as biodiversity attributes that are not directly related to timber assessment and wood harvesting. There is an on-going debate regarding the role of forest inventories in biodiversity assessment and monitoring. This paper presents a review on the topic that aims at providing updated knowledge on the current contribution of forest inventories to the assessment and monitoring of forest biodiversity conditions on a large scale. Specific objectives are fourfold: (i) to highlight the types of forest biodiversity indicators that can be estimated from data collected in the framework of standard forest inventories and the implications of different sampling methods on the estimation of the indicators; (ii) to outline current possibilities for harmonized estimation of biodiversity indicators in Europe from National Forest Inventory data; (iii) to show the added value for forest biodiversity monitoring of framing biodiversity indicators into ecologically meaningful forest type units; and (iv) to examine the potential of forest inventory sample data for estimating landscape biodiversity metrics. [Copyright &y& Elsevier]

Monitoring of tree traits and tree response to the environment can be integrated with sensor technology. High-resolution monitoring requires a frequent data acquisition, that generally results in hourly measurements for the definition of daily processes, then scaled at monthly, seasonal and annual levels, by producing very large databases. The TreeTalker (TT) technology is an example of a tool for forest monitoring that measures hourly and simultaneously tree parameters and environmental variables, by collecting 1032 attributes per tree per day (43 variables, such as sap flow and stem diameter variation measured every hour). Therefore, we present the TreeTalkersCheck R package built to facilitate the remote checking of the functioning of the TreeTalkers devices. This package contains functions to download, process raw data obtained with TreeTalker devices, and produces a report with all the alerts detected and a complete database with preliminary processing of the data. The package was tested on a database obtained by 60 TT installed on 60 trees in three sites in Tuscany (Central Italy). [Display omitted] • TreeTalkers (TT) are useful tools to monitor tree functional traits. • Checking the functionality of TT is challenging, expensive, and time consuming. • We present an R package to remotely monitor TT devices in real time. • Downloading and primary processing of data is performed by the package. • Daily reports of TT sensors status and recorded data are produced by the package. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports on the development and testing of a procedure for assessing large-scale patterns of forest net primary productivity (NPP) in Italy. The work was based on a straightforward forest productivity model (C-Fix) which uses the relationship between absorbed photosynthetically active radiation of plant canopies and relevant biomass productivity. The objective was to develop a procedure of simple implementation, based on widely available Earth Observation (SPOT Vegetation, VGT) and ancillary data, suitable for rapid assessment of forest net primary productivity on a national level. The original C-Fix methodology, which was proposed in 1994 and has been applied all over Europe, was improved by the use of more abundant ancillary information and more efficient techniques for NDVI data processing. In this way, the method was suited to evaluating mean productivity levels of existing forests. The accuracy of the model outputs was assessed through comparison with independent estimates of forest NPP. The results obtained were satisfactory, with some shortcomings mainly due to the limited spatial resolution of SPOT Vegetation VGT data with respect to the high heterogeneity of forest landscapes in Italy. After including correction factors which accounted for the mean canopy covers of existing forest types, the method allowed the computation of NPP estimates for all Italian forests. [Copyright &y& Elsevier]

Model-assisted estimation of forest wood volume is approached exploiting the wall-to-wall information available from satellite data and partial information achieved from airborne laser scanning (ALS) covering a portion of the survey area. If the portion covered by ALS is selected by a probabilistic sampling scheme, two-phase estimators are considered in which the two sources of information are exploited by means of linear and non-linear models. If the portion covered by ALS is fixed because purposively selected, the two sources of information are exploited by the double-calibration estimator. The performance of the proposed strategies is checked by a simulation study from two study areas in Southern and Northern Italy. [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]