Deadwood distribution in European forests Puletti, Nicola; Giannetti, Francesca; Chirici, Gherardo ...
Journal of maps,
11/30/2017, Volume:
13, Issue:
2
Journal Article
Peer reviewed
Open access
National forest inventories are a primary source of data for the assessment of forest resources and lastly more often biodiversity at national scales. The diversity of adopted sampling designs and ...measurements reduces the prospect for a reliable comparison of generated estimates. The ICP Forest dataset represents a unique opportunity for a standardized approach of forest estimates through Europe. This work aims to provide a distribution map of the mean deadwood volume in European forest. A total of 3243 ICP Forests plots were analysed and presented. The study area extends over 3,664,576 km
2
interesting 19 countries. We observed that the highest percentage of plots show a deadwood volume lower than 50 m
3
ha
−1
, with a few of forests attaining around the maximum of 300 m
3
ha
−1
. Forests with more than 100 m
3
ha
−1
are concentrated in mountainous regions, central Europe and other regions, linked to high-forest management types, while coppices-derived forest systems (part of the Great Britain, Mediterranean region) show lower deadwood content. The map of deadwood volume on European Forests is of interests for scientists, land planners, forest managers and decision-makers, as a reference for further evaluation of changes, stratified sampling, ground reference for model validation, restoration and conservation purposes.
Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, ...including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.
In the context of the potential future use of unmanned ground vehicles for forest inventories, we present the first experiences with SPOT, a legged robot equipped with a LiDAR instrument and several ...cameras that have been used with a teleoperation approach for single-tree detection and measurements. This first test was carried out using the default LiDAR system (the so-called enhanced autonomy payload-EAP, installed on the board of SPOT to guide autonomous movements) to understand advantages and limitations of this platform to support forest inventory activities. The test was carried out in the Vallombrosa forest (Italy) by assessing different data acquisition methods. The first results showed that EAP LiDAR generated noisy point clouds where only large trees (DBH ≥ 20 cm) could be identified. The results showed that the accuracy in tree identification and DBH measurements were strongly influenced by the path used for data acquisition, with average errors in tree positioning no less than 1.9 m. Despite this, the best methods allowed the correct identification of 97% of large trees.
Several political initiatives aim to achieve net-zero emissions by the middle of the twenty-first century. In this context, forests are crucial as a carbon sink to store unavoidable emissions. ...Assessing the carbon sequestration potential of forest ecosystems is pivotal to the availability of accurate forest variable estimates for supporting international reporting and appropriate forest management strategies. Spatially explicit estimates are even more important for Mediterranean countries such as Italy, where the capacity of forests to act as sinks is decreasing due to climate change. This study aimed to develop a spatial approach to obtain high-resolution maps of Italian forest above-ground biomass (ITA-BIO) and current annual volume increment (ITA-CAI), based on remotely sensed and meteorological data. The ITA-BIO estimates were compared with those obtained with two available biomass maps developed in the framework of two international projects (i.e., the Joint Research Center and the European Space Agency biomass maps, namely, JRC-BIO and ESA-BIO). The estimates from ITA-BIO, JRC-BIO, ESA-BIO, and ITA-CAI were compared with the 2nd Italian NFI (INFC) official estimates at regional level (NUT2). The estimates from ITA-BIO are in good agreement with the INFC estimates (R2 = 0.95, mean difference = 3.8 t ha−1), while for JRC-BIO and ESA-BIO, the estimates show R2 of 0.90 and 0.70, respectively, and mean differences of 13.5 and of 21.8 t ha−1 with respect to the INFC estimates. ITA-CAI estimates are also in good agreement with the INFC estimates (R2 = 0.93), even if they tend to be slightly biased. The produced maps are hosted on a web-based forest resources management Decision Support System developed under the project AGRIDIGIT (ForestView) and represent a key element in supporting the new Green Deal in Italy, the European Forest Strategy 2030 and the Italian Forest Strategy.
Agricultural terraces are an important element of the Italian landscape. However, abandonment of agricultural areas and increase in the frequency of destructive rainfall events has made it mandatory ...to increase conservation efforts of terraces to reduce hydrological risks. This requires the development of new approaches capable of identifying and mapping failed or prone-to-fail terraces over large areas. The present work focuses on the development of a more cost-effective alternative, to help public administrators and private land owners to identify fragile areas that may be subject to failure due to the abandonment of terracing systems. We developed a simple field protocol to acquire quantitative measurements of the degree of damage—dry stone wall deformation—and establish a damage classification system. This new methodology is tested at two different sites in Tuscany, central Italy. The processing is based on existing DTMs derived from Airborne Laser Scanner (ALS) data and open source software. The main GIS modules adopted are flow accumulation and water discharge, processed with GRASS GIS. Results show that the damage degree and terrace wall deformation are correlated with flow accumulation even if other factors other than those analyzed can contribute to influence the instability of dry stone walls. These tools are useful for local land management and conservation efforts.
Forest parameter estimation is required to support the sustainable management of forest ecosystems. Currently, forest resource assessment is increasingly linked to auxiliary information obtained from ...remote sensing (RS) technologies. In forest parameter estimation, airborne laser scanning (ALS) data have been demonstrated to be an invaluable source of information. However, ALS data are often not available for the entire forest area, whereas images from multiple satellite systems offer new opportunities for large-scale forest surveys. This study aims to assess and estimate the contribution of field plot data and ALS data along with Landsat data to the precision of growing stock volume (GSV) estimates. We compared different approaches for model-assisted estimation of mean forest GSV per unit area using different proportions of field sample data, ALS cover data, and wall-to-wall Landsat data. Model-assisted estimators were used with NFI sample data in an Italian study area using 10 RS predictors, specifically the seven Landsat 7 ETM+ bands and three fine-resolution metrics based on ALS-derived canopy height. We found that relative to the standard simple expansion estimator, the model-assisted estimators produced relative efficiency of 1.16 when using only Landsat data and relative efficiencies as great as 1.33 when using increasing levels of ALS coverage.
The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate ...change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation.
Academic and cultural heritage institutions around the world have made measurable strides in the development of digital sound archives oriented towards research and access, but their impact on ...scholarship and society has been little studied. Traditionally, impact has been measured by citations; yet these are problematic metrics for nontraditional outputs like sound recordings. Social media data provide a promising avenue of investigation for measuring scholarly as well as societal impact. Twitter in particular has been shown to provide a high number of references for cultural and research outputs in all disciplines. This study analyzes Twitter references pertaining to the collections of five digital sound archives: British Library Sounds, Europeana Sounds, the Internet Archive Audio Archive, PennSound, and UbuWeb. By using text analysis methods to identify high‐frequency events and trends, and labeling them with a rubric designed for measuring the impact of digital heritage resources, this study provides preliminary insights on user values as they relate to digital sound collections. Despite the limitations of using social media data, the evidence gathered in this case study characterizes aspects of the use of digital sound collections, and may point to future priorities for the digital preservation of sound.
The following case study describes two library-led text encoding projects involving correspondence collections. The first, a documentary edition of personal papers held by Peter Still, a former ...slave, was conceived as an independent research project involving the participation of two undergraduate research assistants; the second, based upon letters to and from the Rutgers College War Service Bureau (1917–1919), has been designed as a two-week text encoding unit in a proposed undergraduate course on data and culture. These two projects, both featuring the letter as their object of study, are compared and contrasted as models of data and process, affording reflections on the overlapping concerns of the library instruction and digital humanities communities of practice. I propose viewing text encoding projects, particularly those that focus on lesser known creators or on life documents such as letters, as a means of accessing both critical library pedagogy and digital humanities methodology. By developing such projects, librarians address a number of collection and instruction related objectives of the library, while offering a valuable introduction to a set of methods that are of increasing importance to undergraduate education. Furthermore, these projects may be conducted at smaller scales, by reusing and adapting methods and software shared by the digital humanities community, thereby limiting reliance on institutional partners for technology and infrastructure support, which may not be forthcoming in under-resourced institutional contexts.
Large-scale forest monitoring benefits greatly from change detection analysis based on remote sensing data because it enables characterizing forest dynamics of disturbance and recovery by detecting ...both gradual and abrupt changes on Earth’s surface. In this study, two of the main disturbances occurring in Mediterranean forests, harvesting operations and forest fires, were analyzed through the analysis of Landsat Times Series images in a case study in Central Italy (Tuscany region). Disturbances were characterized based on their distinct temporal behaviors before and after the event: a period of 20 years (1999–2018) was used to extract and analyze at pixel level spectral trajectories for each disturbance and produce descriptive temporal trends of the phenomena. Recovery metrics were used to characterize both short- (5 years) and long-term aspects of recovery for harvested and burned areas. Spectral, recovery, and trend analysis metrics were then used with the Random Forest classifier to differentiate between the two disturbance classes and to investigate their potential as predictors. Among spectral bands, the Landsat SWIR 1 band proved the best to detect areas interested by harvesting, while forest fires were better detected by the SWIR 2 band; among spectral indices, the NBR scored as the best for both classes. On average, harvested areas recovered faster in both short- and long-term aspects and showed less variability in the magnitude of the disturbance event and recovery rate over time. This tendency is confirmed by the results of the classifier, which obtained an overall accuracy of 98.6%, and identified the mean of the post-disturbance values of the trend as the best predictor to differentiate between disturbances.
PDF
