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We present a browser-based App for smartphones that is freely available to end-users for collecting geotagged and oriented photos depicting vegetation biomass and fuel characteristics. Our solution builds on advantages of smartphones, allowing their use as easy sensors to collect data by imaging forest ecosystems. The strength and innovation of the proposed solution is based on the following points: (i) using a low memory footprint App, streaming images and data with as little data-volume and memory as needed; (ii) using JavaScript APIs that can be launched from both a browser or as an installed App, as it applies features such as service workers and Progressive Web App; (iii) storing both image and survey data (geolocation and sensor orientation) internally to the device on an indexed database, and synchronizing the data to a cloud-based server when the smartphone is online and when all other safety tests have been successfully passed. The goal is to achieve properly positioned and oriented photos that can be used as training and testing data for future estimation of the surface fuel types based on automatic segmentation and classification via Machine Learning and Deep Learning.

Estimating carbon stocks in wooded systems is crucial to quantify national greenhouse gas balance estimates. However, inaccurate estimates are likely due to the divergent architecture of many species. The monkey puzzle tree Araucaria araucana, with its umbrella-like architecture is a vivid example. This species, often found in monodominant stands at high elevations, is the greatest carbon reservoir in the landscape, hence estimating its carbon storage is crucial. To provide the necessary basis for these estimations, we documented the variation in basic density and moisture content along the stem profile, identified the most suitable biomass estimation models, and quantified biomass allocation for three age ranges. We measured, felled, weighed, and separated trees into three categories: stem wood, stem bark, and foliage (branches + scaly leaves). The log-linear form of the simple allometric equation Y = aXb, based on diameter at breast height as the explanatory variable, covered a large part of the variation and showed good cross-validation performance (>0.96). Models using more covariates achieved lower absolute errors, but the estimation of the additional model parameters was associated with greater uncertainty. A multi-objective model comparison revealed that the best additional covariate to further improve biomass estimation was total tree height. The mean absolute percentage error was 9.8% for the total aboveground biomass, 8% for stem wood, 12% for stem bark and 24% for foliage. Changes in biomass distribution among tree components were related to age. For older trees, there was a relative increase in stem wood, a decreased proportion of foliage, but no change in stem bark. The proportion of stem bark biomass is similar to that of Araucaria angustifolia, but higher than in other conifers and most trees in general. Our results provide key properties for A. araucana and general guidance for the selection of easily-measurable variables allowing for excellent predictive power for local biomass estimation.

On October 29, 2019, the Vaia storm hits the North-Eastern regions of Italy by wind gusts exceeding 200 km h-1. The forests in these regions have been seriously damaged. This contribution illustrates the methodology adopted in the emergency phase to estimate forest damages caused by Vaia storm, both in terms of damaged forest areas and growing stock volume of fallen trees. 494 Municipalities registered forest damages caused by Vaia, destroyed or intensely damaged forest stands amounted to about 42.500 ha, spread in Trentino Alto Adige, Veneto, Friuli Venezia Giulia, Lombardy and, only marginally, Piedmont and Valle d’Aosta. The growing stock volume of fallen trees was about 8.5 millions m3.

Degradation, a reduction of the ecosystem’s capacity to supply goods and services, is widespread in tropical forests and mainly caused by human disturbance. To maintain the full range of forest ecosystem services and support the development of effective conservation policies, we must understand the overall impact of degradation on different forest resources. This research investigates the response to disturbance of forest structure using several indicators: soil carbon content, arboreal richness and biodiversity, functional composition (guild and wood density), and productivity. We drew upon large field and remote sensing datasets from different forest types in Ghana, characterized by varied protection status, to investigate impacts of selective logging, and of illegal land use and resources extraction, which are the main disturbance causes in West Africa. Results indicate that functional composition and the overall number of species are less affected by degradation, while forest structure, soil carbon content and species abundance are seriously impacted, with resources distribution reflecting the protection level of the areas. Remote sensing analysis showed an increase in productivity in the last three decades, with higher resiliency to change in drier forest types, and stronger productivity correlation with solar radiation in the short dry season. The study region is affected by growing anthropogenic pressure on natural resources and by an increased climate variability: possible interactions of disturbance with climate are also discussed, together with the urgency to reduce degradation in order to preserve the full range of ecosystem functions.

Several studies have verified the suitability of LiDAR for the estimation of forest metrics over large areas. In the present study we used LiDAR as support for the characterization of structure, volume, biomass and naturalistic value in mixed-coniferous forests of the Alpine region. Stem density, height and structure in the test plots were derived using a mathematical morphology function applied directly on the LiDAR point cloud. From these data, digital maps describing the horizontal and vertical forest structure were derived. Volume and biomass were then computed using regression models. A strong agreement (accuracy of the map = 97%, Kappa Cohen = 94%) between LiDAR land cover map (i.e., bare soil, forest, shrubs) and ground data was found, while a moderate agreement between coniferous/broadleaf map derived from LiDAR data and ground surveys was detected (accuracy = 73%, Kappa Cohen = 60%). An analysis of the forest structure map derived from LiDAR data revealed a prevalence of even-age stands (66%) in comparison to the multilayered and uneven-aged forests (20%). In particular, the even-age stands, whether adult or mature, were overwhelming (33%). A moderate agreement was then detected between this map and ground data (accuracy = 68%, Kappa Cohen = 58%). Moreover, strong correlations between LiDAR-estimated and ground-measured volume and aboveground carbon stocks were detected. Related observations also showed that stem density can be rightly estimated for adult and mature forests, but not for younger categories, because of the low LiDAR posting density (2.8 points m-2). Regarding environmental issues, this study allowed us to discriminate the different contribution of LiDAR-derived forest structure to biodiversity and ecological stability. In fact, a significant difference in floristic diversity indexes (species richness - R, Shannon index - H’) was found among structural classes, particularly between pole wood (R=15 and H’=2.8; P

The goal of this review is to present leading examples of current methodologies for extracting forest characteristics from full-waveform LiDAR data. Four key questions are addressed: (i) does full-waveform LiDAR provide advantages over discrete-return laser sensors; (ii) will full-waveform LiDAR provide valid results in support of forest inventory operations and allow for a decrease in ground sampling efforts; (iii) is the use of full-waveform LiDAR data cost effective; and (iv) what is the scope of the applied methods (i.e., is full-waveform LiDAR accurate for different forest compositions, structures, and densities, and is it sensitive to leaf-off/leaf-on conditions)? Key forest structure characteristics can be estimated with significant accuracy using full-waveform metrics, although methodologies and their corresponding accuracies differ. For example, some processing methods are valid at the plot scale, whereas other procedures perform well at the regional scale; to be effective, certain LiDAR data analyses require a minimum point density, whereas other methods perform well using large-footprint sensors. Therefore, it is important to match processing methods with the appropriate scale and scope. The aim of this paper is to provide the forest research community and remote sensing technology developers with an overview of existing methods for inferring key forest characteristics, including their applicability and performance.