Emanuele Lingua | Università degli Studi di Padova (original) (raw)

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Papers by Emanuele Lingua

2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

Average data by plot of structure, composition, and regeneration in the Biogradska Gora forest

&lt;p&gt;Forest fires are a natural disturbance largely affected by global change... more &lt;p&gt;Forest fires are a natural disturbance largely affected by global changes, especially by anthropic pressure. At the same time, forest fires can be a menace to human lives and activities, and the phenomenon needs control in the most critical areas. One of the tools available to land managers to assess forest fire risk is fire simulation.&lt;/p&gt;&lt;p&gt;Forest fire simulators can highlight the most critical sectors of a landscape, but they need several input information, some of which is not routinely collected. In addition, for some information expensive procedures or dedicated instruments are required. One example is the value of canopy bulk density (CBD), a parameter often assumed as constant because its direct measurement requires destructive sampling of trees.&lt;/p&gt;&lt;p&gt;Alternatives to direct sampling of CBD have been found, with satisfactory results. One of the best proxies is the leaf are index (LAI), a common parameter collected in agricultural and ecological research. Nonetheless, its use outside academia is not common, often due to the need of specific tools and dedicated software to analyse the data.&lt;/p&gt;&lt;p&gt;In this study, a smartphone with a clip-on fisheye lens, and a free software have been used to overcome the aforementioned limitations. LAI has been sampled in 6 &lt;em&gt;Pinus spp.&lt;/em&gt; forests in North-East Italy in the context of the EU Interreg Project CROSSIT SAFER, and the results have been compared to values from other studies. Despite the lack of destructive sampling in the same forest plots, the methodology seems promising, providing more reliable values compared to constant values often used in simulations.&lt;/p&gt;&lt;p&gt;With this affordable equipment it was possible to give a more detailed figure of CBD over a landscape, consequently giving more detailed input for forest fire simulators. Although results are not conclusive, the procedure can be easily implemented by land managers when assessing the forest fires risk of their territories.&lt;/p&gt;

&lt;p&gt;Landslide susceptibility maps are often not validated after significant ... more &lt;p&gt;Landslide susceptibility maps are often not validated after significant landslide events. In this work, we analyse the impact of the Vaia windstorm on landslide activity in Belluno province (Veneto Region, NE, Italy). The storm hit the area on October 27-30, 2018, causing 8,679 ha of damaged forests and widespread landslides. As shown in the case of windstorm Vivian (1990) and Lothar (1999) (Switzerland), extreme meteorological events can influence slope stability after three to ten years (Bebi et al 2019). Through multi-temporal landslide inventory mapping post Vaia event, we want to access and validate the landslide susceptibility maps produced by using pre-event data from the Italian Landslide Inventory IFFI and assess if the susceptibility has increased in the areas affected by the storm. We used artificial intelligence techniques to prepare multi-temporal inventory and susceptibility maps pre and post-event. In the pre-event event inventory, 5934 landslides and 14 landslide conditioning factors were used to prepare the susceptibility models. We then validate the pre-event landslide susceptibility maps using post-event inventory from the 2018 Vaia windstorm and a following intense rainfall event that occurred in the same area in December 2020. A total of 542 landslides were mapped after the 2018 Vaia storm event, and an update to the landcover map as forest damage layer was used for post-event susceptibility analysis. This study is one of the first attempts to validate pre-event susceptibility maps by utilising multi-temporal artificial intelligence-based landslide inventories in Belluno province (Veneto Region, NE, Italy).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Bebi, P., Bast, A., Ginzler, C., Rickli, C., Sch&amp;#246;ngrundner, K., and Graf, F., 2019, Forest dynamics and shallow landslides: A large-scale GIS-analysis: Schweizerische Zeitschrift fur Forstwesen, v. 170, p. 318&amp;#8211;325, doi:10.3188/szf.2019.0318.&lt;/em&gt;&lt;/p&gt;

In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchm... more In this study, eight airborne laser scanning (ALS)-based single tree detection methods are benchmarked and investigated. The methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest

Forest Ecology and Management, 2021

Root reinforcement is the main contribution of forests in preventing and mitigating shallow soil ... more Root reinforcement is the main contribution of forests in preventing and mitigating shallow soil instabilities, one of the main hazards in mountain areas. Quantifying such factor remains complex because of a wide variability and uncertainty. This study aims to assess how spatial tree distribution affects root reinforcement, and whether the thinning operations can significantly reduce the contribution to the soil stabilization. We measured tree size and position in 103 sampling plots, located in pure and mixed forests with sweet chestnut, Norway spruce, European beech and silver fir in the Southern Alps. We developed, calibrated and validated a model for estimating root reinforcement at the stand scale, using the spatial distribution of tree diameter as input variable. Finally, we simulated how different silvicultural treatments (thinning 18% of the basal area, either randomly or in groups), affects root reinforcement. The average values of root reinforcement were 6.06, 7.97, 8.31 and 8.53 kN/m in chestnut, mixed, spruce, and beech forests respectively. Probability density functions of root reinforcement significantly differ among forest types. Randomly spaced thinning did not significantly modify root reinforcement, while group thinning reduced it five-fold. Such obtained values are consistent with previous works and can be used for assessing slope stability over forested hillslope with a poor availability of forestry data.

Forest Ecology and Management, 2020

Seed regeneration of sweet chestnut (Castanea sativa Miller) under different coppicing approaches.

Protection from landslides is one of the most important regulating services provided by forest ec... more Protection from landslides is one of the most important regulating services provided by forest ecosystems. Tree roots provide an increase in tensile strength, compression and shear resistance, compared to that uniquely due to the soil properties. This additional effect is known as root reinforcement. The degree of soil reinforcement given by roots have been modeled using laboratory and field data. The great spatial and temporal variability of root distribution is one of the main sources of uncertainty for the development of accurate and reliable models to quantify root reinforcement. The relative importance of stand structure remains poorly known. Here, we analyze the relationships between observed stand structure from a sample of spruce, beech, chestnut and mixed stands of the Southeastern Alps, and a spatially explicit model of root reinforcement. Data were collected in 20-m radius sampling units inclined 15-40°and covered by a low-resolution airborne LiDAR-derived canopy height model. Tree size and position were used to calculate root reinforcement through commonly used and calibrated models. Then, we studied the relationships between root reinforcement, stand structural indexes and area-based stand metrics from canopy height model. In specific conditions, the three groups of variables were correlated. Therefore, root reinforcement values might be spatially extrapolated through available canopy height models. Final step is to integrate the extrapolated values into a landslide susceptibility model, which combines other data available from forest plans, digital elevation models, geological and meteorological data. This study provides managers with a tool to periodically update maps of the service given by forest trees to protection of humans from landslides.

2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

Average data by plot of structure, composition, and regeneration in the Biogradska Gora forest

Forest Ecology and Management, 2021

Forest Ecology and Management, 2020

Seed regeneration of sweet chestnut (Castanea sativa Miller) under different coppicing approaches.