<p>Forest fires are a natural disturbance largely affected by global change... more <p>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.</p><p>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.</p><p>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.</p><p>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 <em>Pinus spp.</em> 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.</p><p>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.</p>
<p>Landslide susceptibility maps are often not validated after significant ... more <p>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).</p><p> </p><p><em>Bebi, P., Bast, A., Ginzler, C., Rickli, C., Schöngrundner, K., and Graf, F., 2019, Forest dynamics and shallow landslides: A large-scale GIS-analysis: Schweizerische Zeitschrift fur Forstwesen, v. 170, p. 318–325, doi:10.3188/szf.2019.0318.</em></p>
<p>Forest fires are a natural disturbance largely affected by global change... more <p>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.</p><p>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.</p><p>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.</p><p>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 <em>Pinus spp.</em> 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.</p><p>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.</p>
<p>Landslide susceptibility maps are often not validated after significant ... more <p>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).</p><p> </p><p><em>Bebi, P., Bast, A., Ginzler, C., Rickli, C., Schöngrundner, K., and Graf, F., 2019, Forest dynamics and shallow landslides: A large-scale GIS-analysis: Schweizerische Zeitschrift fur Forstwesen, v. 170, p. 318–325, doi:10.3188/szf.2019.0318.</em></p>
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