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Natural Disturbances and Protection Forests: At the Cutting Edge of Remote Sensing Technologies for the Rapid Assessment of Protective Effects against Rockfall
Written By
Emanuele Lingua, Niccolò Marchi, Francesco Bettella, Maximiliano Costa, Francesco Pirotti, Marco Piras, Matteo Garbarino, Donato Morresi and Raffaella Marzano
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Protection forests can be severely affected by natural disturbances, whose consequences could greatly alter the fundamental ecosystem services they are providing. Assessing and monitoring the status of the protective effects, particularly within disturbed stands, is therefore of vital importance, with timing being a critical issue. Remote sensing technologies (e.g., satellite imagery, LiDAR, UAV) are widely available nowadays and can be effectively applied to quantify and monitor the protective effects of Alpine forests. This is especially important after abrupt changes in forest cover and structure following the occurrence of a disturbance event. In this contribution, we present a brief introduction on remote sensing technologies and their potential contribution to protection forest management, followed by two case studies. In particular, we focus on research areas within protection forests against rockfall affected by windthrow (i.e., the 2018 storm Vaia in the Eastern Italian Alps, where LiDAR and UAV data were used), and forest fires (i.e., the 2017 fall fires in the Western Italian Alps, involving Sentinel-2 image analyses).
*Address all correspondence to: emanuele.lingua@unipd.it
1. Introduction
In mountain areas, forests that directly protect human assets (i.e., houses, roads, touristic and sport facilities, etc.) against rockfall cover an area of more than 20,000 km2 (www.alpine-space.eu/project/rockthealps/). These stands are defined as direct object protection forests (see chapter [1] of this book) and are providing a valuable ecosystem service to the Alpine communities. To perform an effective protection, protection forests should have specific characteristics. These include stand density and average tree size, which could be effective against different natural hazards [2], particularly gravity-driven ones (see chapter [3] of this book). Indeed, forests can provide both an active protection, avoiding the occurrence of natural hazards (e.g., impeding avalanche release), and a passive one, mitigating their impacts (i.e., in the case of rockfall), depending on their position along the slope [4].
The ability to offer a protective effect is not a permanent characteristic of a stand. Throughout their development, forests can be subject to a variety of perturbations, potentially resulting in modifications of their structural attributes that could change their protective effect. Natural disturbances, i.e., discrete events in time that disrupt ecosystem, community, or population structure and change resources, substrate availability, or the physical environment [5], can severely impact protection forests. Avalanches, forest fires, windstorms and landslides are some of the most common disturbances in mountain forests in the Alps, whose effects can profoundly influence stand dynamics.
Natural disturbances are globally expected to increase in frequency, severity, and extent due to both climate change and land use change [6, 7]. These possible alterations in disturbance regimes could result in massive modifications of the structure and composition of protection forests, with potential negative implications on the ecosystem services they are currently providing [8, 9].
Adopting appropriate forest management allows maintaining the ideal protection profile of forest stands and sustaining their protective effect [10]. Silvicultural management can also contribute to mitigating the impact of some types of disturbances, particularly those that have a lower intensity. To guide forest management in this framework, it is necessary to identify protection forests, assess their protective effect and promptly detect any alteration in its efficacy. Available field data are generally not sufficient to properly evaluate the protective effect of forests over large areas, and the costs of specific surveys are usually not sustainable [11]. Furthermore, following the occurrence of a disturbance, an early assessment of the status of protection forests and their residual protective role is fundamental.
Remote sensing tools can provide sound solutions for detecting both abrupt and gradual changes in forest stands. These tools are a valid and well-established source of data for evaluating earth surface characteristics. Active sensors (e.g., laser scanner, radar) can provide useful 3D information on forest structures and are able to extract tree size and spatial arrangement [12], while passive sensors can be used to infer vegetation status and forest cover [13]. For example, active Synthetic Aperture Radar (SAR) sensors emit a polarized signal at wavelengths in the microwave range of the electromagnetic spectrum and record the backscattered intensity at different polarizations. Depending on the emitted wavelength, the local incidence angle and other factors, the backscatter behaves differently according to land-cover type, texture and even vegetation biomass. This allows detecting specific types of land-cover and changes that occur over time. The advantage of active technologies is that they are largely independent from lighting and atmospheric conditions.
In contrast, passive sensors collect light from the sun that is reflected from the Earth surface. Spectral signatures from surface objects are created by sampling reflected light at sensor-specific wavelengths. These spectral signatures can be analyzed through transforming the spectral components (e.g., vegetation indices) and via classification and regression methods, also using modern artificial intelligence approaches (e.g., neural networks, random forest).
Integrating data from active and passive sensors can provide complementary information related to forest attributes, ranging from biomass [14] to structural parameters and canopy characteristics [15]. The European Copernicus programme manages Sentinel 1 and Sentinel 2 missions that respectively provide active and passive remote sensing data at up to 10 m resolution. They were launched in 2014 and 2015, respectively, and have a revisit time of a few days. The Copernicus services offer unprecedented temporal and spatial coverage over forest stands, allowing an accurate assessment of the effects of disturbances and their impacts on forest ecosystem services.
At the forest stand scale, it is now possible to apply new technologies for fast data acquisition. Portable or handheld LiDAR is an innovative solution that can prove very effective since it allows to acquire data (i.e., point clouds) by simply walking in the forest (Figure 1). The sensor can be installed onto a hand-held system or can be carried in a backpack. This system works by emitting a laser beam, collecting the distance between the sensor and object, as well as beam angles and thus generating a point cloud. By coupling a digital camera, the point cloud generation can be enhanced since the color information helps the point matching. The registration of the point clouds is usually made adopting the Simultaneous Localization and Mapping (SLAM) approach, using several computer vision algorithms [16, 17].
Figure 1.
Point cloud generated by a mobile mapping platform for rapid mobile scanning (Kaarta stencil 2) in the Mompantero site (Susa Valley, Piemonte, Italy) after the 2017 Susa fire.
Unmanned Aerial Vehicles (UAV) [18, 19, 20], both multirotor and fixed wings, can be employed to collect data, using different types of sensors. Nowadays, it is possible to install daylight, near infrared, multi/hyper-spectral, and thermal cameras, as well as a laser scanner on a drone. These autonomously flying systems allow collecting data very rapidly and with a very high spatial resolution. Photogrammetric processing of the UAV imagery allows generating orthophotos with different radiometric information and point clouds for digital surface and elevation models.
In the following, we describe two case studies where active sensors at the stand scale and remote sensing products from passive sensors were applied to assess the status of protection forests following high-severity forest disturbances.
2. Assessing the protective effects of forests after high-severity disturbances
The most frequent abiotic disturbances within European forests are windthrow and forest fires. In recent years unprecedented events affected mountain stands, posing serious threats to their ability to provide fundamental ecosystem services (e.g., protection against natural hazards) and creating a series of issues to be solved by their post-disturbance management. Remote sensing applications were tested to assist in the different phases of emergency management.
2.1 The 2018 windthrow events in the Dolomites due to the the storm Vaia (Eastern Italian Alps)
At the end of October 2018, the storm Vaia affected the Central and Eastern Italian Alps, damaging more than 42,000 ha of forests with different levels of severity [21]. The windthrown forests were mostly located in steep terrain or in the valley bottom, where the wind was funneled. The majority of the stands on the slopes were protection forests. Salvage logging operations started right after the event and are still ongoing. However, in those areas where the potential for new avalanche releases in the absence of the forest cover was detected, it was decided to leave all the windthrown material on the ground until permanent or temporary technical protection structures are built. Research conducted in Switzerland after the storm Vivian in 1990 demonstrated that the presence of deadwood could have a positive effect in dissipating the energy of falling blocks [22, 23], by increasing the terrain roughness. However, an exhaustive quantification of this effect and its duration in relation to wood decay dynamics is still missing [24, 25]. Furthermore, leaving deadwood on-site can maintain a higher level of biodiversity [26] and enhance regeneration establishment. To incorporate the friction value provided by windthrown material into natural hazard simulation models, roughness estimation should be performed right after the events to assess the post-disturbance conditions. The spatialization of roughness data assessed through field surveys is quite complex. The spatial arrangement of deadwood elements on the ground affects rockfall in different ways: a continuous layer can for instance maximize rock energy dissipation, while tall clumps can result in a highly effective barrier for larger blocks. In the past this issue was solved by assigning the same value measured on a point location in the field to a homogeneous polygon (e.g., forest management unit, forest cover category). Recent remote sensing techniques allow performing a refined spatialization by providing spatially continuous data. Roughness information, relating to both standing and lying deadwood, can be obtained through LiDAR data. This approach has only recently been applied but has already proven to be effective in providing useful insights into the heterogeneity of the spatial arrangement of elements [27]. The availability of more spatially refined data on this feature improves simulation accuracy.
In the municipality of Colle S. Lucia (BL, Italy), in the framework of the RockTheAlps project (ASP462), the efficiency of a rockfall protection forest has been assessed in 2018, before the storm occurred. This was achieved by adopting a combined remote sensing and field data collection methodology (Figure 2). LiDAR data acquired in 2015 combined with a UAV data acquisition in July 2018 were used to extract single tree positions on the slopes.
Figure 2.
Canopy height models (CHMs) derived from LiDAR data before (left; 2015) and after (right; 2019) the storm Vaia in the Colcuc case study (Colle S. Lucia, BL, Italy).
The protective effect has been assessed using Rockyfor3D (v 5.2; [28]), running 1,000 simulations with a rock size corresponding to the 95th percentile of the rock deposits observed in the field (1.5 x 1.0 x 0.8 m, corresponding to 1.2 m3). Stand data were validated within field plots where other parameters needed for the rockfall simulations were also recorded. Based on the software outputs, the Overall Rockfall Protection Index (ORPI; [29]) was computed for three different positions on the slope: the state road at the bottom, the municipality road to the Colcuc village in the middle, and a severely affected section (checkpoints 1, 2, and 3, respectively, in Figure 3). This index describes and quantifies the protective effect against rockfall by integrating the proportion of stopped rocks (frequency) and the total rock energy reduction due to forest cover (intensity) [29].
Figure 3.
Rockfall simulation with Rockyfor3D in two scenarios: before (left; 2018) and after (right; 2019) the storm Vaia in the Colcuc case study (Colle S. Lucia, BL, Italy). Maps show the cumulative number of rock passages meaning the number of rocks going through a cell based on 1,000 simulations (i.e., number of rocks released per source cells). Checkpoints are specific locations on the ground (the state road at the bottom, the municipality road to the Colcuc village in the middle, and a severely affected section: checkpoints 1, 2, and 3, respectively), where the ORPI [29] was calculated.
Later that year, the storm Vaia hit the site, leaving a large amount of timber on the ground. In July 2019, new LiDAR data and aerial imagery were acquired, providing up-to-date information on the forest status after the event. A new set of simulations was then performed using the forest cover values resulting from the windthrow and new roughness values for the windthrown area, to take into account the obstacles provided by logs and uprooted stumps. This information has been directly extracted from the LiDAR scans adopting an approach that makes use of quantiles of point distribution on a height-normalized point cloud filtered for the first returns [30].
All the other input parameters have been maintained (i.e., rock characteristics, number of simulations, etc.) to consider the same rockfall scenario and compare results before and after the disturbance. Post-disturbance simulations highlighted a peculiar situation since the protective effect, at least in the short term, was even improved along the slope after Vaia. In each analyzed checkpoint, the ORPI value actually increased (Figure 4), in most cases leading to a promotion to the upper class of protective effect (e.g., from low to medium or medium to high; for details on class thresholds see [29]), meaning that the presence of lying deadwood exerted a positive effect in increasing the protection against rockfall. The protective effect should however be assessed through time, monitoring the decay dynamics, the reduction in height above ground of downed logs, and the displacement of logs due to downslope movements [31] and snow pressure. Remote sensing techniques such as LiDAR or UAV will continue to provide useful quantitative information about these dynamics.
Figure 4.
The overall protection (expressed by ORPI) against rockfall provided by the forest in the Colcuc case study (Colle S. Lucia, BL, Italy) above the three checkpoints before and right after the storm Vaia (see Figure 2 for checkpoint locations).
2.2 The 2017 large forest fires in the Piedmont region (Western Italian Alps)
The severe and prolonged summer drought, which occurred in South-Central Europe in 2017 [32] was a major predisposing factor for the simultaneous ignition and spread of several forest fires in the Piedmont Region of Italy during the second half of October. These fires affected nearly 10,000 ha, including more than 7,200 ha of forest stands. Given the importance of fire severity in determining post-fire recovery dynamics, its assessment was considered a key issue to guide post-disturbance management and particularly to identify priority areas where to first intervene. The extensive areas affected by the fires made the application of remote sensing techniques highly useful and different severity indices, commonly applied in other regions of the world, were tested at these sites.
Fire severity maps were produced for the 10 largest forest fires (extent > 50 ha) by adopting the approach formerly developed within the Fire Effects Monitoring and Inventory System [33], which is aimed at integrating optical satellite data and field data (Figure 5). A multitemporal analysis based on multispectral imagery acquired by Sentinel-2 was employed to map spectral changes induced by fire in the near infrared and the shortwave infrared wavelengths using indices that compare pre- and post-fire conditions based on the Normalized Burn Ratio (NBR): the differenced NBR [33], the Relative difference NBR [34], and the Relativized Burn Ratio [35]. Field data collected using the Composite Burn Index protocol (Figure 6) were employed to obtain independent severity ratings, to be used to calibrate and validate remote sensing results, relating detected radiometric change to actual fire effects on the ground (Figure 7) [33]. The Composite Burn Index is obtained within plots (in our case 20 m circular plots) by ocularly evaluating the degree of change induced by fire in five vegetative strata, from the substrates to the dominant trees. Different attributes per stratum are rated on a burn severity scale, ranging from 0.0 (no burn effect) to 3.0 (highest burn effect). Stratum, understory, overstory, and overall composite ratings are then obtained by adding up scores within each hierarchical level and dividing by the number of rated factors. The overall index represents the magnitude of fire effects combined across all strata. In particular, the relationship between the overall Composite Burn Index score assessed in 251 plots and each bi-temporal index was evaluated through nonlinear regression models, which subsequently provided a threshold for classifying bi-temporal indices into burn severity categories.
Figure 5.
Developed workflow to retrieve burn severity maps from remote sensing data based on remote sensing indices (Normalized Burn Ratio, NBR; differenced Normalized Burn Ratio, dNBR; Relative difference Normalized Burn Ratio, RdNBR; Relativized Burn Ratio, RBR) and field data (Composite Burn Index, CBI).
Figure 6.
Patches burned at different severity in protection forest stands dominated by Scots pine (Pinus sylvestris L.) and European larch (Larix decidua Mill.), and corresponding Composite Burn Index (CBI) values. CBI quantifies the degree of change induced by fire thorough ocular evaluation of different attributes in five vegetative strata within field plots, along a burn severity scale, ranging from 0.0 (no burn effect) to 3.0 (highest burn effect). The overall CBI value for a plot is obtained by averaging attribute scores for all strata and used to describe fire severity. This index can then be related to the Normalized Burn Ratio (NBR) index (and other bi-temporal indices based on the NBR index), obtained through remote sensing data.
Figure 7.
Burn severity maps derived from the Relative difference Normalized Burn Ratio (RdNBR) index for (a) the Susa fire and (b) the Cumiana fire (Piemonte, Italy).
The adopted methodology provided satisfying overall classification accuracies of severity maps, ranging from 77.7% to 79.3% depending on the bi-temporal index. Stands dominated by conifers, i.e., Scots pine (Pinus sylvestris L.) and European larch (Larix decidua Mill.), were burned by stand replacing crown fires, killing both the canopy trees and the understory (i.e. regeneration, shrubs), deeply affecting the soil organic layers and potentially compromising the protective effect for a long period of time. In contrast, broadleaf-dominated stands (e.g., European chestnut [Castanea sativa Mill.] and European beech [Fagus sylvatica L.]) were mainly burned by low and moderate severity fires [36], that affected only the understory layers, without major changes in the potential protective effect of the forest.
Overlapping the severity maps with the layers of protection forests against rockfall and the historical avalanche sites, priority areas were identified and mapped. Those stands characterized by high fire severity (namely those experiencing stand replacing crown fires) and a relevant protective function, whose protective effect had thus potentially been highly compromised, were selected to perform interventions devoted to the rapid recovery of the protective effect, adopting ecoengineering techniques (building wooden structures with burned logs) and afforestation.
The availability to collect timely information on the status of protection forests is of fundamental importance for their management, particularly in the aftermath of high-severity disturbances, both in the response and recovery phases. Currently several freely available data sources are accessible to forest and land managers, as well as new tools and software (Table 1), to increase the amount of and improve information required to support a sustainable forest management in the framework of global change challenges. Rapid mapping through remote sensing technologies operating over large areas allows monitoring and updating on-demand knowledge about the protective effect of a stand, providing key data to guide the decision-making process. Characterizing disturbance severity and relating its short- and long-term effects to the stand residual protective effect can have a direct applicability in forest management to spatially define intervention necessities and priorities.
Category
Details
Website
Multispectral satellite data at medium spatial resolution
Examples of freely available data, open-source software, and free-of-charge cloud-computing platforms for remote sensing data analysis (the list in the table is non-exhaustive).
The described methodologies and related technologies are currently operational and require medium level skills in using GIS and remote sensing tools. Concerning LiDAR, some pre-processed data (e.g., canopy height models) are more and more available for end-users, but directly managing the point clouds still remains a task restricted to more skilled experts. Given the rapid progress in the development of new remote sensing technologies and tools for describing, measuring and monitoring forest stands, the forest sector as a whole should invest in training and continuing education in this field to keep its members updated to be competitive in rapidly evolving scenarios.
The presented research was funded by the Interreg Alpine Space project “RockTheAlps” (ASP462), by Dept. TESAF, University of Padova in the frame of the project “Vaia FRONT” (CAVA_SID19_02), by the Veneto Region in the frame of the project Vaia-Land (OCDPC 558/2018), and by the Piemonte Region in the frame of the “Piano straordinario di interventi di ripristino in seguito agli incendi dell’autunno 2017” (DGR 18 Aprile 2019, n. 29-8813).
1.Teich M, Accastello C, Perzl F, Berger F. Protective forests for Ecosystem-based Disaster Risk Reduction (Eco-DRR) in the Alpine Space. In: Teich M, Accastello C, Perzl F, Kleemayr K, editors. Protective Forests as Ecosystem-based Solution for Disaster Risk Reduction (Eco-DRR). London: IntechOpen; 2022. DOI: 10.5772/intechopen.99505
2.Frehner M, Wasser B, Schwitter R. Sustainability and success monitoring in protection forests – Guidelines for silvicultural interventions in forests with protective functions. Umwelt-Vollzug. 2007;27:29
3.Perzl F, Bono A, Garbarino M, Motta R. Protective effects of forests against gravitational natural hazards. In: Teich M, Accastello C, Perzl F, Kleemayr K, editors. Protective Forests as Ecosystem-based Solution for Disaster Risk Reduction (Eco-DRR). London: IntechOpen; 2021. DOI: 10.5772/intechopen.99506
4.Berger F, Rey F. Mountain protection forests against natural hazards and risks: New French developments by integrating forests in risk zoning. Nat. Hazards. 2004;33:395-404. DOI: 10.1023/B:NHAZ.0000048468.67886.e5
5.White PS, Pickett STA. Natural disturbance and patch dynamics, an introduction. In: Pickett STA, White PS, editors. The Ecology of Natural Disturbance and Patch Dynamics. New York: Academic Press; 1985. pp. 3-13
7.Forzieri G, Girardello M, Ceccherini G, Spinoni J, Feyen L. Emergent vulnerability to climate-driven disturbances in European forests. Nat. Commun. 2021:1-12. DOI: 10.1038/s41467-021-21399-7
8.Lingua E, Bettella F, Pividori M, Marzano R, Garbarino M, Piras M, Kobal M, Berger F. The protective role of forests to reduce rockfall risks and impacts in the Alps under a climate change perspective. In: Leal Filho W, Nagy GJ, Borga M, Chavez Muñoz PD, Magnuszwesk A, editors. Climate Change, Hazards and Adaptation Options: Handling the Impacts of a Changing Climate. Cham, Switzerland: Springer; 2020. pp. 333-347. DOI: 10.1007/978-3-030-37425-9_18
9.Mina M, Bugmann H, Cordonnier T, Irauschek F, Klopcic M, Pardos M, Cailleret M. Future ecosystem services from European mountain forests under climate change. J. Appl. Ecol. 2017;54:389-401. DOI: 10.1111/1365-2664.12772
10.Dorren LKA, Berger F, Imeson AC, Maier B, Rey F. Integrity, stability and management of protection forests in the European Alps. For. Ecol. Manage. 2004;195:165-176. DOI: 10.1016/j.foreco.2004.02.057
11.Scheidl C, Heiser M, Vospernik S, Lauss E, Perzl F, Kofler A, Kleemayr K, Bettella F, Lingua E, Garbarino M, Skudnik M, Trappmann D, Berger F. Assessing the protective role of alpine forests against rockfall at regional scale. Eur. J. For. Res. 2020:139. DOI: 10.1007/s10342-020-01299-z
12.Eysn L, Hollaus M, Lindberg E, Berger F, Monnet JM, Dalponte M, Kobal M, Pellegrini M, Lingua E, Mongus D, Pfeifer N. A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the Alpine Space. Forests. 2015;6:1721-1747. DOI: 10.3390/f6051721
13.Piragnolo M, Pirotti F, Zanrosso C, Lingua E, Grigolato S. Responding to large-scale forest damage in an alpine environment with remote sensing, machine learning, and web-GIS. Remote Sensing. 2021:1541. DOI: 10.3390/rs13081541
14.Vaglio GL, Pirotti F, Callegari M, Chen Q, Cuozzo G, Lingua E, Notarnicola C, Papale D. Potential of ALOS2 and NDVI to estimate forest above-ground biomass, and comparison with lidar-derived estimates. Remote Sens. 2017;9:18. DOI: 10.3390/rs9010018
15.Zald HSJ, Wulder MA, White JC, Hilker T, Hermosilla T, Hobart GW, Coops NC. Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sens. Environ. 2016;176:188-201. DOI: 10.1016/j.rse.2016.01.015
16.Fan Y, Feng Z, Mannan A, Khan TU, Shen C, Saeed S. Estimating tree position, diameter at breast height, and tree height in real-time using a mobile phone with RGB-D SLAM. Remote Sens. 2018:10. DOI: 10.3390/rs10111845
17.Pierzchała M, Giguère P, Astrup R. Mapping forests using an unmanned ground vehicle with 3D LiDAR and graph-SLAM. Comput. Electron. Agric. 2018;145:217-225. DOI: 10.1016/j.compag.2017.12.034
18.Aicardi I, Garbarino M, Lingua A, Lingua E, Marzano R, Piras M. Monitoring post-fire forest recovery using multi-temporal digital surface models generated from. EARSeL eProceedings. 2016. DOI: 10.12760/01-2016-1-01
19.Adão T, Hruška J, Pádua L, Bessa J, Peres E, Morais R, Sousa JJ. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 2017:9. DOI: 10.3390/rs9111110
20.Torresan C, Berton A, Carotenuto F, Di Gennaro SF, Gioli B, Matese A, Miglietta F, Vagnoli C, Zaldei A, Wallace L. Forestry applications of UAVs in Europe: A review. Int. J. Remote Sens. 2017;38:2427-2447. DOI: 10.1080/01431161.2016.1252477
21.Chirici G, Giannetti F, Travaglini D, Nocentini S, Francini S, D’Amico G, Calvo E, Fasolini D, Broll M, Maistrelli F, Tonner J, Pietrogiovanna M, Oberlechner K, Andriolo A, Comino R, Faidiga A, Pasutto I, Carraro G, Zen S, Contarin F, Alfonsi L, Wolynski A, Zanin M, Gagliano C, Tonolli S, Zoanetti R, Tonetti R, Cavalli R, Lingua E, Pirotti F, Grigolato S, Bellingeri D, Zini E, Gianelle D, Dalponte M, Pompei E, Stefani A, Motta R, Morresi D, Garbarino M, Alberti G, Valdevit F, Tomelleri E, Torresani M, Tonon G, Marchi M, Corona P, Marchetti M. Forest damage inventory after the “Vaia” storm in Italy. For. – Riv. di Selvic. Ed Ecol. For. 2019;16:3-9. DOI: 10.3832/efor3070-016
22.Schönenberger W, Noack A, Thee P. Effect of timber removal from windthrow slopes on the risk of snow avalanches and rockfall. For. Ecol. Manage. 2005;213:197-208. DOI: 10.1016/j.foreco.2005.03.062
23.Wohlgemuth T, Schwitter R, Bebi P, Sutter F, Brang P. Post-windthrow management in protection forests of the Swiss Alps. Eur. J. For. Res. 2017;136:1029-1040. DOI: 10.1007/s10342-017-1031-x
24.Rammig A, Fahse L, Bugmann H, Bebi P. Forest regeneration after disturbance: A modelling study for the Swiss Alps. For. Ecol. Manage. 2006;222:123-136. DOI: 10.1016/j.foreco.2005.10.042
25.Frey W, Thee P. Avalanche protection of windthrow areas: A ten year comparison of cleared and uncleared starting zones. For. Snow Landsc. Res. 2002;77:89-107
26.Thorn S, Chao A, Georgiev KB, Müller J, Bässler C, Campbell JL, Castro J, Chen YH, Choi CY, Cobb TP, Donato DC, Durska E, Macdonald E, Feldhaar H, Fontaine JB, Fornwalt PJ, Hernández RMH, Hutto RL, Koivula M, Lee EJ, Lindenmayer D, Mikusiński G, Obrist MK, Perlík M, Rost J, Waldron K, Wermelinger B, Weiß I, Żmihorski M, Leverkus AB. Estimating retention benchmarks for salvage logging to protect biodiversity. Nat. Commun. 2020:11
27.Marchi N, Pirotti F, Lingua E. Airborne and terrestrial laser scanning data for the assessment of standing and lying deadwood: Current situation and new perspectives. Remote Sens. 2018;10:1356. DOI: 10.3390/rs10091356
28.Dorren LKA. Rockyfor3D (v5.2) revealed – Transparent description of the complete 3D rockfall model; ecorisQ pa; 2015. 32 p
29.Dupire S, Bourrier F, Monnet JM, Bigot S, Borgniet L, Berger F, Curt T. Novel quantitative indicators to characterize the protective effect of mountain forests against rockfall. Ecol. Indic. 2016;67:98-107. DOI: 10.1016/j.ecolind.2016.02.023
30.Costa M, Marchi N, Bettella F, Bolzon P, Berger F, Lingua E. Biological legacies and rockfall: The protective effect of a windthrown forest. Forests. 2021;12(9):1141. DOI: 10.3390/f12091141
31.Picco L, Scalari C, Iroumé A, Mazzorana B, Andreoli A. Large wood load fluctuations in an Andean basin. Earth Surf. Process. Landforms. 2020. DOI: 10.1002/esp.5030
32.Rita A, Camarero JJ, Nolè A, Borghetti M, Brunetti M, Pergola N, Serio C, Vicente-Serrano SM, Tramutoli V, Ripullone F. The impact of drought spells on forests depends on site conditions: The case of 2017 summer heat wave in southern Europe. Glob. Chang. Biol. 2020;26: 851-863. DOI: 10.1111/gcb.14825
33.Key CH, Benson NC. Landscape Assessment (LA) Sampling and Analysis Methods. USDA For. Serv. – Gen. Tech. Rep. RMRS-GTR. 2006
34.Miller JD, Thode AE. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta normalized burn ratio (dNBR). Remote Sens. Environ. 2007;109:66-80. DOI: 10.1016/j.rse.2006.12.006
35.Parks SA, Dillon GK, Miller C. A new metric for quantifying burn severity: The relativized burn ratio. Remote Sens. 2014;6:1827-1844. DOI: 10.3390/rs6031827
36.Morresi D, Marzano R, Lingua E, Motta R, Garbarino M. Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery. Remote Sens. Environ. 2022;269:112800. DOI: 10.1016/j.rse.2021.112800
Written By
Emanuele Lingua, Niccolò Marchi, Francesco Bettella, Maximiliano Costa, Francesco Pirotti, Marco Piras, Matteo Garbarino, Donato Morresi and Raffaella Marzano
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