Determining forest parameters for avalanche simulation using remote sensing data

Brožová, Natalie; Fischer, Jan-Thomas; Bühler, Yves; Bartelt, Perry; Bebi, Peter

doi:10.1016/j.coldregions.2019.102976

Cited by 29 publications

(21 citation statements)

References 25 publications

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“…It had no pairs of overlapping distributions for all the roughness categories, and it accurately assigned high roughness values to higher vegetation, avalanche barriers and other land-cover categories. In comparison, the DTMbased approach generally underestimated the surface roughness (Brožová et al, 2020). The case study, applying nu-merical avalanche modelling to a DSM and a DTM, showed that surface roughness plays a decisive role in the avalanche runout distance and the flow path.…”

Section: Applications For Natural Hazard Assessmentmentioning

confidence: 94%

“…The forested areas are based on the forest characteristics specified in Swiss law (Brändli and Speich, 2007) and delineated using an orthophoto. We determined the runout distance manually as the projected run length in the main flow of the avalanche, where the maximum flow depth of the simulated avalanche drops to zero (Brožová et al, 2020). We also evaluated the maximum flow height over the simulation duration.…”

Section: Case Study: Snow Avalanche Modellingmentioning

confidence: 99%

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Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Brožová

Baggio

D’Agostino

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Surface roughness influences the release of avalanches and the dynamics of rockfall, avalanches and debris flow, but it is often not objectively implemented in natural hazard modelling. For two study areas, a treeline ecotone and a windthrow-disturbed forest landscape of the European Alps, we tested seven roughness algorithms using a photogrammetric digital surface model (DSM) with different resolutions (0.1, 0.5 and 1 m) and different moving-window areas (9, 25 and 49 m2). The vector ruggedness measure roughness algorithm performed best overall in distinguishing between roughness categories relevant for natural hazard modelling (including shrub forest, high forest, windthrow, snow and rocky land cover). The results with 1 m resolution were found to be suitable to distinguish between the roughness categories of interest, and the performance did not increase with higher resolution. In order to improve the roughness calculation along the hazard flow direction, we tested a directional roughness approach that improved the reliability of the surface roughness computation in channelised paths. We simulated avalanches on different elevation models (lidar-based) to observe a potential influence of a DSM and a digital terrain model (DTM) using the simulation tool Rapid Mass Movement Simulation (RAMMS). In this way, we accounted for the surface roughness based on a DSM instead of a DTM, which resulted in shorter simulated avalanche runouts by 16 %–27 % in the two study areas. Surface roughness above a treeline, which in comparison to the forest is not represented within the RAMMS, is therefore underestimated. We conclude that using DSM-based surface roughness in combination with DTM-based surface roughness and considering the directional roughness is promising for achieving better assessment of terrain in an alpine landscape, which might improve the natural hazard modelling.

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Section: Applications For Natural Hazard Assessmentmentioning

confidence: 94%

Section: Case Study: Snow Avalanche Modellingmentioning

confidence: 99%

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Brožová

Baggio

D’Agostino

et al. 2021

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…Maroschek et al (2015) combined remote sensing data (i.e., a LiDAR-based normalized crown model and a volume map) and inventories to generate realistic fine-grained forest landscapes with single-tree level information as input to the spatially explicit hybrid model used to assess the protective effect of the vegetation. Brožová et al (2020) determined forest parameters for avalanche simulation using remote sensing data, specifically photogrammetry-based vegetation height model and LiDAR-based vegetation height model.…”

Section: Appropriate Remote Sensing Methods For the Monitoring Of Protective Function Indicatorsmentioning

confidence: 99%

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

Torresan

Luyssaert

Filippa

et al. 2021

Climate-Smart Forestry in Mountain Regions

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Monitoring forest responses to climate-smart forestry (CSF) is necessary to determine whether forest management is on track to contribute to the reduction and/or removal of greenhouse gas emissions and the development of resilient mountain forests. A set of indicators to assess “the smartness” of forests has been previously identified by combining indicators for sustainable forest management with the ecosystem services. Here, we discuss the remote sensing technologies suitable to assess those indicators grouped in forest resources, health and vitality, productivity, biological diversity, and protective functions criteria. Forest cover, growing stock, abiotic, biotic, and human-induced forest damage, and tree composition indicators can be readily assessed by using established remote sensing techniques. The emerging areas of phenotyping will help track genetic resource indicators. No single existing sensor or platform is sufficient on its own to assess all the individual CSF indicators, due to the need to balance fine-scale monitoring and satisfactory coverage at broad scales. The challenge of being successful in assessing the largest number and type of indicators (e.g., soil conditions) is likely to be best tackled through multimode and multifunctional sensors, increasingly coupled with new computational and analytical approaches, such as cloud computing, machine learning, and deep learning.

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“…The importance of providing remote sensing and other geo-spatial data for sustainable forest management is increasing persistently (Pasanen et al, 2005;Kivinen et al, 2008;Store & Antikainen, 2010;Brožová et al, 2020;Shang et al, 2020). Today, forest data and especially remote sensing-derived data can be generated more quickly than we can process it, and it could be integrated into daily decision making processes.…”

Section: Introductionmentioning

confidence: 99%

Open geo-spatial data for sustainable forest management: Lithuanian case

Tiškutė-Memgaudienė¹,

Mozgeris²,

Gaižutis³

2020

Research for Rural Development

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In Lithuania, forests are managed by Lithuanian State Forest Enterprise, municipalities, ministries, etc. and private forest owners. About 50% of all forest land is State importance, privately owned forests cover 40% of forest land, and about 10% of forest land belongs to forests reserved for restitution. Forest management of private ownership force many challenges, because private forest owners are people, who have purchased or received the property after restitution, and often lacks knowledge about forest resources, its dynamics and sustainable forest management. As remote sensing is a valuable source for forest monitoring, because it provides periodic data on forest resource and condition status, these methods are gaining increased attention worldwide. In this context, more scientific efforts are made at developing remote sensing derived geo-spatial data services for sustainable forest management through a web service platform, which would integrate geo-information into daily decision making processes and operation for private forest owners. This article presents a review of privately owned forests’ statistics, questionnaire-based survey about GIS usage and demand for forest owners in Lithuania and links available sources of open geo-spatial data useful for sustainable forest management.

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Determining forest parameters for avalanche simulation using remote sensing data

Cited by 29 publications

References 25 publications

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Multiscale analysis of surface roughness for the improvement of natural hazard modelling

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

Open geo-spatial data for sustainable forest management: Lithuanian case

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