Congiungere la modellazione dei movimenti di massa alla realtà

I flussi di massa sono pericoli naturali di tipo gravitativo tipici delle zone montane che causano ogni anno perdite economiche e vittime. I modelli numerici sono strumenti per prevedere la propagazione di potenziali eventi di flussi di massa su una determinata topografia, ma questi richiedono diversi input. Gli input e i processi che sostanzialmente influenzano i risultati dei modelli sono rappresentati dalla dal volume, dalle condizioni di innesco e dalle interazioni topografia – flusso di massa. Pertanto, l'obiettivo principale della tesi è quello di migliorare la quantificazione del volume coinvolto in un evento di flusso di massa e di aumentare la rappresentazione dell’interazione tra il flusso e la topografia. Quindi, sono stati studiati due tipi di flussi di massa: debris flow e valanghe di neve. Per quanto riguarda i debris flow, la tesi vuole migliorare l'affidabilità dei modelli analizzando l'aumento del volume del flusso attraverso l'erosione del letto del canale e il collasso di strutture di mitigazione. Per le valanghe di neve, lo studio ha come obbiettivo quello di migliorare l'identificazione delle possibili aree di distacco. La tesi è strutturata come una raccolta di articoli dei quali tre sono stati pubblicati e uno è in fase di revisione. Il primo articolo ha migliorato la rappresentazione dei fenomeni erosivi nei modelli numerici grazie ai dati di un evento di debris flow avvenuto nel bacino del rio Gere (Veneto, IT). Una funzione basata sui valori di pendenza è stata definita per calcolare il coefficiente di erosione, successivamente utilizzato per riprodurre l’erosione osservata nel canale. I risultati sono utili per migliorare l'accuratezza di futuri scenari da debris flow per i quali l'erosione è un importante processo nella dinamica del flusso. Il secondo studio ha definito una procedura per simulare l'effetto del collasso delle briglie di consolidamento in un evento di debris flow. La metodologia è stata sviluppata nel rio Rotian (Trentino, IT), dove un evento di pioggia estrema ha innescato un debris flow che ha provocato il collasso di una serie di 15 briglie. La metodologia sviluppata può essere direttamente applicata per mappare il rischio residuo dei canali da debris flow in cui siano presenti opere o dove la mancanza di manutenzione delle misure di mitigazione può diminuire la loro stabilità. Il terzo progetto riguarda lo studio della rugosità del terreno. Sette algoritmi di calcolo della rugosità sono stati testati in due aree studio al fine di identificare quale algoritmo possa rappresentare nel modo più appropriato le tipologie del terreno che interagiscono con i fenomeni di massa. I risultati hanno mostrato che il miglior algoritmo è risultato il vector ruggedness e che l’utilizzo di una risoluzione maggiore non ha migliorato le performance. Il quarto progetto ha analizzato la capacità di protezione delle foreste colpite da tempeste di vento. Due nuovi algoritmi per valutare le caratteristiche degli alberi abbattuti sono stati sviluppati. I risultati hanno evidenziato che il momento di protezione minimo delle foreste contro le valanghe di neve è dopo 10 anni l'evento di tempesta. Inoltre, gli algoritmi possono essere applicati direttamente su scala regionale per la gestione e il monitoraggio delle aree forestali colpite da tempeste. I diversi studi hanno analizzato i processi di erosione, l'effetto del collasso di briglie e l'identificazione di potenziali aree di innesco. I risultati dei quattro progetti hanno risposto ai corrispondenti obbiettivi, migliorando la comprensione dei flussi di massa e quindi la previsione di eventi futuri. Inoltre, i progetti forniscono importanti risultati metodologici e nuovi metodi sono stati sviluppati e testati al fine di migliorare la stima del volume dei flussi di massa. Tali metodi sono inoltre applicabili al di fuori delle aree di studio prese in esame, dando supporto a diversi stakeholder nella gestione dei rischi naturali.Mass flows are gravitational natural hazards typical of mountain areas causing economic losses and fatalities every year. Numerical models are a way to predict the propagation of potential mass flow events over a certain topography. To appropriately reproduce future events, models required different inputs. Inputs and processes consistently affecting the outcomes of mass flow models regard the released volume, the triggering conditions and the interaction with the topography and the features on the ground once the flow is in motion. Therefore, the main objective of the thesis is to improve the quantification of the input volume and to improve the implementation of processes of interaction with the basal topography. In this context, the focus has been placed on two types of mass flows: debris flows and snow avalanches. Regarding debris flows, the study aims to improve the reliability of models to capture the increase in flow volume through channel bed erosion and mitigation structure collapse. For snow avalanches, the study wants to improve the identification of possible avalanche release areas taking into account the role of different types of vegetation structures. The thesis was structured as a collection of articles of which three have been published and one is currently under review. The first paper investigated the improvement of debris flow erosion in computational models thanks to data of a severe event occurred in the Gere catchment (Veneto, IT). A function based on a smoothed terrain slope map was calibrated to derive the erosion coefficient, successively used to reproduce the observed erosion process occurred in the channel. Results can improve the reliability of future scenarios related to debris flows for which bed erosion plays an important role in volume increase. The second study defined a procedure to simulate the effect of check dam collapse in a debris flow event. The methodology was developed in the rio Rotian (Trentino, IT) where an extreme rainfall event triggered a debris flow that collapsed a series of 15 check dams. The adopted methodology can be straight applied to map the residual risk of mountain channels or where the lack of maintenance may decrease torrent countermeasure stability. The third project involves the study of terrain roughness. We tested seven algorithms computing terrain roughness in two study areas with the aim to identify which roughness algorithm can represent in the most appropriate way the features on the ground interacting with natural hazards. Outcomes showed that the best algorithm resulted the vector ruggedness and that the increase in data resolution did not improve the classification performance. Results can improve the reliability of mass flow propagation models over natural areas. The fourth project analysed the protection capacity of forests affected by windstorms. We developed and tested two algorithms to assess the characteristics of abated trees. Results assessed that the time of minimum level of forest protection against snow avalanches in 10 years after the storm event. The developed algorithms can be straight applied at regional scale to monitor and improve the management of windthrow areas. The projects investigated entrainment processes, effect of mitigation structure failures and the identification of potential triggering areas. Outcomes of the four projects filled the respective gaps of knowledge, improving the understanding of mass flows and then the prediction of future events. Furthermore, the projects have strong methodological outcomes and new methods to improve the volume estimation of mass flows have been developed and tested. Such methods are further applicable outside of the study areas, supporting different stakeholders in the management of natural hazards of mountain areas

Congiungere la modellazione dei movimenti di massa alla realtà

I flussi di massa sono pericoli naturali di tipo gravitativo tipici delle zone montane che causano ogni anno perdite economiche e vittime. I modelli numerici sono strumenti per prevedere la propagazione di potenziali eventi di flussi di massa su una determinata topografia, ma questi richiedono diversi input. Gli input e i processi che sostanzialmente influenzano i risultati dei modelli sono rappresentati dalla dal volume, dalle condizioni di innesco e dalle interazioni topografia – flusso di massa. Pertanto, l'obiettivo principale della tesi è quello di migliorare la quantificazione del volume coinvolto in un evento di flusso di massa e di aumentare la rappresentazione dell’interazione tra il flusso e la topografia. Quindi, sono stati studiati due tipi di flussi di massa: debris flow e valanghe di neve. Per quanto riguarda i debris flow, la tesi vuole migliorare l'affidabilità dei modelli analizzando l'aumento del volume del flusso attraverso l'erosione del letto del canale e il collasso di strutture di mitigazione. Per le valanghe di neve, lo studio ha come obbiettivo quello di migliorare l'identificazione delle possibili aree di distacco. La tesi è strutturata come una raccolta di articoli dei quali tre sono stati pubblicati e uno è in fase di revisione. Il primo articolo ha migliorato la rappresentazione dei fenomeni erosivi nei modelli numerici grazie ai dati di un evento di debris flow avvenuto nel bacino del rio Gere (Veneto, IT). Una funzione basata sui valori di pendenza è stata definita per calcolare il coefficiente di erosione, successivamente utilizzato per riprodurre l’erosione osservata nel canale. I risultati sono utili per migliorare l'accuratezza di futuri scenari da debris flow per i quali l'erosione è un importante processo nella dinamica del flusso. Il secondo studio ha definito una procedura per simulare l'effetto del collasso delle briglie di consolidamento in un evento di debris flow. La metodologia è stata sviluppata nel rio Rotian (Trentino, IT), dove un evento di pioggia estrema ha innescato un debris flow che ha provocato il collasso di una serie di 15 briglie. La metodologia sviluppata può essere direttamente applicata per mappare il rischio residuo dei canali da debris flow in cui siano presenti opere o dove la mancanza di manutenzione delle misure di mitigazione può diminuire la loro stabilità. Il terzo progetto riguarda lo studio della rugosità del terreno. Sette algoritmi di calcolo della rugosità sono stati testati in due aree studio al fine di identificare quale algoritmo possa rappresentare nel modo più appropriato le tipologie del terreno che interagiscono con i fenomeni di massa. I risultati hanno mostrato che il miglior algoritmo è risultato il vector ruggedness e che l’utilizzo di una risoluzione maggiore non ha migliorato le performance. Il quarto progetto ha analizzato la capacità di protezione delle foreste colpite da tempeste di vento. Due nuovi algoritmi per valutare le caratteristiche degli alberi abbattuti sono stati sviluppati. I risultati hanno evidenziato che il momento di protezione minimo delle foreste contro le valanghe di neve è dopo 10 anni l'evento di tempesta. Inoltre, gli algoritmi possono essere applicati direttamente su scala regionale per la gestione e il monitoraggio delle aree forestali colpite da tempeste. I diversi studi hanno analizzato i processi di erosione, l'effetto del collasso di briglie e l'identificazione di potenziali aree di innesco. I risultati dei quattro progetti hanno risposto ai corrispondenti obbiettivi, migliorando la comprensione dei flussi di massa e quindi la previsione di eventi futuri. Inoltre, i progetti forniscono importanti risultati metodologici e nuovi metodi sono stati sviluppati e testati al fine di migliorare la stima del volume dei flussi di massa. Tali metodi sono inoltre applicabili al di fuori delle aree di studio prese in esame, dando supporto a diversi stakeholder nella gestione dei rischi naturali.Mass flows are gravitational natural hazards typical of mountain areas causing economic losses and fatalities every year. Numerical models are a way to predict the propagation of potential mass flow events over a certain topography. To appropriately reproduce future events, models required different inputs. Inputs and processes consistently affecting the outcomes of mass flow models regard the released volume, the triggering conditions and the interaction with the topography and the features on the ground once the flow is in motion. Therefore, the main objective of the thesis is to improve the quantification of the input volume and to improve the implementation of processes of interaction with the basal topography. In this context, the focus has been placed on two types of mass flows: debris flows and snow avalanches. Regarding debris flows, the study aims to improve the reliability of models to capture the increase in flow volume through channel bed erosion and mitigation structure collapse. For snow avalanches, the study wants to improve the identification of possible avalanche release areas taking into account the role of different types of vegetation structures. The thesis was structured as a collection of articles of which three have been published and one is currently under review. The first paper investigated the improvement of debris flow erosion in computational models thanks to data of a severe event occurred in the Gere catchment (Veneto, IT). A function based on a smoothed terrain slope map was calibrated to derive the erosion coefficient, successively used to reproduce the observed erosion process occurred in the channel. Results can improve the reliability of future scenarios related to debris flows for which bed erosion plays an important role in volume increase. The second study defined a procedure to simulate the effect of check dam collapse in a debris flow event. The methodology was developed in the rio Rotian (Trentino, IT) where an extreme rainfall event triggered a debris flow that collapsed a series of 15 check dams. The adopted methodology can be straight applied to map the residual risk of mountain channels or where the lack of maintenance may decrease torrent countermeasure stability. The third project involves the study of terrain roughness. We tested seven algorithms computing terrain roughness in two study areas with the aim to identify which roughness algorithm can represent in the most appropriate way the features on the ground interacting with natural hazards. Outcomes showed that the best algorithm resulted the vector ruggedness and that the increase in data resolution did not improve the classification performance. Results can improve the reliability of mass flow propagation models over natural areas. The fourth project analysed the protection capacity of forests affected by windstorms. We developed and tested two algorithms to assess the characteristics of abated trees. Results assessed that the time of minimum level of forest protection against snow avalanches in 10 years after the storm event. The developed algorithms can be straight applied at regional scale to monitor and improve the management of windthrow areas. The projects investigated entrainment processes, effect of mitigation structure failures and the identification of potential triggering areas. Outcomes of the four projects filled the respective gaps of knowledge, improving the understanding of mass flows and then the prediction of future events. Furthermore, the projects have strong methodological outcomes and new methods to improve the volume estimation of mass flows have been developed and tested. Such methods are further applicable outside of the study areas, supporting different stakeholders in the management of natural hazards of mountain areas

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

Extraction of thalweg networks from DTMs: application to badlands

Contact: [email protected] study gully spatial patterns in the badlands using a continuous thalweg vector network, this paper presents methods to extract the badlands' thalweg network from a regular grid digital terrain model (DTM) by combining terrain morphology indices with a drainage algorithm. This method will delineate a thalweg only where the DTM denotes a significant curvature with respect to DTM accuracy and relies on three major steps. First, discontinuous concave areas were detected from the DTM using morphological criteria, either the plan curvature or the convergence index. Second, the concave areas were connected using a drainage algorithm, which provides a continuous, thick, tree-structured scheme. We assumed that these areas were physically significant and corresponded to a gully floor. Finally, the thick path was reduced to its main course and vectorised to obtain a thalweg network. The methods were applied to both virtual and actual DTM cases. The actual case was a LiDAR DTM of the Draix badlands in the French Alps. The obtained networks were quantitatively compared, both with a network obtained using the usual drainage area criteria and with a reference network mapped in the field. The CI-based network showed the great potential for thalweg network extraction

Predicting glacier accumulation area distributions

A mass balance model based on energy balance at the terrain surface was developed and used to predict glacier accumulation areas in the Jotunheimen, Norway. Spatially distributed melt modelling used local climate and energy balance surfaces to drive predictions, derived from regional climate and topographic data. Predictions had a temporal resolution of 1 month and a spatial resolution of 100 m, which were able to simulate observed glacier accumulation area distributions. Data were stored and manipulated within a GIS and spatial trends and patterns within the data were explored. These trends guided the design of a suite of geomorphologically and climatologically significant variables which were used to simulate the observed spatial organisation of climatic variables, specifically temperature, precipitation and wind speed and direction. DEM quality was found as a critical factor in minimising error propagation. A new method of removing spatially and spectrally organised DEM error is presented using a fast Fourier transformation. This was successfully employed to remove error within the DEM minimising error propagation into model predictions. With no parameter fitting the modeled spatial distribution of snowcover showed good agreement with observed distributions. Topographic maps and a Landsat ETM+ image are used to validate the predictions and identify areas of over or under prediction. Topographically constrained glaciers are most effectively simulated, where aspect, gradient and altitude impose dominant controls on accumulation. Reflections on the causes of over or under prediction are presented and future research directions to address these are outlined. Sensitivity of snow accumulation to climatic and radiative variables was assessed. Results showed the mass balance of accumulation areas is most sensitive to air temperature and cloud cover parameterisations. The model was applied to reconstruct snow accumulation at the last glacial maximum and under IPCC warming scenarios to assess the sensitivity of melt to changing environmental conditions, which showed pronounced sensitivity to summer temperatures Low data requirements: regional climate and elevation data identify the model as a powerful tool for predicting the onset, duration and rate of melt for any geographical area

Uncertainties in Digital Elevation Models: Evaluation and Effects on Landform and Soil Type Classification

Digital elevation models (DEMs) are a widely used source for the digital representation of the Earth's surface in a wide range of scientific, industrial and military applications. Since many processes on Earth are influenced by the shape of the relief, a variety of different applications rely on accurate information about the topography. For instance, DEMs are used for the prediction of geohazards, climate modelling, or planning-relevant issues, such as the identification of suitable locations for renewable energies. Nowadays, DEMs can be acquired with a high geometric resolution and over large areas using various remote sensing techniques, such as photogrammetry, RADAR, or laser scanning (LiDAR). However, they are subject to uncertainties and may contain erroneous representations of the terrain. The quality and accuracy of the topographic representation in the DEM is crucial, as the use of an inaccurate dataset can negatively affect further results, such as the underestimation of landslide hazards due to a too flat representation of relief in the elevation model. Therefore, it is important for users to gain more knowledge about the accuracy of a terrain model to better assess the negative consequences of DEM uncertainties on further analysis results of a certain research application. A proper assessment of whether the purchase or acquisition of a highly accurate DEM is necessary or the use of an already existing and freely available DEM is sufficient to achieve accurate results is of great qualitative and economic importance. In this context, the first part of this thesis focuses on extending knowledge about the behaviour and presence of uncertainties in DEMs concerning terrain and land cover. Thus, the first two studies of this dissertation provide a comprehensive vertical accuracy analysis of twelve DEMs acquired from space with spatial resolutions ranging from 5 m to 90 m. The accuracy of these DEMs was investigated in two different regions of the world that are substantially different in terms of relief and land cover. The first study was conducted in the hyperarid Chilean Atacama Desert in northern Chile, with very sparse land cover and high elevation differences. The second case study was conducted in a mid-latitude region, the Rur catchment in the western part of Germany. This area has a predominantly flat to hilly terrain with relatively diverse and dense vegetation and land cover. The DEMs in both studies were evaluated with particular attention to the influence of relief and land cover on vertical accuracy. The change of error due to changing slope and land cover was quantified to determine an average loss of accuracy as a function of slope for each DEM. Additionally, these values were used to derive relief-adjusted error values for different land cover classes. The second part of this dissertation addresses the consequences that different spatial resolutions and accuracies in DEMs have on specific applications. These implications were examined in two exemplary case studies. In a geomorphometric case study, several DEMs were used to classify landforms by different approaches. The results were subsequently compared and the accuracy of the classification results with different DEMs was analysed. The second case study is settled within the field of digital soil mapping. Various soil types were predicted with machine learning algorithms (random forest and artificial neural networks) using numerous relief parameters derived from DEMs of different spatial resolutions. Subsequently, the influence of high and low resolution DEMs with the respectively derived land surface parameters on the prediction results was evaluated. The results on the vertical accuracy show that uncertainties in DEMs can have diverse reasons. Besides the spatial resolution, the acquisition technique and the degree of improvements made to the dataset significantly impact the occurrence of errors in a DEM. Furthermore, the relief and physical objects on the surface play a major role for uncertainties in DEMs. Overall, the results in steeper areas show that the loss of vertical accuracy is two to three times higher for a 90 m DEM than for DEMs of higher spatial resolutions. While very high resolution DEMs of 12 m spatial resolution or higher only lose about 1 m accuracy per 10° increase in slope steepness, 30 m DEMs lose about 2 m on average, and 90 m DEMs lose more than 3 m up to 6 m accuracy. However, the results also show significant differences for DEMs of identical spatial resolution depending on relief and land cover. With regard to different land cover classes, it can be stated that mid-latitude forested and water areas cause uncertainties in DEMs of about 6 m on average. Other tested land cover classes produced minor errors of about 1 – 2 m on average. The results of the second part of this contribution prove that a careful selection of an appropriate DEM is more crucial for certain applications than for others. The choice of different DEMs greatly impacted the landform classification results. Results from medium resolution DEMs (30 m) achieved up to 30 % lower overall accuracies than results from high resolution DEMs with a spatial resolution of 5 m. In contrast to the landform classification results, the predicted soil types in the second case study showed only minor accuracy differences of less than 2 % between the usage of a spatial high resolution DEM (15 m) and a low resolution 90 m DEM. Finally, the results of these two case studies were compared and discussed with other results from the literature in other application areas. A summary and assessment of the current state of knowledge about the impact of a particular chosen terrain model on the results of different applications was made. In summary, the vertical accuracy measures obtained for each DEM are a first attempt to determine individual error values for each DEM that can be interpreted independently of relief and land cover and can be better applied to other regions. This may help users in the future to better estimate the accuracy of a tested DEM in a particular landscape. The consequences of elevation model selection on further results are highly dependent on the topic of the study and the study area's level of detail. The current state of knowledge on the impact of uncertainties in DEMs on various applications could be established. However, the results of this work can be seen as a first step and more work is needed in the future to extend the knowledge of the effects of DEM uncertainties on further topics that have not been investigated to date

Analysis of two sources of variability of basin outflow hydrographs computed with the 2D shallow water model Iber: Digital Terrain Model and unstructured mesh size

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract:] Modelling hydrological processes with fully distributed models based on the shallow water equations implies a high computational cost, which often limits the resolution of the computational mesh. Therefore, in practice, modellers need to find a compromise between spatial resolution, numerical accuracy and computational cost. Moreover, this balance is probably related to the accuracy and resolution of the underlying Digital Terrain Model (DTM). In this work, it is studied the effect of the DTM resolution and the size of the computational mesh on the results and on the runtime of a hydrological model based on the 2D shallow water equations. Seven rainfall events in four different basins have been modelled using 3 DTMs and 3 different mesh resolutions. The results obtained highlight the relevance of the vertical accuracy versus the horizontal resolution of the DTMs. Furthermore, it has been observed that mesh resolutions greater than 25 m, together with LiDAR-based DTMs with horizontal resolution greater than 25 m, provide comparable outflow hydrographs.Xunta de Galicia; ED481B-2021-108Xunta de Galicia; ED431C 2021/44Xunta de Galicia; ED431C 2018/56Fondo Europeo de Desarrollo Regional (FEDER); 0034_RISC_ML_6_

GIS and remote sensing based mapping of microtopography and vegetation composition in a boreal mire complex

Small scale variations in mire surface elevation referred to as microtopography are fundamental characteristics of mire ecosystems especially in the boreal region. Microtopography commonly classified into hummocks and hollows play a major role in several ecological, hydrological and biogeochemical processes including vegetation composition and carbon and methane dynamics. This makes microforms a crucial factor to account on when aiming for modeling ecosystem fluxes and monitoring peatland ecosystem change and resilience under shifting climatic conditions. However, quantitively modelling approaches using the technological advantages of remote sensing applications are limited so far and current methods lack of simplicity and straight forward mapping ability. In this study a new novel and simple modelling approach for classifying mire microtopography, only based on a digital elevation model (DEM), got applied and tested on four study sites of Kulbäcksliden peatland research infrastructure (northern Sweden). Furthermore, a vegetation classification was performed on the same sites using random forest (RF) classifiers with and without microtopography as an input to evaluate the effect of microforms on the classification accuracy results. The results indicate promising tendencies for the applicability of the new microtopographic approach even though the accuracy results point out an over estimation of hummock and hollow features, which could be resolved by adapting new height thresholds. The highest overall accuracy of for the vegetation classification was reached using all possible input parameters including microtopography. Still only minor improvements can be observed using microtopography with regards to fine resolution spectral data and the need of optimized height thresholds for microtopography