Structure-from-Motion Derived Snow Cover in Burned Forests of the Western Oregon Cascades

Forest fire occurrence in the western US has increased rapidly since the 1980s, and most western US fires occur in the seasonal snow zone. Burned forests influence snow accumulation and melt patterns for years following fire, and understanding drivers of variability in snow cover across a burned landscape at the basin-scale is necessary for accurate hazard prediction and water resource forecasting. Basin-scale surveys of snowpack are possible with remote sensing, but accurate sensing methods such as Light Detection and Ranging (LiDAR) are often cost-prohibitive. In the last decade, structure-from-motion (SfM), an optical remote sensing technique, has emerged as an affordable alternative to LiDAR for high resolution snow depth mapping. While SfM technique has been used to survey snow in unforested regions, this method is not suitable in forested regions due to the inability of RGB cameras to penetrate the forest canopy. Yet the reduced canopy cover of burned forests may offer a unique opportunity to employ this method in regions otherwise not suitable for SfM surveys prior to burn occurrence. To understand the potential and limitations of SfM-derived snow depth and extent maps in burned forests, we collected aerial stereopair imagery over a 27 km2 region of the burned Breitenbush Watershed in the Oregon Cascades in September of 2022 and February of 2023. We surveyed a smaller region that overlaps the initial survey region in April of 2023. With SfM techniques, we created digital elevation models (DEMs) for each survey. The September DEM was subtracted from February and April DEMs to isolate February and April snowpack. Coincident with the April survey, 200 depth measurements were taken across five 0.8 km transects along a burn severity gradient. We compared modeled snow depth to measured snow depth at point locations through simple regression to understand how variability in modeled snow was driven by actual snow, and this regression was used to adjust SfM snow depth estimates. We used multilinear regressions to assess how variability in adjusted modeled snow was driven by burn severity, pre-fire vegetation, and topography. We then compared binary snow extent maps to Landsat fSCA through confusion matrices to assess how well SfM snow maps predicted snow extent. Lastly, we limited snow depth maps to an ideal region -high or moderate severity burned forest and snow-covered- and assessed how variability in modeled snow constrained by these conditions was driven by burn severity, topography, and vegetation. Multilinear regression showed that in the sampling region, variability in modeled snow was driven by only burn severity. We observed striking differences in the way terrain was modeled in low severity burn versus moderate and high severity burn. SfM modeled snow in low severity burn was significantly different from modeled snow in moderate and high severity burn. Modeled snow variability was far greater in low severity burn than in high and moderate severity burn, and modeled snow was greater in moderate and high severity burn. Our work indicates that SfM snow modeling in high and moderate severity burn regions is distinct from and likely more reliable than snow modeling in low severity burn regions where canopy cover obscures snow from the sensor

Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

Environmental disturbances in semi-arid ecosystems have highlighted the need to monitor current and future vegetation conditions across the landscape. Imaging spectroscopy provide the necessary information to derive vegetation characteristics at high-spatial resolutions across large geographic areas. The work of this thesis is divided into two sections focused on using imaging spectroscopy to estimate and classify vegetation cover, and approximate aboveground biomass in a semi-arid ecosystem. The first half of this thesis assesses the ability of imaging spectroscopy to derive vegetation classes and their respective cover across large environmental gradients and ecotones often associated with semi-arid ecosystems. Optimal endmember selection and endmember bundling are coupled with classification and spectral unmixing techniques to derive vegetation species and abundances across Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho at high spatial resolution (1 m). Results validated using field data indicated classification of aspen, Douglas fir, juniper, and riparian classes had an overall accuracy of 57.9% and a kappa coefficient of 0.43. Plant functional type classification, consisting of deciduous and evergreen trees, had an overall accuracy of 84.4% and a kappa coefficient of 0.68. Shrub, grass, and soil cover were predicted with an overall accuracy of 67.4% and kappa coefficient of 0.53. I conclude that imaging spectroscopy can be used to map vegetation communities in semi-arid ecosystems across large environmental gradients at high-spatial resolution and with high accuracy. The second half of this thesis focuses on monitoring the changes of aboveground biomass (AGB) from the 2015 Soda Fire, which burned portions of southwest Idaho and southeastern Oregon. Classifications derived in the first study are used to estimate AGB loss within a portion of RCEW, and these estimates are used to compare to gross estimates made over the full extent of the Soda Fire. I found that there was an AGB loss of 174M kg within RCEW and approximately 1.8B kg lost over the full extent of the Soda Fire. Additionally, a post-fire analysis was performed to provide insight into the amount of AGB that returned to both RCEW and the full extent of the Soda Fire. An estimated 2,100 – 208,000 kg of AGB had returned to the burned portion of RCEW one-year post fire, and approximately 3.2M kg of AGB had returned over the full extent of the Soda Fire. These AGB loss and re-growth estimates can be used by researchers and practitioners to monitor carbon flux across the Soda Fire and as baseline data for wildfires in semi-arid ecosystems

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

New Computational Methods for Automated Large-Scale Archaeological Site Detection

Aquesta tesi doctoral presenta una sèrie d'enfocaments, fluxos de treball i models innovadors en el camp de l'arqueologia computacional per a la detecció automatitzada a gran escala de jaciments arqueològics. S'introdueixen nous conceptes, enfocaments i estratègies, com ara lidar multitemporal, aprenentatge automàtic híbrid, refinament, curriculum learning i blob analysis; així com diferents mètodes d'augment de dades aplicats per primera vegada en el camp de l'arqueologia. S'utilitzen múltiples fonts, com ara imatges de satèl·lits multiespectrals, fotografies RGB de plataformes VANT, mapes històrics i diverses combinacions de sensors, dades i fonts. Els mètodes creats durant el desenvolupament d'aquest doctorat s'han avaluat en projectes en curs: Urbanització a Hispània i la Gàl·lia Mediterrània en el primer mil·lenni aC, detecció de monticles funeraris utilitzant algorismes d'aprenentatge automàtic al nord-oest de la Península Ibèrica, prospecció arqueològica intel·ligent basada en drons (DIASur), i cartografiat del patrimoni arqueològic al sud d'Àsia (MAHSA), per a la qual s'han dissenyat fluxos de treball adaptats als reptes específics del projecte. Aquests nous mètodes han aconseguit proporcionar solucions als problemes comuns d'estudis arqueològics presents en estudis similars, com la baixa precisió en detecció i les poques dades d'entrenament. Els mètodes validats i presentats com a part de la tesi doctoral s'han publicat en accés obert amb el codi disponible perquè puguin implementar-se en altres estudis arqueològics.Esta tesis doctoral presenta una serie de enfoques, flujos de trabajo y modelos innovadores en el campo de la arqueología computacional para la detección automatizada a gran escala de yacimientos arqueológicos. Se introducen nuevos conceptos, enfoques y estrategias, como lidar multitemporal, aprendizaje automático híbrido, refinamiento, curriculum learning y blob analysis; así como diferentes métodos de aumento de datos aplicados por primera vez en el campo de la arqueología. Se utilizan múltiples fuentes, como lidar, imágenes satelitales multiespectrales, fotografías RGB de plataformas VANT, mapas históricos y varias combinaciones de sensores, datos y fuentes. Los métodos creados durante el desarrollo de este doctorado han sido evaluados en proyectos en curso: Urbanización en Iberia y la Galia Mediterránea en el Primer Milenio a. C., Detección de túmulos mediante algoritmos de aprendizaje automático en el Noroeste de la Península Ibérica, Prospección Arqueológica Inteligente basada en Drones (DIASur), y cartografiado del Patrimonio del Sur de Asia (MAHSA), para los que se han diseñado flujos de trabajo adaptados a los retos específicos del proyecto. Estos nuevos métodos han logrado proporcionar soluciones a problemas comunes de la prospección arqueológica presentes en estudios similares, como la baja precisión en detección y los pocos datos de entrenamiento. Los métodos validados y presentados como parte de la tesis doctoral se han publicado en acceso abierto con su código disponible para que puedan implementarse en otros estudios arqueológicos.This doctoral thesis presents a series of innovative approaches, workflows and models in the field of computational archaeology for the automated large-scale detection of archaeological sites. New concepts, approaches and strategies are introduced such as multitemporal lidar, hybrid machine learning, refinement, curriculum learning and blob analysis; as well as different data augmentation methods applied for the first time in the field of archaeology. Multiple sources are used, such as lidar, multispectral satellite imagery, RGB photographs from UAV platform, historical maps, and several combinations of sensors, data, and sources. The methods created during the development of this PhD have been evaluated in ongoing projects: Urbanization in Iberia and Mediterranean Gaul in the First Millennium BC, Detection of burial mounds using machine learning algorithms in the Northwest of the Iberian Peninsula, Drone-based Intelligent Archaeological Survey (DIASur), and Mapping Archaeological Heritage in South Asia (MAHSA), for which workflows adapted to the project’ s specific challenges have been designed. These new methods have managed to provide solutions to common archaeological survey problems, presented in similar large-scale site detection studies, such as the low precision in previous detection studies and how to handle problems with few training data. The validated approaches for site detection presented as part of the PhD have been published as open access papers with freely available code so can be implemented in other archaeological studies

Multi-Scale Modelling of Cold Regions Hydrology

Numerical computer simulations are increasingly important tools required to address both research and operational water resource issues related to the hydrological cycle. Cold region hydrological models have requirements to calculate phase change in water via consideration of the energy balance which has high spatial variability. This motivates the inclusion of explicit spatial heterogeneity and field-testable process representations in such models. However, standard techniques for spatial representation such as raster discretization can lead to prohibitively large computational costs and increased uncertainty due to increased degrees of freedom. As well, semi-distributed approaches may not sufficiently represent all the spatial variability. Further, there is uncertainty regarding which process conceptualizations are used and the degree of required complexity, motivating modelling approaches that allow testing multiple working hypotheses. This thesis considers two themes. In the first, the development of improved modelling techniques to efficiently include spatial heterogeneity, investigate warranted model complexity, and appropriate process representation in cold region models is addressed. In the second, the issues of non-linear process cascades, emergence, and compensatory behaviours in cold regions hydrological process representations is addressed. To address these themes, a new modelling framework, the Canadian Hydrological Model (CHM), is presented. Key design goals for CHM include the ability to: capture spatial heterogeneity in an efficient manner, include multiple process representations, be able to change, remove, and decouple hydrological process algorithms, work both at point and spatially distributed scales, reduce computational overhead to facilitate uncertainty analysis, scale over multiple spatial extents, and utilize a variety of boundary and initial conditions. To enable multi-scale modelling in CHM, a novel multi-objective unstructured mesh generation software *mesher* is presented. Mesher represents the landscape using a multi-scale, variable resolution surface mesh. It was found that this explicitly captured the spatial heterogeneity important for emergent behaviours and cold regions processes, and reduced the total number of computational elements by 50\% to 90\% from that of a uniform mesh. Four energy balance snowpack models of varying complexity and degree of coupling of the energy and mass budget were used to simulate SWE in a forest clearing in the Canadian Rocky Mountains. It was found that 1) a compensatory response was present in the fully coupled models’ energy and mass balance that reduced their sensitivity to errors in meteorology and albedo and 2) the weakly coupled models produced less accurate simulations and were more sensitive to errors in forcing meteorology and albedo. The results suggest that the inclusion of a fully coupled mass and energy budget improves prediction of snow accumulation and ablation, but there was little advantage by introducing a multi-layered snowpack scheme. This helps define warranted complexity model decisions for this region. Lastly, a 3-D advection-diffusion blowing snow transport and sublimation model using a finite volume method discretization via a variable resolution unstructured mesh was developed. This found that the blowing snow calculation was able to represent the spatial redistribution of SWE over a sub-arctic mountain basin when compared to detailed snow surveys and the use of the unstructured mesh provided a 62\% reduction in computational elements. Without the inclusion of blowing snow, unrealistic homogeneous snow covers were simulated which would lead to incorrect melt rates and runoff contributions. This thesis shows that there is a need to: use fully coupled energy and mass balance models in mountains terrain, capture snow-drift resolving scales in next-generation hydrological models, employ variable resolution unstructured meshes as a way to reduce computational time, and consider cascading process interactions

Light detection and ranging (LiDAR) : what we can and cannot see in the forest for the trees

Recently concerns over anthropogenic carbon pollution have received increased global attention and research in forest biomass and carbon sequestration has gained momentum. Light Detection and Ranging (LiDAR) remote sensing has in the last decade demonstrated forest measurement and biomass estimation potential. The project objective was to compare LiDAR forest biomass estimates to traditional field biomass estimates in a conifer predominant forest located in the Pacific Northwest region of the United States. Chapter 2 of this dissertation investigated mapping-grade GPS accuracy in determining tree locations. Results indicated that post processing of coded pseudorange satellite signals is the most accurate of those we tested for GPS surveying under a conifer dominant forest canopy. Chapter 3 compared LiDAR, total station, and GPS receiver discrete point elevations and DEMs across a range of forest settings. Average total station plot elevation differences ranged from -0.06 m (SD 0.40) to -0.59 m (SD 0.23) indicating that LiDAR elevations are higher than actual elevations. Average plot GPS elevation differences ranged from 0.24 (SD 1.55) to 2.82 m (SD 4.58), and from 0.27 (SD 2.33) to 2.69 m (SD 5.06) for LiDAR DEMs. Chapter 4 assessed LiDAR’s ability to measure three-dimensional forest structure and estimate biomass using single stem (trees and shrubs) remote sensing. The LiDAR data tree extraction computer software programs FUSION, TreeVaW, and watershed segmentation were compared. LiDAR spatial accuracy assessment resulted in overall average error and standard deviation (SD) for FUSION, TreeVaW, and watershed segmentation of 2.05 m (SD 1.67 m), 2.19 m (SD 1.83 m), and 2.31 m (SD 1.94 m) respectively. Overall average LiDAR tree height error and standard deviations (SD) respectively for FUSION, TreeVaW and watershed segmentation were -0.09 m (SD 2.43 m), 0.28 m (SD 1.86 m), and 0.22 m (2.45 m) in even-age, uneven-age, and old growth plots combined; and for one clearcut plot 0.56 m (SD 1.07 m), 0.28 m (SD 1.69 m), and 1.17 m (SD 0.68 m), respectively. Biomass comparisons included feature totals per plot, mean biomass per feature by plot, and total biomass by plot for each extraction method. Overall LiDAR biomass estimations resulted in FUSION and TreeVaW underestimating by 25 and 31% respectively, and watershed segmentation overestimating by approximately 10%. LiDAR biomass underestimation occurred in 66% and overestimation occurred in 34% of the plot comparisons

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

Post-fire Tree Establishment Patterns at the Subalpine Forest-Alpine Tundra Ecotone: A Case Study in Mount Rainier National Park

Climatic changes have induced striking altitudinal and latitudinal vegetation shifts throughout history. These shifts will almost certainly recur in the future; threatening other flora and fauna, and influencing climate feedback loops. Changes in the spatial distribution of vegetation are most conspicuous at physiognomically distinct ecotones, particularly between the subalpine forest and alpine tundra. Traditionally, ecological research has linked abiotic variables with the position of this ecotone (e.g., cold temperatures inhibit tree survival at high elevations). Thus, the prevailing assumption states that this ecotone is in equilibrium or quasi-equilibrium with the surrounding physical environment and that any dynamic shifts express direct linkages with the physical environment. This dissertation employs a landscape ecology approach to examine the abiotic and biotic ecological mechanisms most important in controlling tree establishment at this ecotone. The study site is on the western slopes of Mount Rainier, which was severely burned by a slash fire in 1930. Therefore, a crucial underlying assumption is that the ecological mechanisms controlling tree establishment are similar at disturbed and undisturbed sites. I exploited the use of 1970 CORONA satellite imagery and 2003 aerial photography to map 33 years of changes in arboreal vegetation. I created detailed maps of abiotic variables from a LIDAR-based DEM and biotic variables from classified remotely sensed data. I linked tree establishment patterns with abiotic and biotic variables in a GIS, and analyzed the correlations with standard logistic regression and logistic regression in the hierarchical partitioning framework at multiple spatial resolutions. A biotic factor (proximity to previously existing trees) was found to exert a strong influence on tree establishment patterns; equaling and in most cases exceeding the significance of the abiotic factors. The abiotic setting was more important at restricted spatial extents near the extreme upper limits of the ecotone and when analyzing coarse resolution data, but even in these cases proximity to existing trees remained significant. The strong overall influence of proximity to existing trees on patterns of tree establishment is unequivocal. If the underlying assumption of this dissertation is true, it challenges the long-held ecological assumption that vegetation in mountainous terrain is in equilibrium with and most strongly influenced by the surrounding physical environment

Quantifying scale relationships in snow distributions

2007 Summer.Includes bibliographic references.Spatial distributions of snow in mountain environments represent the time integration of accumulation and ablation processes, and are strongly and dynamically linked to mountain hydrologic, ecologic, and climatic systems. Accurate measurement and modeling of the spatial distribution and variability of the seasonal mountain snowpack at different scales are imperative for water supply and hydropower decision-making, for investigations of land-atmosphere interaction or biogeochemical cycling, and for accurate simulation of earth system processes and feedbacks. Assessment and prediction of snow distributions in complex terrain are heavily dependent on scale effects, as the pattern and magnitude of variability in snow distributions depends on the scale of observation. Measurement and model scales are usually different from process scales, and thereby introduce a scale bias to the estimate or prediction. To quantify this bias, or to properly design measurement schemes and model applications, the process scale must be known or estimated. Airborne Light Detection And Ranging (lidar) products provide high-resolution, broad-extent altimetry data for terrain and snowpack mapping, and allow an application of variogram fractal analysis techniques to characterize snow depth scaling properties over lag distances from 1 to 1000 meters. Snow depth patterns as measured by lidar at three Colorado mountain sites exhibit fractal (power law) scaling patterns over two distinct scale ranges, separated by a distinct break at the 15-40 m lag distance, depending on the site. Each fractal range represents a range of separation distances over which snow depth processes remain consistent. The scale break between fractal regions is a characteristic scale at which snow depth process relationships change fundamentally. Similar scale break distances in vegetation topography datasets suggest that the snow depth scale break represents a change in wind redistribution processes from wind/vegetation interactions at small lags to wind/terrain interactions at larger lags. These snow depth scale characteristics are interannually consistent, directly describe the scales of action of snow accumulation, redistribution, and ablation processes, and inform scale considerations for measurement and modeling. Snow process models are designed to represent processes acting over specific scale ranges. However, since the incorporated processes vary with scale, the model performance cannot be scale-independent. Thus, distributed snow models must represent the appropriate process interactions at each scale in order to produce reasonable simulations of snow depth or snow water equivalent (SWE) variability. By comparing fractal dimensions and scale break lengths of modeled snow depth patterns to those derived from lidar observations, the model process representations can be evaluated and subsequently refined. Snow depth simulations from the SnowModel seasonal snow process model exhibit fractal patterns, and a scale break can be produced by including a sub-model that simulates fine-scale wind drifting patterns. The fractal dimensions provide important spatial scaling information that can inform refinement of process representations. This collection of work provides a new application of methods developed in other geophysical fields for quantifying scale and variability relationships