Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

COMPARING DATA ACQUISITION METHODOLOGIES FOR DTM PRODUCTION

In this paper we present the results related to several field campaigns conducted in the last three years in a small (4.4 km2) wilderness basin in Carnia, a tectonically active alpine region in northeast Italy. The study area is a typical alpine debris-flow dominated catchment where several landslides, including a significantly large one, were observed and mapped. The field survey carried out in 2007, 2008, 2009 and 2010 were focused on the large landslide of the basins and they consisted in the following steps: 1 – development of an accurate GPS network, 2 – make use of a long range Terrestrial Laser Scanner (TLS) for a detailed and local analysis of landslide movements, 3 – merge the data with an airborne LiDAR for a large scale analysis of the processes. Preliminary analysis consist in the comparison of different high resolution Digital Terrain Models (DTMs) in order to estimate the debris volume that has been triggered during the last movements of the landslides. Achieved results show that the integration between ALS and TSL data allows to produce DTMs of limited extent, with higher quality and level of detail. Such DTMs improve the capabilities for landslides analysis and modelling with respect to the use of LiDAR data only, even in areas providing limited or difficult access for the survey activity

Heroic viticulture: Environmental and socioeconomic challenges of unique heritage landscapes

Steep-slope agricultural landscapes cover a small fraction of global agricultural areas.1 Despite the limited coverage, they are relevant for high-quality food and wine production, history, and landscape value. On steep slopes, centuries of effort and tradition have created a unique cultural heritage to be preserved. Here, peculiar traditional local knowledge of soil and water conservation combined with agronomic practices (e.g., dry-stone wall terracing) has been handed down for generations. However, such landscapes are fragile and under threat

Remote sensing, modelling-based hazard and risk assessment, and management of agro-forested ecosystems

Agricultural and forested landscapes cover large areas over many countries; they are a very important natural resource that needs to be managed sustainably for both the environment and the local communities. Food security, population growth, urbanization, and intensive agricultural development are some of the factors that generate increasing demands for water and land resources in the context of global change. Therefore, potential impacts deriving from a changing climate, from more frequent and intense extreme events, and from anthropogenic activities can pose serious threats to economic infrastructure and development in the coming decades and also severely undermine food, fodder, water, and energy security for a growing global population. Significant recent changes in climate and in the hydrological cycle will impact land suitability for agricultural production and forest ecosystems. In particular, we can expect an increase, in some regions, in the frequency and intensity of extreme weather and weather-related events such as heat waves, floods, wind and snowstorms, droughts, etc. (IPCC, 2012; IPCC, 2021). Furthermore, anthropogenic activities can exacerbate consequences of an unbalanced environment, such as water quality degradation, groundwater depletion, land subsidence, erosion, and sedimentation (Delkash et al., 2018; Tarolli and Straffellini, 2020). Therefore, sustainable management and exploitation of first-order agricultural resources and forested areas, e.g. available land with favourable climate, soil, and water, will become even more important in the lives and activities of people. The 10 original papers included in this special issue address several of these aspects. In particular one review paper provides a general introduction to risk assessment for natural hazards, six papers focus on water- and weather-related hazards (four related to agriculture and two related to water quality at river basin scale), two papers address hazard assessment for the insurance sector, and one paper is related to challenges in agriculture–forest frontiers. The presented researches adopt different types of quantitative and qualitative modelling and spatial analysis and use remote sensing data, when relevant

Major threats caused by climate change to grapevine

The main worrying feature of climate change is its rapid evolution, in extent and variation, becoming less and less predictable. In this paper, we have reviewed the available literature and elaborated original data to outline how climate change will affect the grapevine cultivation and wine quality. We start by discussing which features of climate change will impact grapevine production most. The effects of heatwaves, air and soil temperature, extreme rainfall events, atmospheric evaporative demand, wildfires, and smoke are addressed. An increased frequency and intensity of heat waves since 2010 is shown in four grapevine production areas of Northern Italy. The focus then shifts to the impacts of the predicted increase in temperature and drought on frost risks, grapevine phenology, yield, berry quality and water needs as well as vine and vineyard carbon budgets. Climate change will challenge the achievement of current yields and wine quality as well as the ability of vineyards to sequester atmospheric carbon, but such effects will likely depend on the characteristics of the growing environments and on the varieties present. Climate change-related threats to grapevine call for a rapid implementation of adaptation strategies

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity:The Mobile Parkinson Disease Score

IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. OBJECTIVES: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. DESIGN, SETTING, AND PARTICIPANTS: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. MAIN OUTCOMES AND MEASURES: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. RESULTS: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. CONCLUSIONS AND RELEVANCE: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics

Characteristics of a prototype matrix of Silicon PhotoMultipliers (SiPM)

International audienceThis work reports on the electrical (static and dynamic) as well as on the optical characteristics of a prototype matrix of Silicon Photomultipliers (SiPM). The prototype matrix consists of 4 × 4 SiPM's on the same substrat fabricated at FBK-irst (Trento, Italy). Each SiPM of the matrix has an area of 1 × 1mm2 and it is composed of 625 microcells connected in parallel. Each microcell of the SiPM is a GM-APD (n+/p junction on P+ substrate) with an area of 40 × 40 μm2 connected in series with its integrated polysilicon quenching resistance. The static characteristics as breakdown voltage, quenching resistance, post-breakdown dark current as well as the dynamic characteristics as gain and dark count rate have been analysed. The photon detection efficiency as a function of wavelength and operation voltage has been also estimated

A multi-component flood risk assessment in the Maresme coast (NW Mediterranean)

Coastal regions are the areas most threatened by natural hazards, with floods being the most frequent and significant threat in terms of their induced impacts, and therefore, any management scheme requires their evaluation. In coastal areas, flooding is a hazard associated with various processes acting at different scales: coastal storms, flash floods, and sea level rise (SLR). In order to address the problem as a whole, this study presents a methodology to undertake a preliminary integrated risk assessment that determines the magnitude of the different flood processes (flash flood, marine storm, SLR) and their associated consequences, taking into account their temporal and spatial scales. The risk is quantified using specific indicators to assess the magnitude of the hazard (for each component) and the consequences in a common scale. This allows for a robust comparison of the spatial risk distribution along the coast in order to identify both the areas at greatest risk and the risk components that have the greatest impact. This methodology is applied on the Maresme coast (NW Mediterranean, Spain), which can be considered representative of developed areas of the Spanish Mediterranean coast. The results obtained characterise this coastline as an area of relatively low overall risk, although some hot spots have been identified with high-risk values, with flash flooding being the principal risk process

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

The depth to bedrock controls a myriad of processes by influencing subsurface flow paths, erosion rates, soil moisture, and water uptake by plant roots. As hillslope interiors are very difficult and costly to illuminate and access, the topography of the bedrock surface is largely unknown. This essay is concerned with the prediction of spatial patterns in the depth to bedrock (DTB) using high‐resolution topographic data, numerical modeling, and Bayesian analysis. Our DTB model builds on the bottom‐up control on fresh‐bedrock topography hypothesis of Rempe and Dietrich (2014) and includes a mass movement and bedrock‐valley morphology term to extent the usefulness and general applicability of the model. We reconcile the DTB model with field observations using Bayesian analysis with the DREAM algorithm. We investigate explicitly the benefits of using spatially distributed parameter values to account implicitly, and in a relatively simple way, for rock mass heterogeneities that are very difficult, if not impossible, to characterize adequately in the field. We illustrate our method using an artificial data set of bedrock depth observations and then evaluate our DTB model with real‐world data collected at the Papagaio river basin in Rio de Janeiro, Brazil. Our results demonstrate that the DTB model predicts accurately the observed bedrock depth data. The posterior mean DTB simulation is shown to be in good agreement with the measured data. The posterior prediction uncertainty of the DTB model can be propagated forward through hydromechanical models to derive probabilistic estimates of factors of safety.Key Points:We introduce an analytic formulation for the spatial distribution of the bedrock depthBayesian analysis reconciles our model with field data and quantifies prediction and parameter uncertaintyThe use of a distributed parameterization recognizes geologic heterogeneitiesPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137555/1/wrcr22005.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137555/2/wrcr22005_am.pd