Standard Deviation Of Elevation Research Articles

Scaling use of the rust fungus Puccinia punctiformis for biological control of Canada thistle (Cirsium arvense (L.) Scop.): First report on a U.S. statewide effort

Canada thistle (Cirsium arvense (L.) Scop., CT) is one of the worst weeds threatening temperate regions of the world. A host-specific rust fungus, Puccinia punctiformis (F. Strauss) Rohl., is known to cause systemic disease of CT, ultimately killing individuals and reducing stand densities. In 2013, it was demonstrated that fall inoculation of rosettes with coarsely ground leaves bearing P. punctiformis telia can successfully initiate epiphytotics. In the same year, a cooperative project between the Colorado Department of Agriculture and United States Department of Agriculture was initiated, in which CT patches across the state of Colorado (USA) were inoculated and tracked over subsequent years for changes in stem density. Here, we report our findings from 8 years (2014–2021) of monitoring effort. At most sites (N = 87), CT stem densities declined, from a mean (±SE) of 87.9 (±6.5) stems to 44.7 (±4.2). These declines however were spatially-autocorrelated, and likely attributable to local growing conditions, as mean annual daily maximum temperature and standard deviation of elevation, as well as climatic conditions around the times of both treatment and monitoring, were found to be important predictors of CT decline. Further, we observed that the amount of inoculum deployed, timing since last release, and method in which it was spread locally at a site were also associated with the magnitude of CT stem decline. These results are indicative of the value of P. punctiformis as a CT biological control agent.

Explanation of the influence of geomorphometric variables on the landform classification based on selected areas in Poland

In recent years, automatic image classification methods have significantly progressed, notably black box algorithms such as machine learning and deep learning. Unfortunately, such efforts only focused on improving performance, rather than attempting to explain and interpret how classification models actually operate. This article compares three state-of-the-art algorithms incorporating random forests, gradient boosting and convolutional neural networks for geomorphological mapping. It also attempts to explain how the most effective classifier makes decisions by evaluating which of the geomorphometric variables are most important for automatic mapping and how they affect the classification results using one of the explainable artificial intelligence techniques, namely accumulated local effects (ALE). This method allows us to understand the relationship between predictors and the model’s outcome. For these purposes, eight sheets of the digital geomorphological map of Poland on the scale of 1:100,000 were used as the reference material. The classification results were validated using the holdout method and cross-validation for individual sheets representing different morphogenetic zones. The terrain elevation entropy, absolute elevation, aggregated median elevation and standard deviation of elevation had the greatest impact on the classification results among the 15 geomorphometric variables considered. The ALE analysis was conducted for the XGBoost classifier, which achieved the highest accuracy of 92.8%, ahead of Random Forests at 84% and LightGBM at 73.7% and U-Net at 59.8%. We conclude that automatic classification can support geomorphological mapping only if the geomorphological characteristics in the predicted area are similar to those in the training dataset. The ALE plots allow us to analyze the relationship between geomorphometric variables and landform membership, which helps clarify their role in the classification process.

An Elevation Editing Method for Water Bodies in InSAR derived DSM

In the InSAR-DSM, which is the Digital Surface Model (DSM) acquired through Interferometric Synthetic Aperture Radar (InSAR), the presence of significant noise, voids, and elevation anomalies in water bodies is pervasive. These disparities arise from factors such as diminished backscattering due to electromagnetic wave absorption by water, surface-based specular reflection, and the impact of temporal decorrelation. To tackle this challenge, an automated elevation editing method for water bodies based on InSAR-DSM is introduced. Water bodies are automatically categorized as oceans, lakes, or rivers based on their morphological characteristics and location criteria. Distinct algorithms are subsequently deployed for elevation editing within each category. Results demonstrate that after editing water body elevations, all missing values within the original InSAR-DSM for water bodies are effectively addressed. Elevation anomalous pixels within water bodies are reduced by 68.5%, and the elevation standard deviation, which reflects elevation variability in water bodies, is decreased by 47.31%. This process effectively rectifies water body elevations within InSAR-DSM.

From peaks to people: The association between physical topography and generalized trust in China

According to frontier settlement theory, physical topography is a critical factor that shapes a region's psychological landscape. To determine whether physical topography also impacts generalized trust, we conducted an exploratory study to investigate the relationship between mountainousness and individual's generalized trust (N = 26,910) in China. The multilevel modeling analysis revealed positive associations between the standard deviation of elevation and generalized trust. Random Forest analyses confirmed mountainousness indicators as significant predictors of generalized trust and also uncovered a complex nonlinear relationship between these indicators and generalized trust. Our results revealed that individuals residing in China's mountainous areas are more prone to trust others.

Sensitivity of tidal range assessments to harmonic constituents and analysis timeframe

Tides exhibit variability over time. This study proposes a methodology for selecting a representative timeframe for tidal range energy analyses, when constrained to a typical, short-term, lunar month-long period. We explore how the selection of particular timeframes skews findings of energy assessments, especially for cross-comparisons across studies. This exercise relies on metrics assessing the magnitude and variability of a tidal signal relative to longer-term nodal cycle quantities. Results based on UK tide gauges highlight that tide characteristics exhibit significant variations temporally within a lunar month. Relative to quantities of tidal elevation standard deviation or average potential energy, values can vary by 15% and 30% respectively. For each lunar month, interquartile range values for tidal height and energy can deviate by 45% from the mean. Spatially, we observe a satisfactory correlation only once sufficient constituents are considered. In that case, a representative timeframe can be identified for comparative tidal range scheme assessments within the same tidal system. In contrast, timeframes with high tidal variability distort individual project performance, particularly under fixed operation. The methodology, if integrated to marine energy resource and environmental impact assessments, would deliver marine power generation insights over a project lifetime that enable robust design comparisons across sites.

Errors of five satellite precipitation products for different rainfall intensities

Revealing the errors of the satellite precipitation products (SPPs) for various rainfall intensity groups is crucial for their applications in the hydro-meteorological field. However, the spatial maps of errors of SPPs for different rainfall intensity groups and the impact of elevation on them are still unclear. In this study, the spatial maps of errors of the five satellite precipitation products (i.e., IMERG-Late, IMERG-Final, GSMaP-MVK, GSMaP-Gauge, and PERSIANN-CCS) for the four rainfall intensity groups (i.e., light precipitation, moderate precipitation, heavy precipitation, and storm) are depicted. A novel score, namely the relative rainfall occurrence bias (ROB), is proposed as a means to evaluate the reproducibility of the spatial patterns of SPPs for different surface rainfall intensity occurrences. All five SPPs cannot reproduce the spatial patterns of the four surface rainfall intensity occurrences. Despite that, all SPPs except for PERSIANN-CCS can capture almost all rainfall intensity groups (except light precipitation) because their probability of detection (POD) values exceeds 0.8. Moreover, we found that the detection capability of SPPs for light precipitation decreases with the increasing standard deviation of elevation (SDE) but is the opposite for bias, while the impacts of elevation on the errors of SPPs for other rainfall intensity groups are unapparent. The overestimations of the number of surface heavy precipitation and storm occurrences for IMERG-Late and GSMaP-MVK are mainly due to overestimating the rainfall amount of the lower rain-rate surface rainfall events, while their total biases for heavy precipitation and storms originate from hit bias. The potential limitations of the bias correction algorithms used in the IMERG and GSMaP retrieval systems in correcting different rainfall intensity groups are identified by comparing the performance between gauge-adjusted products and satellite-only products. Finally, the optimal SPP precipitation product for each rainfall intensity group is discussed. The findings reported in this study can provide valuable information for data users to select a product that best meets the requirements of the applications.

Statistical roughness properties of the gravel bed surfaces in a meandering channel

To investigate the statistical roughness properties of gravel bed surfaces in meandering channels, a meandering channel model is constructed, the flowrate is selected as the control variable, and the cross-sectional water levels, cross-sectional velocities and bed surface elevations are measured. The experimental results show that the elevation probability distributions of the detrended water-worked bed surfaces in the meandering channel are positively skewed and leptokurtic. With the increasing flowrate, the elevation standard deviation and the elevation skewness increases, but the elevation kurtosis decreases. The armoring layer in the meandering channel can resist a certain extent of increasing flow strength, but its protective ability is very limited. Once the armoring layer is destructed, the bed surface roughness will increase significantly. The bed surface roughness and the averaged influence scale of the constituent grains are large in the main stream path, while those in the main stream separating areas are small and decrease along the longitudinal direction. For any local zone, the roughness and the averaged influence scale in the longitudinal direction are obviously larger compared with the corresponding values in the lateral direction.

The influence of bedrock river morphology and alluvial cover on gravel entrainment. Part 2: Modelling critical shear stress

AbstractThe critical shear stress (τc) at which grains are entrained on a bedrock surface is important for determining how bedrock rivers evolve through changes in sediment cover and bedrock erosion. The difference in τc for grains on bedrock and alluvial surfaces also determines whether a channel may be susceptible to runaway alluviation. Bedrock channel beds can have a wide variety of morphologies, but we do not fully understand how this variation affects τc. Here we address how bedrock morphology alters the grain entrainment parameters of pivoting angle, grain exposure and roughness height z0, and thus τc. In our companion article we used scaled, 3D printed replicas of seven bedrock surfaces to measure grain pivoting angles for four grain sizes. For three surfaces, pivoting angles were also measured with 25–100% sediment cover. In this second article, we combine these pivot angle data with measurements of grain exposure and surface roughness (standard deviation of elevations, σz) to predict τc using a force–balance model. The bedrock topography produces substantial variation in τc; for a given grain diameter (D), a 3.6× range of σz across the surfaces without sediment cover produces up to a 5.1× variation in τc. For comparison, for any single surface, τc varies by up to 2.5× for a fourfold range in grain size. Comparison to previous models with less representation of grain‐scale geometry shows that in our results grains move at lower values of dimensionless critical shear stress (τ*c), and that τ*c decreases more quickly with increasing D/σz. However, direct comparison is difficult because previous relationships are based on a hydraulic roughness length that cannot be easily predicted without hydraulic data. Our results propose a new relationship between D/σz and τ*c, but further development and testing require datasets that combine measurements of flow, τc and grain‐scale geometry.

Free amino acid quantification in cloud water at the Puy de Dôme station (France)

Abstract. Eighteen free amino acids (FAAs) were quantified in cloud water sampled at the Puy de Dôme station (PUY – France) during 13 cloud events. This quantification has been performed without concentration or derivatization, using liquid chromatography hyphened to mass spectrometry (LC-MS) and the standard addition method to correct for matrix effects. Total concentrations of FAAs (TCAAs) vary from 1.2 to 7.7 µM, Ser (serine) being the most abundant AA (23.7 % on average) but with elevated standard deviation, followed by glycine (Gly) (20.5 %), alanine (Ala) (11.9 %), asparagine (Asn) (8.7 %), and leucine/isoleucine (Leu/I)​​​​​​​ (6.4 %). The distribution of AAs among the cloud events reveals high variability. TCAA constitutes between 0.5 and 4.4 % of the dissolved organic carbon measured in the cloud samples. AA quantification in cloud water is scarce, but the results agree with the few studies that investigated AAs in this aqueous medium. The environmental variability is assessed through a statistical analysis. This work shows that AAs are correlated with the time spent by the air masses within the boundary layer, especially over the sea surface before reaching the PUY. The cloud microphysical properties' fluctuation does not explain the AA variability in our samples, confirming previous studies at the PUY. We finally assessed the sources and the atmospheric processes that potentially explain the prevailing presence of certain AAs in the cloud samples. The initial relative distribution of AAs in biological matrices (proteins extracted from bacterial cells or mammalian cells, for example) could explain the dominance of Ala, Gly, and Leu/I. AA composition of aquatic organisms (i.e., diatom species) could also explain the high concentrations of Ser in our samples. The analysis of the AA hygroscopicity also indicates a higher contribution of AAs (80 % on average) that are hydrophilic or neutral, revealing the fact that other AAs (hydrophobic) are less favorably incorporated into cloud droplets. Finally, the atmospheric aging of AAs has been evaluated by calculating atmospheric lifetimes considering their potential transformation in the cloud medium by biotic or abiotic (mainly oxidation) processes. The most concentrated AAs encountered in our samples present the longest atmospheric lifetimes, and the less dominant ones are clearly efficiently transformed in the atmosphere, potentially explaining their low concentrations. However, this cannot fully explain the relative contribution of several AAs in the cloud samples. This reveals the high complexity of the bio-physico-chemical processes occurring in the multiphase atmospheric environment.

Using geomorphometric approach to investigate spatial pattern and intensity of erosional dissection in a block-faulted topography (Orlickie-Bystrzyckie Mountains, Central Europe)

The paper presents an attempt to recognize areas of enhanced erosional dissection, based on a combined analysis of several geomorphometric variables and using four different approaches (rank-based, cold and hot spot analysis, k-means clustering of ‘raw’ data, and k-means clustering of local measures of spatial autocorrelation). The study area includes the Orlickie-Bystrzyckie Mountains Block (OBMB), which is a block-faulted range within the Sudetes Mountains, Central Europe, typified by fault-generated range fronts, remnant planation surfaces and topographic ramps due to large-scale tilting. Geology is diverse, with basement rocks and sedimentary cover, and the relevant timescale is late Cenozoic. Morphometric variables derived from a LiDAR-based DEM include contour length, standard deviation of elevation, median valley depth and standard deviation of curvature, and are considered to account for various morphometric signatures of erosional dissection. While deriving compound measures of dissection, three grid cell sizes were considered. Within the OBMB several areas were identified as erosionally dissected to a considerable degree. Their spatial pattern is complex and reflects several factors simultaneously. Differential uplift appears as the main trigger, but the erosional response varies, depending mainly on the size of runoff contributing area – itself related to the pattern of uplift, and the distance from the margins of elevated areas. Lithology (bedrock resistance to erosion) seems to play a subordinate role. We also discuss various issues related to the procedures themselves, pointing out their advantages and limitations. Procedural setups proposed in this paper are not site-specific and may be used in various topographic and geological settings.

Land-Surface Quantitative Analysis to Investigate the Spatial Distribution of Gravitational Landforms along Rocky Coasts

The increasing availability of high-quality digital elevation models (DEMs) has been associated with a growing interest in developing quantitative analyses aimed at taking advantage of these detailed, updated, and promising digital datasets. Land-surface quantitative (LSQ) analysis is valuable for describing the land-surface topography and performing measures of the signature of specific geomorphic processes, taking into account site-specific geological contexts and morphoclimatic settings, proving to be particularly effective in transitional environments, such as rocky coasts. This paper presents the results of research aimed at investigating the spatial distribution of gravitational landforms along rocky coasts, by means of LSQ analysis based on a DEM with a ground resolution of 2 m, derived from airborne LiDAR (light detection and ranging) surveys. The study area is at Mt. San Bartolo (Northern Marche, Italy) and characterized by a sea cliff diffusely affected by gravitational phenomena of different sizes and types. Geomorphological and geological field data, interpretations of remotely sensed datasets derived from ad hoc unmanned aerial vehicle (UAV) flights, and DEM-derived hillshades were also adapted to support LSQ analysis. In detail, four morphometric variables (slope, roughness, terrain ruggedness index, and elevation standard deviation) were computed and the outputs evaluated based on visual–spatial inspections of derived raster datasets, descriptive statistics, and joint comparison. Results reveal the best performing variables and how combined interpretations can support the identification and mapping of zones characterized by varying spatial distribution of gravitational landforms of different types. The findings achieved along the Mt. San Bartolo rocky coast confirm that an approach based on land-surface quantitative analysis can act as a proxy to efficiently investigate gravitational slope processes in coastal areas, especially those that are difficult to reach with traditional field surveys.

Bilateral Differential Topography-A Novel Topographic Algorithm for Keratoconus and Ectatic Disease Screening.

Purpose: The purpose of this study was to establish a novel bilateral differential topographic algorithm and assess its efficacy for screening of keratoconus and corneal ectasia before corneal refractive surgery. Methods: One hundred and sixty-one consecutive patients (115 men and 46 women, aged 22.8 ± 6.8 years) with keratoconus, including clinical keratoconus, subclinical keratoconus, forme fruste keratoconus (FFK), and corneal ectasia (KC group) and one hundred and seventy-four consecutive patients (97 men and 77 women, aged 25.1 ± 6.7 years) with ametropia (control group) visiting the Eye and ENT hospital of Fudan University from June 2018 to April 2021 were included. Bilateral differential keratometry, elevation, and pachymetry topographies were composed based on raw topographic data obtained by a Scheimpflug imaging anterior segment analyzer. Key bilateral differential characteristic parameters were calculated. SPSS 20 (SPSS Inc., IBM) was used for statistical analyses and the receiver operating characteristic (ROC) curves were used to determine the diagnostic efficacies. Results: Mann-Whitney tests detected that the front keratometry, front elevation, corneal pachymetry, and back elevation maximal, mean, and standard deviation values within a 1.5-mm radius of the bilateral differential topography were all significantly higher in the KC group than in the control group (all p-values <0.001). The front keratometry mean (ΔFKmean) and standard deviation (ΔFKsd) and the front elevation standard deviation (ΔFEsd) and maximal (ΔFEmax) values within a 1.5-mm radius of the bilateral differential topography yielded the four highest accuracies (area under the ROC curve = 0.985, 0.985, 0.984, and 0.983, respectively) for discriminating KC cases (including FFK cases) from normal cases. Cut-off values of 0.75 diopters (D) for the ΔFKmean, 0.67 D for the ΔFKsd, 2.9 μm for the ΔFEsd, and 14.6 μm for the ΔFEmax had the highest sensitivities (95.7, 95.0, 96.9, and 95.0%, respectively) and specificities (96.0, 97.7, 94.8, and 95.4%, respectively). Conclusion: Bilateral differential topographic parameters may be efficient for the early detection of keratoconus and corneal ectasia secondary to corneal refractive surgery. This bilateral differential topographic algorithm may complement conventional diagnostic models by improving the sensitivity and specificity of screening for early keratoconus and ectasia before corneal refractive surgeries.

Quantifying Topographic Ruggedness Using Principal Component Analysis

The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones.

Representation of Stony Surface‐Atmosphere Interactions in WRF Reduces Cold and Wet Biases for the Southern Tibetan Plateau

AbstractCurrent regional and global climate models often have significant cold and wet biases in the simulation of climate for the Southern Tibetan Plateau (STP). This region has complex terrain and steep slopes, and the underlying surface in many areas is stony with little soil and vegetation, which causes negligible infiltration and rapid runoff. The default soil type (loam) in the Weather Research and Forecasting (WRF) is thus replaced with bedrock to improve the representation of the mountainous hydrological processes in this region. The domain of bedrock is identified by using satellite‐based Normalized Difference Vegetation Index (NDVI) and terrain elevation standard deviation. The model results demonstrate that the WRF‐simulated precipitation and temperature are sensitive to soil type setting in this region. After the soil type is corrected, the mean precipitation bias at weather stations in the STP is reduced from 35 mm to −6 mm and the cold bias of 2 m air temperature is reduced from 1.5°C to 0.8°C when evaluated against weather station data. The relevant land and atmospheric processes are explored. The revision of the soil type from loam to bedrock results in increased sensible heat flux and decreased evaporation. A higher sensible heat flux results in higher air temperature, and the suppressed evaporation attenuates precipitation‐evaporation feedback. The altered energy budget enhances the valley wind, resulting in a sinking motion anomaly and reduced precipitation over lower elevations. The study highlights the critical role of soil type in the realistic representation of land‐atmosphere interactions by numerical models for the unique region.

Using landslide-inventory mapping for a combined bagged-trees and logistic-regression approach to determining landslide susceptibility in eastern Kentucky, USA

High-resolution LiDAR-derived datasets from a 1.5 m digital elevation model and a detailed landslide inventory (n ≥ 1000) for Magoffin County, Kentucky, USA were used to develop a combined machine-learning and statistical approach to improve geomorphic-based landslide-susceptibility mapping.An initial dataset of 36 variables was compiled to investigate the connection between slope morphology and landslide occurrence. Bagged trees, a machine-learning random-forest classifier, was used to evaluate the geomorphic variables, and 12 were identified as important: standard deviation of plan curvature, standard deviation of elevation, sum of plan curvature, minimum slope, mean plan curvature, range of elevation, sum of roughness, mean curvature, sum of curvature, mean roughness, minimum curvature and standard deviation of curvature. These variables were further evaluated using logistic regression to determine the probability of landslide occurrence and then used to create a landslide-susceptibility map.The performance of the logistic-regression model was evaluated by the receiver operating characteristic curve, area under the curve, which was 0.83. Standard deviations from the probability mean were used to set landslide-susceptibility classifications: low (0–0.10), low–moderate (0.11–0.27), moderate (0.28–0.44), moderate–high (0.45–0.61) and high (0.62–1.0). Logistic-regression results were validated by using a separate landslide inventory for the neighbouring Prestonsburg 7.5-minute quadrangle, and running the same regression function. Results indicate that 74.9% of the landslide deposits were identified as having moderate, moderate–high or high landslide susceptibility. Combining inventory mapping with statistical modelling identified important geomorphic variables and produced a useful approach to landslide-susceptibility mapping.Supplementary material: The statistical data used in the combined machine-learning functions are available at https://doi.org/10.6084/m9.figshare.c.5351313.v3Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology

Minimum temperature mapping augments Australian grain farmers’ knowledge of frost

Economic losses due to crop damage caused by frost in Australia are estimated to be many hundreds of millions of dollars. In broadacre cropping, pre-sowing management to alleviate frost risk, and timely post-frost decisions rely on locally relevant information about the extent and severity of frosts. Yet temperature information currently available to grain farmers often has limited local relevance due to distance from meteorological stations. The availability of minimum temperature (Tmin) maps at an appropriate scale would facilitate improved farmer decision-making in response to frost risk and frost events.This study deployed temperature loggers and utilised Multivariate Adaptive Regression Splines (MARS) modelling to develop maps of Tmin at farm scale (30 × 30 m grid). We use terrain derived variables to generate nightly Tmin maps across a whole farm, based on data from a single on-farm weather station. Based on cross-validation, only elevation and elevation standard deviation were found to be useful predictors. Validation of the model against different years and locations resulted in good predictive RMSE values in the range 0.72 to 1.61°C. Classification accuracy scores (F1) for prediction of temperatures being above or below a 2°C threshold ranged from 83% to 96%.A priority of this work was to understand how the minimum temperature mapping complements farmers’ understanding of frost, and how it would contribute to improved management of frost in their cropping systems. Evaluation of the maps by farmers showed general agreement that the maps complemented local knowledge, with the main interest in the maps being as a guide to know where to start looking for frost damage.We have developed a method to generate Tmin maps based on a data from a single on-site temperature logger combined with terrain data and demonstrated that these maps are appropriately accurate and at a scale that is relevant to farmer management actions.

Influence of motor instruction words on body movements in step-over motions.

[Purpose] To quantitatively analyze the characteristics of movements evoked by certain motor instruction words on the basis of measurements of ankle elevation and related body movements in step-over motion tasks. [Participants and Methods] Sixty-one healthy adult participants were presented with motor instructions and asked to step over an obstacle in accordance with the instructions. The motor instructions were as follows: “Raise your XX (body part) up YY (expression)” in four combinations using “thigh” and “knee” for body part and “high” and “firmly” for expression. Using Kinect to analyze movements, ankle elevation, trunk-anteversion angle, hip-flexion angle, and knee-flexion angle were measured and statistically processed. [Results] With respect to body part, there was no significant difference in the mean and standard deviation (individual variation) values for ankle elevation. With respect to expression, hip joint and knee joint were bent significantly more for “high” than for “firmly”, and although the mean value for ankle elevation was high, ankle elevation standard deviation (individual variation) values were significantly lower for “firmly” than for “high”. [Conclusion] Explicit motor instruction words such as “high” may be effective in improving performance, while ambiguous motor instruction words like “firmly” may be effective in stabilizing movements.

Terrain Hazard Risk Analysis for Flood Disaster Management in Chaohu Basin, China, Based on Two-Dimensional Cloud

Terrain analysis is essential to flood disaster risk evaluation. It is a complicated evaluation process, involving both quantitative and qualitative data analysis. However, quantitative and qualitative data cannot be put into operation directly. Based on stochastic and fuzzy mathematics, cloud models allow interchange between qualitative and quantitative data, dealing with randomness and ambiguity. Two- or multi-dimensional cloud models can solve the problem of multivariable analysis. This study used absolute elevation and neighborhood elevation standard deviation as main factors. Using the model, it demonstrated the construction of qualitative conditions and risk evaluation clouds and established a set of two-dimensional cloud reasoning rules to calculate the joint certainties with all the grids in reasoning rules. By selecting the highest certainty of cloud reasoning, preliminary evaluation results were obtained. For more accurate results, the model algorithm was improved, and further iterations were performed. The results of two-dimensional cloud reasoning showed better dispersion and precision than traditional methods did. The terrain risk distribution of Chaohu Basin, China, agreed with reality with great detail. A new method regarding the risk assessment of flood disaster was also proposed.

The role of vegetation and large wood on the topographic characteristics of braided river systems

Riparian vegetation and large wood play a crucial role in shaping rivers. On the one hand, vegetation tends to increase bank stability, reduce channel width and reduce the braiding index in gravel-bed rivers. Conversely, large wood tends to increase channel dynamics and promote avulsions. The effects of vegetation and large wood have been rarely simulated together in flume experiments. In this paper we present a series of experimental runs conducted in a large flume facility, using cylindrical wooden dowels and alfalfa seedlings to represent logs and vegetation, respectively, in order to investigate their role on determining the topographical nature of braided river systems. A terrestrial laser scanner was used to measure in detail the topography of the channels and bars, and distribution of the elevations and second-order structure functions were used to explore the topography that large wood and vegetation imposed on the braided pattern in the flumes. Results show that vegetation reduced the braiding index and produced wider and deeper channels. The standard deviation of the bed elevations was higher than in flumes without vegetation. Large wood alone was not effective in changing the braiding index or the standard deviation of the elevations. When vegetation and large wood acted together in the flume, the braiding index reached a minimum value, and the skewness of the distribution of elevation increased, revealing shallower pools if compared with runs conducted with only vegetation.

Remote detection of ecosystem degradation in the Everglades ridge-slough landscape

Indicators of landscape condition should be sensitive and specific to environmental change and provide early warning detection of ncipient changes. We assessed a suite of five spectral metrics derived from Landsat 5 TM imagery spanning a decade (1996–2007) to quantify ecosystem condition in the ridge-slough mosaic of the Everglades (South Florida, USA). These included the normalized difference vegetation index (NDVI), the same index using green instead of red band as the visible reference (NDVIg), the normalized difference water index (NDWI), the simple ratio of NIR and red bands (SR) and the moisture stress index (MSI). Mean and variance from pure ridge or slough pixels (i.e., those >30 m from a mapped patch edge) were quantified for twentyfour 2 × 5 km blocks across a gradient of hydrologic and ecological condition. Metrics were compared with field measures of landscape condition from block-scale soil elevation surveys, which capture dramatic spatial gradients between conserved and degraded locations. Elevationbased measures of landscape condition, validated as diagnostic in previous work, included soil elevation bi-modality (BISE), a binary measure of ecosystem condition, and the soil elevation standard deviation (SDSE), a continuous measure of condition. Spectral metric performance was assessed based on the strength (sensitivity) and shape (leading vs.lagging) of the relationship with elevation-based measures. We observed significant logistic regression slopes with BISE for only 3 metrics (mean ridge NDVI and NDVIg, mean ridge SR). In prediction of variance SDSE, the standard deviation of our VIs was more informative than the mean, and measures from sloughs more informative than ridges. The strongest predictions are variance in slough NDVIg and SR, suggesting that spatial heterogeneity in slough biomass is most informative for predicting the status of soil elevation variance. None of the vegetation metrics were leading indicators of change; a multivariate model using three VIs selected based on consistent performance across years (variance in Slough SR, slough NDVIg, and ridge NDWI) substantially improve predictions of SDSE and yielded more plausible prediction maps. These findings suggest that soil elevation changes from altered peat accretion dynamics precede visible changes in vegetation reflectance. While this constrains predictions of incipient ecosystem change, the reasonable performance of spectral metrics indicates that efficient monitoring of ridge-slough landscape health is possible as part of the ongoing Everglades restoration effort.