Regional Morpho-Kinematic Inventory of Slope Movements in Northern Norway

Mountain slopes in periglacial environments are affected by frost- and gravity-driven processes that shape the landscape. Both rock glaciers and rockslides have been intensively inventoried worldwide. Although most inventories are traditionally based on morphologic criteria, kinematic approaches based on satellite remote sensing have more recently been used to identify moving landforms at the regional scale. In this study, we developed simplified Interferometric Synthetic Aperture Radar (InSAR) products to inventory ground velocity in a region in Northern Norway covering approximately 7,500 km2. We used a multiple temporal baseline InSAR stacking procedure based on 2015–2019 ascending and descending Sentinel-1 images to take advantage of a large set of interferograms and exploit different detection capabilities. First, moving areas are classified according to six velocity brackets, and morphologically associated to six landform types (rock glaciers, rockslides, glaciers/moraines, talus/scree deposits, solifluction/cryoturbation and composite landforms). The kinematic inventory shows that the velocity ranges and spatial distribution of the different types of slope processes vary greatly within the study area. Second, we exploit InSAR to update pre-existing inventories of rock glaciers and rockslides in the region. Landform delineations and divisions are refined, and newly detected landforms (54 rock glaciers and 20 rockslides) are incorporated into the databases. The updated inventories consist of 414 rock glacier units within 340 single- or multi-unit(s) systems and 117 rockslides. A kinematic attribute assigned to each inventoried landform documents the order of magnitude of the creep rate. Finally, we show that topo-climatic variables influence the spatial distribution of the rock glaciers. Their mean elevation increases toward the continental interior with a dominance of relict landforms close to the land-sea margin and an increased occurrence of active landforms further inland. Both rock glaciers and rockslides are mostly located on west-facing slopes and in areas characterised by strongly foliated rocks, which suggests the influence of geological preconditioning factors. The study demonstrates the value of semi-quantitative InSAR products to characterise kinematic information at large scale and exploit the results for periglacial research. It highlights the complementarity of both kinematic and morphologic approaches for inventorying slope processes.

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Assessment of the heterogeneity of hydraulic properties in gravelly outwash plains: a regionally scaled sedimentological analysis in the Munich gravel plain, Germany

Hydrogeology Journal ◽

10.1007/s10040-020-02205-y ◽

2020 ◽

Vol 28 (8) ◽

pp. 2657-2674

Author(s):

Markus Theel ◽

Peter Huggenberger ◽

Kai Zosseder

Keyword(s):

Spatial Distribution ◽

Hydraulic Conductivity ◽

Large Scale ◽

Regional Scale ◽

Pumping Test ◽

Small Scale ◽

Significant Heterogeneity ◽

Conductivity Distribution ◽

Braided Rivers ◽

Sedimentary Deposits

AbstractThe favorable overall conditions for the utilization of groundwater in fluvioglacial aquifers are impacted by significant heterogeneity in the hydraulic conductivity, which is related to small-scale facies changes. Knowledge of the spatial distribution of hydraulically relevant hydrofacies types (HF-types), derived by sedimentological analysis, helps to determine the hydraulic conductivity distribution and thus contribute to understanding the hydraulic dynamics in fluvioglacial aquifers. In particular, the HF-type “open framework gravel (OW)”, which occurs with the HF-type “bimodal gravel (BM)” in BM/OW couplings, has an intrinsically high hydraulic conductivity and significantly impacts hydrogeological challenges such as planning excavation-pit drainage or the prognosis of plumes. The present study investigates the properties and spatial occurrence of HF-types in fluvioglacial deposits at regional scale to derive spatial distribution trends of HF-types, by analyzing 12 gravel pits in the Munich gravel plain (southern Germany) as analogues for outwash plains. The results are compared to the reevaluation of 542 pumping tests. Analysis of the HF-types and the pumping test data shows similar small-scale heterogeneities of the hydraulic conductivity, superimposing large-scale trends. High-permeability BM/OW couples and their dependence on recognizable discharge types in the sedimentary deposits explain sharp-bounded small-scale heterogeneities in the hydraulic conductivity distribution from 9.1 × 10−3 to 2.2 × 10−4 m/s. It is also shown that high values of hydraulic conductivity can be interpolated on shorter distance compared to lower values. While the results of the HF-analysis can be transferred to other fluvioglacial settings (e.g. braided rivers), regional trends must be examined with respect to the surrounding topography.

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Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

10.26434/chemrxiv.9894224.v1 ◽

2019 ◽

Author(s):

Ryther Anderson ◽

Achay Biong ◽

Diego Gómez-Gualdrón

Keyword(s):

Neural Network ◽

Machine Learning ◽

Molecular Simulation ◽

Large Scale ◽

Learning Model ◽

Operating Conditions ◽

Small Subset ◽

Screening Methods ◽

Large Set ◽

Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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Large-scale mapping of the catenas vegetation in Subarctic tundra

Geobotanical mapping ◽

10.31111/geobotmap/1993.3 ◽

1995 ◽

pp. 3-21

Author(s):

S. S. Kholod

Keyword(s):

Spatial Distribution ◽

Vegetation Cover ◽

Large Scale ◽

Block Diagram ◽

Abiotic Factors ◽

Ecological Factors ◽

Spatial Arrangement ◽

Geological Time ◽

Ecological Barriers ◽

Functional Zones

One of the most difficult tasks in large-scale vegetation mapping is the clarification of mechanisms of the internal integration of vegetation cover territorial units. Traditional way of searching such mechanisms is the study of ecological factors controlling the space heterogeneity of vegetation cover. In essence, this is autecological analysis of vegetation. We propose another way of searching the mechanisms of territorial integration of vegetation. It is connected with intracoenotic interrelation, in particular, with the changing role of edificator synusium in a community along the altitudinal gradient. This way of searching is illustrated in the model-plot in subarctic tundra of Central Chukotka. Our further suggestion concerns the way of depicting these mechanisms on large-scale vegetation map. As a model object we chose the catena, that is the landscape formation including all geomorphjc positions of a slope, joint by the process of moving the material down the slope. The process of peneplanation of a mountain system for a long geological time favours to the levelling the lower (accumulative) parts of slopes. The colonization of these parts of the slope by the vegetation variants, corresponding to the lowest part of catena is the result of peneplanation. Vegetation of this part of catena makes a certain biogeocoenotic work which is the levelling of the small infralandscape limits and of the boundaries in vegetation cover. This process we name as the continualization on catena. In this process the variants of vegetation in the lower part of catena are being broken into separate synusiums. This is the process of decumbation of layers described by V. B. Sochava. Up to the slope the edificator power of the shrub synusiums sharply decreases. Moss and herb synusium have "to seek" the habitats similar to those under the shrub canopy. The competition between the synusium arises resulting in arrangement of a certain spatial assemblage of vegetation cover elements. In such assemblage the position of each element is determined by both biotic (interrelation with other coenotic elements) and abiotic (presence of appropriate habitats) factors. Taking into account the biogeocoenotic character of the process of continualization on catena we name such spatial assemblage an exolutionary-biogeocoenotic series. The space within each evolutionary-biogeocoenotic series is divided by ecological barriers into some functional zones. In each of the such zones the struggle between synusiums has its individual expression and direction. In the start zone of catena (extensive pediment) the interrelations of synusiums and layers control the mutual spatial arrangement of these elements at the largest extent. Here, as a rule, there predominate edificator synusiums of low and dwarfshrubs. In the first order limit zone (the bend of pediment to the above part of the slope) one-species herb and moss synusiums, oftenly substituting each other in similar habitats, get prevalence. In the zone of active colonization of slope (denudation slope) the coenotic factor has the least role in the spatial distribution of the vegetation cover elements. In particular, phytocoenotic interactions take place only within separate microcoenoses of herbs, mosses and lichens. In the zone of the attenuation of continualization process (the upper most parts of slope, crests) phytocoenotic interactions are almost absent and the spatial distribution of vegetation cover elements depends exclusively on the abiotic factors. The principal scheme of the distribution of vegetation cover elements and the disposition of functional zones on catena are shown on block-diagram (fig. 1).

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Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

Remote Sensing ◽

10.3390/rs13020284 ◽

2021 ◽

Vol 13 (2) ◽

pp. 284

Author(s):

Dan Lu ◽

Yahui Wang ◽

Qingyuan Yang ◽

Kangchuan Su ◽

Haozhe Zhang ◽

...

Keyword(s):

Spatial Distribution ◽

Relative Error ◽

Urban Areas ◽

Large Scale ◽

Population Distribution ◽

Spatial Optimization ◽

Distribution Data ◽

Mountainous Areas ◽

Mean Relative Error ◽

Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

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Nebula: ultra-efficient mapping-free structural variant genotyper

Nucleic Acids Research ◽

10.1093/nar/gkab025 ◽

2021 ◽

Author(s):

Parsoa Khorsand ◽

Fereydoun Hormozdiari

Keyword(s):

Large Scale ◽

Structural Variants ◽

Sequencing Technologies ◽

Generic Framework ◽

Common Genetic Variants ◽

Order Of Magnitude ◽

Complex Events ◽

Comparable Accuracy ◽

Using Data ◽

Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

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Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

Sustainability ◽

10.3390/su13147782 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7782

Author(s):

Wenjing Zeng ◽

Yongde Zhong ◽

Dali Li ◽

Jinyang Deng

Keyword(s):

Large Scale ◽

Regional Scale ◽

Hunan Province ◽

Social Settings ◽

Recreation Opportunity Spectrum ◽

City District ◽

Night Lights ◽

Recreational Resources ◽

Metropolitan Counties ◽

Land Types

The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

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Spatial Agglomeration of China’s Forest Products Manufacturing Industry: Measurement, Characteristics and Determinants

Forests ◽

10.3390/f12081006 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1006

Author(s):

Zhenhuan Chen ◽

Hongge Zhu ◽

Wencheng Zhao ◽

Menghan Zhao ◽

Yutong Zhang

Keyword(s):

Spatial Distribution ◽

Spatial Data ◽

Large Scale ◽

Manufacturing Industry ◽

Economies Of Scale ◽

Forest Products ◽

Spatial Data Analysis ◽

Forest Protection ◽

Spatial Agglomeration ◽

Continuous Space

China’s forest products manufacturing industry is experiencing the dual pressure of forest protection policies and wood scarcity and, therefore, it is of great significance to reveal the spatial agglomeration characteristics and evolution drivers of this industry to enhance its sustainable development. Based on the perspective of large-scale agglomeration in a continuous space, in this study, we used the spatial Gini coefficient and standard deviation ellipse method to investigate the spatial agglomeration degree and location distribution characteristics of China’s forest products manufacturing industry, and we used exploratory spatial data analysis to investigate its spatial agglomeration pattern. The results show that: (1) From 1988 to 2018, the degree of spatial agglomeration of China’s forest products manufacturing industry was relatively low, and the industry was characterized by a very pronounced imbalance in its spatial distribution. (2) The industry has a very clear core–periphery structure, the spatial distribution exhibits a “northeast-southwest” pattern, and the barycenter of the industrial distribution has tended to move south. (3) The industry mainly has a high–high and low–low spatial agglomeration pattern. The provinces with high–high agglomeration are few and concentrated in the southeast coastal area. (4) The spatial agglomeration and evolution characteristics of China’s forest products manufacturing industry may be simultaneously affected by forest protection policies, sources of raw materials, international trade and the degree of marketization. In the future, China’s forest products manufacturing industry should further increase the level of spatial agglomeration to fully realize the economies of scale.

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The development of regional differentiation of office construction in the Czech Republic: 1990–2010

Moravian Geographical Reports ◽

10.1515/mgr-2015-0003 ◽

2015 ◽

Vol 23 (1) ◽

pp. 21-33

Author(s):

Pavel Domalewski ◽

Jan Baxa

Keyword(s):

Spatial Distribution ◽

Czech Republic ◽

Questionnaire Survey ◽

Large Scale ◽

Capital City ◽

Office Building ◽

Regional Differentiation ◽

Key Factors ◽

The Czech Republic ◽

Dominant Position

Abstract The factors that were crucial for the construction of administrative buildings in the regional capitals of the Czech Republic are subject to examination in this article. One primary question is whether the development of office construction reflects the qualitative importance of the cities, or whether there are some other regularities in the spatial distribution of construction. To identify the key factors, controlled interviews with experts professionally involved in the construction of administrative buildings were carried out, and these data were then extended as part of a large-scale questionnaire survey with other experts on the issue. The results have confirmed the dominant position of the capital city of Prague in terms of its qualitative importance, as the remaining regional capitals have less than one-tenth of the volume of modern office building areas. The greatest differences in the construction of administrative buildings have been noted in Brno and Ostrava, despite the fact that they exhibit similar characteristics when considered in the light of respondent-determined factors.

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