Landform Features Research Articles

A geomorphological study of Auranga river basin, Jharkhand using geospatial techniques

The geomorphological setting defines a condition that influences landform development and morphological and structural characteristics of landforms. They are useful for land use planning, hazard mapping, and natural resource and environmental management. The present study is an investigation of the geomorphological characteristics of the Auranga river basin located in Jharkhand using a geospatial approach. The lineaments are extracted using ASTER DEM data and statistically analyzed to determine length, orientation, etc. Geomorphological units are identified and mapped using remote sensing data and GIS software. Slope and relief maps are prepared for a detailed understanding. It is observed that the maximum area of the basin is under the pediment pediplain complex (1075 sq. km) whereas the least expanded geomorphological unit is anthropogenic terrain (1.02 sq. km). The lineament analysis indicates major orientation and structural trends mainly in the NE-SW direction and zones of high lineament density are potential for groundwater prospecting in the basin. The result thus reveals the geomorphological setting of the Auranga river basin, hence proving the efficiency of the use of geospatial techniques for geomorphological studies.

Effect of the Mazar Tagh Mountains on Aeolian landforms in the western Taklamakan Desert, northwestern China

Topographic obstacles across the Earth and other planetary bodies are important factors affecting the morphology of aeolian landforms. Compared to general-scale obstacles (e.g., shrubs, nebkhas, residual hills), the effects of mountain-scale obstacles on aeolian landforms have rarely been investigated. Here, we studied the effect of the Mazar Tagh Mountains as a natural mountain-scale obstacle on aeolian landforms in the western Taklamakan Desert, northwestern China. A comprehensive analysis was conducted on the characteristics of aeolian landforms under the effect of the Mazar Tagh Mountains through aeolian sand parameters derived from the wind regime, grain-size parameter and the recent dune growth mechanisms. Our findings show that the Mazar Tagh Mountains change the spatial distribution of drift potential and grain-size parameters from the north to the south, resulting in distinctly different dune orientations and dune morphologies on both sides of the mountains. It was also found that mountains could block most sand transported from the north to the south. The Mazar Tagh Mountains block approximately 13.4 % of the resultant drift potential and 62.1 % of sand supply from north to south, respectively. However, aeolian landforms inside the sand transport channel or notch show high consistency since there is no obstacle. Combined with wind flow simulations, we suggest that mountains affect aeolian landforms by disrupting the wind flow and sand supply. Finally, we infer that the effects of mountain-scale obstacles on the dynamics of aeolian landform may be used to bring new constraints on the spatial distribution patterns of dune fields.

Geodiversity in Khorat Geopark, Thailand: Approaches to geoconservation and sustainable development

This work aims to investigate the geodiversity of the Khorat Geopark area, Northeastern Thailand, and assess the geological sites that are connected to biological and cultural heritage, as well as community activities that enhance geoconservation and sustainable development. The geological diversity of Khorat Geopark dates back to the late Jurassic period and continues to the present day, resulting from the deposition of terrestrial sedimentary rocks from the Khorat Group. The Khorat rock group has been uplifted and eroded to form the Khorat Plateau with a unique landscape and important geological features of the cuesta landform on the western part of the Khorat Geopark. On the four corners of Nakhon Ratchasima City, there are numerous fossils of outstanding Early Cretaceous dinosaurs and other reptiles and Cenozoic fossils of both flora and fauna, mainly mammalian remains and, in particular, ancient elephants. Geological sites in the Khorat Geopark area were weighted and scored based on their scientific value (SV), potential value for education (EV), potential value for tourism (TV), and degradation risk variables (DV). According to the assessment, the Khorat Geopark's 24 geosites are of moderate to high value, with 7 sites having 301–400 scores and 5 sites having a high level of international value. Most of them are related to fossils and cuestas. Thus, the fossil diversity of the Khorat Geopark is of international scientific significance. Khorat Geopark is managed under the conservation, education, and sustainable development initiatives using a bottom-up method with multiple networks, both local communities and local governments, especially through the Home-Temple-School (HTS) model.

Organogenic landforms on the regressive mires of the Lower Amur and Southern Priokhotie

The article examines the distribution features of specific organocryogenic landforms in the waterlogged areas of the cryolithozone of the Northwest Coast of the Pacific, within the borders of the Lower Amur and Southern Priokhotie. It is suggested that the considered landforms are closely related to the regressive processes occurring in certain mire areas due to thermokarst phenomena. Attention is focused on the specifics of morphometric features of the ridge-hollow reliefwith a parallel arrangement of elements formed in local runoff hollows and low-flow basins.

A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations

Abstract. Karst trough and valley landforms are prone to flooding, primarily because of the unique hydrogeological features of karst landforms, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting flooding are highly dependent on distributed modelling, complicated boundary parameter settings and extensive hydrogeological data processing, which consumes large amounts of both time and computational power. Proposed here is a distributed physically based karst hydrological model known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and fewer parameters are included in this model than in the current distributed models. This allows karst areas to be modelled with only a small amount of necessary hydrogeological data. Eighteen flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations: the average values of the Nash–Sutcliffe coefficient and the water balance coefficient are both 0.92, while the average relative flow process error is 10 % and the flood peak error is 11 %. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding enabled by the proposed QMG model promotes a better mechanistic depiction of runoff generation and confluence in karst trough valleys.

Outdoor scene understanding of mobile robot via multi-sensor information fusion

The present research on the multi-sensor information fusion technology of mobile robots aims to better understand the outdoor scene and improve the robot's perception of the environment. Firstly, a conversion algorithm is proposed based on two point-cloud-to-image algorithms, including the point cloud plane fitting and point cloud projection transformation. Moreover, the elevation map is constructed to describe the terrain characteristics of the scene based on the three-dimensional laser ranging data. Meanwhile, the conditional random field model is used to obtain landform characteristics from visual information. Besides, the projection transformation and information statistics methods are used to effectively integrate the laser information and the visual information with the grid in the elevation map as the carrier. Ultimately, the convolution neural network is used to realize the three-dimensional scene understanding. It is found that the average recognition rate of the outdoor scene understanding model based on multi-sensor information fusion is as high as 89.36%, and the image segmentation time of the proposed algorithm is not more than 180 ms.The latest research results refer to the use of SSAE in combination with the CRF algorithm. On the whole, the proposed model improves the real-time performance of the mobile robot under the premise of accuracy, and realizes the recognition and analysis ability of complex scenes through the construction of multi-sensor information. This study has important practical significance for promoting the development of the mobile robot autonomous industry.

Agglomeration spillover, accessibility by high-speed rail, and urban innovation in China: A focus on the electronic information industry

This study examines the impacts of different sources and types of agglomeration economies on urban innovation in the context high-speed rail (HSR), using the Electronic Information (EI) industry in China as an example. The impacts of inter-city access to different innovation factors such as knowledge sources (e.g., universities/research institutions), human capital (e.g., scientific/technical workers), input suppliers (e.g., producer services) and final markets through HSR networks are explored while local agglomeration effects and local proximity to HSR stations are controlled. Historical courier routes and stations and landform characteristics are used to construct instruments for endogenous HSR accessibility measures. Results indicate that local agglomeration benefits such as overall urban size, level of industrialization specialization and local access to top science/engineering universities/research institutions and producer service suppliers are positively associated with innovation performance in the EI sector. When longer travel time thresholds (e.g., >2 h) are applied, inter-city access to knowledge sources, human capital, producer services and final customers through HSR network yields significant impacts on innovation outputs of Type-II large cities (population in 1–3 million).

Opak Fault Strand Delineation Using Merapi Slope Shifted Indicator

Abstract After the Yogya earthquake occurred on May 27, 2006, the opinions of experts split into two groups regarding location of responsible fault to earthquake shacking; (1), First group argues that Opak Fault displacement caused the earthquake. Where fault line is commonly known runs along Opak River, striking from Parangtritis Beach to Prambanan. (2) Second group of experts stated at deferent opinion, that another fault displacement triggered the earthquake shock. Where is located at the East side of the Opak River about 10 Km. On the other-hand, this paper proposes an idea to unravel Opak Fault position by understanding, that main active fault movement underlying Bantul region has been influencing continuum mechanical process, onto Merapi Sediments surface since the early periods of continuing Merapi materials sedimentation. It should has been reflected to the surface landform at above the fault strand. Further, indicates to morphotectonic feature as an en echelon slope shifted alignment. Thus, it can be the way to assist in defining attribute of main fault (PDZ) areas by using its en echelon indicator of shifting slope alignment. This paper also presents the results of determining Opak fault line location by using Digital Elevation Model (DEM-NAS) to generate custom shading in approaching landform features. With a further doing specific landscapes observations over the entire of Young Merapi deposits. There are other challenges to alternate identification of buried basin faults fill, by doing carefully identifications in morphotectonic aspects over the entire South Slope Merapi flank. It traces from Kepurun village at about elevation 400 m towards Parangtritis at 25 m of elevation. Independent field morphotectonic data sets of such scarp, terraces, water springs alignment and active cracks are encountered in the vicinity of Merapi sediments slope shifted, particularly in Tirtomartani Jetis village Kalasan. Identification of such structures obtained from morphotectonic analysis results are regarded as reliable indicator of faults, which is efficiently can be found in field. Key word: Opak Fault, Morphotectonic, Slope Shifted, Delineation.

The global distribution and morphologic characteristics of fan-shaped sedimentary landforms on Mars

Fan-shaped sedimentary landforms on Mars are important geomorphic markers of past water flow and characterizing the distribution and morphology of these features can yield insights into the planet's climatic evolution. We present a new database of 1501 martian fan-shaped sedimentary landforms that builds upon previous surveys and uses Context Camera images. This comprehensive global survey includes both alluvial fans and putative deltas, which we term scarp-fronted fan deposits (SFDs), across the entire martian surface. We classified each fan-shaped landforms based on their morphology as alluvial fans, channelized SFDs, smooth SFDs, and terraced SFDs. Martian alluvial fans are highly concentrated in the high southern tropical latitudes while SFDs are more common near the equator. Both alluvial fans and SFDs are found at lower elevations than Late Noachian to Early Hesperian valley networks, suggesting a climatic change in the locations of stable liquid water. Alluvial fans within impact craters are preferentially located on north, south, and east facing slopes, and are concentrated near the location of highest rim topographic relief; together these observations support orographically-influenced precipitation and snowmelt as a runoff source. For every fan in the database we collected morphologic data, which we compare with terrestrial alluvial fans. Relationships between alluvial fan catchment and fan morphology vary little across the martian surface, suggesting relatively similar processes. Although stratigraphic evidence of a deltaic deposition is lacking, previously conducted experiments along with the depositional basins of the channelized and terraced SFDs generally support deltaic formation. Crater counts indicate that many alluvial fans are within Hesperian and Amazonian impact craters, indicating that significant fluvial erosion occurred during Mars' relatively recent history.

Remote sensing of water use efficiency in Southwest China's karst area.

As the largest contiguous karst area in China, the southwestern karst area is a typical ecologically fragile area affecting local vegetation dynamics. Ecosystem water use efficiency (WUE) is an important factor reflecting the ability of vegetation to produce organic matter with a limited water supply. Therefore, determining the WUE variation trends in this ecologically fragile region is important. In this paper, we used MODIS remote sensing datasets, meteorological data, and land cover data to analyze the spatiotemporal changes in vegetation water use efficiency in the southwestern karst region from 2001 to 2017. We also further quantitatively analyzed the effects of climate change and human activities on the spatial and temporal patterns of vegetation WUE in the study area. The main conclusions were as follows. (1) From 2001 to 2017, in terms of temporal characteristics, the interannual variation in WUE fluctuated greatly, ranging from 1.33 to 1.51g C kg-1 H2O, with a multiyear average of 1.43g C kg-1 H2O and an average rate of change of - 0.0046g C kg-1 H2O year-1. In terms of spatial characteristics, areas with a higher WUE were concentrated in central Sichuan and northeastern Yunnan. (2) The annual average WUE of each vegetation type decreased in the following order: evergreen coniferous forest > evergreen broad-leaved forest > mixed forest > deciduous broad-leaved forest > cultivated land > deciduous coniferous forest > grassland > cultivated land and natural vegetation > shrub forest. (3) The vegetation WUE of 70.66% in this area was positively correlated with temperature. Additionally, 79.68% of the vegetation WUE was negatively correlated with precipitation. The relative contribution rates of climate change and human activities to the change trend in WUE were 15% and 85%, respectively. Compared with WUE results in other studies, the WUE of different karst landform areas obtained in this study was quite different, indicating that the geological and landform features of the karst area are complex. Our study provides scientific support for local vegetation restoration and protection policies and promotes the understanding of the principle of the carbon-water cycle in karst areas.

Spatial distribution of potential wild turkey nest predators in west‐central Louisiana

AbstractWild turkey nest success is variable spatially and temporally, with predation being the primary driver of nest loss. Historically, characteristics of nest site vegetation have been used to predict the probability of a nest being successful, a corollary to not being preyed upon. Implicit in nest success studies is that the density, distribution, and habitat preferences of predator species, relative to wild turkey nest distribution, drive the predation risk of nests on the landscape. We quantified diversity of wild turkey nest predator species within 11‐ha incubation ranges centered on active nest sites, scent baited passive nest sites, and scent baited and unbaited random sites and developed a spatially explicit predictive model for potential nest predator distribution based on vegetation and landform characteristics that occurred within incubation ranges. We used 210 camera sites on passive (nesting area from previous year), active (nesting area same year), and random sites over 5,144 trap nights (TN) and recorded 2,925 photographs of potential nest predator species. Relative abundance indices (RAI) indicated greater abundance in passive (14.2/100 TN) and active (12.7/100 TN) nesting sites relative to random sites (3.13/100 TN). For active nesting sites, successful nests had lower RAI (1.8/100 TN) than unsuccessful nests (10.4/100 TN) and lower RAI than baited and unbaited random sites (2/100 TN and 4.3/100 TN, respectively). Our results indicate that potential wild turkey nest predator species regularly occurred within incubation ranges, but were more likely to occur in locations with nests than in random locations on the landscape. We did not document a strong relationship between potential nest predator occurrence and spatial vegetation metrics, which do not appear to affect the occurrence of generalist potential predator species. However, we did show evidence of avoidance by certain potential predators for certain vegetation types. Thus, our work indicated that the most important factor affecting a nest's outcome may be the placement of the nest on the landscape relative to use by potential predator species. We suggest future research evaluate how vegetation characteristics within wild turkey incubation ranges impact nest predator use.

PTRM: Perceived Terrain Realism Metric

Terrains are visually prominent and commonly needed objects in many computer graphics applications. While there are many algorithms for synthetic terrain generation, it is rather difficult to assess the realism of a generated output. This article presents a first step toward the direction of perceptual evaluation for terrain models. We gathered and categorized several classes of real terrains, and we generated synthetic terrain models using computer graphics methods. The terrain geometries were rendered by using the same texturing, lighting, and camera position. Two studies on these image sets were conducted, ranking the terrains perceptually, and showing that the synthetic terrains are perceived as lacking realism compared to the real ones. We provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, and so forth. We show that the presence or absence of certain features has a significant perceptual effect. The importance and presence of the terrain features were confirmed by using a generative deep neural network that transferred the features between the geometric models of the real terrains and the synthetic ones. The feature transfer was followed by another perceptual experiment that further showed their importance and effect on perceived realism. We then introduce Perceived Terrain Realism Metrics (PTRM), which estimates human-perceived realism of a terrain represented as a digital elevation map by relating the distribution of terrain features with their perceived realism. This metric can be used on a synthetic terrain, and it will output an estimated level of perceived realism. We validated the proposed metrics on real and synthetic data and compared them to the perceptual studies.

Looking Into the Entanglement Between Karst Landforms and Fault Activity in Carbonate Ridges: The Fibreno Fault System (Central Italy)

The entanglement between active tectonics and karst systems is well-known in the literature. Karst systems are sound recorders of continental deformation in terms of brittle structures and seismic features and have been successfully used as markers for reconstructing tectonic stresses and assessing preferential directions of increased permeability in oil and gas fields. Karst systems could also be exploited to evaluate the past activity of faults bounding karst hydrostructures, thus providing useful data for the assessment of the seismic hazard of a specific area. In this work, we look into the complex relationship among karst development, recent tectonics and groundwater flow, which appear to be strongly interconnected with each other, to assess the activity of faults bounding karst hydrostructures. We focused our attention on an active karst area located in the Mesozoic and Cenozoic carbonate reliefs of the Italian central Apennines. In this context, the morphological and morphometric features of the karst landforms (dolines, dry valleys, and cave entrances), identified with geomorphological surveys, and their mutual relationship with fractures and fault segments, identified employing geostructural analysis, document stasis and deepening events in karst evolution. Such events are related to changes in the groundwater table and the consequent variation of the paleokarst base level associated with the Quaternary fault activity. A comprehensive evaluation of the evolution of karst systems at local and regional scales, considering the hydrogeological influence on base levels, allows us to use karst landforms as a proxy to unravel fault activity and evolution in Italy and in other similar karst environments.

Investigating geometrical characteristics of collapsed pipes and the changing role of driving factors

Determination of the amount (i.e., area and volume) of soil losses due to erosional landforms, especially collapsed pipes, plays a considerable role in different decision-making approaches. Further, mapping the spatial distribution and predicting the volumetric and areal losses of collapsed pipes (CPs) are essential for supporting ecosystem health. The study was conducted in relation to the area and volume of CPs and their related covariables. It focused on the estimation of soil losses due to collapsed pipes using unmanned aerial vehicle (UAV) images as well as field covariates at the Chatal Watershed, Golestan Province, Iran. A total of 481 soil samples were collected from homogeneous units with an area of approximately 1,410 ha. The potential relationship between the area/volume of collapsed pipes and land use, several topographic attributes (i.e., altitude, slope, and aspect), and soil properties, including soil stability, soil organic matter, clay, silt, and sand contents were analyzed using five distance-based methods (i.e., kernel density (KD), average nearest neighbor (ANN), spatial autocorrelation, hotspot analysis (HSA), and ordinary least square (OLS) analysis. The average nearest neighbor (Ratio = 0.12, Z score = −20.30, p-value < 0.05) and Moran space solidarity (Moran index = 0.258, Z score = 5.50, p-value < 0.05) showed the cluster distribution of area and volume of CPs. Hot spots and cold spots in the southwestern part of the study area were identified using KD and HSA. The relationship between existing independent and dependent variables (area of CPs) using regression analysis of OLS showed that slope and aggregate stability (>2.5 standard deviation) had the highest positive relationship with the dependent variable. Regarding the volume of CPs, land use (especially agricultural lands) had the strongest relationship with the dependent variable. Thus, geometrical characteristics of collapsed pipes can be applied as a quantitative indicator for the identification of hotspot zones (hazardous areas), land use planning, and erosion hazard mitigation. However, more studies are required to measure geometrical characteristics of soil landforms.

Mechanism of rainfall induced landslides in Yunnan Province using multi-scale spatiotemporal analysis and remote sensing interpretation

In order to explore the disaster mechanism of rainfall induced landslides in Yunnan Province, a new method is developed in the field of landslide research, which provides a strong basis for the study of rainfall induced landslide disaster mechanism in different regions. First, the mechanism of rainfall induced landslide initiation and the mechanism of rainfall impacting slope are introduced, and the temporal and spatial characteristics of landslide disasters in Yunnan Province are analyzed comprehensively. The influence of rainfall on landslide density is analyzed in 8 regions randomly, and a semi supervised feature selection algorithm based on manifold is proposed to optimize the remote sensing image of Zhaojiapo rainfall type landslide area in Yunnan Province. The results show that in the province the monthly rainfall days account for a large proportion from June to September, and the months with the largest number of landslides in the whole year are from June to August. The seasonal change of rainfall belt and the frequent occurrence time of landslide disasters and the spatial characteristics of landslide distribution and rainfall distribution basically belong to corresponding relationship; the greater the rainfall of 8 regions, the greater the probability of landslide occurrence, and the greater the landslide density, which belongs to the proportional relationship; the landform characteristics in the optimized remote sensing image can be clearly displayed, and the classification accuracy of the data is enhanced. The results show that based on multi-scale spatiotemporal analysis combined with optimized remote sensing technology, the cause of rainfall induced landslides in Yunnan Province is the change of landslide density caused by heavy rainfall, which eventually leads to disasters. It is conducive to scientifically understanding the inducement of rainfall induced landslides in Yunnan Province, provides a scientific basis for further disaster control and prevention, and provides a new possible method for landslide real-time prediction and alarm system.

Soils and landforms of war — Pedological investigations 75 years after World War II

Seventy-five years after World War II, the legacy of the last war in Germany is still visible in near-natural and anthropogenically shaped urban and rural soilscapes of the Berlin/Brandenburg Metropolitan Area (BMA). Shooting ranges, trenches, and artificial mountains consisting of war debris can be identified across the study area. Our study has been carried out to show how acts of war influence the pedosphere and the geomorphology in the study area. Analysis of war-related landforms and soil profiles in the BMA has been conducted to achieve morphological and pedological characteristics of “soils of war”. Emphasis was placed on geochemical investigations on shooting ranges and war debris soils as acts of war may induce elevated soil heavy metal contents.Our study displays how humankind, by relocating and reworking natural soil material and depositing war debris, substantially altered the rural and urban landscapes within the BMA. Approximately, 4% of the BMA have been used for indirect or direct military purposes. Our research revealed that “soils of war” on anthropogenic landforms show distinct soil physical and chemical characteristics as well as pedoturbations. We furthermore found out that “soils of war” often contain elevated amounts of heavy metals. On the other hand, these ruderal soils are often habitats for endangered species of the red list and can provide specific ecosystem services. As studies on “landforms and soils of war” in urban areas are still insufficient, further efforts are needed in the future to research the characteristics of these specific landforms, evaluate their ecological risk but also the potential worth of protection.

Generating Terrain Data for Geomorphological Analysis by Integrating Topographical Features and Conditional Generative Adversarial Networks

With the development of artificial intelligence techniques for geographical knowledge discovery, simulated terrain generation based on deep-learning algorithms has become one practical way to construct accurate terrain data. However, it is still necessary to discuss whether the simulated topographic data contain the characteristics of specific landforms and can support related geographical studies. Therefore, in this study, a deep learning-based model inspired by previous research is constructed to generate loess landform data. We analyzed the influence of inputting different topographic features on terrain generation and evaluated the similarity between the simulated and reference data. The results show that the deep learning-based model can generate simulated topographic data that include similar elevation and slope probability distributions to the reference data of the loess landform. In addition, the generated results may have inaccurate terrain details, which can be regarded as noise in some cases. This indicates that the selection of input features should be carefully considered. Finally, the simulated data can subsequently support landform and terrain research, especially with intelligence algorithms that require large sets of topographic data.

Dating of the late Quaternary high lake levels in the Jilantai area, northwestern China, using optical luminescence of quartz and K-feldspar

High lake levels were developed in wide areas of western China during the late Quaternary as revealed by an increasing number of studies on dating shorelines; however, whether the high lake level of these lakes occurred simultaneously and what drove them are unresolved problems. In this study, in the Jilantai sub-depression within the Jilantai–Hetao Basin, dating results of the single-aliquot quartz and potassium-enriched feldspar (K-feldspar) single grains from the sediments at the western bank of the present Jilantai Salt Lake indicate that the ∼1070 m and 1060–1050 m shorelines formed after ∼136 ka and 49–22 ka, respectively, supporting the hypothesis that higher lake levels developed at intervals of marine isotope stage (MIS) 5e and MIS 3a–Last Glacial Maximum (LGM) in the Jilantai sub-depression. Considering the previous assumption that the lake at an altitude of 1080–1050 m was fed mainly by water from the Yellow River, the changes in the lakeshore landform features are sensitive to climate change in the Jilantai area, among which the development of lakeshore during MIS 3a–LGM was likely a geomorphic response to the development of dry climatic conditions rather than a representative of the humid climate in arid regions.

Coastal Erosion Vulnerability in Mainland China Based on Fuzzy Evaluation of Cloud Models

Global climate change-induced sea-level rise and storm wave intensification, along with the large population densities and high-intensity human development activities in coastal areas, have caused serious burden and damage to China’s coasts, led to the rapid growth of artificial shorelines development, and formed a “new Great Wall” of reinforced concrete against the laws of nature. After the last ice age, transgression formed the different features of China’s coast. Depending on the types of geological and landform features, coasts are divided into 36 evaluation units, and 10 indicators are selected from natural aspects (including tectonics, geomorphology, sediment, and storms) and aspects of social economy (population, GDP, Gross Domestic Product), and cloud model theory is used to build a coastal erosion vulnerability evaluation index system in China. The results show that high grade (V), high-middle grade (IV), middle grade (III), low-middle grade (II), and low grade (I) coastal erosion vulnerability degrees account for 5.56, 13.89, 41.67, 33.33, and 5.56% of the Chinese coastlines, respectively. The coastal erosion vulnerability of the subsidence zone is significantly higher than that of the uplift zone. Reverse cloud model and analytic hierarchy process calculation show that the main factors that control coastal erosion vulnerability since the transgression after the last ice age are geological structure, topography and lithological features, and in recent years, the decrease in sea sediment loads and increase in reclamation engineering. Mainland China must live with the basic situation of coastal erosion, and this study shows that the index system and method of cloud modeling are suitable for the evaluation of the coastal erosion vulnerability of the Chinese mainland. This study provides a scientific basis for the adaptive management of coastal erosion, coastal disaster assessment and the overall planning of land and sea.

A Fine-Grained Genetic Landform Classification Network Based on Multimodal Feature Extraction and Regional Geological Context

Deep learning networks have facilitated the automated scene recognition of landforms based on geomorphogenesis. However, current genetic landform classification methods do not consider regional geological context, which can more accurately reflect the formation and evolution mechanism of geomorphic landforms than local ones. Therefore, this study proposes a multimodal, deep learning landform recognition framework based on a joint contextual geological and channel attention module (GCMENET). First, the multibranch feature extraction network of DenseNet121 is used to extract the respective features from the target scene and the contextual geological scene. Second, the features similar to the landform features of the target scene are extracted from the contextual geological features based on the cosine method and then combined with geomorphic features of the target scene. Third, channel attention mechanism is used to reduce the interference caused by redundant contextual geological information after fusion of data. To measure the classification accuracy of GCMENET, we establish a fine geomorphogenic dataset consisting of remote sensing images of six landform types with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$64\times64$ </tex-math></inline-formula> -pixel size and 10-m resolution (JOS10m). During the training process of two geomorphogenic datasets, the feature extraction network without batchnorm2d (batch normalization) could preserve the distribution and spatial alignment of data from the components. Using different training-to-validation data ratios and combinations of input components, the results of the GCMENET supplemented with the joint contextual geological and channel attention module exhibited greater accuracy than those obtained without the module. This observation confirms the importance of contextual geological information in automated geomorphogenic landforms.