Landform Types Research Articles

DEBRIS FLOW RELIEF OF THE MALAYA PAIPUDYNA BASIN (THE POLAR URAL MOUNTAINS)<a href="#FN1"><sup>1</sup></a>

We studied the debris flow relief of the Malaya Paipudyna basin, the Polar Ural Mountains. Based on the analysis of remote sensing data and field surveys, we established that 14 debris flow basins are located on the territory. We found traces of five slushflow occurrence in the streams in the spring of 2021. Typical landforms for different morphodynamic zones of debris flow basins were identified. The initiation zones of debris flows are mainly represented by catchment funnels on the slopes of the Bolshoi and Maliy PaIpudynskii ranges. The transit zones V-shaped bottom incisions alternate with box- and trough-shaped transverse profile. Within the debris flow fans, two generations of accumulative debris flow relief are clearly distinguished. Young accumulation zones are represented by pebble-boulder ridges up to 0.5 m high, localized directly in the near-channel areas of debris flow fans. Usually, they are either devoid of soil and vegetation cover, or are overgrown only by herbs. Ancient debris flow fans are triangular in shaped with convex transverse profile, consisting of a system of ridges and hollows, and overgrown with shrubs. The area of young accumulation zones for each debris flow basin is no more than 0.03 km2, the area of ancient accumulation zones is 0.4 km2. Debris flow fans are superimposed on the bottom of the trough valley of Malaya Paipudyna, which is mainly the surface of glacial accumulation. Probably, the formation of these fans began after the degradation of the last extensive glaciation of the territory. We calculated the morphometric features of the debris flow basins.

Discerning geomorphological aspects of tsunami risk in Pangandaran, West Java, Indonesia

The geomorphological understanding of earth dynamics, including the relationship between landforms and their processes, was one of the earliest and most specific contributions to disaster prevention. Disaster geomorphology is one of the approaches in disaster studies, which includes aspects of landforms, processes, and results of physical processes that have the potential and can cause disasters. The landform is of risk factors that can turn natural hazards into natural disasters and determines the damage that disasters can cause to human activities. Following the 2006 South Java Tsunami, infrastructural development occurred massively in the 2006 South Java Tsunami inundation areas. Several tsunami risk mitigation efforts were conducted but solely based on the 2006 tsunami scenario and ignored the existence of more considerable tsunami hazards from the Sunda Megathrust. This consideration may lead to an increasing risk of future tsunamis. We evaluate and appraise favorable and unfavorable geomorphological features to reduce the risk of future tsunamis. Pangandaran has a unique landform compared to other areas on the south coast of Java, and this landform has the potential to reduce future tsunami risk. Typical landforms studied include coastal plains, alluvial plains, Tombolo, tied islands, and structural hills. The results show that the morphological features of the Tombolo and the coastal plain area are categorized as high risk when a tsunami occurs. The tied island is categorized as a favorable morphology where these morphological units have the advantage of elevation and efficient distance to the tsunami risk zone. Evacuation facilities are also needed, especially in the coastal plain and Tombolo areas (with a height of >20 meters), to reduce disaster risk, particularly mortality caused by tsunami events.

Towards accurate mapping of loess waterworn gully by integrating google earth imagery and DEM using deep learning

Accurate mapping of loess waterworn gully (LWG) is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau (CLP). Due to the vertical joint and collapsibility of loess, LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity. This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency. However, simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions. Here, we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data. We selected four study areas representing typical loess landforms to test the performance of our method. The proposed method can achieve satisfactory mapping results, with the F1 score, mean Intersection-over-Union (mIoU), and overall accuracy of 90.5%, 85.3%, and 92.3%, respectively. In addition, the proposed model also showed significant accuracy improvement by inputting additional topographic information (especially the slope of slope). Compared with existing algorithms (Random forests, original DeepLabV3+, and Unet), the proposed approach in this study achieved a better accuracy-efficiency trade-off. Overall, the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation.

ML based approach for inverting penetration depth of SAR signals over large desert areas

Penetration depth of synthetic aperture radar (SAR) signals over a desert is a key parameter to understand the internal properties of the desert. Existing approaches for obtaining the penetration depth require good quality interferometric SAR (InSAR) data of very short temporal and long spatial baselines. Such data are often difficult to obtain in a highly dynamic desert. We propose a new machine learning (ML) based approach for inverting penetration depth of SAR signals over large desert areas by jointly using InSAR, polarimetric SAR (PolSAR) and optical remote sensing data. First, SAR scattering parameters and terrain properties are determined based on PolSAR and Landsat 5 TM multispectral data and a DEM. The penetration depth of SAR signals over a small desert area is obtained based on methods such as using a scattering model. A random forest model is then used to establish the relationship between the SAR scattering parameters and site features, and the penetration depth, and then is used to derive the penetration depth over a large desert area. The approach is applied to calculate the penetration depth of ALOS-1 PALSAR L-band signals for a large part of the Kufra Basin, an area of about 60, 000 km2. The penetration depths of four types of typical landforms in area (i.e., sandy plains, paleochannels, rocks and man-made features) are discussed in relation to the geological and climatic conditions. The average signal penetration depths over the paleochannels, sandy plains, and rocks and man-made features are 2.84 m, 1.97 m, 1.21 m, respectively. It is found that the backscattering coefficient, dielectric constant, surface roughness and mineral composition are the most important parameters in determining the signal penetration depths. An interesting point is that the existence of hematite in the sand can increase the dielectric dissipation of the sand medium and shorten the signal penetration depth.

Landscape characteristics shape surface soil microbiomes in the Chihuahuan Desert.

Soil microbial communities, including biological soil crust microbiomes, play key roles in water, carbon and nitrogen cycling, biological weathering, and other nutrient releasing processes of desert ecosystems. However, our knowledge of microbial distribution patterns and ecological drivers is still poor, especially so for the Chihuahuan Desert. This project investigated the effects of trampling disturbance on surface soil microbiomes, explored community composition and structure, and related patterns to abiotic and biotic landscape characteristics within the Chihuahuan Desert biome. Composite soil samples were collected in disturbed and undisturbed areas of 15 long-term ecological research plots in the Jornada Basin, New Mexico. Microbial diversity of cross-domain microbial groups (total Bacteria, Cyanobacteria, Archaea, and Fungi) was obtained via DNA amplicon metabarcode sequencing. Sequence data were related to landscape characteristics including vegetation type, landforms, ecological site and state as well as soil properties including gravel content, soil texture, pH, and electrical conductivity. Filamentous Cyanobacteria dominated the photoautotrophic community while Proteobacteria and Actinobacteria dominated among the heterotrophic bacteria. Thaumarchaeota were the most abundant Archaea and drought adapted taxa in Dothideomycetes and Agaricomycetes were most abundant fungi in the soil surface microbiomes. Apart from richness within Archaea (p = 0.0124), disturbed samples did not differ from undisturbed samples with respect to alpha diversity and community composition (p ≥ 0.05), possibly due to a lack of frequent or impactful disturbance. Vegetation type and landform showed differences in richness of Bacteria, Archaea, and Cyanobacteria but not in Fungi. Richness lacked strong relationships with soil variables. Landscape features including parent material, vegetation type, landform type, and ecological sites and states, exhibited stronger influence on relative abundances and microbial community composition than on alpha diversity, especially for Cyanobacteria and Fungi. Soil texture, moisture, pH, electrical conductivity, lichen cover, and perennial plant biomass correlated strongly with microbial community gradients detected in NMDS ordinations. Our study provides first comprehensive insights into the relationships between landscape characteristics, associated soil properties, and cross-domain soil microbiomes in the Chihuahuan Desert. Our findings will inform land management and restoration efforts and aid in the understanding of processes such as desertification and state transitioning, which represent urgent ecological and economical challenges in drylands around the world.

Transformation of Paddy Field Use in Intermountain-Type Basins Using Evidence from the Structure and Function Perspective of Karst Mountain Areas in Southwest China

China’s southwestern karst area exhibits many mountains and little flat lands, and intermountain basins (IBs) (locally named “bazi”) are one of their typical landform types. Comparative studies on land use in the core of typical landforms in karst mountain areas are relatively lacking. Studying the evolution and transformation patterns of intermountain basin paddy fields use (IBPFU) to optimize land resources in mountainous areas is important. Based on structural and functional perspectives, this study established a research framework on the evolution and transformation of IBPFU in karst mountainous areas, and analyzed the evolution and transformation patterns of IBPFU in Guizhou Province from 1966 to 2020 by measuring land use change and applying the moving window method and morphological spatial pattern analysis (MSPA) model. The study showed that the evolution of IBPFU is characterized by reversibility and irreversibility, diversity, and stages. The transition of IBPFU requires a trade-off among regional socioeconomic development, ecological protection, and food security. The commonality of land use transitions in IB and sloped land (SL) is reflected in the diversity of their land use functions. This study provides a reference for a comprehensive understanding of land use transitions and sustainable development in the mountainous karst regions of southwest China by providing in-depth examinations of the land use transition of IB, which is different from SL, via the long time series evolution of core land use types. The research framework and research method constructed here also apply to other regions.

Using geochemical elements to discriminate sediment sources in a typical karst watershed

Knowledge of temporal variations in sediment sources is necessary to identify the principal sources of sediment for effective implementation of soil conservation measures and to improve the calibration and validation of process-based soil erosion models. Quantitative information on sediment provenance is often limited in karst regions because of their complex surfaces and underground three-dimensional erosion systems. Peak-cluster depressions are typical landforms in karst areas and can well intercept most sediment from hillsides such as dams or reservoirs. Thus, peak-cluster depressions are suitable areas for sediment source identification. The objective of this study was to quantify the contribution of sediment sources in a typical karst watershed and to identify temporal variations in sediment sources in the watershed in the past 60 years. We collected 194 soil samples from three sediment profiles in a karst depression and 58 source samples from cropland, fissure soil, and forestland. We used a composite fingerprinting method based on 45 geochemical properties and an IsoSource multivariate mixed model in a typical karst watershed. The results indicate that cropland was the principal sediment source (89.2 %) in the last 60 years, followed by fissure soil (9.6 %) and forestland (1.2 %). Importantly, compared with the period from 1949 to 1999, the contribution of sediment from fissure soil reached a maximum value of approximately 20 % from 1999 to 2015. This result implies that the “Grain for Green” program, which was implemented in 1999, clearly reduced the amount of soil erosion from cropland. Therefore, the program may greatly affect sediment deposition in karst depressions. Using metal ratios as fingerprint factors, the IsoSource mixed model can be effectively used to study the source of sediments in karst watersheds quantitatively. This study can aid the existing understanding of soil erosion processes and contributes to the sustainable development of agro-ecology in karst watersheds.

A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples

The quality of samples is crucial in constructing a data-driven landslide susceptibility model. This article aims to construct a data-driven landslide susceptibility model that takes into account the selection of non-landslide samples. First, 21 conditioning factors are selected, including four types of topography and landform, geological conditions, environmental conditions, and human activities. Grid units with 30 m resolution are established by combining 942 historical landslide events in study area. Second, non-landslide samples are selected using both the traditional method and the information quantity method. Two landslide susceptibility models are established using the Bayesian optimization-LightGBM model. The accuracy of the model is evaluated by significance test and the area under curve (AUC). Finally, the SHAP algorithm is used to analyse the internal mechanism of the model’s decision-making. Based on the information quantity method, the LightGBM model identifies very high-high susceptibility areas that account for 77.92% of the total number of landslides. Additionally, the AUC of test set and the AUC of training set are 23.2% and 17.1% higher, respectively, compared to the traditional model. The selection of different sample data, whether landslide or non-landslide, impacts the factor rank, model accuracy, and the interal decision-making mechanism of the model. This finding provides valuable for the selection of sample data in the binary classification model.

Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research.

Analysis of Spatio-Temporal Evolution of Regional Settlements under the Background of Rapid Urbanization: A Case Study in Sishui County, China

China’s urbanization has achieved rapid development in the past 20 years, with towns expanding in size and the population increasing, while rural society has also undergone dramatic changes. An in-depth study on the evolution process of rural settlements in the context of rapid urbanization is beneficial to the rational planning of villages and the promotion of green and sustainable urban development. Located in East China, Sishui County is in the transition area between three types of landforms: hills, plains, and mountains. The spatial distribution of rural settlements in the urbanization process shows obvious regional differences. To our best knowledge, research on the spatio-temporal evolution of regional settlements in Sishui County is rare. In this study, we chose Sishui County as the study area, utilized Landsat5 (TM) and Landsat8 (OLI) satellite data as remote sensing data sources, and applied Geographic Information System (GIS) spatial analysis methods, central place theory, and core–periphery theory to explore the evolution process for the pattern, scale, and structure of rural settlements in this region from 2000 to 2021 and to investigate the influencing factors. The results show that: (1) in terms of the evolution of the rural settlement pattern, its distribution shows a gradual increase in the degree of dispersion, which indicates an overall development trend in the region of Sishui County in recent years and that the trend is gradually increasing; (2) in terms of scale evolution, the degree and speed of expansion in rural settlements of Sishui County have gradually decreased, and the scale grade has gradually increased; (3) in terms of structural evolution, the hierarchy system in rural settlements of Sishui County is constantly being improved and optimized from a simple to a complex core–periphery structure. These results will provide data support for the rational planning of villages and sustainable, high-quality urban development. They will also help local governments take appropriate measures to achieve coordinated and sustainable socio-economic and environmental development in the region.

Predictive modeling for neolithic settlements in the Lingnan Region, South China

Predictive modeling is an effective tool to extrapolate the distribution pattern of ancient settlements, and the adaptative strategies of ancient populations to ever-changing environment. In this article, we build predictive models for settlements of three Neolithic periods in the Lingnan region, South China, which primarily encompasses the two present-day provinces of Guangxi and Guangdong. We process the geographic and topographic data of 793 sites to establish Logistic models, which we test with classification accuracy, AUC, Kvamme’s Gain statistic, and 5-fold cross-validation. In the meantime, we incorporate the subsistence strategies and landform types of 245 sites to observe how ancient populations responded to the matrix of environmental, technological, and cultural development. The results show that the models are powerful for predicting the distribution of Neolithic settlements in the region. Furthermore, they show that the distribution patterns of sites differ widely in the two provinces of Guangxi and Guangdong as well as in the three periods of the Early, Middle, and Late Neolithic.

An Optimised Region-Growing Algorithm for Extraction of the Loess Shoulder-Line from DEMs

The positive and negative terrains (P–N terrains) of the Loess Plateau of China are important geographical topography elements for measuring the degree of surface erosion and distinguishing the types of landforms. Loess shoulder-lines are an important terrain feature in the Loess Plateau and are often used as a criterion for distinguishing P–N terrains. The extraction of shoulder lines is important for predicting erosion and recognising a gully head. However, existing extraction algorithms for loess shoulder-lines in areas with insignificant slopes need to be improved. This study proposes a regional fusion (RF) method that integrates the slope variation-based method and region-growing algorithm to extract loess shoulder-lines based on a Digital Elevation Model (DEM) at a spatial resolution of 5 m. The RF method introduces different terrain factors into the growth standards of the region-growing algorithm to extract loess-shoulder lines. First, we employed a slope-variation-based method to build the initial set of loess shoulder-lines and used the difference between the smoothed and real DEMs to extract the initial set for the N terrain. Second, the region-growing algorithm with improved growth standards was used to generate a complete area of the candidate region of the loess shoulder-lines and the N terrain, which were fused to generate and integrate contours to eliminate the discontinuity. Finally, loess shoulder-lines were identified by detecting the edge of the integrated contour, with results exhibiting congregate points or spurs, eliminated via a hit-or-miss transform to optimise the final results. Validation of the experimental area of loess ridges and hills in Shaanxi Province showed that the accuracy of the RF method based on the Euclidean distance offset percentage within a 10-m deviation range reached 96.9% compared to the manual digitalisation method. Based on the mean absolute error and standard absolute deviation values, compared with Zhou’s improved snake model and the bidirectional DEM relief-shading methods, the proposed RF method extracted the loess shoulder-lines highly accurately.

Deep learning-based automated terrain classification using high-resolution DEM data

Landforms are a fundamental component of the natural environment, and digital terrain mapping on a large spatial scale is important when studying landforms. In this study, we adopted a semantic segmentation model in computer vision to classify elementary landform types using AW3D30 digital elevation model (DEM) data. We built a semantic segmentation model with an FCN-ResNet architecture that extracts features using a residual network (ResNet) and obtains pixel-level segmentation of the DEM using a fully convolutional network (FCN). A lightweight decoder based on skip connections was adopted to maintain detailed information at different scales. We used the 1:1,000,000 Chinese landform map as the label and tested different combinations of terrain factors. The experiments indicate that increasing the terrain factors has no significant influence on the model, and the semantic information can be learned using only DEM data. The model has strong feature extraction capability and is tolerant to noise and error. The results of landform category prediction confirm that deep learning methods have strong potential for landform classification and will have great application prospects in the field of geomorphological research.

Vegetation Dynamics and Their Response to Climate Changes and Human Activities: A Case Study in the Hanjiang River Basin, China

The Hanjiang River Basin (HJRB) is an important water conservation and ecological barrier area for the South–North Water Transfer Central Project. The quantitative analysis of regional differences in vegetation changes and their main drivers is important for the monitoring of the ecological environment of the basin and formulation of ecological protection measures. Based on MODIS13Q1 data from 2000 to 2020, spatiotemporal variation characteristics of vegetation in the HJRB were analyzed using Theil–Sen + Mann–Kendall, the Hurst index, and correlation analysis. Then, we detected the drivers using an optimal parameter geographic detector. The results showed that from 2000 to 2020, the average NDVI value increased from 0.651 to 0.737, with a spatial distribution pattern of “high in the northwest and low in the southeast”, and 88.68% of the study area showed an increase in vegetation cover, while 5.80% showed a significant degradation. The positive persistence of future vegetation changes is stronger than the negative. It may show a slowdown or degradation trend, among which the vegetation restoration along the Han River and urbanized areas need to be strengthened. The factor detector indicated that the main factors influencing vegetation change were topography and climate, for which the most influential variables, respectively, were elevation (0.1979), landform (0.1720), slope (0.1647), and soil type (0.1094), with weaker influence from human activity factors. The interaction test results showed that the interaction of various geographic factors enhanced the explanatory power of vegetation changes and showed mainly nonlinear and two-factor enhancements. The dominant factor varies between sub-basins; for example, the interaction between wind speed and land use conversion was the dominant factor in the middle reaches of the HJRB; the dominant factor in the lower reaches of the HJRB was expressed as the interaction between land use conversion and temperature. Finally, the effects of the range or category of different drivers on vegetation growth were systematically analyzed. The results of the study contribute to the understanding of the dynamic changes of vegetation based on a comprehensive consideration of the interaction of topography, climate, and human activities, taking into account the totality and variability of the geographical environment, and provide a reference for the ecological restoration and rational use of vegetation resources in the HJRB.

Glacial geomorphology of the northwest Laurentide Ice Sheet on the northern Interior Plains and western Canadian Shield, Canada

ABSTRACT The majority of the Northwest Territories of mainland Canada was covered by the Laurentide Ice Sheet during the Last Glacial Maximum. The increasing coverage of high resolution remotely sensed data provides new opportunities to map the glacial geomorphology and study the glacial history of this remote location. Here we present a comprehensive map of glacial landforms within the northern Interior Plains and adjacent areas of the Canadian Shield, comprising around 6% of the Laurentide Ice Sheet bed. Twelve landform types were mapped from the high resolution ArcticDEM: ice flow parallel lineations, subglacial ribs, crevasse-squeeze ridges, major and minor moraine crests, hummocky terrain complexes and ridges, shear margin moraines, major, minor and lateral and submarginal meltwater channels, esker ridges and complexes, glaciofluvial complexes, perched deltas, raised shorelines and aeolian dunes. Together, these landforms provide a record of the highly dynamic behaviour of the northwest sector of the Laurentide Ice Sheet.

Ecological niche modelling of a critically endangered species Commiphora wightii (Arn.) Bhandari using bioclimatic and non-bioclimatic variables

BackgroundThe aim of this study is to examine the effects of four different bioclimatic predictors (current, 2050, 2070, and 2090 under Shared Socioeconomic Pathways SSP2-4.5) and non-bioclimatic variables (soil, habitat heterogeneity index, land use, slope, and aspect) on the habitat suitability and niche dimensions of the critically endangered plant species Commiphora wightii in India. We also evaluate how niche modelling affects its extent of occurrence (EOO) and area of occupancy (AOO).ResultsThe area under the receiver operating curve (AUC) values produced by the maximum entropy (Maxent) under various bioclimatic time frames were more than 0.94, indicating excellent model accuracy. Non-bioclimatic characteristics, with the exception of terrain slope and aspect, decreased the accuracy of our model. Additionally, Maxent accuracy was the lowest across all combinations of bioclimatic and non-bioclimatic variables (AUC = 0.75 to 0.78). With current, 2050, and 2070 bioclimatic projections, our modelling revealed the significance of water availability parameters (BC-12 to BC-19, i.e. annual and seasonal precipitation as well as precipitation of wettest, driest, and coldest months and quarters) on habitat suitability for this species. However, with 2090 projection, energy variables such as mean temperature of wettest quarter (BC-8) and isothermality (BC-3) were identified as governing factors. Excessive salt, rooting conditions, land use type (grassland), characteristics of the plant community, and slope were also noticed to have an impact on this species. Through distribution modelling of this species in both its native (western India) and exotic (North-east, Central Part of India, as well as northern and eastern Ghat) habitats, we were also able to simulate both its fundamental niche and its realized niche. Our EOO and AOO analysis reflects the possibility of many new areas in India where this species can be planted and grown.ConclusionAccording to the calculated area under the various suitability classes, we can conclude that C. wightii's potentially suitable bioclimatic distribution under the optimum and moderate classes would increase under all future bioclimatic scenarios (2090 > 2050 ≈ current), with the exception of 2070, demonstrating that there are more suitable habitats available for C. wightii artificial cultivation and will be available for future bioclimatic projections of 2050 and 2090. Predictive sites indicated that this species also favours various types of landforms outside rocky environments, such as sand dunes, sandy plains, young alluvial plains, saline areas, and so on. Our research also revealed crucial information regarding the community dispersion variable, notably the coefficient of variation that, when bioclimatic + non-bioclimatic variables were coupled, disguised the effects of bioclimatic factors across all time frames.

Variability of precipitation-stable isotopes and moisture sources of two typical landforms in the eastern Loess Plateau, China

Study regionTwo typical landforms in the eastern Loess Plateau, China Study focusHydrogen and oxygen stable isotopes in precipitation are highly efficient tracers for quantitatively identifying the regional water cycle. Based on 237 precipitation event data points from seven precipitation stable isotope monitoring stations in the eastern Loess Plateau (ECLP), this study first presented the spatial-temporal variation of precipitation isotopes. It explored the mainly controlling factor and source of water vapor for regional precipitation. New hydrological insights for the regionThis research found a consistent temporal trend was observed in the ECLP except for the Jiexiu station, characterized by enrichment from December to May and depletion from June to October. Signals of the summer monsoon reaching the ECLP were recorded using precipitation δ18O (δ18Op), especially in July. Relatively smaller slope of the local meteoric water line (LMWL) of summer precipitation reflects that sub-cloud evaporation strongly influences regional precipitation process. δ18Op values did not exhibited significant relationships with air temperature and an apparent "precipitation amount effect" was observed in precipitation samples with precipitation amount less than 10 mm. An apparent "anti-altitude" effect appeared in precipitation samples with the elevation between 400 and 750 m of the ECLP. The sub-cloud evaporation had stronger influence on the precipitation process of the the Fenhe river basin valley (FRV) especially in the Jiexiu station. The water vapor of precipitation in the ECLP mainly originated from the Bohai Sea and the East China Sea in the near-surface and southeast path directions, especially in summer. These studies are essential in regional water resources allocation, especially under the influence of climate change.

Topographic Position Index to Landform Classification and Spatial Planning, Using GIS, for Wadi Araba, South West Jordan

This study aimed to carry out a classification of landforms in Wadi Araba, southwest Jordan, using the topographic Position Index (TPI) by GIS. TPI was used at different spatial scales (mean, 50 m, 75 m, 100 m, 200 m, 500 m, 1000 m, 1500 m, and 2000 m) for such classification. Landform classification goes by ten levels: Valleys Bottoms, Midslope Drainage, Upland Drainages, Gentle Valleys, Plains, Open Slopes, Upper Slopes, Local Ridges, Midslope Ridges, and High Ridges. This requires defined classes using the TPI for all scales. The analysis showed that there were ten types of landforms; most of them were ridges, valleys, and steep slopes. In addition, the proportions of these types varied according to the variation of indicator values in the scale. Landform map according to the Neighborhood Values mean (m) showed that areas with upland drainages were about 14%, while areas with Gentle Valleys represented about 5.52%, of the total area of the study area. Finally, it was found possible to rely on the classification maps of the landforms for the purpose of spatial planning, as this included determining the land uses that are appropriate for each landform, as well as determining the levels of risk for landslides and floods.

Signatures of Life Detected in Images of Rocks Using Neural Network Analysis Demonstrate New Potential for Searching for Biosignatures on the Surface of Mars.

Microorganisms play a role in the construction or modulation of various types of landforms. They are especially notable for forming microbially induced sedimentary structures (MISS). Such microbial structures have been considered to be among the most likely biosignatures that might be encountered on the martian surface. Twenty-nine algorithms have been tested with images taken during a laboratory experiment for testing their performance in discriminating mat cracks (MISS) from abiotic mud cracks. Among the algorithms, neural network types produced excellent predictions with similar precision of 0.99. Following that step, a convolutional neural network (CNN) approach has been tested to see whether it can conclusively detect MISS in images of rocks and sediment surfaces taken at different natural sites where present and ancient (fossil) microbial mat cracks and abiotic desiccation cracks were observed. The CNN approach showed excellent prediction of biotic and abiotic structures from the images (global precision, sensitivity, and specificity, respectively, 0.99, 0.99, and 0.97). The key areas of interest of the machine matched well with human expertise for distinguishing biotic and abiotic forms (in their geomorphological meaning). The images indicated clear differences between the abiotic and biotic situations expressed at three embedded scales: texture (size, shape, and arrangement of the grains constituting the surface of one form), form (outer shape of one form), and pattern of form arrangement (arrangement of the forms over a few square meters). The most discriminative components for biogenicity were the border of the mat cracks with their tortuous enlarged and blistered morphology more or less curved upward, sometimes with thin laminations. To apply this innovative biogeomorphological approach to the images obtained by rovers on Mars, the main physical and biological sources of variation in abiotic and biotic outcomes must now be further considered.

Geopedology in complex landform setting: A case study in eastern Parangtritis Village, Indonesia

The structural mountain range of Baturagung continue to undergo soil development, influenced by various factors, including landform, geomorphological processes, and land use management. This research is grounded in the potential of soil resources based on landform, demanding precise land management and conservation. The study involved transecting and soil sampling across different landform features in the eastern part of Parangtritis Village. The delineation of these features was aided by the modification of the topographic position index-based landform classification. Research data revealed that the landform types encompass shoulders, backslopes, footslopes, toeslopes, depressions, and floodplains. Each of these landform features exhibits distinct soil characteristics concerning depth, color, texture, and mottling, as well as organic matter, manganese, and carbonate content. These characteristics also represent soil problems that require special management, including shallow soils, steep soils, Vertisols, and anthropogenic soils experiencing compaction. Proper soil management is essential to mitigate land degradation. Therefore, investigating the variations in soil characteristics based on geomorphology is crucial for comprehending their formation and development processes and for factoring them into land management decisions.