Search Results (104)

Mapping coastal landforms is critical for the sustainable management of ecosystems influenced by both natural dynamics and human activity. This study investigates the application of Transformer-based semantic segmentation models for pixel-level classification of key surface types such as water, sandy shores, rocky areas, vegetation, and built structures. We utilize a diverse, multi-resolution dataset that includes NAIP (1 m), Quadrangle (6 m), Sentinel-2 (10 m), and Landsat-8 (15 m) imagery from U.S. coastlines, along with high-resolution aerial images of the Greek coastline provided by the Hellenic Land Registry. Due to the lack of labeled Greek data, models were pre-trained on U.S. datasets and fine-tuned using a manually annotated subset of Greek images. We evaluate the performance of three advanced Transformer architectures, with Mask2Former achieving the most robust results, further improved 11 through a coastal-class weighted focal loss to enhance boundary precision. The findings demonstrate that Transformer-based models offer an effective, scalable, and cost-efficient solution for automated coastal monitoring. This work highlights the potential of AI-driven remote sensing to replace or complement traditional in-situ surveys, and lays the foundation for future research in multimodal data integration and regional adaptation for environmental analysis. Full article

The Colombian Orinoquia was shaped within a tectonic and sedimentary framework linked to the uplift of the Andean cordilleras during the Oligocene–Miocene. This orogenic event generated two tectonic fronts and facilitated extensive fluvial sedimentation across a broad alluvial geosyncline. The present geomorphological configuration reflects the cumulative interaction of tectonic and erosional processes with Quaternary climatic dynamics, which together produced complex landscape assemblages characterized by plains with distinctive drainage patterns. To delineate and characterize geomorphological units, we employed multidimensional imagery and Machine Learning techniques within the Google Earth Engine platform. The classification model integrated dual polarizations of synthetic aperture radar (L-band) with key topographic variables including elevation, slope, aspect, convexity, and roughness. The analysis identified three major physiographic units: (i) the Foothills and the Floodplain, both dominated by fluvial environments; (ii) the High plains and Serranía de La Macarena (Macarena Mountain Range), where denudational processes predominate; and (iii) localized aeolian environments embedded within the Floodplain. These contrasting dynamics have generated a broad spectrum of landforms, ranging from terraces and alluvial fans in the Foothills to hills and other erosional features in La Macarena. The Floodplain, developed over a sedimentary depression, illustrates the combined action of fluvial and aeolian processes, whereas the High plains is characterized by rolling plains and peneplains formed through the uplift and erosion of Tertiary sediments. Such geomorphic heterogeneity underscores the interplay between tectonic activity, climatic forcing, and surface processes in shaping the Orinoquia landscape. The geomorphological classification using Random Forest demonstrated high effectiveness in discriminating units at a regional scale, with accuracy levels supported by confusion matrices and associated Kappa indices. Nevertheless, some degree of classificatory overlap was observed in fluvial environments, likely reflecting their transitional nature and complex sedimentary dynamics. Overall, this methodological approach enhances the objectivity of geomorphological analysis and establishes a replicable framework for assessing landform distribution in tropical sedimentary basins. Full article

Contour lines, as the primary elements of fundamental geospatial data, have long been a research focus for similarity measurement. With the evolution of cartographic generalization, the representation of contour lines across varying scales must maintain the consistency of specific information. Typically, the rationality of the generated results is assessed based on their similarity values. However, current measurements for measuring contour similarity predominantly focus on geometric and topological aspects, and are often less concerned with the terrain-specific similarities that are intrinsic to contour lines. Contour line groups contain a wealth of topographic information, and the similarity of their terrain features reflects both the variations in relief and the intrinsic nature of landform development. In this study, we propose a novel metric for assessing the similarity of contour line groups by considering topographic features, aiming to evaluate the similarity of contour line groups from a holistic perspective. First, we analyze and define the geometric, topological, and topographic similarity calculation metrics for contour line groups. Next, we apply the Analytic Hierarchy Process using ten criteria, which are encompassed by these three similarity metrics. To validate the effectiveness of the proposed metric, we select hillock areas within Suide County, China, as a case study for examining the similarity of contour line groups. The results demonstrate that the proposed metric provides a more precise quantitative framework for delineating the subtle differences and similarities among multi-source and multi-scale contour line groups within the overall similarity. Moreover, the metric also establishes a foundation for the quantitative assessment of surface morphology and the classification of geomorphological types. Full article

Landscape character assessment (LCA) is a systematic approach used to classify, describe, and analyze the physical and cultural attributes that define the landscape. The traditional approaches to LCA are fundamentally subjective and descriptive, relying on human evaluations of aesthetic value, and they often show inconsistencies in results when assessed by different observers for the same landscape. This research aims to establish a spatial and quantitative methodology through GIS for evaluating the landscape character of King Khalid University (KKU)’s campus in the Southern Province of Saudi Arabia, which is considered crucial for designing a sustainable and context-sensitive landscape. To identify the feasible developed areas and their sustainable characteristics, three key landscape variables were measured and spatially expressed, subsequently averaged to categorize landscape character. The variables include land use and land cover, which were obtained from Sentinel 2 remote sensing data through supervised classification, as well as landforms and hydrological settings derived from a digital elevation model (DEM) utilizing GIS functionalities. The findings revealed three distinct landscape characters, each characterized by quantifiable landscape attributes. The landscapes exhibiting the most significant character encompass approximately 20% (1074 ha) of the study area, whereas those with the least significance account for 6.5% (342 ha). The remaining 73.5% (3884 ha) is classified as landscapes with an average significance character. The results provide a solid scientific basis for choosing locations in the campus’s study area that promote environmentally friendly and sustainable landscape development. This method improves objectivity in LCA and offers a reproducible framework for implementation in arid and semi-arid areas. Full article

Understanding spatial variability is central to precision agriculture, yet terrain features are often overlooked in remote sensing workflows that inform agronomic decision-making. This work introduces a terrain-aware visual analytics approach that integrates landform classification with crop performance analysis to better support field-level decisions. Terrain features are an important contributor to yield variability, alongside environmental conditions, soil properties, and management practices. However, they are rarely integrated systematically into performance analysis and decision-making workflows—limiting the potential for terrain-aware insights in precision agriculture. Addressing this gap requires approaches that incorporate terrain attributes and landform classifications into agricultural performance analysis and management zone (MZ) delineation—ideally through visual analytics that offer interpretable insights beyond the constraints of purely data-driven methods. We introduce an interactive focus+context visualization tool that integrates multiple data layers—including terrain features, vegetation index–based performance metric, and management zones—into a unified, expressive view. The system leverages freely available remote sensing imagery and terrain data derived from Digital Elevation Models (DEMs) to evaluate crop performance and landform characteristics in support of agronomic analysis. The tool was applied to eleven agricultural fields across the Canadian Prairies under diverse environmental conditions. Fields were segmented into depressions, hilltops, and baseline areas, and crop performance was evaluated across these landform groups using the system’s interactive visualization and analytics. Depressions and hilltops consistently showed lower mean performance and higher variability (measured by coefficient of variation) compared to baseline regions, which covered an average of 82% of each field. We also subdivided baseline areas using slope and the Sediment Transport Index (STI) to investigate soil erosion effects, but field-level patterns were inconsistent and no systematic differences emerged across all sites. Expert evaluation confirmed the tool’s usability and its value for field-level decision support. Overall, the method enhances terrain-aware interpretation of remotely sensed data and contributes meaningfully to refining management zone delineation in precision agriculture. Full article

This study develops a geomorphological identification methodology for wall reefs in the microcontinental environment of Wangiwangi Island, Indonesia, using medium-resolution Landsat 8 satellite imagery and morphological analysis based on Maxwell’s geomorphological framework. The uniqueness of the wall reef landform lies in the fact that the lagoon elongates on limestone, resulting in a habitat and ecosystem that develops differently from those of other shelf reefs, namely, platform reefs and plug reefs. Using Optimum Index Factor (OIF) optimization and RGB image composites, four reef types were successfully identified: cuspate reefs, open ring reefs, closed ring reefs, and resorbed reefs. A field check was conducted at fifteen observation sites, which included measurements of depth, turbidity, and water quality parameters, as well as an in situ benthic habitat inventory. The analysis results showed a strong correlation between image composites, geomorphological reef classes, and ecological conditions, confirming the successful adaptation of Maxwell’s classification to the Indonesian reef system. This hybrid integrated approach successfully maps the distribution of reefs on a complex continental shelf, providing an essential database for shallow-water spatial planning, ecosystem-based conservation, and sustainable management in the Coral Triangle region. Policy recommendations include zoning schemes for protected areas based on reef landform morphology, strengthening integrative monitoring systems, and utilizing high-resolution imagery and machine learning algorithms in further research. Full article

Physical and chemical processes related to global and regional climate changes are important factors in shaping the Earth’s surface. These processes form various erosion and deposition landforms on the Earth’s surface. These landforms reflect the traces of past and present climate conditions. This study shows that geomorphometric parameters can effectively distinguish between geomorphometrically and climatically distinct geomorphic provinces. In this context, supervised machine learning models were developed using geomorphometric parameters and the Köppen-Geiger climate classes observed in Türkiye. These models, Random Forest, Support Vector Machines, and K-Nearest Neighbor algorithms, were developed using a training data set. Classification analysis was performed using these models and a test dataset that was independent of the training dataset. According to the classification results, the overall accuracy values for the Random Forest, Support Vector Machines, and K-Nearest Neighbor models were calculated as 99.27%, 99.70%, and 99.30%, respectively. The corresponding kappa values were 0.99, 0.99, and 0.99, respectively. This study shows that among the geomorphometric parameters used in the analyses, maximum altitude, elevation, and valley depth were determined as important parameters in distinguishing geomorphic provinces. Full article

As a perceptual representation of ecosystem structure and function, the soundscape has become an important indicator for evaluating ecological health and assessing the impacts of human disturbances. Understanding the spatiotemporal heterogeneity of soundscapes is essential for revealing ecological processes and human impacts in protected areas. This study investigates such heterogeneity in Zhangjiajie National Forest Park using deep learning approaches. To this end, we constructed a dataset comprising eight representative sound source categories by integrating field recordings with online audio (BBC Sound Effects Archive and Freesound), and trained a classification model to accurately identify biophony, geophony, and anthrophony, which enabled the subsequent analysis of spatiotemporal distribution patterns. Our results indicate that temporal variations in the soundscape are closely associated with circadian rhythms and tourist activities, while spatial patterns are strongly shaped by topography, vegetation, and human interference. Biophony is primarily concentrated in areas with minimal ecological disturbance, geophony is regulated by landforms and microclimatic conditions, and anthrophony tends to mask natural sound sources. Overall, the study highlights how deep learning-based soundscape classification can reveal the mechanisms by which natural and anthropogenic factors structure acoustic environments, offering methodological references and practical insights for ecological management and soundscape conservation. Full article

Interferometric Synthetic Aperture Radar (InSAR)-derived Digital Elevation Models (DEMs) provide critical landform data for monitoring the stability of urban infrastructure, especially for linear infrastructure such as roads and transportation corridors. Traditional landform classification methods are often hindered by incomplete results and require significant manual intervention. To address these challenges, we propose an automated landform classification method based on an enhanced Random Forest (RF) model that integrates Optimization of Decreasing Reduction (ODR) for majority class undersampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVM-SMOTE) for minority class oversampling, specifically to address class imbalance. The method was validated using a dataset of 82,450 expert-labeled samples from approximately 100 km of highway corridors, with independent test sets and ten-fold cross-validation. The enhanced RF model achieved a classification completeness rate of 100% and a macro F-score of 97.0%, significantly outperforming traditional rule-based and standard RF methods. This approach provides robust post-processing support for InSAR-based urban infrastructure monitoring and environmental modeling. Full article

Cultivated land quality is a key factor in ensuring sustainable agricultural development. Exploring differences in cultivated land quality under distinct cropping systems is essential for developing targeted improvement strategies. This study takes place in Shenyang City—located in the typical black soil region of Northeast China—as a case area to construct a cultivated land quality evaluation system comprising 13 indicators, including organic matter, effective soil layer thickness, and texture configuration. A minimum data set (MDS) was separately extracted for paddy and upland fields using principal component analysis (PCA) to conduct a comprehensive evaluation of cultivated land quality. Additionally, an obstacle degree model was employed to identify the limiting factors and quantify their impact. The results indicated the following. (1) Both MDSs consisted of seven indicators, among which five were common: ≥10 °C accumulated temperature, available phosphorus, arable layer thickness, irrigation capacity, and organic matter. Parent material and effective soil layer thickness were unique to paddy fields, while landform type and soil texture were unique to upland fields. (2) The cultivated land quality index (CQI) values at the sampling point level showed no significant difference between paddy (0.603) and upland (0.608) fields. However, their spatial distributions diverged significantly; paddy fields were dominated by high-grade land (Grades I and II) clustered in southern areas, whereas uplands were primarily of medium quality (Grades III and IV), with broader spatial coverage. (3) Major constraint factors for paddy fields were effective soil layer thickness (21.07%) and arable layer thickness (22.29%). For upland fields, the dominant constraints were arable layer thickness (27.57%), organic matter (25.40%), and ≥10 °C accumulated temperature (23.28%). Available phosphorus and ≥10 °C accumulated temperature were identified as shared constraint factors affecting quality classification in both systems. In summary, cultivated land quality under different cropping systems is influenced by distinct limiting factors. The construction of cropping-system-specific MDSs effectively improves the efficiency and accuracy of cultivated land quality assessment, offering theoretical and methodological support for land resource management in the black soil regions of China. Full article

Rockfalls on railways are considered a natural disaster under the topic of landslides. It is an event that varies regionally due to landforms and climate. In addition to traffic density, the Karabük–Yenice railway line also passes through mountainous areas, river crossings, and experiences heavy seasonal rainfall. These conditions necessitate the implementation of proactive measures to mitigate risks such as rockfalls, tree collapses, landslides, and other geohazards that threaten the railway line. Undetected environmental events pose a significant threat to railway operational safety. The study aims to provide early detection of environmental phenomena using vibrations emitted through fiber optic cables. This study presents a real-time hazard detection system that integrates Distributed Acoustic Sensing (DAS) with a hybrid ensemble learning model. Using fiber optic cables and the Luna OBR-4600 interrogator, the system captures environmental vibrations along a 6 km railway corridor in Karabük, Türkiye. CatBoosting, Support Vector Machine (SVM), LightGBM, Decision Tree, XGBoost, Random Forest (RF), and Gradient Boosting Classifier (GBC) algorithms were used to detect the incoming signals. However, the Voting Classifier hybrid model was developed using SVM, RF, XGBoost, and GBC algorithms. The signaling system on the railway line provides critical information for safety by detecting environmental factors. Major natural disasters such as rockfalls, tree falls, and landslides cause high-intensity vibrations due to environmental factors, and these vibrations can be detected through fiber cables. In this study, a hybrid model was developed with the Voting Classifier method to accurately detect and classify vibrations. The model leverages an ensemble of classification algorithms to accurately categorize various environmental disturbances. The system has proven its effectiveness under real-world conditions by successfully detecting environmental events such as rockfalls, landslides, and falling trees with 98% success for Precision, Recall, F1 score, and accuracy. Full article

Vulnerability impacts have increased in an unprecedented way with the effects of global warming, climate change, erosion, sea level rise, tsunami, flood, and drought—natural events that jointly cause geomorphological changes, especially in coastal zones. There are no analytical mathematical formulations under a set of assumptions due to the complexity of the interactive associations of these natural events, and the only way that seems open in the literature is through empirical formulations that depend on expert experiences. Among such empirical formulations are the Coastal Vulnerability Index (CVI), the Environmental Vulnerability Index (EVI), the Socioeconomic Vulnerability Index (SVI), and the Integrated Coastal Vulnerability Index (ICVI), which is composed of the previous indices. Although there is basic experience and experimental information for the establishment of these indices, unfortunately, logical aspects are missing. This paper proposes a Coastal Fuzzy Vulnerability Index (CFVI) based on fuzzy logic, aiming to improve the limitations of the traditional Coastal Vulnerability Index (CVI). Traditional CVI relies on binary logic and calculates vulnerability through discrete classification (such as “low”, “medium”, and “high”) and arithmetic or geometric means. It has problems such as mutation risk division, ignoring data continuity, and unreasonable parameter weights. To this end, the author introduced fuzzy logic, quantified the nonlinear effects of various parameters (such as landforms, coastal slope, sea level changes, etc.) through fuzzy sets and membership degrees, and calculated CFVI using a weighted average method. The study showed that CFVI allows continuous transition risk assessment by fuzzifying the parameter data range, avoiding the “mutation” defect of traditional methods. Taking data from the Gulf of Mexico in the United States as an example, the calculation result range of CFVI (0.38–3.04) is significantly smaller than that of traditional CVI (0.42–51), which is closer to the rationality of actual vulnerability changes. The paper also criticized the defects of traditional CVI, being that it relies on subjective experience and lacks a logical basis, and pointed out that CFVI can be expanded to integrate more variables or combined with other indices (such as the Environmental Vulnerability Index (EVI)) to provide a more scientific basis for coastal management decisions. This study optimized the coastal vulnerability assessment method through fuzzy logic, improved the ability to handle nonlinear relationships between parameters, and provided a new tool for complex and dynamic coastal risk management. Further research possibilities are also mentioned throughout the text and in the Conclusion section. Full article

The semi-automated extraction of landforms using GIS analysis is one of the main topics in computer analyses. The use of digital elevation models (DEMs) in GIS applications makes the extraction and classification procedure of landforms easier and faster. In the present paper, we assess the accuracy of semi-automated landform maps by means of a comparison with hand-made landform maps realized in the Pleistocene Agri intermontane basin (southern Italy). In this study, landform maps at three different scales of 1:50,000, 1:25,000, and 1:10,000 were used to ensure a good level of detail in the spatial distribution of landforms. The semi-automated extraction and classification of landforms was performed using a GIS-related toolbox, which identified ~48 different landform types. Conversely, the hand-made landform map identified ~57 landforms pertaining to various morphogenetic groups, such as structural, fluvial, karst landforms, etc. An overlap of the two landform maps was produced using GIS applications, and a 3D block diagram visualization was realized. A visual inspection of the overlapping maps was conducted using different spatial scales of patch frames and then analyzed to provide information on the accuracy of landform extraction using the implemented tools. Full article

The advent of geomatic techniques and novel sensors has opened the road to new approaches in mapping, including morphological ones. The evolution of a land portion and its graphical representation constitutes a fundamental aspect for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, which are useful for modernizing traditional, geomorphological mapping, become necessary for the creation of scalable digital representation of processes and landforms. A fully remote mapping approach, based on multi-source and multi-sensor applications, was implemented for the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of ‘calanchi’ (i.e., badlands). Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e., Geomorphons, was adopted to map landforms at the slope scale. Simultaneously, by collecting and digitizing a time-series of historical orthoimages, a multi-temporal analysis was performed. Finally, surveying the area with an unmanned aerial vehicle, exploiting the high-resolution digital terrain model and orthoimage, a local-scale geomorphological map was produced. The proposed approach has proven to be well capable of identifying the variety of processes acting on the pilot area, identifying various genetic types of geomorphic processes with a nested hierarchy, where runoff-associated landforms coexist with gravitational ones. Large ancient mass movement characterizes the upper part of the basin, forming deep-seated gravity deformation, highly remodeled by a set of widespread runoff features forming rills, gullies, and secondary shallow landslides. The extended badlands areas imposed on Plio-Pleistocene clays are typically affected by sheet wash and rill and gully erosion causing high potential of sediment loss and the occurrence of earth- and mudflows, often interfering and affecting agricultural areas and anthropic elements. This approach guarantees a multi-scale and multi-temporal cartographic model for a full-coverage representation of landforms, representing a useful tool for land planning purposes. Full article

The Münchberg Gneiss Complex (Central European Variscides, Germany) is separated by a deep-seated lineamentary fault zone, the Franconian Lineamentary Fault Zone, from its Mesozoic foreland. The study area offers insight into a great variety of landforms created by fluvial and mass wasting processes together with their bedrocks, covering the full range from unmetamorphosed sediments to high-grade regionally metamorphic rocks. It renders the region an ideal place to conduct a study of compositional and numerical geomorphology and their landscape-forming indices and parameters. The landforms under consideration are sculpted out of the bedrocks (erosional landforms) and overlain by depositional landforms which are discussed by means of numerical landform indices (LFIs), all of which are coined for the first time in the current paper. They are designed to be suitable for applied geosciences such as extractive/economic geology as well as environmental geology. The erosional landform series are subdivided into three categories: (1) The landscape roughness indices, e.g., VeSi_val (vertical sinuosity—valley of landform series) and the VaSlAn_alti (variation in slope angle altitude), which are used for a first order classification of landscapes into relief generations. The second order classification LFIs are devoted to the material properties of the landforms’ bedrocks, such as the rock strength (VeSi_lith) and the bedrock anisotropy (VaSlAn_norm). The third order scheme describes the hydrography as to its vertical changes by the inclination of the talweg and the different types of knickpoints (IncTal_lith/grad) and horizontal sinuosity (HoSi_lith/grad). The study area is subjected to a tripartite zonation into the headwater zone, synonymous with the paleoplain which undergoes some dissection at its edge, the step-fault plain representative of the track zone which undergoes widespread fluvial piracy, and the foreland plains which act as an intermediate sedimentary trap named the deposition zone. The area can be described in space and time with these landform indices reflecting fluvial and mass wasting processes operative in four different stages (around 17 Ma, 6 to 4 Ma, <1.7 Ma, and <0.4 Ma). The various groups of LFIs are a function of landscape maturity (pre-mature, mature, and super-mature). The depositional landforms are numerically defined in the same way and only differ from each other by their subscripts. Their set of LFIs is a mirror image of the composition of depositional landforms in relation to their grain size. The leading part of the acronym, such as QuantSan_heav and QuantGrav_lith, refers to the process of quantification, the second part to the grain size, such as sand and gravel, and the subscript to the material, such as heavy minerals or lithological fragments. The three numerical indices applicable to depositional landforms are a direct measurement of the hydrodynamic and gravity-driven conditions of the fluvial and mass wasting processes using granulometry, grain morphology, and situmetry (clast orientation). Together with the previous compositional indices, the latter directly translate into the provenance analysis which can be used for environmental analyses and as a tool for mineral exploration. It creates a network between numerical geomorphology, geomorphometry, and the E&E issue disciplines (economic/extractive geology vs. environmental geology). The linguistics of the LFIs adopted in this publication are designed so as to be open for individual amendments by the reader. An easy adaptation to different landform suites worldwide, irrespective of their climatic conditions, geodynamic setting, and age of formation, is feasible due to the use of a software and a database available on a global basis. Full article