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Geomorphons-a pattern recognition approach to classification and mapping of landforms
... However, geomorphometric parameters [61] can describe the morphology of the land surface and geomorphons can be used to classify and map landform elements. Recent research outlined a "novel method for classification and mapping of landform elements […] based on the principle of pattern recognition" [62]. Since various types of urban geometry are not included in the geomorphons we extended and adapted this method to the study and development of urban form understood as continuous terrain. ...
... Since this notation refers to the relative height difference, no assumptions about the absolute vertical position of the points constituting the pattern can be made. Figure 1 in Jasiewicz and Stepinski [62] provides an intuitive description of this property. Secondly, the algorithmic definition of the geomorphons is intentionally constructed to accommodate for different sizes of the terrain features representing the same geomorphon. ...
... The original geomorphon algorithm calculates nadir and zenith angles to derive the horizontal position of the eight neighbouring points. The original paper [ref] [62], explains the concept of the zenith and nadir angles and demonstrates how four different geometries are representative of a valley geomorphon. Finally, since the original geomorphons approach is based on a 2.5D terrain surface representation, it does not take into consideration surface thickness, surface discontinuity or undercuts, vertical surfaces and transitions between horizontal and vertical surfaces. ...
The thematic context for this article is the topic of sustainable and ecological green city and building design. More specifically, this article reports the development of a conceptual framework for early stage design of ecological building envelopes, which are enclosures of buildings that make provisions for humans, plants and animals, as well as soil microbiota. This is done with the intent to advance multi-species design in architecture, urban ecology and biodiversity, as well as ecological accessibility and ecosystem services and therefore health benefits for humans. For this purpose a mixed method approach, including literature research, research-by-design through case studies, was utilized to develop an ontology-aided generative computational design process. The latter combines ontologies, a voxel model, and rule based processes that generate design variety that can be evaluated, and from which specific instances can be selected and optimised. The process was developed for two types of projects that occur in architectural practice, masterplan design and building design. The process is organised into two stages and three steps that are elaborated in the article. The development of key computational components of this process is outlined. Furthermore, technical requirements and developments towards accomplishing a technical resolution for each step are indicated and further research questions are outlined.
... This association generates a visual fragmentation, suggesting that automatic extraction is not well-suited, in this case. The question concerning the recognition and delimitation of relict and engraved erosion surfaces has already been outlined by many authors [2,15,41] and the semi-automated procedure seems not to be able to detect this morphological feature. The reason could be attributed to the physical complexity of this kind of landscape feature, which contains diverse landforms. ...
... Based on this study, the main issues that require improvement are i) the fragmentation of relict erosion surfaces and ii) differentiation between low-angle areas related to river terraces, marine terraces or sinkhole bottoms. The question concerning the recognition and delimitation of relict and engraved erosion surfaces has already been outlined by many authors [2,15,41] and the semi-automated procedure seems unable to detect this morphological feature. The reason could be attributed to the physical complexity of this kind of landscape feature, which may contain diverse landforms. ...
... The question concerning the recognition and delimitation of relict and engraved erosion surfaces has already been outlined by many authors [2,15,41] and the semi-automated procedure seems unable to detect this morphological feature. The reason could be attributed to the physical complexity of this kind of landscape feature, which may contain diverse landforms. ...
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.
... The combination and overlapping of these land surface parameters allow the manual identification and mapping of geomorphological features by visualization techniques [32,33]. Moreover, automated and semi-automated landform classification, based on terrain analysis via DTMs, has been developed [34][35][36] and is currently available in the form of GIS tools [37][38][39]. These landform classifications are based on diverse geomorphometric parameters and their combination to derive geomorphic units [40] or specific landforms (e.g., channel morphology [41], landslide crown and bank erosion [42], and gully [43]); these are often compared with each other [44,45]). ...
... The topographic variability is assessed by computing the increase, decrease, or unchanged elevation of a grid DTM cell. This approach [35] considers the line-of-sight principle to evaluate the 8 neighboring grid cells, labelled either 0 if the grey level of a neighbor is smaller than the grey level of the central cell, or 1 otherwise. By this approach the ternary pattern is determined, considering not only the elevation parameter but also the zenith and nadir angles in the lookup distance using the line-of-sight principle. ...
... The topo iability is assessed by computing the increase, decrease, or unchanged elevat DTM cell. This approach [35] considers the line-of-sight principle to evaluate boring grid cells, labelled either 0 if the grey level of a neighbor is smaller th level of the central cell, or 1 otherwise. By this approach the ternary pattern is considering not only the elevation parameter but also the zenith and nadir a lookup distance using the line-of-sight principle. ...
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.
... Geomorphons [24,25] offer a means to differentiate the landscape into meaningful landforms by categorizing each pixel into one of the following ten features: flat, summit, ridge, shoulder, spur, slope, hollow, footslope, valley, or depression. To accomplish this, the landscape patterns in all eight directions from the cell being classified are characterized. ...
... This results in patterns defined by a tuple with a length of eight. The large number of possible eighttuple combinations are subsequently reclassified into the meaningful landforms listed above [24,25]. ...
... Attempting to differentiate the entire landscape into general landform categories, primarily defined based on topographic position and steepness and with gradational boundaries between adjacent units, may not transfer well to the extraction of specific landscape features, which are defined based on texture and specific topographic presentations, such as the elongated nature of terraces and their associated slope breaks and the terraced, steep slopes of valley fill faces. It should also be noted that the landscape classification generated by the geomorphon method will vary based on the user-defined settings provided to the algorithm [24,25]. The settings used in this study are described in Section 3.3 above and were selected based on visualization of the resulting landform maps generated over different geographic extents. ...
Semantic segmentation algorithms, such as UNet, that rely on convolutional neural network (CNN)-based architectures, due to their ability to capture local textures and spatial context, have shown promise for anthropogenic geomorphic feature extraction when using land surface parameters (LSPs) derived from digital terrain models (DTMs) as input predictor variables. However, the operationalization of these supervised classification methods is limited by a lack of large volumes of quality training data. This study explores the use of transfer learning, where information learned from another, and often much larger, dataset is used to potentially reduce the need for a large, problem-specific training dataset. Two anthropogenic geomorphic feature extraction problems are explored: the extraction of agricultural terraces and the mapping of surface coal mine reclamation-related valley fill faces. Light detection and ranging (LiDAR)-derived DTMs were used to generate LSPs. We developed custom transfer parameters by attempting to predict geomorphon-based landforms using a large dataset of digital terrain data provided by the United States Geological Survey’s 3D Elevation Program (3DEP). We also explored the use of pre-trained ImageNet parameters and initializing models using parameters learned from the other mapping task investigated. The geomorphon-based transfer learning resulted in the poorest performance while the ImageNet-based parameters generally improved performance in comparison to a random parameter initialization, even when the encoder was frozen or not trained. Transfer learning between the different geomorphic datasets offered minimal benefits. We suggest that pre-trained models developed using large, image-based datasets may be of value for anthropogenic geomorphic feature extraction from LSPs even given the data and task disparities. More specifically, ImageNet-based parameters should be considered as an initialization state for the encoder component of semantic segmentation architectures applied to anthropogenic geomorphic feature extraction even when using non-RGB image-based predictor variables, such as LSPs. The value of transfer learning between the different geomorphic mapping tasks may have been limited due to smaller sample sizes, which highlights the need for continued research in using unsupervised and semi-supervised learning methods, especially given the large volume of digital terrain data available, despite the lack of associated labels.
... While they offer a high degree of automation, they often struggle to effectively capture complex terrain features, resulting in classifications that may lack clear geological significance 7 . Rule-based methods, on the other hand, rely on expert knowledge to define classification rules 8 . While they are highly interpretable, their lack of flexibility limits their effectiveness in handling complex or dynamic terrain features. ...
... As a result, no specific slope threshold was defined for this category. Conversely, for the uplift type in Table 4, it was found that this type was distributed within the range of TPI between − 0. 8 ...
Detecting micro-terrain is essential for the effective layout and maintenance of transmission lines. To address the issues of detection incompleteness, classification ambiguity, and inefficiency in traditional methods, particularly the challenge of distinguishing between saddle and canyon micro-terrain, this paper optimizes the calculation of micro-terrain features and the strategy of micro-terrain detection, and explores a detection method of micro-terrain around transmission lines based on the GPU parallel random forest. This paper employs the GPU parallel random forest model as the extraction framework, leveraging the computational speed advantage of GPU parallel technology for handling large datasets and the robustness inherent in the ensemble approach of random forests. The DEM data of 49 transmission lines in the study area was used for micro-terrain detection experiments. Most of these 49 routes are situated in mountainous regions with complex terrain and contain diverse micro-terrain categories along their paths, rendering them highly representative. The experimental results demonstrate that the proposed method effectively identifies atypical micro-terrain types and four typical micro-terrain types—saddle, canyon, alpine watershed, and uplift—with a classification accuracy of 97.96% and a Kappa coefficient of 0.974. Compared to the traditional method, which achieves a classification accuracy of 75.19% and a Kappa coefficient of 0.642, the proposed method demonstrates a clear improvement in performance. Moreover, by employing the parallel model, the acceleration ratios for training and classification reach 50.57 and 109.06, respectively, significantly improving the efficiency of micro-terrain detection for large-scale regions. These findings could significantly enhance transmission line maintenance and layout planning by providing more accurate micro-terrain data, enabling better decision-making and resource allocation for infrastructure development and disaster risk mitigation.
... The Martian basin boundaries from Luo et al. (2023) was adopted, but we only included basins that have a drainage density greater than 0.003 km -1 (using the latest global VN data of Alemanno et al. (2018)) for analysis, because some lower density areas were complicated by lava flows (Fig. 3). To extract basin boundaries of the Moon and fractal surfaces, we first used the geomorphon (Jasiewicz and Stepinski, 2013) method to classify the landscape into 10 types (e.g., pit, valley, ridge, etc.) based on the relative elevations of the cell under consideration and surrounding cells along the line of sight in 8 cardinal/intercardinal directions (Jasiewicz and Stepinski, 2013). Next, we input the local pit and valley geomorphon types as sinks to the IPBA algorithm (Luo et al., 2023) to extract basin boundaries "draining" to those sinks. ...
... The Martian basin boundaries from Luo et al. (2023) was adopted, but we only included basins that have a drainage density greater than 0.003 km -1 (using the latest global VN data of Alemanno et al. (2018)) for analysis, because some lower density areas were complicated by lava flows (Fig. 3). To extract basin boundaries of the Moon and fractal surfaces, we first used the geomorphon (Jasiewicz and Stepinski, 2013) method to classify the landscape into 10 types (e.g., pit, valley, ridge, etc.) based on the relative elevations of the cell under consideration and surrounding cells along the line of sight in 8 cardinal/intercardinal directions (Jasiewicz and Stepinski, 2013). Next, we input the local pit and valley geomorphon types as sinks to the IPBA algorithm (Luo et al., 2023) to extract basin boundaries "draining" to those sinks. ...
... Slope was derived from the DEM via the Slope tool in QGIS (QGIS Development Team 2022). The raster layer of landforms was generated from the DEM using r.geomorphon in GRASS within QGIS (Jasiewicz and Stepinski 2013;Olaya 2022b), utilizing default settings. ...
... The TPI categorizes areas as greater than zero (higher), equal to zero (same), or less than zero (lower) relative to surrounding elevations (Gallant and Wilson 2000;Reu et al. 2013;Costanzo et al. 2021). In contrast, r.geomorphon identifies 10 types of landforms based on combinations of relative elevations between a location and its neighboring areas, distinguishing between the same, lower, and higher elevations (Jasiewicz and Stepinski 2013). For example, "flat/pit/valley/ footslope" indicates areas with lower or similar relative elevations, "spur/slope/hollow" denotes areas with higher relative elevations adjacent to lower or similar elevations, and "peak/ ridge/shoulder" signifies areas with higher relative elevations or those with a combination of higher and similar relative elevations. ...
Settlement systems are often characterized by a mixture of different site types, each with potentially different locational properties reflected by differences in their functions and uses. Prehistoric settlements in China are commonly known for their wooden defense structures and rammed earth. However, from the late Neolithic period, ca. 2800 BCE, a new type of stone-wall site emerged in northern China, coexisting with earth-wall sites. Examining differences in the locational properties of stone-wall and earth-wall settlements is essential for understanding regional settlement patterns and human–environment interactions in prehistoric northern China. Studies of this topic have so far been limited to descriptive qualitative accounts, and formal statistical comparisons of their differences have yet to be carried out. This paper contributes to this research agenda by examining, via point process models (PPMs), stone-wall and earth-wall sites associated with the Lower Xiajiadian Culture (2000–1400 BCE) in the Aohan Banner, northern China. We fitted log-linear and generalized additive models (GAMs) and identified the relevance of key spatial covariates via information criterion importance for both site types. Our results highlight not only the spatial preferences of stone-wall and earth-wall sites but also some differences, suggesting a defensive function of the former site type.
... Geomorphons are a new method of digital representation and analysis of landforms which does not rely on differential geometry (Stepinski & Jasiewicz, 2011). They are the basic microstructures of landscapes, considered to be simultaneously relief attributes and relief types (Jasiewicz & Stepinski, 2013). This method of autoclassification is based on the concept of a local ternary pattern, the values of which may be 0, +1, or -1. ...
... The values 0, +1, or -1 are defined by flatness threshold (t), which is the minimal value of the right triangle (line-of-sight angle, zenith or nadir) considered to be considerably different from the horizon (Stepinski & Jasiewicz, 2011). Theoretically, the ternary pattern may support 6561 possible variations, but they can be mainly depicted with 10 geomorphons: flat, peak, ridge, shoulder, spur, slope, pit, valley, footslope and hollow (Jasiewicz & Stepinski, 2013). Input data for geomorphons are of raster character (DEM), as well as scale parameters (Frankl et al. 2016 eters, the Outer Search Radius was used (distance in meters or cells from the main cell, L) and flatness threshold (t) (Atkinson et al., 2020). ...
Valjevo–Mionica karst is a limestone area within the eastern-most flanks of the Internal Dinarides in western Serbia. In the spatial extent of 380 km2, it hosts typical karst landforms, pri-marily dolines, blind valleys, dry valleys and caves. Dolines are present on 75% of the total area, and the exact number of them within the area outline is 5319. The aim of the study is to de-termine the guidance factors for the spatial distribution of do-lines, primarily morphological, while lithological, tectonic and climatic factors are presented at the basic level. Morphologi-cal factors in this study are analysed through morphometrical characteristics and calculations, which include the elevation, the mean topographical slope and the landform classification based on geomorphons. Digital elevation models with resolu-tions 90 m and 30 m are used. Data sources for doline positions were the topographical maps of 1:25,000 scale.Spatial distribution of dolines in the study area is rather un-even. Three zones (clusters) of higher concentration may be distinguished, where 34% of the total study area hosts 72% of dolines. These three zones are the karsts of the villages Lelić, Bačevci and Robaje, divided by deep canyon valleys. Maximum density of dolines, judging by the Kernel density method, is 33 dolines per km2 in the zone of the Stapar village, at the north-western outskirt of the study area. The main factors influencing the spatial distribution of dolines are the topographical slope and the phases of morphological/hydrographical evolution of the area
... Italy recently published by Bucci et al. (2022), at 1:100,000 scale. Topographic quantities such as slope, drainage network and landforms (namely, forms singled out by r.geomorphon, a model by Jasiewicz and Stepinski, 2013) were derived from the DEM. ...
... Since detailed geomorphological data are not continuously available for large area, these methods resort to Earth observation data and statistical modeling. On the other hand, the GmI adopted in this work considers morphometric types defined by the r.geomorphons model for landforms (Jasiewicz and Stepinski, 2013). For this reason, validation with real world geomorphological maps is of utmost importance. ...
Urban geomorphology studies the landscape in cities, and changes induced by human activities to the natural
landscape. Cities have different geological-geomorphological substrates, and humans as “geomorphic agents”
have been operating within them in different times since the Paleolithic, threatening the Earth-surface heterogeneity
and ecological sustainability, especially in urban areas. Urban geomorphology helps understanding
natural and historical landscape evolution, changes to natural morphologies, and the effects of the development
of cities on natural geomorphological processes. Quantitative geomorphodiversity describes the variety of
landforms and morphological processes characterizing the landscape. Geomorphodiversity maps can be prepared
using heterogeneous spatial data, at different geographical scales. Here, we adopt the land surface diversity index
of Italy, which approximates field-based geomorphological maps. One relevant example of the latter, in Italy, is
the geomorphological survey carried out in Rome, which integrates field surveys, historical maps, aerial photographs,
archaeological and geomorphological literature. In this work, we compare the land surface diversity
index, obtained with a simple and objective approach, with comprehensive geomorphological maps of locations
describing the rural-urban gradient within the Rome urban area. We aim at understanding the representativeness
of the geomorphodiversity index at the local scale, and its advantages and limitations, in urban areas. We
describe a simple approach to compare the geomorphodiversity index and the geomorphological dataset. The
method pins down to a common ground the five diversity classes, in the raster index, and the number of
landforms mapped in the field, in the geomorphological map. Most notably, the latter distinguishes natural and
anthropogenic landforms, allowing us a different assessment for these substantially different geomorphological
elements. Results highlight that both natural and anthropogenic processes contribute to geomorphodiversity in
urban environment, and in areas having different urbanization level. They are relevant to understand the
anthropogenic morphogenesis impact on geomorphodiversity in urban environment.
... We used the geomorphons classification method proposed by Jasiewicz & Stepinski (2013) to categorize the topographies into 80 ten different landforms ( Figure 1b). Geomorphons-defined landforms are italicized throughout the text for improved readability. ...
... Unlike the direct cell neighbor method (e.g. slope, curvature, or roughness), the geomorphons method allows to capture landforms at larger scales by defining a search radius around the reference cell, the look-up distance in Jasiewicz & Stepinski (2013). Here we defined it as a function 85 of the hillslope characteristic length: ...
This study investigates the influence of topography on the desaturation rates of groundwater-dependent wetlands in response to changes in recharge. We examined sixty catchments across northern Chile, which feature a wide variety of landforms. We categorized the landforms using geomorphon descriptors, identifying three distinct clusters: lowland, transition, and mountain settings. Using steady-state 3D groundwater models, we derived flow partitioning and seepage area extent for each catchment. Each cluster revealed consistent seepage areas evolution under varying wet-to-dry conditions. Our findings indicate that mountains exhibit reduced seepage area compared to lowlands at equivalent hydraulic conductivity to recharge (K/R) ratios but are less sensitive to recharge fluctuations with slower rates of seepage area variation. Statistical evidence demonstrates that geomorphons-defined landforms correlate with desaturation indicators, enabling the prediction of catchment sensitivity to climate change based solely on a topographic analysis.
... The steps of PCA mainly include the following: (1) the deviation standardization of the original 9 topographic factors; (2) calculating the correlation and covariance matrix, eigenvalues, and eigenvectors of topographic factors (Table 2); and (3) determining the first four principal components as the classification basis by the cumulative contribution rate. The Geomorphons algorithm uses the topographic openness index to determine the elevation relationships between the central grid and the neighborhood grids in the other eight directions [23]. The optimal analysis window size is the key to ensuring that the terrain factors are representative in a certain range and effectively reflect the integrity of the landform. ...
Optimizing the habitat quality (HQ) assessment and revealing its nonlinear influence mechanisms, particularly by considering the mountain micro-topographic characteristics, are critically important for promoting sustainable development and safeguarding the ecological environment of mountain cities. Taking the Chongqing main city (CMC) as the study area, first, the Geomorphons algorithm was used to identify the mountain micro-topographic positions. On this basis, the HQ assessment of the InVEST model was optimized by collecting the multispectral data from UAV, and its spatiotemporal change trend was analyzed by the least-squares method. Secondly, hotspot analysis was used to explore the spatiotemporal differentiation of HQ on different land use and geomorphological types. Finally, based on the generalized additive model, the dominant influencing factors were determined, and their nonlinear effects were analyzed. The results showed the following: (1) The average HQ of the CMC showed an increasing trend from 2000 to 2020. The HQ of the four mountains and two rivers was higher, while it was lower in the central urban area. (2) The HQ hotspots were mainly distributed in parallel mountain areas and composed of forests, grasslands, and waters. The heterogeneity of HQ at the mountain micro-topographic scale was manifested in that the summits were always the hotspots of HQ. (3) HQ was influenced by a range of factors, including both natural environmental conditions and socio-economic drivers, among which the normalized difference vegetation index was the most important influencing factor.
... Goldgof et al. (1988) calculated Gaussian curvature to extract terrain feature points and identify specific terrain regions. Pixel-based approaches primarily rely on terrain derivatives based on pixel values to distinguish between different landforms, often overlooking the contextual relationships among neighboring pixels and the integrity of landform units (Jasiewicz and Stepinski 2013). Object-based approaches use neighboring region expansion techniques to generate distinct and ambiguous spatial entities, recognizing landform types based on terrain indices (Verhagen andDragut 2012, Blaschke et al. 2014). ...
Landform type recognition presents significant implications for understanding landform origins, evolutionary mechanisms, and morphological differences. Artificial intelligence (AI) techniques based on sample learning often lead to unsatisfactory outcomes due to the intricate genesis and regional heterogeneity of land-forms. This study combines domain knowledge with a deep learning (DL) model to improve landform type recognition. Contour data serves as a valuable resource, offering rich morphological information across horizontal, vertical, local, and macro scales. Our approach incorporated morphological knowledge and proximity relationships derived from contours into a graph convolu-tional network using the DiffPool technique (GCN-DP). Guided by the First Law of Geography, contours within each landform unit were represented as graphs, incorporating morphological knowledge as node features. The GCN-DP model then employed con-volution and pooling to extract hierarchical features from these graphs for landform type recognition. A performance evaluation demonstrated the effectiveness of our method with an F1-score of 87.40%, surpassing RF and GCN methods by 5.24-12.50%, respectively. Ablation experiments confirmed the usefulness of morphological knowledge. This study offers an efficient strategy for landform type recognition, improving the level of intelligent mining using contour data. ARTICLE HISTORY
... Separate raster layers with a resolution of 30 m pixel -1 were created for each topographical predictor. Landform types were classified using ASTER DEM data based on the system proposed by (Jasiewicz and Stepinski, 2013), containing 10 classes: 1peak, 2ridge, 3shoulder, 4spur, 5hillslope, 6footslope, 7flat, 8hollow, 9valley, 10peat (visualization in Fig. 4). We created a raster attribute of landform classes using the R toolkit r.geomorphon (available 155 on https://github.com/OSGeo/grass/tree/main/raster/r.geomorphon), ...
Accurate assessment of forest losses and evaluation of future damage risks are crucial for effective forest management and conservation, particularly as global warming intensifies natural disturbance agents. This study introduces a novel approach combining convolutional neural networks (CNNs) with Random Forest (RF) machine learning classifiers to enhance the precision of forest disturbance detection and risk evaluation. We tested this approach on a large-scale dataset (1490 km2) with diverse forest types and environmental conditions of cool temperate-south boreal forests on Kunashir Island (Northwest Pacific). Using the U-Net deep learning architecture, we precisely identified windthrow patches from (VHR) Pléiades-1 optical satellite imagery. Resulted windthrow map was integrated with an RF classifier that utilized environmental predictors, including elevation, slope aspect, slope inclination, slope curvature, forest canopy closure, landform type, and forest vegetation type, to assess forest damage risk. Our analysis revealed approximately 21.73 km2 of the forested area as significantly disturbed, predominantly within dark coniferous forests. Elevation emerged as the most critical predictor of disturbance risk, with complex interactions observed among predictors such as canopy closure and slope steepness. This integrated approach allowed for highly accurate forest loss detection and provided valuable insights into the risk of future damage events. By combining advanced deep learning techniques with RF and detailed environmental predictors, this approach offers a robust framework for evaluating forest disturbance risk. This method pushes forward the frontiers in the precision of forest loss detection but also aids in developing effective strategies for managing and mitigating risks from future disturbance events.
... flat, peak, ridge, shoulder, spur, slope, hollow, footslope, valley and pit (Jasiewicz & Stepinski, 2013). For this study, geomorphons were calculated in ArcPro using search distances of 15.2, 30.5 and 45.7 m. ...
Surficial geologic maps contribute to decisions regarding natural hazard mitigation, land‐use planning and infrastructure development. However, geologic maps may not adequately convey the uncertainty inherent in the information shown. In this study, we use machine learning and lidar elevation data to produce surficial geologic maps for parts of two quadrangles in Kentucky. We measured the performance of eight supervised machine learning methods by comparing the overall accuracy and F1 scores for each geologic unit. Surficial geologic units include residuum, colluvium, alluvial and lacustrine terraces, high‐level alluvial deposits and modern alluvium. The importance of 41 moving‐window geomorphic variables, including slope, roughness, residual topography, curvature, topographic wetness index, vertical distance to channel network and topographic flatness, was reduced to 12 variables by ranking the importance of each variable. The gradient‐boosted trees model produced the classifier with the greatest overall accuracy, producing maps with overall accuracies of 87.4% to 90.7% in areas of simple geology and 80.7% to 81.6% in areas with more complex geology. The model produced high F1 scores of up to 96.2% for colluvium but was not as good at distinguishing between units found in the same geomorphic position, such as high‐level alluvium and residuum, both of which are found on ridgelines. Probability values for each geologic unit at each cell are conveyed using gradations of colour and eliminate the need for drawn boundaries between units. Machine learning may be used to create accurate surficial geologic maps in areas of simple geology; in more complex areas, highlight that additional information obtained in the field is necessary to distinguish between units.
... Hence, the geomorphological diversity of the Coromandel Peninsula provides a record of volcanic events followed by sedimentation, thereby resulting in landscape forms such as plains, valleys, hills, mountain ranges, and cliffs. However, current research has not found a geomorphological map/ model of the Coromandel Peninsula, so we have created the best possible representation with the Geomorphons tool of Hydrothermal alteration data were extracted from the GNS database (Edbrooke, 2001 QGIS (Saga plugin) (Stepinski and Jasiewicz, 2011;Jasiewicz and Stepinski, 2013), which we calculated with default parameters. The model describes 10 landforms based on the differences in elevation between the calculated pixel and its neighbors. ...
Unexpected natural hazardous events can lead communities to create preparedness plans and identify risks associated with future devastating events. In the case of Cyclone Gabrielle , which resulted in catastrophic damage throughout the North Island of New Zealand, we recognised a need for models that could define the most hazardous areas in the Coromandel Peninsula with respect to the potential risk of hazardous influences on the anthroposphere as shaped by geodiversity. In this research, we utilise a qualitative-quantitative methodology for the assessment of hazard susceptibility applied to locations with varying levels of geodiversity on the Coromandel Peninsula. Because most of the geological sites displaying high values are located near cliff sides and/or along valleys, they are likely to align with hazardous areas. Utilising the same methodology for the recognition of two different parameters will provide an opportunity to compare results to find a potential similarity and/or correlation between geological locations and hazardous zones. Meanwhile, a flood prediction model has been analysed along with hazard susceptibility to recognise potential risks in the anthropological sphere (presented as buildings) on the Coromandel Peninsula. Our research results demonstrate a significant correlation between hazard susceptibility and geodiversity models, while flood prediction models together with the hazard model define vulnerable regions in the event of future natural events on the Coromandel Peninsula.
... These flow networks are clipped to exact catchment boundaries and visualized according to the Strahler stream order [9] and [10]. Further inputs for runoff modeling parametrization can be generated with the same approach, either extracting data like other land cover or simulated land cover change by watershed extent from Living Atlas services, or using hosted algorithms to generate e.g., landform classes from geomorphons [11] by on-demand server-side processing (Figure 3). ...
Continuity of water supply is critical for sustaining livelihoods of (Central Asian) mountain communities, which are typically not connected to any regional water utility. Water supply is indispensable for household usage incl sanitation, livestock survival, farming of agricultural crops and small-scale hydropower generation. In mountain environments seasonal as well as multi-annual buffering and storage are limited, surface runoff into streams is the main source of water. The WaterFlow project has been designed to enable academic partners in the Kyrgyz Republic to perform hydrographic analysis of small mountain watersheds to explore current and future potentials for local communities. One key objective is the quantification of the impact of regional climate change scenarios on streamflow as a basis for assessment of future village-level livability and potential mitigation efforts. Hydrologic surface runoff estimation and simulation is a data-intensive process requiring detailed topographic, landcover / landuse and climate change scenario data for distributed modeling. While access to national level or regional data frequently poses a major obstacle to large scale local research. The current and increasing availability of high-resolution global data sets facilitates analyses essentially anywhere on the planet. This paper serves as a case study and process documentation how the 'Living Atlas' as a platform consolidating a rich and continuously updated collection of worldwide openly accessible data supports, inter alia, the foundation data for distributed modeling of surface runoff essentially anywhere on the planet. This research does not aim at achieving results specifically valid for the sample study areas, the latter rather serve as demonstrators for analyses based on cloud-based data services. Steps beyond data preparation, including local observations and integration of climate and land cover change scenarios are briefly mentioned to contextualize the data preparation and pre-processing steps presented below. Altogether, the WaterFlow initiative aims at supporting the development of science-based local capacity for environmental monitoring, within the explicit context of appreciating the potential impacts of likely climate changes on the local water balance.
... The two datasets of reference hills were extracted by classifying topographical variation of the study area with GRASS GIS tool r.geomorphons. The geomorphons approach utilizes image analysis methodology to autosegment the landscape into 498 patterns that correspond to different landform types (Stepinski & Jasiewicz 2011;Jasiewicz & Stepinski 2013). The r.geomorphons tool simplifies the patterns to the 10 most common geomorphons that can be used to describe the landscape: flat, peak, ridge, shoulder, spur, slope, hollow, footslope, valley and pit (Stepinski & Jasiewicz 2011;Grass Development Team 2023). ...
A defense and guarding system consisting of fire beacon networks was commonly used in Scandinavian coastal areas from the Late Iron Age until the Early Modern period. A chain-like system of signal fire stations was established in locations strategically important for defense and activated if the threat of an enemy attack concretized. Historical sources evidence that the same defense system was also utilized in the archipelago and coastline of southern Finland. Also, certain place names are considered to reflect ancient warning fire activities. Using GIS-based analyses, we examine whether these place names in the Turku archipelago can be distinguished from other locations based on their visibility and topography. In addition, we investigate how visible other potential signal fire stations and certain sailing routes are from the sites selected for the analysis, and the possibility of them having an interactive connection based on visual observation.
As the climate continues to warm, the thawing of ice-rich permafrost leads to changes in the polygonal patterned ground (PPG) landscape, exhibiting an array of spatial heterogeneity in trough patterns, governing permafrost stability and hydrological and ecosystem dynamics. Developing accurate methods for detecting trough areas will allow us to better understand where the degradation of PPG occurs. The Geomorphon approach is proven to be a computationally efficient method that utilizes digital elevation models (DEMs) for terrain classification across multiple scales. In this study, we firstly evaluate the appliance of the Geomorphon algorithm in trough mapping in Prudhoe Bay (PB) in Alaska and the Wudaoliang region (WDL) on the central Qinghai–Tibet Plateau. We used the optimized DEM resolution, flatness threshold (t), and search radius (L) as input parameters for Geomorphon. The accuracy of trough recognition was evaluated against that of hand-digitized troughs and field measurements, using the mean intersection over union (mIOU) and the F1 Score. By setting a classification threshold, the troughs were detected where the Geomorphon values were larger than 6. The results show that (i) the lowest t value (0°) captured the microtopograhy of the troughs, while the larger L values paired with a DEM resolution of 50 cm diminished the impact of minor noise, improving the accuracy of trough detection; (ii) the optimized Geomorphon model produced trough maps with a high accuracy, achieving mIOU and F1 Scores of 0.89 and 0.90 in PB and 0.84 and 0.87 in WDL, respectively; and (iii) compared with the polygonal boundaries, the trough maps can derive the heterogeneous features to quantify the degradation of PPG. By comparing with the traditional terrain indices for trough classification, Geomorphon provides a direct classification of troughs, thus advancing the scientific reproducibility of comparisons in PB and WDL. This work provides a valuable method that may propel future pan-Arctic studies of trough mapping.
Soil erosion poses a considerable threat to ecosystem services around the world. Among these, it is extremely problematic for archaeological sites, particularly in arable landscapes where accelerated soil degradation has been widely observed. Conversely, some archaeological deposits may obtain a certain level of protection when they are covered by eroded material, thereby lessening the impacts of phenomena such as plow damage or bioturbation. As a result, detailed knowledge of the extent of colluvial deposition is of great value to site management and the development of appropriate methodological strategies. This is particularly true of battlefield sites, where the integrity of artifacts in the topsoil is of great importance and conventional metal detection (with its shallow depth of exploration) is relied upon as the primary method of investigation. Using the Napoleonic battlefield of Waterloo in Belgium as a case study, this paper explores how different noninvasive datasets can be combined with ancillary data and a limited sampling scheme to map colluvial deposits in high resolution and at a large scale. Combining remote sensing, geophysical, and invasive sampling datasets that target related phenomena across spatial scales allows for overcoming some of their respective limitations and derives a better understanding of the extent of colluvial deposition.
O avanço desordenado da ação humana sobre o meio e suas consequências mais intensas evidenciam a necessidade de abrangentes estudos ambientais, para possibilitar uma interpretação do espaço e das articulações a serem realizadas, onde devem ser considerados aspectos físicos, bióticos, químicos e antrópicos. Com isso, este artigo apresenta a aplicação da Análise Hierárquica Ponderada (AHP) em Sistemas de Informação Geográfica na bacia hidrográfica do arroio Ferreira, Santa Maria - RS. Com o objetivo de delimitar e espacializar a suscetibilidade a impactos ambientais na área da bacia hidrográfica, a partir dos critérios de geologia, solos, uso e ocupação da terra e geomorfologia. O método AHP foi utilizado para sintetizar esses critérios e seus respectivos subcritérios, permitindo uma análise espacial da suscetibilidade a impactos ambientais. Foram identificadas 5 classes de suscetibilidade, sendo que as classes mais altas ocupam 31,29% da área de estudo, na porção sudeste de BH. Os resultados evidenciam a necessidade de planejamento ambiental, mitigação de impactos, recuperação de áreas degradadas e implementação de atividades de menor impacto, como as desenvolvidas pela agroecologia. Conclui-se que o método AHP é confiável para análises multicritério em estudos, oferecendo subsídios para o planejamento e ações de mitigação de impactos ambientais em bacias hidrográficas.
This contribution analyzes the processes related to the development of small, temporary, non-floodplain wetlands (NFWs) of neotectonic origin in the loessic North Pampa Plain under wet conditions. The study focuses on the Vila-Cululú upstream sub-basin (973 km2), a tributary of the Salado River belonging to the Parana River basin. Under wet conditions, the flat landscape influences the surface runoff and favors the development of
temporary NFWs, increasing flood vulnerability and compromising human activities. Late Pleistocene fluvial palaeochannels intercept small tectonic depressions and significantly restrict the present drainage network (low-order streams and artificial channels). This research involves an integrated approach, including geomorphic and morphometric analyses based on remotely sensed satellite imagery in a GIS platform and fieldwork, and 2D hydrologic-hydraulic simulations using HydroBID Flood to capture the system behavior during a rainfall event that recently occurred over several weeks and caused extended flooded areas. Simulation results show that the model significantly captures the system’s hydrodynamics. The resulting flooded areas were comparable to those obtained from the analysis of satellite images. The dendritic runoff pattern towards the tectonic depressions, the water storage evolution, and the hydraulic connectivity were numerically replicated. Results indicates the Vila- Cululú sub-basin shows a significant delay in the hydraulic response downstream since the system must first satisfy groundwater and surface water storage. Once storage capacity is exceeded, the hydraulic behavior results
in a dynamic process that involves the spilling and merging of ponds generated in small deflation hollows, generally nested within fluvial palaeochannels. Such a hierarchical structure controls surface runoff towards the shallow tectonic depressions. This mechanism allows the development of NFWs as simulation time progresses. Additionally, the surface runoff flow pattern exhibits significant lateral mobility and low connectivity down-
stream, although this varies depending on the magnitude of the storm. Despite the dense network of artificial channels developed to evacuate water excess to the outlet, geomorphological thresholds further control hydrodynamics by adding to surface water storage and limiting channel conveyance. Identifying critical geomorphological thresholds adds to the knowledge of different levels of hydrological connectivity, providing a better assessment of flood hazards. This work is one of the first studies in North Pampa in which hydrological and geomorphological data are combined to explain the present hydrodynamics and that could be applied to other fluvial basins of large plains of South America.
While mesophotic assemblages in the Gulf of Aqaba have been described in some detail, in the Red Sea proper, data are rare. Here we present a first report on a detached carbonate platform fragment from the Northern Red Sea off Al Wajh that stretches over a water depth range of 25 to 130 m. The assemblages observed comprise depth-typical large benthic foraminifers, crustose coralline algae, and mesophotic scleractinian corals (e.g., Leptoseris spp.). Encrusting bryozoans and foraminifers are abundant. A soft sediment cover in the deeper areas indicates the absence of strong currents. Radiocarbon ages confirm that this community is currently thriving. This example resembles the previously known occurrences of mesophotic reefs in the Gulf of Aqaba with respect to assemblage despite different oceanographic conditions. At the same time, it reminds of Cenozoic, in particular pre-Upper Miocene (i.e., before 8.5 million years ago), coral-associated assemblages, when coral bioherms preferentially occurred in mesophotic rather than euphotic settings. The present study thus contributes to improving the interpretation of those fossil bioherms with respect to their ecosystem dynamics and environmental significance.
While wetlands have been extensively studied using optical and radar satellite imagery, thermal imagery has been used less often due its low spatial – temporal resolutions and challenges for emissivity estimation. Since 2018, spaceborne thermal imagery has gained interest due to the availability of ECOSTRESS data, which are acquired at 70 m spatial resolution and a 3–5 revisit time. This study aimed at comparing the contribution of ECOSTRESS time-series to wetland mapping to that of other thermal time-series (i.e., Landsat-TIRS, ASTER-TIR), Sentinel-1 SAR and Sentinel-2 optical satellite time-series, and topographical variables derived from satellite data. The study was applied to a 209 km2 heathland site in north-western France that includes riverine, slope, and flat wetlands. The method used consisted of four steps: (i) four-year time-series (2019–2022) were aggregated into dense annual time-series; (ii) the temporal dimension was reduced using functional principal component analysis (FPCA); (iii) the most discriminating components of the FPCA were selected based on recursive feature elimination; and (iv) the contribution of each sensor time-series to wetland mapping was assessed based on the accuracy of a random forest model trained and tested using reference field data. The results indicated that an ECOSTRESS time-series that combined day and night acquisitions was more accurate (overall F1-score: 0.71) than Landsat-TIRS and ASTER-TIR time-series (overall F1-score: 0.40–0.62). A combination of ECOSTRESS thermal images, Sentinel-2 optical images, Sentinel-1 SAR images, and topographical variables outperformed the sensor-specific accuracies (overall F1-score: 0.87), highlighting the synergy of thermal, optical, and topographical data for wetland mapping.
This chapter discusses the basic physical and mathematical concepts of digital terrain analysis. First, we introduce the notion of the topographic surface and its limitations. Second, we discuss contour lines, special and nonspecial points, slope lines, and flow lines. Third, we present definitions, formulas, and physical meanings for five main groups of morphometric variables, such as local, nonlocal, structural, two-field-specific, and combined morphometric attributes. In particular, we present the derivation of formulas for slope gradient, slope aspect, 12 curvatures, rotor, as well as horizontal and vertical deflections. Among nonlocal morphometric variables, we are concerned with catchment and dispersive areas as well as the topographic position index. Among two-field-specific morphometric variables, we discuss solar attributes (reflectance, insolation, and sky view factor), wind variables (wind effect index and wind exposition index), as well as view attributes (terrain visibility, total viewshed, and topographic openness). The list of combined morphometric variables includes the topographic wetness index, stream power index, shape index, and curvedness. We discuss three fundamental types of landform classifications: the Gaussian classification, the Efremov–Krcho classification, and the Shary classification. Finally, we review some other landform classifications, such as the Pennock classification, the MacMillan classification, the Schmidt–Hewitt classification, the topographic position index-based classification, the Iwahashi–Pike classification, and the geomorphon classification.
This chapter deals with calculation methods of digital terrain modeling. First, we describe the Evans–Young method, a classic finite-difference technique for computing local topographic attributes. Second, we present two methods to derive models of local morphometric variables from digital elevation models based on plane square grids as well as spheroidal equal angular grids at a sphere and an ellipsoid of revolution. The method for plane square grids uses the approximation of the third-order polynomial to the 5 � 5 moving window by the least squares. The method for spheroidal equal angular grids is based on the approximation of the second-order polynomial to the 3 � 3 moving window by the least squares. We briefly review techniques to compute nonlocal and combined morphometric variables on plane square grids and spheroidal equal angular grids, approaches to detect structural lines including calculation of horizontal deflection, as well as algorithms for derivation of two-field specific morphometric variables and landform classifications. Finally, we present analytical and computational solutions, which provide the basis for geomorphometric modeling on a triaxial ellipsoid.
The ocean, covering 71% of Earth’s surface, remains largely unexplored due to the challenges of the marine environment. This study focuses on the Kuźnica Deep in the Baltic Sea, aiming to develop an automatic seabed mapping methodology using multibeam echosounders (MBESs) and machine learning. The research integrates various scientific fields to enhance understanding of the Kuźnica Deep’s underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. Advances in remote sensing, particularly, object-based image analysis (OBIA) and machine learning, have significantly improved geospatial data analysis for underwater landscapes. The study highlights the importance of using a reduced set of relevant features for training models, as identified by the Boruta algorithm, to improve accuracy and robustness. Key geomorphometric features were crucial for seafloor composition mapping, while textural features were less significant. The study found that models with fewer, carefully selected features performed better, reducing overfitting and computational complexity. The findings support hydrographic, ecological, and geological research by providing reliable seabed composition maps and enhancing decision-making and hypothesis generation.
Riparian forests are among the most vulnerable ecosystems to biological invasions. Effective management
strategies require a nuanced understanding of both species invasiveness and environmental conditions, but
analysing these interactions is complex. We designed an Agent-Based Model (ABM) to simulate the spatio-
temporal spread of Gleditsia triacanthos in the riparian forest of the Esteros de Farrapos e Islas del Río
Uruguay National Park; Uruguay. This is a parsimonious and generalisable model that incorporates distinctive
features of woody invasions in riparian systems—such as environmental heterogeneity and flooding regimes—as
well as common characteristics of woody invaders. Management simulations suggest that if control measures are
maintained over time, the abundance of the invader will remain constant at low levels; thus, the invasion can be
controlled, even if not completely eradicated. The results also indicate that without control, the invasion process
would spread even into unsuitable areas. The SWIRS model facilitates the evaluation of targeted management
strategies, ultimately providing a framework to enhance biodiversity conservation efforts in riparian ecosystems.
Landslides and floods are among the most prevalent hazards in tropical and developing regions, often exacerbated by rapid urbanization and unregulated land management. Santa Ana, a rapidly expanding municipality in Costa Rica, presents a unique mix of high-value residential properties, commercial hubs, agricultural lands, and
protected areas, making it vulnerable to both landslides and floods. This study integrates geomorphological (morphometric and morphogenetic) methods to assess landslide and flood hazards, considering key variables such as slope, L-S factor, terrain roughness, topographic wetness index, and flow accumulation. Additionally, socioeconomic data—including population density, infrastructure, and land use—are combined with the Social Development Index (SDI) to evaluate exposure and vulnerability. Through the use of normalized indices, a comprehensive risk map is developed, highlighting high-risk zones, particularly in districts like Salitral and Brasil, where steep slopes and dense hydrological networks coincide with higher socio-economic vulnerabilities. The study provides an adaptable framework for disaster risk management, applicable to regions with limited data availability, facilitating timely and informed territorial planning decisions aimed at mitigating risks and enhancing community resilience.
A Zona Costeira na região nordeste do Estado do Pará (Amazônia Oriental), apresenta ambientes como praias, dunas, manguezais, falésias, entre outros, que expressam a complexidade e dinamicidade que caracterizam os espaços litorâneos, assim como as grandes potencialidades ambientais a eles inerentes. No local em questão, localiza-se a microbacia litorânea do Rio Cuinarana, abrangida pelo município de Magalhães Barata, compreende uma faixa territorial que passou por diversas transformações sociais, culturais e espaciais ocasionando a sua fragmentação e vulnerabilidade ambiental às mudanças de uso da terra com o passar das décadas. O ambiente costeiro ao qual essa pesquisa teve como foco, caracteriza-se pelas frequentes mudanças, tanto espaciais quanto temporais, que resultam em uma variedade de feições geomorfológicas e geológicas. Esse dinamismo costeiro advém da complexa interação entre de processos deposicionais e erosivos relacionados com a ação de ondas, correntes de maré e correntes litorâneas e influências antrópicas que podem proporcionar alterações das paisagens naturais dessas áreas. Para tal, analisou-se a paisagem através de estudo sistêmico e holístico de dados produzidos em técnicas de sensoriamento remoto e geoprocessamento para a compreensão de suas mudanças durante os anos de 2003 e 2023.
As ocorrências de inundações estão diretamente ligadas ao relevo do local onde ocorre o processo, nesta perspectiva a análise das áreas com maior suscetibilidade à inundação são de suma importância, pois auxilia o poder público a tomar a melhor decisão. Neste sentido este trabalho tem como objetivo realizar o zoneamento de suscetibilidade a processos hidrológicos de inundação no município de Candelária, RS. Para a realização deste trabalho foi feita uma análise ponderada que determina pesos e notas para diferentes parâmetros. Os parâmetros definidos para a análise são Distância Euclidiana da rede de drenagem, Declividade, Hipsometria e Elementos do relevo. Para gerar o mapa de suscetibilidade utilizou-se a ferramenta raster calculator, onde foram multiplicados os parâmetros pelos pesos e notas de suscetibilidade determinando as classes: não suscetível, baixa suscetibilidade, média suscetibilidade e alta suscetibilidade. A classe denominada Alta suscetibilidade representa cerca de 23,33% da área do município. No perímetro urbano de Candelária foram identificadas as áreas próximas ao rio Pardo, as quais apresentam alta suscetibilidade além dos principais arroios que cruzam a área urbana, denominadas o molha grande e molha pequeno. Foi observado que a metodologia é adequada para a delimitação preliminar de áreas suscetíveis à inundação no município de Candelária, tendo um grande potencial para ser utilizado no planejamento e ordenamento do município.
Joshimath watershed in Uttarakhand, India, is known for experiencing creep, subsidence, and frequent minor and major landslides. Identifying zones prone to landslides through landslide susceptibility zonation (LSZ) is crucial for city planners to mitigate risks and reduce potential losses. This study employs three widely used and accurate statistical models: Frequency Ratio (FR), Modified Frequency Ratio (MFR), and Information Value (IV) to assess LSZ. A dataset of 271 landslides, derived from time-series satellite images, was utilized, with 70% (190 events) allocated for model training and 30% for validation. The analysis considered fifteen factors influencing landslide susceptibility, including slope, aspect, curvature, proximity to drainage, proximity to faults, proximity to roads, geomorphon, landform, altitude, lithology, and LULC data from both Google Earth and ESRI, RR, SPI, and TWI were evaluated, offering a comprehensive view of the various factors that may affect landslide occurrence. Based on ranking, the most influential factors are geomorphon, proximity to faults and drainage, proximity to roads, and aspect. In contrast, LULC (ESRI), RR, altitude, lithology, and slope demonstrate limited influence, while TWI, SPI, and curvature are the least influential factors. The susceptibility maps were classified into three categories. The FR model identified 59.5% of the area as low susceptibility, 32.1% as medium, and 8.3% as high, with 65.9% of landslides occurring in high-susceptibility zones. The MFR model classified 48.3% of the area as low susceptibility, 27.1% as medium, and 24.6% as high, with 78.4% of landslides located in high-susceptibility zones. The IV model classified 37.8% of the area as low susceptibility, 41.1% as medium, and 21.2% as high, with 77.4% of landslides occurring in high-susceptibility zones. ROC analysis validated the models’ predictive capabilities, with the FR model achieving the highest accuracy in both the Landslide Susceptibility Index (LSI) and LSZ at 83.1% and 84.5% AUC, respectively. The MFR and IV models also demonstrated commendable performance, providing valuable insights for landslide risk assessment. The findings emphasize the importance of model selection in LSZ mapping, highlighting the FR and MFR models as effective tools for risk management and land-use planning in landslide-prone areas. This study contributes to landslide susceptibility modeling and provides a framework for future research in geological hazard assessment.
North America is characterized by an expansive continental plain that has been described as platter‐flat. Yet this central continental plain includes isolated uplands that some people call mountains. The hill‐mountain muddle is a classic problem of geomorphology, arising from the challenge of discriminating continuous, attached forms. Here we approach this problem initially by using crisp, terrain‐only classification approaches. We overlay a global mountain classifier with a plain and prairie designation to identify 20 mountains in Canada's continental plain, then apply a landform classification tool to delineate their spatial extents and to locate adjacent, equal‐area lowlands. We then characterize and compare the attributes of uplands and adjacent lowlands with 15 geobiocultural indicators reflective of the intersections of land, life, and people. Supporting our hypothesis that small, isolated uplands in the continental plain have distinctiveness disproportionate to their dimensions, the 20 uplands are indeed modest in elevation, prominence, and isolation, but distinct in geobiological characteristics relative to adjacent lowlands. The geobiocultural distinctiveness of uplands in the plain relative to surrounding lowlands causes these local prominences to stand out, to seem higher than they are, and to be hills described or named as mountains .
Lunayyir Volcanic Field is one of the youngest (~600 ka to Historic) Cenozoic mafic intracontinental volcanic fields in the western margin of the Arabian Microplate. It consists of about 150 volcanic edifices that worked around 700 vents indicating strong vent alignments and potential longevity of activity to build each of its volcanoes. The volcanoes are strongly aligned following the Neoproterozoic basement structure determining the surface valley network that commonly captured lava flows. The volcanoes are typical magmatic effusive and explosive types of volcanoes ranging from minor fissure vents of spatter ramparts to complex multi-crater scoria cones. Effusive hazards are dominated by transitional lava flows with restricted pāhoehoe and ‘a‘ā lava emplacement while explosive eruptions are spatter cone formation with lava fountaining, scoria cone growth and destruction, paroxysmal sub-Plinian and/or violent Strombolian style activity, directed ballistic curtain formation and a possibility of development of passive volcanic fog (vog). According to these eruption types, effusive eruption scenarios are the most likely and long-term impactful events, while explosive eruptions are dominated by low intensity low impact scenarios. The area however shows evidence of extensive tephra blanket that explosive phases capable to reach volcanic explosivity index (VEI) 4 capable to carry ash away over 250 km.
El trabajo clásico Contribución al conocimiento de la geología de la zona central de Antioquia, publicado por el doctor Gerardo Botero, en 1963, abordó por primera vez aspectos morfoclimáticos relevantes en la porción norte de las cordilleras Central y Occidental, e incorporó altimetría, geomorfología, tectónica y clima como elementos de lo geológico que dan forma a una relevante provincia morfotectónica: el Altiplano Antioqueño (AA) en el Macizo Oriental (MO) de Antioquia, sensu Botero, y los cañones de los ríos Medellín-Porce que lo disectan, y Cauca y Magdalena que lo delimitan al occidente y oriente, respectivamente. Dadas las restricciones tecnológicas de su época, no tuvo el doctor Botero posibilidades de aproximar estos tópicos con base en el análisis digital del terreno. Su trabajo fue principalmente descriptivo-cualitativo y orientado a establecer elementos morfogenéticos en la evolución del paisaje. Presentamos una propuesta del uso de modelos de elevación digital (DEM-SRTM) y las herramientas GRASS y extensiones de R que permiten un tratamiento cuantitativo-geomorfométrico del terreno para abordar la expresión topográfica del AA-MO como conjunto de señales periódicas-cuasiperiódicas, específicamente mediante transformadas wavelets. Evaluamos preliminarmente el papel de las transformadas wavelet Haar tanto en un relieve sintético como en un caso real sobre una porción del MO con elementos del AA y de los cañones/valles de los ríos Medellín y Cauca. El segmento diádico de DEM del MO (~512×512 pixeles de 90×90 m) permitió diseccionar preliminarmente el paisaje, sin pérdida de información espacial, en varios niveles jerárquicos, cada uno con accidentes geográficos diferenciados dentro de un dominio de escala espacio-frecuencia similar. Nuestra aproximación tiene potencial para el análisis de diversas geoformas que incluyen sistemas complejos de colinas, valles y mesetas. Con esta aplicación pionera de las wavelets a problemas de geomorfología en Colombia, demostramos el potencial de estos novedosos métodos para descomposiciones-recomposiciones bajo ambientes GRASS-R.
A área de estudo desse trabalho se localiza na região Sudoeste do Rio Grande do Sul no município de Cacequi que se destaca pela alta ocorrência de voçorocas em seu território. O arcabouço geológico e morfoestrutural pode interferir na forma, intensidade e orientação das voçorocas em Cacequi. Assim, esse artigo busca analisar a relação entre os lineamentos morfoestruturais e os elementos do relevo na ocorrência de voçorocas nas bacias Sanga Areal da Limeira, Sanga da Tapera, Sanga da Vaca Velha e Arroio Areal Grande em Cacequi – RS, através da aplicação de técnicas automatizadas em MDEs. A extração automatizada de lineamentos morfoestruturais apresentou um resultado de feições coerentes com as características fisiográficas da área. A orientação de voçorocas e de lineamentos são coincidentes em direção predominante Leste – Oeste. Com a análise dos elementos do relevo pode-se verificar as microestruturas que elas constituem além de ser possível identificar áreas de avanço de voçorocas. As morfoestruturas se relacionam com os vales e cristas e as voçorocas com os vales, escavados e encostas. Dessa forma, conclui-se que o controle morfoestrutural caracterizado pelas condições geológicas e geomorfológicas é condicionante para a gênese e evolução das incisões erosivas presentes na área de estudo.
Digital soil mapping (DSM) is a significant advancement in soil mapping systems, enabling efficient mapping of soil patterns across different temporal and spatial scales. This computer-assisted method surpasses the traditional soil mapping techniques in terms of both compatibility and accuracy. This study employed multinomial logistic regression to map the soil subgroup levels in the Coimbatore district. Primary sample points and Natural Resource Information System (NRIS) database points serve as the dependent variables, while significant covariate layers act as independent variables. The accuracy assessment showed an overall mapping accuracy of 52.58%, with a kappa statistic of 0.50. Additionally, the calculated disagreement measures, including quantity and allocation disagreements, were 21.50% and 25.92%, respectively. The approach provides spatial soil maps at 30 m resolution and was extended for the Coimbatore district of Tamil Nadu, considering the lack of organized high resolution soil maps for operational use. The area statistics calculated from the digital soil map showed that the soil orders Vertisols cover the largest area, accounting for approximately 25.97% (122,630.38 ha) of the total land area. Soil subgroups like Ultic Haplustalfs and Vertic Ustorthents occupy substantial portions of the land, accounting for 9.95% and 9.62% of the total area, respectively. The total land area classified by the map accounts for 427,432.10 ha, i.e., 90.53% of the total land area, of which 44,696.46 ha (9.467%) remains unclassified. The study also presents the statistics on soil order at the block level. These findings provide valuable insights into soil classification, offering a comprehensive understanding of soil distribution and characteristics that support effective decision-making for sustainable land management and agricultural practices.
Landforms are fundamental components of the Earth surface, providing the base on which surface processes operate. Understanding and classifying global landforms, which record the internal and external dynamics of the planet's evolution, constitutes a critical aspect of Earth system science. Advances in Earth observation technologies have enabled access to higher resolution data, for example remote sensing imagery and digital elevation models (DEMs). However, landform data with a resolution of approximately 1 arc-second (approximately 30 m) are lacking at the global scale, which limits the progress of geomorphologic studies at finer scales. Here, we propose a novel framework for global landform classification and release a unique dataset called Global Basic Landform Units (GBLU), which incorporates a comprehensive set of objects that constitute the range of landforms on Earth. Constructed from multiple 1 arc-second DEMs, GBLU ranks among the highest-resolution global geomorphology datasets to date. Its development integrates geomorphological ontologies and key derivatives to strike a balance between mitigating local noise and preserving valuable landform details. GBLU categorizes the Earth's landforms into three levels with 26 classes, yielding discrete vector units that record landform type and distribution. Comparative analyses with previous datasets reveal that GBLU enhances capture of landform details, enabling more precise depiction of geomorphological boundaries. This refinement facilitates the identification of novel spatial disparities in landform patterns, exemplified by marked contrasts between Asia and other continents, and highlights the distinct prominence of China in terms of landform diversity. Given that the fundamental data resolution of GBLU accords well with available remote sensing datasets, it is readily incorporated into analytical workflows, exploring the relationship between landforms, climate and land cover. The full data set is available on the Deep-time Digital Earth Geomorphology platform and Zenodo (Yang et al., 2024; https://doi.org/10.5281/zenodo.13187969).
We introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these appropriate scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble reasonably patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of classes satisfy the regionalization requirements of maximizing internal homogeneity while minimizing external homogeneity. Most objects have boundaries matching natural discontinuities at regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as a customized process for the eCognition(R) software, available as online download. The results are embedded in a web application with functionalities of visualization and download.
ABSTRACT of stereoscopic landscape analysis, and then determine A method to enhance manual landform delineation using photointerpretation to map a larger area is described. Conventional aerial photo-interpretation (API) maps using a geo-pedological legend of the soil types that occur in each map unit by field inspection of the soil at representative sites. A common inspec-tion density is one observation per one to four map 21 classes were prepared for six sample areas totaling 111 km2 in the centimeters squared (Western, 1978, Table 3.3), which Baranja region, eastern Croatia. Nine terrain parameters extracted at this scale represents 25 to 100 ha. Often, the surveyors from a digital elevation model (DEM) (ground water depth, slope, plan curvature, profile curvature, viewshed, accumulation flow, wetness index, sediment transport index, and the distance to nearest watercourse) were used to extrapolate photo-interpretation over the entire survey area (1062 km make sure that there is also at least one observation
A b s t r a c t . The limits of four major ice sheets can be traced in the present landscape of Poland. Glacial deposits and ice-dammed lakes indicate a stream-like pattern of advancing ice bodies, dependent both on ice dynamics in the marginal zones and on the pre-existing landscape in their forefields. The southernmost extent of the Pleistocene ice sheets is indicated by the Scandinavian erratics and was formed by the South Polish Glaciations (Elsterian), partly replaced in the west by the Odranian of the Middle Polish Glaciations (Saalian I). The subsequent Wartanian Glaciation of the Middle Polish Glaciations (Saalian II) and the Vistulian Glaciation (North Polish Gla-ciation, Weichselian) were limited to areas further to the north.
This paper provides an algorithm for partitioning grayscale images into disjoint regions of coherent brightness and texture. Natural images contain both textured and untextured regions, so the cues of contour and texture differences are exploited simultaneously. Contours are treated in the intervening contour framework, while texture is analyzed using textons. Each of these cues has a domain of applicability, so to facilitate cue combination we introduce a gating operator based on the texturedness of the neighborhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. Experimental results on a wide range of images are shown.
An exact solution is presented for the hydromagnetic natural convection boundary layer flow past an infinite vertical flat
plate under the influence of a transverse magnetic field with magnetic induction effects included. The transformed ordinary
differential equations are solved exactly, under physically appropriate boundary conditions. Closed-form expressions are obtained
for the non-dimensional velocity(u), non-dimensional induced magnetic field component (B
x
) and wall frictional shearing stress i.e. skin friction function (τ
x
) as functions of dimensionless transverse coordinate(η), Grashof free convection number (G
r
) and the Hartmann number(M). The bulk temperature in the boundary layer (Θ) is also evaluated and shown to be purely a function ofM. The Rayleigh flow distribution (R) is derived and found to be a function of both Hartmann number (M) and the buoyant diffusivity parameter (ϑ
*). The influence of Grashof number on velocity, induced magnetic field and wall shear stress profiles is computed. The response
of Rayleigh flow distribution to Grashof numbers ranging from 2 to 200 is also discussed as is the influence of Hartmann number
on the bulk temperature. Rayleigh flow is demonstrated to become stable with respect to the width of the boundary layer region
and intensifies with greater magnetic field i.e. larger Hartman numberM, for constant buoyant diffusivity parameterϑ
*. The induced magnetic field(B
x
), is elevated in the vicinity of the plate surface with a rise in free convection (buoyancy) parameterG
r
, but is reduced over the central zone of the boundary layer regime. Applications of the study include laminar magneto-aerodynamics,
materials processing and MHD propulsion thermo-fluid dynamics.
KeywordsMHD free convection-Magnetic induction-Boundary layer control-Bulk temperature-Frictional drag-Hyperbolic functions-Rayleigh flow
The main objective of this chapter is to segment and classify Shuttle Radar Topography Mission (SRTM) data into specific landforms.
Based on the results of previous research (DRĂGUŢ and Blaschke 2006), a classification system of landform elements was improved
and adapted for SRTM 3 arc second data. Terrain derivatives such as elevation, slope gradient, slope aspect, profile curvature,
and plane curvatures were classified in a multi-resolution object-oriented approach comprising four different scale levels.
We carried out object-based image analysis, using a software program called eCognition Professional 4.0, to segment terrain
derivatives into relatively homogeneous objects, which were further classified using fuzzy logic rule sets. Special emphasis
was put on the accuracy assessment of the results as well as on the transferability of the procedure between study areas.
We classified two SRTM datasets comprising a rolling hill landscape, which covers small areas of the states of Arkansas, Missouri
and Oklahoma, USA, and a high mountain area of 50 square km around Hochkalter Peak, Berchtesgaden National Park, Germany.
Results were visually compared and accuracy assessments using fuzzy classification options and an error matrix were performed.
The classification system proved to be transferable between hilly and high mountain areas, its outcomes being satisfactorily
accurate
In this paper, we present a methodology for automatic geomorphic mapping of planetary surfaces that incorporates machine-learning techniques. Our application transforms remotely sensed topographic data gathered by orbiting satellites into semantically meaningful maps of landforms; such maps are valuable research tools for planetary science. As topographic data become increasingly available, the ability to derive geomorphic maps efficiently is becoming essential. In our proposed framework, the mapping is achieved by means of scene segmentation followed by supervised classification of segments. The two mapping steps use different sets of features derived from digital elevation models of planetary surfaces; selection of appropriate features is discussed. Using a particular set of terrain attributes relevant to annotating cratered terrain on Mars, we investigate the design choices for both segmentation and classification components. The segmentation assessment includes K -means-based agglomerative segmentation and watershed-based segmentation. The classification assessment includes three supervised learning algorithms: Naive Bayes, Bagging with decision trees, and support-vector machines (SVMs); segments are classified into the following landforms: crater floors, crater walls (concave and convex), ridges (concave and convex), and intercrater plains. The method is applied to six test sites on Mars. The analysis of the results shows that a combination of K -means-based agglomerative segmentation and either SVM with a quadratic kernel or Bagging with C4.5 yields best maps. The presented framework can be adopted to generate geomorphic maps of sites on Earth.
In the Vistulian there originated the young glacial relief of northern Poland (in the macro- and mesoscale), and the older relief of southern and central Poland acquired the features of periglacial transformation superimposed on the older forms. The Holocene morphogenesis prepares a new regolith by chemical weathering. More active processes are restricted to less resistant rocks, steeper slopes, and areas with a lowered base level. In the young glacial zone the formation of valley patterns is going on. -from Author
Morphometric types and map units were computed for the entire state of New Mexico from an eight-million-point digital elevation model (DEM). An automated version of the landform-classification procedure, developed for contour maps over 30 years ago by E.H. Hammond, sequentially evaluates the same three descriptive measures: slope, relief and profile type. The DEM, at a spatial resolution of 200 m, was derived from the same 1:250 000-scale topographic maps used by Hammond. Although size of the 9.8 × 9.8 km moving sample-space also is close to Hammond's (9.65 × 9.65 km), the frequency of its placement by the computer is 2500× greater. -from Authors
The ability to analyze and quantify morphology of the surface of the Earth in terms of landform characteristics is essential for understanding of the physical, chemical, and biological processes that occur within the landscape. However, because of the complexity of taxonomic schema for landforms which include their provenance, composition, and function, these features are difficult to map and quantify using automated methods. The author suggests geographic information systems (GIS) based methods for mapping and classification of the landscape surface into what can be understood as fourth-order-of-relief features and include convex areas and their crests, concave areas and their troughs, open concavities and enclosed basins, and horizontal and sloping flats. The features can then be analyzed statistically, aggregated into higher-order-of-relief forms, and correlated with other aspects of the environment to aid fuller classification of landforms.
Terrain position (eg ridge, mid-slope, valley) is a potentially useful variable with which to model environmental parameters and processes using geographical information systems. Digital elevation data spaced on a regular 30 m grid were generated over an area of flat to moderate topography in south-east Australia. Streams and ridges were mapped from the digital elevation model using a new algorithm that utilizes basic geographical principles. Ridge and stream lines closely followed the original contour map and improved upon the results from three alternative algorithms. Mid-slope positions were successfully interpolated from the stream and ridge lines by a modified measure of Euclidean distance. -Author
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
An iterative procedure that implements the classification of continuous topography as a problem in digital image-processing automatically divides an area into categories of surface form; three taxonomic criteria–slope gradient, local convexity, and surface texture–are calculated from a square-grid digital elevation model (DEM). The sequence of programmed operations combines twofold-partitioned maps of the three variables converted to greyscale images, using the mean of each variable as the dividing threshold. To subdivide increasingly subtle topography, grid cells sloping at less than mean gradient of the input DEM are classified by designating mean values of successively lower-sloping subsets of the study area (nested means) as taxonomic thresholds, thereby increasing the number of output categories from the minimum 8 to 12 or 16. Program output is exemplified by 16 topographic types for the world at 1-km spatial resolution (SRTM30 data), the Japanese Islands at 270 m, and part of Hokkaido at 55 m. Because the procedure is unsupervised and reflects frequency distributions of the input variables rather than pre-set criteria, the resulting classes are undefined and must be calibrated empirically by subsequent analysis. Maps of the example classifications reflect physiographic regions, geological structure, and landform as well as slope materials and processes; fine-textured terrain categories tend to correlate with erosional topography or older surfaces, coarse-textured classes with areas of little dissection. In Japan the resulting classes approximate landform types mapped from airphoto analysis, while in the Americas they create map patterns resembling Hammond's terrain types or surface-form classes; SRTM30 output for the United States compares favorably with Fenneman's physical divisions. Experiments are suggested for further developing the method; the Arc/Info AML and the map of terrain classes for the world are available as online downloads.
Automated approaches for identifying different types of glaciated landscapes using digitally processed elevation data were evaluated. We tested the ability of geomorphic measures (e.g. elevation, relative relief, roughness, and slope gradient) derived from digital elevation models (DEMs) to differentiate glaciated landscapes using maximum likelihood classification and artificial neural networks (ANN). The automated methods were trained and validated using an existing Quaternary geology map and a manual interpretation of the contour data portrayed on topographic quadrangles. The need for such methods arises from efforts to classify types of landscapes (e.g. ecoregions) in Michigan. One fundamental control of the landscape structure in Michigan, including soil type and vegetation, is the underlying sedimentary and landform assemblages produced by an array of glacial processes during the waning phase of the Pleistocene. Traditional methods for identifying different landscapes (e.g. ice-contact landscapes, stagnation landscapes) have relied on printed topographic maps and have been very effective, but time consuming. The maps resulting from the four supervised classification trials had between 51% and 61% agreement with the original Quaternary geology map. The output from the maximum likelihood classification had slightly higher agreements than the output from the neural net, which is attributed to the generalization inherent in the Quaternary geology map compared with the nature of the classifier for the neural net. The neural net, however, identifies significant detail and non-linear relationships between classification inputs and output classes. Future work should incorporate a map of soils into the classification.
The following values have no corresponding Zotero field:
ID - 222
Environmental Modeling Using Open Source Tools
MapWindow GIS
A landform evolution model is used to investigate the historical evolution of a fluvial landscape along the Potomac River in Virginia, USA. The landscape has developed on three terraces whose ages span 3.5Ma. The simulation model specifies the temporal evolution of base level control by the river as having a high-frequency component of the response of the Potomac River to sea level fluctuations superimposed on a long-term epeirogenic uplift. The wave-cut benches are assumed to form instantaneously during sea level highstands. The region is underlain by relatively soft coastal plain sediments with high intrinsic erodibility. The survival of portions of these terrace surfaces, up to 3.5Ma, is attributable to a protective cover of vegetation. The vegetation influence is parameterized as a critical shear stress to fluvial erosion whose magnitude decreases with increasing contributing area.The simulation model replicates the general pattern of dissection of the natural landscape, with decreasing degrees of dissection of the younger terrace surfaces. Channel incision and relief increase in headwater areas are most pronounced during the relatively brief periods of river lowstands. Imposition of the wave-cut terraces onto the simulated landscape triggers a strong incisional response.By qualitative and quantitative measures the model replicates, in a general way, the landform evolution and present morphology of the target region.
Landform types are conceptualize as mainly consisting of waveform features that exhibit entire repeating cycles of variation in morphological properties, such as slope gradient, slope lengths, relief, curvatures, and moisture regime. These cyclic patterns can be identified and characterized by analyzing the distribution of variation in morphological attributes within neighborhoods defined by windows of appropriate dimensions and shape, as morphological variables computed for any given cell describe only a small portion of the total cyclic variation that characterizes a repeating landform type. Landform types provide information on the size and scale of landform features and how this size and scale might affect the amounts of energy available for geomorphic, pedogenic, and hydrological processes. Landform types provide context that can be used to inform and improve the further sub-division of the landscape into landform elements. Landform elements have been classified based solely on consideration of their local surface shape, on consideration of a combination of surface shape and slope gradient, and on consideration of a combination of surface shape, slope gradient, and contextual measures of relative landform position. Procedures for automatically extracting and classifying landform types and landform elements differ in terms of the kinds of classification methods applied to extract the entities. Repeating landform types have mainly been classified using Boolean rules based on expert knowledge and Heuristic beliefs. Classification of landform elements has been achieved using a wide variety of classification methods including knowledge-based heuristic approaches, supervised classification, and unsupervised classification.
This chapter presents different formulations, each one of them with a different degree of mathematical complexity, to extract basic morphometric parameters from the digital elevation model (DEM) itself and then discuss importance and interpretation of each parameter. The study of morphometric land-surface parameters start with those related to the local morphometry of each point. They are divided into two main groups: (1) Geometric and (2) statistical measures. For each type of land-surface parameter, a detailed description has been given including further information on the physical phenomena described by each one. The chapter introduces some well-known parameters, such as slope or aspect, and then extends that to some less common ones, such as the Shape Complexity Index, the Anisotropic Coefficient of Variation, geostatistical land-surface parameters, or fractal-based ones that represent the most actual trend in this field.
A new parameter, here termed openness, expressing the degree of dominance or enclosure of a location on an irregular surface, is developed to visualize topographic character. Openness is an angular measure of the relation between surface relief and horizontal distance. For angles less than 90", it is equivalent to the internal angle of a cone, its apex at a DEM location, constrained by neighboring elevations within a specified radial distance. Openness incorporates the terrain line-of-sight, or viewshed, concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Openness has two viewer perspectives. Positive values, expressing openness above the surface, are high for convex forms, whereas negative values describe this attribute below the surface and are high for concave forms. Openness values are mapped by gray-scale tones. The emphasis of terrain convexity and concavity in openness maps facilitates the interpretation of landforms on the Earth's surface and its seafloor, and on the planets, as well as features on any irregular surface-such as those generated by industrial
Starting from a concept of the land surface, its definition and subdivision from Digital Elevation Models (DEMs) is considered. High-resolution DEMs from active remote sensing form a new basis for geomorphological work, which is moving on from consideration of whether data are accurate enough to how the surface of interest can be defined from an overabundance of data. Discussion of the operational definition and delimitation of specific landforms of varying degrees of difficulty, from craters to mountains, is followed by the applicability of ‘fuzzy’ boundaries. Scaling, usually allometric, is shown to be compatible with the scale-specificity of many landforms: this is exemplified by glacial cirques and drumlins. Classification of a whole land surface is more difficult than extraction of specific landforms from it. Well-dissected fluvial landscapes pose great challenges for areal analyses. These are tackled by the delimitation of homogeneous elementary forms and/or land elements in which slope position is considered. The boundaries are mainly breaks in gradient or aspect, but may also be in some type of curvature: breaks in altitude are rare. Elementary forms or land elements are grouped together into functional regions (landforms) such as ‘hill sheds’. It may often be useful to recognise fuzziness of membership, or core and periphery of a surface object.
Traditional manual methods have been employed for decades to measure geomorphometric properties from topographic maps. Such measurement techniques tend to be tedious and time-consuming and the designated landform elements cannot be easily overlaid on any digital map and imagery for further applied research. This study deals with a new quantitative geomorphometric procedure, based on the multivariate statistical analysis of local topographic gradients within a part of north-central Crete. This method employs sets of computer algorithms that automatically extract and classify geomorphometric properties from Digital Elevation Models (DEMs). This was done by evaluating the morphological setting around each pixel of the DEM along the eight azimuth directions. ISODATA unsupervised classification was implemented to generate 10 morphometric classes showing the spatial distribution of areas with a similar geomorphic scenario. Results revealed that this approach permitted a quick estimation of the spatial distribution of morphologically homogeneous terrain units. It also demonstrated the ability of the delineated landform elements to be superimposed on any digital map and imagery for further investigation. This became apparent during the examination of the relationship between the geomorphological units and the land-cover/land-use types in the study area. Both relative association and the dominant land cover/land use types in relation to geomorphological units are presented.
Geomorphology may be defined as the science, which studies the nature and history of landforms, and the processes that created them. This chapter focuses on the uses of digital elevation models (DEMs) for geomorphological mapping that is, extraction of geological and geomorphological features out of DEMs. Basic concepts in geomorphology include the magnitude and frequency of processes, spatial scales of landforms and processes, temporal scales of adjustment, equilibrium and historical inheritance, relations between internal and external processes, and the sediment cascade. Most of these involve the use of geomorphometric measures, increasingly from DEMs. A major application of geomorphometry in geomorphological studies is the automated extraction of geological/hydrological features and landforms. Using a small case study, the chapter demonstrates various approaches to extraction of predefined, generic, and empirically defined landform objects. It has also shown that geomorphometry has gone beyond the experimental stage and produced many substantive results in geomorphology.
The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world.
This paper proposes a quantitative method to classify landforms using four morphometric parameters from DEM-derived thematic raster maps of slope and topographic openness. Because the different surficial processes and stages in the evolution of slopes create landscapes with different shapes, these parameters may lead to a genetic interpretation of topography. The raster maps of slope and topographic openness were constructed for Northeast Honshu, Japan, from 50-m DEMs. The mean and standard deviation of morphometric parameters within a 3050m by 3050 m moving window on the raster maps were calculated. The results for some training areas show that constructional/depositional and erosional landforms with different relief have different morphometric characteristics. A supervised landform classification for Northeast Honshu using the knowledge from the training areas revealed a ladder geomorphological structure composed of high mountains, ranges and volcanoes. The close relationship between the ladder geomorphological structure and volcano distribution indicates that the structure reflects the magmatic plumbing system from the upper mantle to the crust of the Northeast Honshu arc.
One fundamental objective in geomorphometry is to extract signatures of geomorphologic processes on different spatial scales from digital terrain models (DTMs) and to describe the complexity of landforms as the synthesis of those individual imprints. We present an approach for characterizing land surfaces on multiple, spatially varying local scales. We approximate terrain surfaces locally to calculate surface derivatives at different window sizes. Local scale behaviour diagrams are used to define dominant scale ranges and multiple curvatures for each surface point. Multi-scale landform analysis leads to improved models of surface derivatives and new landform classifications, applicable in geomorphology, soil science and hydrology.
The landscape in which people live is made up of many features, which are named and have importance for cultural reasons. Prominent among these are the naming of upland features such as mountains, but mountains are an enigmatic phenomenon which do not bear precise and repeatable definition. They have a vague spatial extent, and recent research has modelled such classes as spatial fuzzy sets. We take a specifically multi-resolution approach to the definition of the fuzzy set membership of morphometric classes of landscape. We explore this idea with respect to the identification of culturally recognized landscape features of the English Lake District. Discussion focuses on peaks and passes, and the results show that the landscape elements identified in the analysis correspond to well-known landmarks included in a place name database for the area, although many more are found in the analysis than are named in the available database. Further analysis shows that a richer interrogation of the landscape can be achieved with Geographical Information Systems when using this method than using standard approaches.
Developing an effective method for automatic mapping of physiography is of great interest because such maps have wide range of applications, but creating them manually is expensive and suffers from lack of standards. Many automapping methods have been proposed, but most yield pixel-based maps that do not quite match an appearance and usability of manually drawn maps. In this letter, we propose a method for autocreation of a physiographic map that has handmadelike appearance and functionality. The new method relies on the concept of stacked classification. First, the outcome of existing pixel-based classification is used to construct new features that contain contextual information around each pixel. Second, these new features are used by a segmentation/classification algorithm to create a final map showing generalized landform classes. We describe the design of our method and demonstrate its utility by mapping the physiography of Tharsis region on Mars. A framework of the new method is general enough to improve upon maps created by all previous pixel-based methods. Potential applications include the following: facilitating efficient geologic mapping, enabling computational comparative geomorphology, more effective visualization of topography, and fusion with other data layers within the Geographic Information System framework. The method can also be applied without modification to create segmentation-based maps of land covers.
The local binary pattern (LBP) operator is a computationally efficient local texture descriptor and has found many useful applications. However, its sensitivity to noise and the high dimensionality of histogram associated with a mediocre size neighborhood have raised some concerns. In this paper, we attempt to improve the original LBP by proposing a novel extension named extended local ternary pattern (ELTP). We will investigate the characteristics of ELTP in terms of noise sensitivity, discriminability and computational efficiency. Preliminary experimental results have shown better efficacy of ELTP over the original LBP.
This study presents a classification of distinct, three-dimensional landform elements and examines the relationship between these landform elements and selected soil morphological properties of Udic Boroll soils in southern Saskatchewan, Canada. The classification is based on defined ranges of three criteria derived from topographic data: gradient, profile (downslope) curvature, and plan (across-slope) curvature. Seven landform elements are recognized: convergent shoulders, divergent shoulders, convergent backslopes, divergent backslopes, convergent footslopes, divergent footslopes, and level elements. All of the elements are easily identified in the field.The thicknesses of A horizons and depths to calcium carbonate of the soils were consistently greater in convergent versus divergent elements in the same profile group (e.g. shoulders), and showed an overall increase in the sequence shoulders<backslopes<level<footslope elements. Distinct groups of elements emerge from a statistical analysis of the differences between individual and bivariate means of soil morphological properties associated with each landform element. The differences in soil properties among the elements can be largely explained by differences in water movement and distribution in hillslope systems. The results of the study highlight the need to consider land-surface morphology during the development of research designs for soil related studies.
Land surface morphology is fundamental to geomorphological mapping and many GIS applications. Review and comparison of various approaches to segmentation of the land surface reveals common features, and permits development of a broad theoretical basis for segmentation and for characterization of segments and their boundaries. Within the context of defining landform units that maximise internal homogeneity and external differences, this paper introduces the concept of elementary forms (segments, units) defined by constant values of fundamental morphometric properties and limited by discontinuities of the properties. The basic system of form-defining properties represents altitude and its derivatives, constant values of which provide elementary forms with various types of homogeneity. Every geometric type of elementary form can be characterized by a defining function, which is a specific case of the general polynomial fitted function. Various types of boundary discontinuity and their connections and transformations into other types of morphological unit boundaries are analysed.The wealth of types of elementary forms and their boundaries is potentially unbounded and thus is sufficient to cover the real variety of landforms. Elementary forms in the basic set proposed here have clear potential for genetic and dynamic interpretation. A brief worked example documents the possibility of analytical computation of various models of ideal elementary forms for particular segments of landform. Ideal elementary forms can be considered as attractors, to which the affinity of surface segments can be measured by multivariate statistical methods. The use of the concept of elementary forms in landscape segmentation is promising and it could be adapted for elementary segmentation of various other spatial fields.
Numerical classification methods may provide an alternative to manual landform delineation using aerial photographs, a subjective process that requires much knowledge of the landscape in question. Continuous classification (fuzzy set) methods and unsupervised (ISODATA) classification techniques were used to classify the landscape of a study area in southwestern Wisconsin, USA. Each pixel of a 10-m resolution digital elevation model (DEM) was grouped according to its membership in a continuous landform class. These classes were determined by the natural clustering of the data in attribute space. Attributes used for the classification were elevation, slope, profile and tangent (related to plan) curvature, compound topographic (wetness) index, and incident solar radiation. The ISODATA classification assigned pixels to one, and only one, landform class while the continuous classification allocated relative class memberships to each pixel. The resulting classifications roughly follow subjective manual delineation lines but give more detailed results. These classification methods may prove useful for statistical analyses and determination of sample schemes.
Previous attempts to devise automated methods of landscape classification have been frustrated by computational issues related to the size of the data set and the fact that most automated classification methods create discrete classes while ‘natural’ interpreted landscape units often have overlapping property sets. Methods of fuzzy k-means have been used by other workers to overcome the problem of class overlap but their usefulness maybe reduced when data sets are large and when the data include artefacts introduced by the derivation of landform attributes from gridded digital elevation models.This paper presents ways to overcome these limitations using spatial sampling methods, statistical modelling of the derived stream topology, and fuzzy k-means using the Distance metric. Using data from Alberta, Canada, and the French pre-Alps it is shown how these methods may easily create meaningful, spatially coherent land form classes from high resolution gridded DEMs.
Visual perception operates in a preattentive mode and in an attentive mode. In the preattentive mode no complex forms are processed, and yet in parallel, without effort or scrutiny, differences in a few local conspicuous features (called textons) are detected over the entire visual field. It is these loci of texton differences to which the narrow aperture of attention is directed in steps each lasting about 50 ms. Only in this aperture of attention are the positional relations between textons preserved, permitting form recognition.
A robust new approach for describing and segmenting landforms which is directly applicable to precision farming has been developed in Alberta. The model uses derivatives computed from DEMs and a fuzzy rule base to identify up to 15 morphologically defined landform facets. The procedure adds several measures of relative landform position to the previous classification of Pennock et al. (Geoderma 40 (1987) 297–315; 64 (1994) 1–19). The original 15 facets can be grouped to reflect differences in complexity of the area or scale of application. Research testing suggests that a consolidation from 15 to 3 or 4 units provides practical, relevant separations at a farm field scale. These units are related to movement and accumulation of water in the landscape and are significantly different in terms of soil characteristics and crop yields. The units provide a base for benchmark soil testing, for applying biological models and for developing agronomic prescriptions and management options.
In this paper a semi-automated method is presented to recognize and spatially delineate geomorphological units in mountainous forested ecosystems, using statistical information extracted from a 1-m resolution laser digital elevation dataset. The method was applied to a mountainous area in Austria. First, slope angle and elevation characteristics were determined for each key geomorphological unit occurring in the study area. Second, a map of slope classes, derived from the laser DTM was used in an expert-driven multilevel object-oriented approach. The resulting classes represent units corresponding to landforms and processes commonly recognized in mountain areas: Fluvial terrace, Alluvial Fan, Slope with mass movement, Talus slope, Rock cliff, Glacial landform, Shallow incised channel and Deep incised channel. The classification result was compared with a validation dataset of geomorphological units derived from an analogue geomorphological map. For the above mentioned classes the percentages of correctly classified grid cells are 69%, 79%, 50%, 64%, 32%, 61%, 23% and 70%, respectively. The lower values of 32% and 23% are mainly related to inaccurate mapping of rock cliffs and shallow incised channels in the analogue geomorphological map. The accuracy increased to 76% and 54% respectively if a buffer is applied to these specific units. It is concluded that high-resolution topographical data derived from laser DTMs are useful for the extraction of geomorphological units in mountain areas.
Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. We describe an approach in terrain classification, with the objective of deriving a method for classifying land elements from DTMs based on their fundamental characteristics. The methodology for modelling land elements is implemented as a two-step process: first, form elements are classified based on local geometry, and second, land elements are derived by evaluating the form elements in their landscape context. Form elements are derived by fuzzy classification of slope and curvature at a specified global scale (window size). The form elements are reclassified according to their geomorphometric context using a higher scale terrain position index. The resulting land elements are evaluated with respect to their predictive value for modelling soil properties. It is shown that scaling geomorphometric properties is important for applying them to predict soil properties and to model landform units. The presented model, based on scaled geomorphometric properties and geomorphometric context, using a limited number of model parameters, is capable of modelling fundamental land elements that can be utilized in soil–landscape modelling and in other applications in land resource management.
This paper presents an automated classification system of landform elements based on object-oriented image analysis. First, several data layers are produced from Digital Terrain Models (DTM): elevation, profile curvature, plan curvature and slope gradient. Second, relatively homogenous objects are delineated at several levels through image segmentation. These object primatives are classified as landform elements using a relative classification model, built both on the surface shape and on the altitudinal position of objects. So far, slope aspect was not used in classification. The classification has nine classes: peaks and toe slopes (defined by the altitudinal position or the degree of dominance), steep slopes and flat/gentle slopes (defined by slope gradients), shoulders and negative contacts (defined by profile curvatures), head slopes, side slopes and nose slopes (defined by plan curvatures). Classes are defined using flexible fuzzy membership functions. Results are visually analyzed by draping them over DTMs. Specific fuzzy classification options were used to obtain an assessment of output accuracy. Two implementations of the methodology are compared using (1) Romanian datasets and (2) Berchtesgaden National Park, Germany. The methodology has proven to be reproducible; readily adaptable for diverse landscapes and datasets; and useful in respect to providing additional information for geomorphological and landscape studies. A major advantage of this new methodology is its transferability, given that it uses only relative values and relative positions to neighboring objects. The methodology introduced in this paper can be used for almost any application where relationships between topographic features and other components of landscapes are to be assessed.
Numerous investigators have examined ways in which digital elevation data can be used to define landform-based units that act as basic spatial and structural entities for soil, terrain or ecological maps. No proposed system of automated terrain or ecological mapping has, to our knowledge, advanced beyond research investigation to achieve routine operational use. This paper describes a conceptual design for creating landform-based spatial entities from digital elevation data to support multi-level, hierarchical integrated natural resource inventory. The spatial entities defined by our procedures embrace both geomorphic and hydrological considerations. They are intended to provide a framework for mapping more specific ecological entities of interest.
Terrain position (e.g., ridge, mid-slope, valley) is a potentially useful variable with which to model environmental parameters and processes using geographical information systems. Digital elevation data spaced on a regular 30 m grid were generated over an area of flat to moderate topography in south-east Australia. Streams and ridges were mapped from the digital elevation model using a new algorithm that utilizes basic geographical principles. Ridge and stream lines closely followed the original contour map and improved upon the results from three alternative algorithms. Mid-slope positions were successfully interpolated from the stream and ridge lines by a modified measure of Euclidean distance.
This paper introduces the analyses of visibility sites on topographic surfaces as a new area of application in geographical information systems. The analysis of visibility sites are important in several related research fields such as problems of surface representation in geographical information systems, visibility problems in computer graphics and the location allocation and the set covering problems in operations research. The application areas include pilot training simulation, navigation, scenic landscape assessment, terrain exploration, military surveillance, forest fire monitoring, locations of radio transmission stations and many others. Five types of site analysis using visibility information are discussed in this paper, including both fixed and variable heights of problems in visibility sites. Triangulated irregular networks are used to approximate an actual topographic surface. Algorithms are developed for extracting visibility information. Finally, three heuristic algorithms and their combinations are used to demonstrate the solutions for five problems of visibility sites.