Landform classification and mapping provide fundamental data for Earth science research, natural resource management, environmental monitoring, urban planning, and various other domains. Despite the availability of DEMs with 1-arc second resolution, global-scale studies on landform classification and mapping are inconsistent in terms of general classification systems and methods.Landforms represent not only assemblages of morphological characteristics but also encompass the human understanding of the Earth, which is constrained by the nature and scale of quantitative analysis. Here, we propose a novel framework for global landform mapping to significantly improve the quantitative evaluation of geomorphological features.The proposed framework incorporates geomorphological ontology that takes account of their conceptualization to construct classified objects. We propose the accumulated slope (AS) and mountain uplift index (MUI) to emphasize the integrity and continuity of geomorphological units, providing more precise results compared to traditional methods. Aggregating local terrain features into global metrics, AS effectively overcomes the potential negative influence of increased resolution on landform integrity. MUI aligns better with human perception of mountainous morphology and surpasses the limitations of window-based computing.In presenting the new framework, we have developed and made available a public dataset, Global Basic Landform Unit (GBLU), which incorporates a comprehensive set of objects that constitute the range of landforms on Earth. In emphasizing the integration of classification with quantitative analysis, GBLU highlights the connection between natural objects and human understanding in geomorphology and the Earth sciences. The GBLU outperforms previous datasets (the basic landform classification and global mountain assessment) in expressing landform details. GBLU can be downloaded at https://geomorph.deep-time.org. It serves as a valuable resource in facilitating a deeper understanding of landform spatial distribution and evolution, and supporting research in a diverse range of fields.

Sand dunes significantly impact climate, water resources, and human infrastructure, reflecting sand sources and wind conditions. However, their complex and heterogeneous terrain leads to fragmented and poorly continuous dune extraction results. Focusing on the Badain Jaran Desert, this study developed a sand dune extraction method using slope cost distance analysis with FABDEM data. First, positive and negative terrains were segmented using 30 m resolution digital elevation data. Next, slope patches were classified based on specific criteria: slope less than 1.5 degrees, patch area greater than 0.01 km2, and patches entirely within negative terrains. The filtered patches served as source data, and slope raster data were used to calculate slope cost distance. Sand dunes and interdune areas were delineated based on a predefined slope cost threshold. Post-processing, such as smoothing and vector conversion, yielded the final extraction results. The method effectively extracted compound transverse megadunes and pyramid megadunes, addressing misclassification issues and accurately defining interdune areas. It demonstrated faster processing speeds compared to traditional methods, with high precision (89.38 % & 90.37 %), accuracy (96.5 % & 92.89 %), and recall (94.77 % & 98.63 %). The results are more complete and less fragmented, aiding in the extraction of micro-scale dune features and revealing macro-scale spatial patterns, which are beneficial for studying dune formation, development, and wind dynamics. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Karst landforms are one of the most typical geographical units with a specific physical process on the earth's surface. The classification of karst landforms is an important aspect for understanding their landform processes and mechanisms. However, influenced by various interior and external forces, karst landforms have an extremely complex surface morphology, increasing the difficulty of their automatic classification. In this study, we considered hydrological features as an important factor in characterizing karst landforms and proposed a method that considers surface flow for karst landform classification. In this method, terrain was reversed for hydrological analysis to achieve the landform units. Then, the watershed boundary of the reversed terrain is extracted by hydrological analysis. The boundary of the karst landform unit is determined by erasing the plain area from the watershed boundary. Thereafter, the graph theory segmentation method is employed to merge the landform units belonging to the same karst landform entity. The proposed approach is validated and applied in two sample karst areas, Fenglin and Fengcong, located in Guilin, China, using digital elevation model data with 30 m spatial resolution. In addition, a comparative analysis is conducted to evaluate the accuracy of the proposed method. The results demonstrated that the typical karst landform units of Fenglin and Fengcong can be effectively classified. The overall classification accuracy is 94.44%. The proposed method produced more reasonable and accurate boundaries compared with the contour tree and terrain feature point methods. Furthermore, the classification results indicate various landform development stages of the karst landform process in the study area. The proposed method considering surface flow characteristics can be further extended to other landform types with highly complex landforms.

Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes.

Terrain visibility analysis is vital in geospatial research, employing computer geometry and graphics principles for computing and visualizing visibility between observation and target points. The observation point setup problem is crucial for tasks like sentry point selection and signal base station placement. This problem involves choosing the minimum viewpoints on terrain for optimal joint view coverage, presenting a combinatorial optimization challenge. As technology advances, data can be acquired with increasing precision, the number of viewpoints that can be extracted from the same area is gradually increasing as well, obtaining candidate viewpoints and determining optimal combinations become challenging. This paper proposes a novel method, Stepwise Maximum Viewshed (SMV), addressing observation point setup by stepwise filtering the maximum viewshed and can customize the size of the observation point combination. In complex mountainous terrain, the SMV algorithm demonstrates superior joint view coverage compared to Candidate Viewpoints Filtering (CVF) and Simulated Annealing (SA) algorithms. Experimental results reveal up to 5.59% improvement over CVF and a maximum of 12.52% over SA in joint viewshed coverage.

The intervalley plain is an important type of landform for mapping, and it has good connectivity for urban construction and development on the Loess Plateau. During the global landform mapping of the Deep-time Digital Earth (DDE) Big Science Program, it was found that slope and relief amplitude hardly distinguished intervalley plains from intermountain flats. This study established a novel descriptive method based on a digital elevation model to describe the difference between intervalley plains and intermountain flats. With the proposed method, first the pattern of variation in the elevation angle is described using a sight line on the terrain profile, and the lowest elevation angle (LEA) is extracted. The maximum value of the LEA is subsequently used among multiple terrain profiles to represent the maximum velocity of the elevation decrease, that is, the three-dimensional lowest elevation angle (3D LEA), to represent the intervalley plains with lower 3D LEA values. The sight parameters of the 3D LEA are evaluated to optimize the intervalley plain mapping. The functional mechanism of the sight parameters is presented from a mathematical perspective and a comparative analysis of the 3D LEA is performed for the relief amplitude and slope angle at multiple scales. This study explores sight-line analysis in a novel way, providing a new terrain factor for landform mapping involving intervalley plains.

In recent years, applications and analyses based on slope units have become increasingly widespread. Compared with grid units, slope units can better represent terrain features and boundaries and allow a more complete view of the morphology of the Earth’s surface. Maps based on slope units also offer significant improvements for disaster prediction and the analysis of slope land resources. Therefore, we need a reasonable method of slope unit classification. Although some methods have been proposed for slope unit classification, they have been too focused on morphological variations and have not fully considered the importance of geomorphology, and the geomorphological and physical significance of slope partitioning remain unclear. Therefore, we propose a novel slope unit classification method by combining terrain feature lines (CTFL) derived from the meaning of geomorphology ontology that use several terrain feature lines, such as geomorphic water division lines, valley shoulder lines, slope toe lines, and shady/sunny slope boundary lines, to classify slopes. The Jiuyuangou and Lushan study areas were selected to test the CTFL method. Compared with the traditional hydrological method, the CTFL method can effectively overcome topographic abruptness and distortions, improve the uniformity of slope and aspect within individual units, and increase the accuracy of slope unit applications and analyses. This work fully considers the importance of geomorphology and is conducive to future studies of slope unit division.

Multi-scale habitat selection modeling (HSM) has garnered attention due to its ability to incorporate scale dependence of species. The key of multi-scale HSM is to select the appropriate combination of scales for different resources or environmental conditions, and then construct a set of multi-scale environmental covariates as the features of HSM. However, the existing scale selection methods do not determine the combination of scales under a unified model. In this study, a combinatorial optimization approach is proposed. We regard the combination of different scales as a search space, and use a heuristic optimization algorithm to search for the best-fitting model to determine the optimal scales for each resource or environmental condition. In a case study conducted in Yancheng National Nature Reserve, the proposed approach is applied to model the habitat selection of the endangered red-crowned crane. We compare the proposed method with single-scale, random-scale and other multiscale approaches. The results show that the combination of scales selected based on the proposed method obtained the best accuracy in the spatial prediction of habitat suitability with a test AUC of 0.865 for the daytime scenario and 0.932 for the nighttime scenario. Moreover, the selected scales are utilized to generate response curves, providing suggestions for habitat restoration and management of the red-crowned crane population in the nature reserve.

Deserts have obvious textural features. In detail, different types of sand dunes have significant differences in their morphological texture features. Existing studies on desert texture have mainly focused on extracting dune ridges or sand ripples using remote sensing images. However, comprehensive understanding of desert texture at multiple scales and quantitative representation of texture features are lacking. Our study area is in the Badain Jaran Desert. Four typical sand dunes in this desert are selected, namely, starlike chain megadune, barchans chain, compound chain dune, and schuppen chain megadune. Based on Sentinel-2 and ASTER 30m DEM data, the macroscopic and microscopic texture features of the desert are extracted using positive and negative topography, edge detection and local binary pattern (LBP) methods, respectively. Eight texture indexes based on gray level co-occurrence matrix(GLCM) are calculated for the original data and the abstract texture data respectivelyThen these texture parameters are clustered based on the result of Spearman correlation. Finally, the coefficient of variation is used to determine representative indicators for each cluster in order to construct a geomorphological texture information spectrum library of typical dune types. The results show that the macroscopic and microscopic texture features of the same type of sand dunes have high similarity. And geomorphological texture information spectrum can well distinguish different types of sand dunes by curve features.

Three-dimensionality (3D), the main feature of urban morphology since rapid urban development, has been a trending topic on urban morphology research. Many 3D morphological indices have been proposed to describe urban morphology in current studies. However, the understanding of those indices is independent and isolated, and the spatial relationship of these indices is rarely considered, which makes it difficult to grasp the overall morphological characteristics and its spatial structure. Based on 3D buildings in the central area of Chengdu City, nine morphological indices are constructed. Then, a spatially constrained multivariate clustering method is applied for the comprehensive regionalization of urban morphology after index correlation inspection. The results show that: (1) four morphological indices from height, density and volume can effectively describe the characteristics of urban morphology in the study area respectively; (2) similar to the landform map, a ten-region map is generated that can clearly and intuitively demonstrate 3D morphological zones of integrated characteristics; and (3) a "two-ring with multi-sector" spatial structure is further abstracted from the regional map. This study contributes to acknowledging the spatial structure of urban morphology from a vertical perspective, and the framework can be reduplicated in other cities, which are meaningful to urban management, planning, and construction.

Interactions of fluvial and eolian processes are prominent in dryland environments and can significantly change Earth surface morphology. Here, we report on sediment records of eolian and fluvial interactions since the last glacial period, in the semiarid area of northwest China, at the limit of the Southeast Asian monsoon. Sediment sequences of last glacial and Holocene terraces of the Yellow River are composed of channel gravels, overlain by flood sands, eolian dunes, and flood loams. These sequences, dated by optically stimulated luminescence, record interlinks between fluvial and eolian processes and their response to climate change. Sedimentologic structures and grain-size analysis show flood loams, consisting of windblown sediment, deposited from floodwater suspended sediment. The gravel and sand were deposited during cold periods. During transitions from cold to warm phases, the river incised, and dunes were formed by deflation of channel and floodplain deposits (>70 and 21-16 ka). Dunes also formed at similar to 0.8 ka, probably after human intervention. After dune formation, flood loam covered dunes without erosion during peak discharges at the beginning of the subsequent warm period. The fluctuations of the Southeast Asian monsoon as a moisture-transporting agent have perhaps been the driving force for interactions between fluvial and eolian processes in this semiarid environment.

The interactions of fluvial and Aeolian processes are a pronounced feature in dry land environments, which can significantly change Earth surface morphology. The interactions of fluvial and Aeolian processes are common in the Lanzhou-Yinchuan reach of Huanghe river catchment at present, but it is rarely reported in the geological time. Here, we analysis the sedimentology including grain size distribution of a sediment sequence on the T2 terrace of the Huanghe River in this area. Aeolian sand dunes (2-3.8 m thickness) situated between fluvial coarse sands and floodplain silts are distinguished, which show mass structure, and mainly composed of well sorted very fine sand. The poor sorting laminar silt mixed with clay and very fine silt covers these dunes. We consider this sediment unit (laminar silt) as floodplain sediment including some aeolian dust input, settling from static water. The grain size of the aeolian dune is slightly smaller than that of the underlying horizontal fluvial sand. Both of the amounts of the coarse and fine component in the sand dunes are less than that in the underlying fluvial sand. These may indicate that the aeolian sand dunes in this region are built through the wind eroding, sorting, carrying the underlying fluvial sand and accumulating in situ. The elemental compositions of the aeolian sand dunes are consistent with the underlying fluvial sand and different from the sandy loess in this area. This also indicates that the source of the dunes on the terrace is the fluvial sand below the dunes. The OSL ages show that the aeolian dunes were deposited during 21～16 ka, and the covering floodplain silts deposited during 14～13 ka. The correlation of the deposit of dunes with climate change records in this area show the sand dunes were built during the period of strong winter monsoon with cold and dry climate during the Last Glacial Maximum (LGM). And the dunes were covered by floodloam later, when the period of strong summer monsoon with warm and wet climate during the last deglaciation. The sediment sequence composed by fluvial gravel, sand, aeolian dune, floodloam on the second terrace of the Huanghe river in this area show the interactions of fluvial and Aeolian process as a response to the climate and environment changes since LGM in this semi-arid environments.