Explore the vast range of titles available.

Granite geomorphological sceneries are important components of global geoheritage, but international awareness of their significance seems insufficient. Based on existing literature, ten distinctive types of relief are identified, along with several sub-types, and an overview of medium-size and minor landforms characteristic for granite terrains is provided. Collectively, they tell stories about landscape evolution and environmental changes over geological timescale, having also considerable aesthetic values in many cases. Nevertheless, representation of granite landscapes and landforms on the UNESCO World Heritage List and within the UNESCO Global Geopark network is relatively scarce and only a few properties have been awarded World Heritage status in recognition of their scientific value or unique scenery. Much more often, reasons for inscription resided elsewhere, in biodiversity or cultural heritage values, despite very high geomorphological significance. To facilitate future global comparative analysis a framework is proposed that can be used for this purpose.

The value of the high-resolution data lies in the high-precision information discovery. The fine-detailed landform element extraction is thus the basis of high-fidelity application of the high-resolution digital elevation models (DEMs). However, the results of landform element extraction generated by classical methods might be ungrounded on high-resolution DEMs. This paper presents our research on using the aspect to reinforce the applicability and robustness of the classical approaches in landform element extraction. First, according to the research of pattern recognition, we assume that aspect-enhanced landform representation is robust to rotation, scaling and affine variance. To testify the role of aspect, we respectively integrated the aspect into three classical approaches: mean curvature-based fuzzy classification, elevation-based feature descriptor, and object-based segmentation. In the experiment, based on four types of high-resolution DEMs (1 m, 2 m, 4 m and 8 m), we compare each classical approaches and their corresponding aspect-enhanced approaches based on extracting the rims of two craters having different landforms, and the ridgelines and valleylines of a region covered by few vegetables and man-made buildings. In comparison to the results generated by curvature-based fuzzy classification, the aspect enhanced curvature-based fuzzy classification can effectively filter a number of noises outperforms the curvature-based one. Otherwise, the aspect-enhanced feature descriptor can detect more landform elements than the elevation-based feature descriptor. Moreover, the aspect-based segmentation can detect the main structure of landform, while the boundaries segmented by classical approaches are messing and meaningless. The systematic experiments on meter-level resolution DEMs proved that the aspect in topography could effectively to improve the classical method-system, including fuzzy-based classification, feature descriptors-based detection and object-based segmentation. The value of aspect is significantly great to be worthy of attentions in landform representation.

The automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. However, landform types are rather complex and gradual changes often occur in these landforms, thus increasing the difficulty in automatically recognizing and classifying landforms. In this study, small-scale watersheds, which are regarded as natural geomorphological elements, were extracted and selected as basic analysis and recognition units based on the data of SRTM DEM. In addition, datasets integrated with terrain derivatives（e.g., average slope gradient, and elevation range） and texture derivatives（e.g., slope gradient contrast and elevation variance） were constructed to quantify the topographical characteristics of watersheds. Finally, Random Forest（RF） method was employed to automatically select features and classify landforms based on their topographical characteristics. The proposed method was applied and validated in seven case areas in the Northern Shaanxi Loess Plateau for its complex andgradual changed landforms. Experimental results show that the highest recognition accuracy based on the selected derivations is 92.06%. During the recognition procedure, the contributions of terrain derivations were higher than that of texture derivations within selected derivative datasets. Loess terrace and loess mid-mountain obtained the highest accuracy among the seven typical loess landforms. However, the recognition precision of loess hill, loess hill–ridge, and loess sloping ridge is relatively low. The experiment also shows that watershed-based strategy could achieve better results than object-based strategy, and the method of RF could effectively extract and recognize the feature of landforms.

Landform recognition is one of the most significant aspects of geomorphology research, which is the essential tool for landform classification and understanding geomorphological processes. Watershed object-based landform recognition is a new spot in the field of landform recognition. However, in the relevant studies, the quantitative description of the watershed generally focused on the overall terrain features of the watershed, which ignored the spatial structure and topological relationship, and internal mechanism of the watershed. For the first time, we proposed an effective landform recognition method from the perspective of the watershed spatial structure, which is separated from the previous studies that invariably used terrain indices or texture derivatives. The slope spectrum method was used herein to solve the uncertainty issue of the determination on the watershed area. Complex network and P–N terrain, which are two effective methodologies to describe the spatial structure and topological relationship of the watershed, were adopted to simulate the spatial structure of the watershed. Then, 13 quantitative indices were, respectively, derived from two kinds of watershed spatial structures. With an advanced machine learning algorithm (LightGBM), experiment results showed that the proposed method showed good comprehensive performances. The overall accuracy achieved 91.67% and the Kappa coefficient achieved 0.90. By comparing with the landform recognition using terrain indices or texture derivatives, it showed better performance and robustness. It was noted that, in terms of loess ridge and loess hill, the proposed method can achieve higher accuracy, which may indicate that the proposed method is more effective than the previous methods in alleviating the confusion of the landforms whose morphologies are complex and similar. In addition, the LightGBM is more suitable for the proposed method, since the comprehensive manifestation of their combination is better than other machine learning methods by contrast. Overall, the proposed method is out of the previous landform recognition method and provided new insights for the field of landform recognition; experiments show the new method is an effective and valuable landform recognition method with great potential as well as being more suitable for watershed object-based landform recognition.

Environmental variation is a crucial driver of ecological pattern, and spatial layers representing this variation are key to understanding and predicting important ecosystem distributions and processes. A national, standardised collection of different environmental gradients has the potential to support a variety of large-scale research questions, but to date these data sets have been limited and difficult to obtain. Here we describe the New Zealand Environmental Data Stack (NZEnvDS), a comprehensive set of 72 environmental layers quantifying spatial patterns of climate, soil, topography and terrain, as well as geographical distance at 100 m resolution, covering New Zealand’s three main islands and surrounding inshore islands. NZEnvDS includes layers from the Land Environments of New Zealand (LENZ), additional layers generated for LENZ but never publicly released, and several additional layers generated more recently. We also include an analysis of correlation between variables. All final NZEnvDS layers, their original source layers, and the R-code used to generate them are available publicly for download at https://doi.org/10.7931/m6rm-vz40.

To establish what effect the Late Cenozoic cooling climate shift might have had on global erosion, inverse modelling of thermochronometric ages is used to show that erosion rates are increased by cooling, especially in glaciated mountain ranges. A cooling climate moves mountains Climatic and tectonic changes are thought to influence topography and erosion rates. A prime example of a major climate shift is the Late Cenozoic cooling, but its impact on global erosion remains uncertain. This paper quantifies erosion rates on the basis of inverse modelling and thermochronometric data from around the world and finds an increase in erosion rates at all latitudes, coinciding with enhanced cooling during the Late Cenozoic. In particular, mountain erosion rates have increased in the past 6 million years and most rapidly in the past 2 million years. The increase in erosion is most pronounced in glaciated mountain ranges, suggesting an important influence of glacial processes on erosion. Climate influences the erosion processes acting at the Earth’s surface. However, the effect of cooling during the Late Cenozoic era, including the onset of Pliocene–Pleistocene Northern Hemisphere glaciation (about two to three million years ago), on global erosion rates remains unclear 1 , 2 , 3 , 4 . The uncertainty arises mainly from a lack of consensus on the use of the sedimentary record as a proxy for erosion 3 , 4 and the difficulty of isolating the respective contributions of tectonics and climate to erosion 5 , 6 , 7 . Here we compile 18,000 bedrock thermochronometric ages from around the world and use a formal inversion procedure 8 to estimate temporal and spatial variations in erosion rates. This allows for the quantification of erosion for the source areas that ultimately produce the sediment record on a timescale of millions of years. We find that mountain erosion rates have increased since about six million years ago and most rapidly since two million years ago. The increase of erosion rates is observed at all latitudes, but is most pronounced in glaciated mountain ranges, indicating that glacial processes played an important part. Because mountains represent a considerable fraction of the global production of sediments 9 , our results imply an increase in sediment flux at a global scale that coincides closely with enhanced cooling during the Pliocene and Pleistocene epochs 10 , 11 .

Assam, located in the Northeast of India, is highly flood-prone, and the erosional and depositional processes highly influence the landforms. The formation and development of landforms are directly related to the geology, geomorphology, drainage basin characteristics, and soil types of the region. In the present study, a remote sensing and GIS-based geomorphodiversity index (GMI) assessment of Assam is performed using three sub-indices: geodiversity, morphometric diversity, and drainage diversity index. Sixty-six potential geomorphosites are identified with their geological, geomorphological, and GMI classes. With the help of a flood inundation map, the inundated area of each GMI class is calculated. According to the result, 27.02%, 10.76%, and 3.7% of the total area of Assam fall under moderate, high, and very high GMI classes, respectively. Barak Valley and Central Assam region exhibit high to very high GMI values. Geology and geomorphology have a strong influence on GMI values. About 22.32%, 28.33%, 37.18%, 38.25%, and 35.37% of areas with low, moderate, high, and very high GMI are inundated, respectively. This study determined that areas having high GMI can increase the geomorphological heritage value of the region and can play a significant role in promoting geotourism with an increase in the scientific, educational, and aesthetic value of geomorphosites. This study can also help the local governing authorities to conduct and implement better management and conservation policies for vulnerable locations.

Many forested landscapes around the world are severely altered during mining for their rich mineral and energy reserves. Herein we provide an overview of the challenges inherent in efforts to restore mined landscapes to functioning forest ecosystems and present a synthesis of recent progress using examples from North America, Europe and Australia. We end with recommendations for further elaboration of the Forestry Reclamation Approach emphasizing: (1) Landform reconstruction modelled on natural systems and creation of topographic heterogeneity at a variety of scales; (2) Use and placement of overburden, capping materials and organic amendments to facilitate soil development processes and create a suitable rooting medium for trees; (3) Alignment of landform, topography, overburden, soil and tree species to create a diversity of target ecosystem types; (4) Combining optimization of stock type and planting techniques with early planting of a diversity of tree species; (5) Encouraging natural regeneration as much as possible; (6) Utilizing direct placement of forest floor material combined with seeding of native species to rapidly re-establish native forest understory vegetation; (7) Selective on-going management to encourage development along the desired successional trajectory. Successful restoration of forest ecosystems after severe mining disturbance will be facilitated by a regulatory framework that acknowledges and accepts variation in objectives and outcomes.

Carex yankouensis , a new species of Cyperaceae (Carex section Rhomboidales) from the limestone landform in northern Guangdong, China is described and illustrated. The new species is similar to C. brevicuspis C. B. Clarke, but differs in having shorter culms (10–15 cm vs 20–55 cm) and spikes (1–1.5 cm vs 3.7–7 cm), leaves wider (15–20–35 mm vs 5–10 mm) and lighter colored (pale green or yellow-green vs dark green), nutlet beak oblique (vs erect or slightly curved), and slightly thickened (vs thickened) style base. Following the IUCN Red List Criteria (IUCN 2024), Carex yankouensis is assessed as ‘Data Deficient (DD)’.

The Earth's surface is shaped by tectonics and climate. This simple statement implies that we should, in principle, be able to use the landscape as an archive of both tectonic rates and of changes to climate regime. To solve this inverse problem, and decipher the geomorphic record effectively, we need a sound understanding of how landscapes respond and erode in response to changes in tectonic or climatic boundary conditions. Rivers have been a major focus of research in this field because they are patently sensitive to tectonic and climatic forcing via their channel gradient and discharge. Theoretical, field, and numerical modeling techniques in the last few years have produced a wealth of insight into the behavior of fluvial landscapes, while the increasing availability of high-resolution topographic models have provided the data sets necessary to address this research challenge across the globe. New work by Miller et al. (2012) in Papua New Guinea highlights the progress we have made in extracting tectonics from topography due to these developments, but also illustrates the problems that still remain. This paper reviews our current knowledge of how fluvial landscapes record tectonics at topographic steady-state and under \"transient\" conditions, assesses why the climate signal has proven so challenging to interpret, and maps out where we need to go in the future.