Automated Landform Classification of China Based on Hammond’s Method

The automatic classification of Macro landforms was processed with the program developed by Hammond’s Manual procedures, which based on properties of slope, local relief, and profile type, which consists of 5 landform types, 24 landform class and 96 landform subclasses. This program identified landform types by moving a square window with size of 9.8 km × 9.8 km. The data includes 816 sheets of topological map with a scale of 1:250,000. The DEM were buildup with the contours and mark points based on this data with a cell size of 200 m, and merge into one sheet. The automated classification was processed on this DEM data with a AML program of ArcGIS 10.X Workstation. The result indicates it produced a classification that has good resemblance to the landforms in China. The maps were produced respectively with 5 types, 16 classes and, 90 subclasses The 5 Landform types of landforms were Plains (PLA), 20.25% of whole areas;Tablelands (TAB) of 3.56%;Plains with Hills or Mountains (PHM) of 32.84%;Open Hills and Mountains (OHM) of 18.72%;Hills and Mountains (HM) of 24.63%. In the result of 24 landform classes, there are not some classes, such as irregular plains with low relief;open very low hills, open low hills;very low hills, low hills, moderate hills. The result of 96 landform subclass is similar to the 24 class.

Landform Classification for Community Siting: A case Study in Quxian County, China

This study is to explore a suitable method to classify landform, in order to support the decision making for community siting in mountainous areas.It first proposes the landform classification for community siting(LCCS) method with detailed discussions on its rationality and the chosen parameters.This method is then tested and verified in Quxian county.The LCCS method entails twograde parameters, which uses relative relief as the first grading parameter, slope as the second, followed by a synthesis process to form a suitable landform classification system.By applying the LCCS method in Quxian county, the result shows that its use of watershed to identify geomorphometric units, and its use of the altitude datum concept, can effectively classify landform according to the local cultural traditions, and the economic and environmental conditions.The verification result shows that comparing to the conventional methods, the LCCS method respects to people's daily experience due to its bottom-up approach.It not only help to minimize the disturbance to the nature when choosing locations for community development, but also helps to prepare more precise land management policies,which maximizes agricultural production and minimizes terrain transformation.

Landform classification using soil data and remote sensing in northern Ordos Plateau of China

Landform classification is commonly done using topographic altitude only. However practice indicates that locations at a same altitude may have distinctly different landforms, depending on characteristics of soils underneath those locations. The objectives of this study were to： 1） develop a landform classification approach that is based on both altitude and soil characteristic; and 2） use this approach to determine landforms within a watershed located in northern Ordos Plateau of China. Using data collected at 134 out of 200 sampling sites, this study determined that D10 （the diameter of soil particles 10% finer by weight） and long-term average soil moisture acquired in 2010, which can be estimated at reasonable accuracy from remote sensing imagery, can be used to represent soil characteristics of the study watershed. Also, the sampling data revealed that this watershed consists of nine classes of landforms, namely mobile dune （MD）, mobile semi-mobile dune （SMD）, rolling fixed semi-fixed dune （RFD）, flat sandy land （FD）, grassy sandy land （GS）, bedrock （BR）, flat sandy bedrock （FSB）, valley agricultural land （VA）, and swamp and salt lake （SW）. A set of logistic regression equations were derived using data collected at the 134 sampling sites and verified using data at the remaining 66 sites. The verification indicated that these equations have moderate classification accuracy （Kappa coefficients K 〉 43%）. The results revealed that the dominant classes in the study watershed are FD （36.3%）, BR （27.0%）, and MD （23.5%）, while the other six types of landforms （i.e., SMD, RFD, GS, FSB, VA, and SW） in combination account for 13.2%. Further, the landforms determined in this study were compared with the classes presented by a geologically-based classification map. The comparison indicated that the geologically-based classification could not identify multiple landforms within a class that are dependent upon soil characteristics.

Landform classification based on landform geospatial structure-a case study on Loess Plateau of China

Landform classification,which is a key topic of geography,is of great significance to a wide range of fields including human construction,geological structure research,environmental governance,etc.Previous studies of landform classification generally paid attention to the topographic or texture information,whilst the watershed spatial structure has not been used.This study developed a new landform classification method based on watershed geospatial structure.Via abstracting the landform into the internal and marginal structure,we adopted the gully weighted complex network(GWCN)and watershed boundary profile(WBP)to simulate the watershed geospatial structure.Introducing various indices to quantitatively depict the watershed geospatial structure,we conducted the landform classification on the Northern Shaanxi of Loess Plateau with a watershed-based strategy and established the classification map.The classified landform distribution has significant spatial aggregation and clear regional boundaries.Classification accuracy reached 89%and the kappa coefficient reached 0.87%.Besides,the proposed method has a positive response to some similar and complex landforms.In general,the present study first utilized the watershed geospatial structure to conduct landform classification and is an efficient landform classification method with well accuracy and universality,offering additional insights for landform classification and mapping.

Deep learning of DEM image texture for landform classification in the Shandong area,China

Landforms are an important element of natural geographical environment,and textures are the research basis for the spatial differentiation,evolution features,and analysis rules of the landform.Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification.Digital elevation model(DEM)image texture,which gives full expression to texture difference,is key data source to reflect the surface features and landform classification.Following the texture analysis,landform features analysis is assistant to different landforms classification,even in landform boundary.With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping,hierarchical landform classification has become the focus and difficulty in research.Recently,the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research,whose multichannel feature fusion structure satisfies the network structure of different landform classification.In this paper,DEM image texture was taken as the data source,and gray level co-occurrence matrix was applied to extract texture measures.Owing to the similarity of similar landform and the difference of different landform in a certain scale,a comprehensive texture factor reflecting landform features was proposed,and the spatial distribution pattern of landform features was systematically analyzed.On this basis,the coupling relationship between texture and landform type was explored.Thus,the deep learning method of Convolutional Neural Network is used to train the texture features,and the second-class landform classification is carried out through softmax.The classification results in small relief and mid-relief low mountains,overall accuracy are 84.35%and 69.95%respectively,while kappa coefficient are 0.72 and 0.40 respectively,were compared to that of traditional unsupervised landform classification results,and the superiority of Convolutional Neural Network classification was verified,it approximately improved 6%in overall accuracy and 0.4 in kappa coefficient.

Feathered sand ridges in the Kumtagh Desert and their position in the classification system

Feathered sand ridges in the northeastern Kumtagh Desert in China cover an area of 4016 km 2 and consist of crescent sand ridges and interridge tongue-shaped dunes.Differences in grain size,mineral composition and albedo between crescent sand ridges and tongue-shaped dunes,and between windward and leeward slopes of tongue-shaped dunes,result in their feathery appearance in aerial and satellite imagery.Measurements of the sand drift potential in the region show that the sand-moving wind for feathered sand ridges can be divided into three sectors;i.e.north-northeasterly,easterly and east-northeasterly sectors roughly corresponding to the southeast,northwest and southwest slip faces.Our findings suggests that the crescent sand ridges resulting from the connection of barchan dunes along the prevailing wind direction are longitudinal dune ridges rather than transverse ones.Tongue-shaped dunes and quasi-dune shapes have obvious distinctions and are new transverse dune types.According to McKee's dune shape classification,the feathered sand ridges are not a deformation dune type but a complex one.According to Wu's dune morphological and genetic classification,they are not dune ridges or compound dune ridges that form under the action of unidirectional winds or two winds intersecting at an acute angle,but are complex dune ridges that form under the action of three winds intersecting at an acute angle.