Classification and Development Laws of Karst Landform in Suzhou Area, North Anhui Province of China

Suzhou area in north Anhui Province is a low hilly area on the Huaibei Plain where carbonate rocks and karstification are widely distributed, and karst landscapes form major physical contours of the bedrock outcrops. Through field investigation, karst landscapes of Suzhou area were divided into two categories based on their morphological characteristics: macro-geomorphologic landscapes including normal hills, dry valleys, karst springs and caves, and micro-corrosion landscapes including corrosion pits, dissolved pores, dissolution traces, corrosion cracks, clints and karrens. Distribution, development and scale of karst landscapes in this region are controlled by climate, rock type, structure, topography and other factors. It was suggested that karst landscapes in the study area could be used as a representative of karst landforms in North China.

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.

Hierarchical pattern recognition of landform elements considering scale adaptation

Landform elements with varying morphologies and spatial arrangements are recognized as feature indicator of landform classification and play a critical role in geomorphological studies.Differential geometry method has been extensively applied in prior landform element research,while its efficacy in differentiating similar morphological characteristics remains inadequate to date.To reduce reliance on geomorphometric variables and increase awareness of landform patterns,geomorphons method was generated in previous study corresponding to specific landform reclassification map based on lookup table.Besides,to address the problem of feature similarity,hierarchical classification was proposed and effectively utilized for terrain recognition through the analytical strategy of fuzzy gradient features.Thus,combining the advantages of these two aspects,a hierarchical framework was proposed in this study for landform element pattern recognition considering the morphology and hierarchy factors.First,the local triplet patterns derived from geomorphons were enhanced by setting the flatness threshold,and subsequently adopted for the primary landform element recognition.Then,as geomorphic units with the same morphology possess different spatial analytical scales,the unidentified landform elements under the principle of scale adaptation were determined by calculating the spatial correlation and entropy information.To ensure the effectiveness of this proposed method,the sampling points were randomly selected from NASADEM data and then validated against a real 3D terrain model.Quantitative results of landform element pattern recognition demonstrate that our approach can reach above 77%average accuracy.Additionally,it delineates local details more effectively than geomorphons in visual assessment,resulting in a 7%accuracy improvement in overall scale.

广东韶关丹霞山生态系统多样性及其空间定位

丹霞山复杂地形地貌塑造了很高的生态系统多样性和复杂的景观多样性,全面的系统分类、空间定位和生态系统类型描述对该区域的生态特征研究具有重要意义。本研究基于IUCN全球生态系统分类框架,结合植被类型和特殊地形地貌特征,对韶关丹霞山国家级自然保护区全境的生态系统类型进行研究,并利用遥感数据与实地调查数据,对各级生态系统进行空间定位和可视化制图。结果显示:(1)该保护区生态系统类型可划分为81类,包括3个I级类型、6个Ⅱ级类型、12个Ⅲ级类型、28个Ⅳ级类型和81个Ⅴ级类型;(2)除空间定位与格局分析外,特别对丹霞山的4个大类的特色生态系统进行了单独的空间展示,包括用面来展示的山顶生态系统23类和沟谷生态系统13类,用点来代表的洞穴生态系统4类,用线来代表崖壁生态系统4类。本研究关于生态系统类型的划分及空间定位,揭示了丹霞山地区的独特地形地貌特征和生态系统多样性特征,深化了丹霞山生态系统多样性和形成机制的理解,结果可为开展生态系统保护和管理提供重要的参考。

基于正负地形的喀斯特地貌分类研究——以贵州喀斯特区为例

地形因子具有直观性、可视性,常被作为喀斯特地貌分类的主要依据。已有关于喀斯特地貌分类研究所用的地形因子较多且复杂,分类特征不易被公众学习和理解,导致成果的应用和推广难度较大。因此以贵州喀斯特区为研究对象,运用地形开度法和随机森林模型,筛选重要性正负地形指标,探讨喀斯特地貌分类方法。结果表明:1)贵州喀斯特区的蚕食度均值为1.35,深切度均值为215.36 m,平均粗糙度比均值为1.01,负地形面积大于正地形面积,正负地形不规则且破碎度较大,正地形被沟谷深切明显,地貌的异质性强烈。2)蚕食度和深切度2个指标的重要性指数分别为0.93和1.16,能较好地反映喀斯特地貌的差异性。3)根据蚕食度和深切度贵州喀斯特地貌分为23类,分类精度优良达93.33%。在不同的喀斯特地貌区,因为蚕食度和深切度不同,导致水、土、光、热资源丰度不同,因而它们的土地利用方式各异。研究简化了分类判别喀斯特地貌的地形指标,降低了公众对喀斯特地貌的认识和理解难度,并为数字地形技术在喀斯特地貌分类中的推广应用提供参考。

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 optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.

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.

中国陆地基本地貌类型及其划分指标探讨

平原、台地、丘陵、山地等是地表最基本的地貌形态，这些名称不仅为专业领域广泛引用，也为普通人所知晓。近100年来多种地貌分类方案中都涉及这些地貌类型名称，有的称其为地貌“基本形态”。由于每种地貌形态都不仅包含形态特征，而且还有一定的成因意义，因此应称其为基本地貌类型。通过对已有的基本地貌分类及其划分指标进行系统分析和评估，认为中国陆地基本地貌类型按照起伏高度和海拔高度两个分级指标组合来划分的原则符合起伏复杂、多台阶中国地貌的基本特点。在传统的平原、台地、丘陵和山地分类的基础上，按起伏高度对山地进一步细分，即划分平原、台地、丘陵（〈200m）、小起伏山地（200～500m）、中起伏山地（500～1000m）、大起伏山地（1000～2500m）和极大起伏山地（〉2500m）等7个基本地貌“形态”。本文对前人以现代雪线、多年冻土下线和森林上线高度为依据确定地貌面海拔高度的分级指标进行了全面分析，由于它们的海拔高度在全国各地存在巨大差异，我们认为海拔高度等级指标并不符合中国实际。通过全国重点地区1：500000地形图山地顶面海拔高度分布和1：1000000国家数字高程模型（DTM）数据库编制的中国地面高程分布图进行较系统的分析，我们提出了应以1000m，2000m，4000m和6000m作为划分低海拔（〈1000m）、中海拔（1000～2000m）、亚高海拔（2000～4000m）、高海拔（4000～6000m）和极高海拔（〉6000m）地貌海拔高度分级指标。根据7个地貌起伏高度形态和5个海拔高度等级，将全国组合成从低海拔平原至极大起伏极高山28个基本地貌类型。

青藏高原及其邻近地区地貌类型划分

我国至今尚未形成一个公认的地貌分类系统,传统地貌类型的划分主要依据海拔及起伏度,较少考虑地貌的完整性原则,且分类结果琐碎。为了避免此类问题,以我国青藏高原及其邻近地区1000 m分辨率的SRTM DEM为数据源,采用面向对象思想,基于eCognition软件,利用多尺度分割、局部方差法以及决策树分类法自动划分了地貌形态。结果表明:(1)在分割尺度范围为10-1400、步长为100时,青藏高原及其邻近地区的最佳分割尺度为400;(2)依据平均高程及标准差的大小,青藏高原及其邻近地区可划分为极高山、大起伏高山、高丘、小起伏高山、大起伏中山、小起伏中山、高海拔平地、低海拔平地八种地貌类型。相比依据海拔及起伏度的划分方法,分类结果更能考虑地貌的完整性原则,且具有高效便捷性,划分结果更平滑,为我国地貌类型的划分提供了参考。

梯田地形形态特征及其综合数字分类研究

梯田地形具有独特的平面和剖面形态特征,而现有梯田地形分类无法准确反映梯田地形的平面形态特征,导致其难以满足未来构建梯田地形数值模拟模型的需求。以黄土高原旱梯田地形为切入点,对梯田地形的总体特征、平面和剖面形态特征及几何量测特征进行深入研究,提出了基于梯田平面形态特征的梯田地形分类,并在结合现有梯田地形分类的基础上,构建出梯田地形综合数字分类。与传统梯田地形分类相比,该分类综合考虑梯田的总体特征和平面及剖面形态,能更好地反映梯田独特的形态特征和几何量测特征。研究结果为未来构建梯田地形数值模拟模型奠定了坚实基础,对于探讨利用DEM实现梯田地形的有效数字表达与分析具有重要理论意义。

雅鲁藏布江河谷风沙地貌分类与发育问题

雅鲁藏布江河谷风沙地貌分类体系由４级２１个类型组成，其中风积地貌划分为谷底与谷坡风积地貌２个亚类。河谷风沙地貌面积１９２９．９４６ｋｍ２，可称为“雅鲁藏布江河谷沙地”。河谷区具有沙源、风动力和堆积场地等风沙地貌形成的基本条件。风沙地貌总体上沿河谷呈带状不连续分布，其形成发育受到风力和流水两种营力交替作用，沙丘、沙丘群在同一地貌部位的分布与排列有一定的规整性。

环京津贫困带土地利用变化的地形梯度效应分析

地形因素与土地利用变化的关系研究是土地利用/覆被变化研究的重要内容。该文以环京津贫困带为研究区,选取1985年、1995年、2000年和2010年Landsat TM影像为数据源,运用地学图谱分析法,从地形起伏度、坡度变率、坡向和地形位角度,系统分析了环京津贫困带土地利用变化的地形梯度特征,探讨了土地利用变化的地形梯度效应及其成因。结果表明:1)1985-2010年环京津贫困带土地利用类型分布呈现明显的层级性。耕地、水域和建设用地的优势分布区集中于低地形梯度区,草地优势分布区集中于中高、高地形梯度区,林地和未利用地优势分布区集中于高地形梯度区。2)研究区土地利用变化以稳定型图谱和反复变化型图谱为主,林地、耕地和草地是主要的土地利用类型,林地向耕地转换再又转换为林地是反复变化型图谱的主要类别。1985-2000年人地矛盾突出,土地利用变化以"林地-耕地"、"草地-耕地"和"未利用地-耕地"为主,耕地向较高地形梯度区扩展;2000年后社会经济发展和退耕还林政策的实施,土地利用变化以"耕地-建设用地"、"耕地-林地"和"草地-林地"为主,耕地在原优势地形位的优势度增大、草地和未利用地的优势分布区向更高级地形位集中,林地优势分布区向较低地形位扩张。3)土地利用变化地形梯度分布特征及分异效应是自然因素、社会经济因素和政策因素共同作用的结果,自然因素是基础,社会经济因素和政策因素是重要推动力。该文为研究区土地利用动态优化配置和生态环境建设提供了科学依据和决策支持。

雷达地形测绘DEM用于青藏高原地貌分类

第一次全国地理国情普查工作的一项重要内容就是调查地形地貌。针对在青藏高原等艰险地区开展相应工作仍存在诸多困难,该文基于美国"奋进号"航天飞机获取的SRTM-DEM数据,运用GIS技术提取了青藏高原的8个地貌因子,通过相关性分析最终选取地表粗糙度、高程、地势起伏度、坡度变率及坡向变率5个相关性相对较小的因子,开展宏观地貌类型划分及地貌分布特征分析工作,并基于一种简易模型提取并讨论了对青藏高原生态环境有重要影响的冰缘(冻土)地貌。研究表明:应用SRTM-DEM数据可快速有效地提取青藏高原等艰险地区的地貌因子,但某些因子之间具有极高的相关性,应用中需慎重选择。此外,通过SRTM-DEM数据提取地貌因子以进行宏观地貌类型划分及地貌特征分析的方法简单易行,能够为在青藏高原等艰险地区开展地理国情普查工作提供可靠的基础资料。

地貌形态类型面向对象分类法的改进

提出一种改进的基于随机森林因子重要性分析和灰度共生矩阵纹理的地貌形态类型面向对象划分方法。以中国1∶100万DEM为数据源,利用相关分析和雪氏熵值法筛选确定地貌分类的地形因子组合,并利用随机森林分类树评价各地形因子的重要性,将求得的各因子重要性数值作为面向对象多尺度分割各图层的阈值,最后基于灰度共生矩阵纹理信息构成分类样本的知识库进行地貌分类。全国地貌分类以《中国及毗邻地区1∶400万地貌图》为精度评价标准,结果显示该文提出的分类方法总体精度为71.4%,比ISODATA非监督分类法精度提高5.7%,比常用的面向对象分类法精度提高15.7%;陕西省地貌分类以《中华人民共和国1∶100万地貌图》为精度评价标准,分类的总体精度为72.9%。通过分析该文方法对不同分辨率DEM分类精度的影响,得出分辨率越高总体精度越高。

新疆农业地貌分类──以编制新疆1:100万农业地貌图为例

针对新疆地域辽阔，地形起伏巨大，山地层状地貌显著，盆地封闭、干旱，风成和流水地貌发育等特点，着眼于地貌与农业的关系，采用形态与成因相结合的分类原则，以地貌与农业关系密切的海拔高程、物质组成、相对高差、坡度等要素为指标，制定新疆农业地貌分类系统，划分农业地貌类型。

沉积盆地类型划分及其相关问题讨论

沉积盆地类型划分是盆地及其相关领域研究的重要基础。已有的盆地分类方案较多,各有侧重和所长,但对陆内盆地的划分和研究较为薄弱。沉积盆地是一个典型的复杂巨系统,对其分类是一个复杂的系统工程。盆地分类的原则和结果应体现此系统的整体性、层次性、关联性、典型性(代表性)和可对比性(预测性)。盆地类型划分的依据主要包括盆地发育鼎盛时期所处的大地构造位置、地壳-岩石圈类型、沉降机制和动力环境、盆地结构构造特征与基底性质、沉积环境及充填特征等。导致盆地沉降的动力主要源自地球深部,可分为热力、应力、重力及其复合4种。小行星等天体撞击地球所形成的盆地属特殊类型,将其划归重力成因盆地大类。还有一种值得注意的地貌成因盆地,其形成与内动力地质作用联系不密切,主要由地表负向地形存在而导致沉积物充填和水体汇聚。这类盆地在不同大地构造环境中均有发育,但其地球动力学意义和沉积矿产赋存条件等均与内动力成因的盆地差别颇大。故将其作为新的盆地和成因类型单独列出。根据上述沉积盆地分类的原则和基础,以全球板块构造动力环境、大陆内部动力活动的独立性、主动性和盆地沉降成因的主动力为主线,综合和归纳前人分类方案和盆地研究成果,将盆地发育的区域构造动力环境分为6大类(大洋和大陆板块内部,离散型、消减型、碰撞型、转换型大陆(板块)边缘),增加了天体撞击的特殊型和后期改造的复合型2大类与之并列;将前6大类构造动力环境中发育的沉积盆地分为44(亚)类,按构造动力环境、盆地主要构造力学性质(即应力)两大系统,分别进行了划分和归类。对本盆地分类方案中新的类型、或内涵有变、或需说明的部分盆地及术语,进行了进一步讨论或说明。这些盆地如后陆盆地、侧陆盆地、转换-补偿盆地、拉裂盆地、地貌成因盆地、(天体)撞击盆地、中间地块盆地、陆内前陆盆地、改造(型)盆地等。本盆地分类方案是对不同地区相同或相近构造环境所发育盆地类型的理性归纳和综合,相对较为系统全面。但在地史上和现世界中,因地质条件的差异或发展进程的不同,其中某类盆地可能在具相同构造环境的某地并未出现,或发育特征并不典型。任一个全球沉积盆地分类方案,从问世之日起就处于检验、争议和修补之中,周而复始,日臻完善。

海南岛地貌分区和分类

海南岛地貌单元可分为地貌区、地貌亚区、地貌形态成因类型和微地貌4级.全岛有北部台地平原区和南部山地丘陵区2个地貌区,包括14个地貌亚区.岛内均有台地、山地、阶地、丘陵以及平原,但以台地和山地为主.考虑外营力种类和作用方向以及地貌发育历史,在海南岛内可划出5种地貌成因类型:即1)侵蚀、剥蚀构造地貌、2)剥蚀侵蚀地貌、3)河积地貌、4)海成地貌、5)火山地貌.