Drainage basin object-based method for regional-scale landform classification: a case study of loess area in China

Landform classification is one of the most important procedures in recognizing and dividing earth surface landforms. However, topographical homogeneity and differences in regional-scale landforms are often ignored by traditional pixel- and object-based landform classification methods based on digital elevation models (DEMs). In this work, a drainage basin object-based method for classifying regional-scale landforms is proposed. Drainage basins with least critical areas are first delineated from DEMs. Then, terrain derivatives of mean elevation, mean slope, drainage density, drainage depth, and terrain texture are employed to characterize the morphology of the drainage basins. Finally, a decision tree based on the topographical characteristics of the drainage basins is constructed and employed to determine the landform classification law. The experiment is validated in the landform classification of regional-scale loess areas in China. Results show that clear boundaries exist in different landforms at the regional scale. Landform type in a specific region shows significant topographical homogeneity under its specific regional geomorphological process. Classification accuracies are 87.3 and 86.3% for the field investigation and model validation, respectively. Spatial patterns of classified landforms and their proximity to sediment sources and other factors can be regarded as indicators of the evolutionary process of loess landform formation.     

Morphometric characterisation of landform from DEMs

We describe a method of morphometric characterisation of landform from digital elevation models (DEMs). The method is implemented first by classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single‐scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi‐scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi‐scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R ² goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single‐ and multi‐scale terrain indices perform well when characterising the different geomorphometric features.     

Supervised landform classification of Northeast Honshu from DEM-derived thematic maps

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 3050 m 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.     

五台山冰缘地貌植物群落的数量分类与排序

在野外样方调查的基础上,采用双向指示种分析法(TWINSPAN)和除趋势对应分析法(DCA)对五台山冰缘地貌植被进行了数量分类和排序研究,将五台山冰缘地貌植被分为13个群丛,13个群丛是不同冰缘地貌生态景观的重要组成要素,各群丛的群落学特征与冰缘地貌生境有密切的生态关系。DCA样方排序结果和种排序结果表明,五台山冰缘地貌植物群落分布格局是土壤、水分、热量等综合环境因子决定的。而不同冰缘地貌上植物群丛优势种的分布格局在很大程度上决定着植物群丛类型的分布格局,反映出植物群落类型和物种分布随冰缘地貌类型及环境梯度变化的趋势。     

The geographic distribution of small dams in Oregon using ecoregion and landform classification

Abstract

Small dams fragment river landscapes, disrupting channel connectivity and impacting water quality and quantity. Although they impound volumetrically less total water than large dams, the ubiquity of small dams suggests their cumulative impacts could be significant. Documenting the distribution and characteristics of small dams is necessary to understanding and mitigating their impacts. In this study, we compare datasets of small dams in Oregon, compile a new comprehensive dataset, and document geographic variations in small dam distributions between different ecoregions. We used Oregon Water Resources Department dam and Oregon Department of Fish and Wildlife fish passage barrier datasets to compare dam heights and contributing drainage areas between different ecoregions. Over 61% of Oregon’s land area is above one or more small dam. We highlight the location of Oregon’s small dams at valley margins, transition zones between flat plains and mountains, and areas of high population density. Given the hidden nature of small dams, evaluation of small dam impacts using public imagery is not effective. However, knowledge of small dam distributions given their association with landforms can aid in finding unrecorded dams, assessing approaches for evaluating their geomorphic impacts, and informing their management.     

Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature

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.     

An automated approach to the classification of the slope units using digital data

Digital elevation and remote sensing data sets contain different, yet complementary, information related to geomorphological features. Digital elevation models (DEMs) represent the topography, or land form, whereas remote sensing data record the reflectance/emittance, or spectral, characteristics of surfaces. Computer analysis of integrated digital data sets can be exploited for geomorphological classification using automated methods developed in the remote sensing community. In the present study, geomorphological classification in a moderate- to high-relief area dominated by slope processes in southwest Yukon Territory, Canada, is performed with a combined set of geomorphometric and spectral variables in a linear discriminant analysis. An automated method was developed to find the boundaries of geomorphological objects and to extract the objects as groups of aggregated pixels. The geomorphological objects selected are slope units, with the boundaries being breaks of slope on two-dimensional downslope profiles. Each slope unit is described by variables summarizing the shape, topographic, and spectral characteristics of the aggregated group of pixels. Overall discrimination accuracy of 90% is achieved for the aggregated slope units in ten classes.     

On the application of SAR interferometry to geomorphological studies: estimation of landform attributes and mass movements

This paper presents two examples of application of Synthetic Aperture Radar (SAR) interferometry (InSAR) to typical geomorphological problems. The principles of InSAR are introduced, taking care to clarify the limits and the potential of this technique for geomorphological studies. The application of InSAR to the quantification of landform attributes such as the slope and to the estimation of landform variations is investigated. Two case studies are presented. A first case study focuses on the problem of measuring landform attributes by interferometric SAR data. The interferometric result is compared with the corresponding one obtained by a Digital Elevation Model (DEM). In the second case study, the use of InSAR for the estimation of landform variations caused by a landslide is detailed.     

应用点群分析研究柴达木盆地盐湖的地球化学分类

应用数学地质中常用的点群（聚类）分析方法研究了柴达木盆地盐湖的地球化学分类，并与其它学者盐湖水化学分类法进行了对比研究，探讨了二者之间的共同点与差异，并且对新的分类中各类元素的地球化学特征进行了总结     

Housing submarket classification: The role of spatial contiguity

Understanding housing submarket structure is of crucial importance to both public and private agencies. It can also help current and future homeowners make informed decisions on their residential choices. Current research on submarket focuses on comparative analyses of different classification techniques. Few studies, however, have examined the function of spatial contiguity on housing submarket classification. To address this issue, this paper developed a spatially constrained data-driven submarket classification methodology to obtain spatially integrated housing market segments. Specifically, a data-driven model based on principal component analysis and cluster analysis was developed for delineating housing submarkets. Within the model, a number of location attributes were used for principal component analysis, and the geographic locations of houses were also incorporated in the cluster analysis. The performance of this method was compared with other unconstrained data-driven techniques and a priori classifications using three measurements: substitutability, spatial integrity, and similarity. Results indicate that spatially contiguous submarkets can be obtained without compromising housing hedonic model accuracy and attribute homogeneity.     

月表地貌起伏形态分异特征及分级标准研究

月球地貌是月球表面发生的地质和地貌过程的结果,月球地貌单元的划分和等级分类体系的构建是月球地貌学研究的基础,也是月球地貌图制图的基础和关键科学问题。地貌学是研究形态和成因的科学,高程和起伏度是最基本的地貌指标。本文基于LOLA（Lunar Orbiter Laser Altimeter） DEM数据以及LOLA和SELENE TC（Terrain Camera）融合的DEM数据（SLDEM2015,文中简称SLDEM）,利用均值变点法确定月表起伏度计算的最佳窗口,并以起伏度100 m、200 m、300 m、700 m、1500 m及2500 m为阈值将月球表面分为微起伏平原（< 100 m）、微起伏台地[100 m, 200 m）、微小起伏丘陵[200 m, 300 m）、小起伏山地[300 m, 700 m）、中起伏山地[700 m, 1500 m）、大起伏山地[1500 m, 2500 m）及极大起伏山地（≥ 2500 m）地貌7个类型。划分结果显示：微起伏平原主要分布在月海平原区域、部分有玄武岩充填的撞击盆地的盆底区域以及撞击坑坑底区域;微起伏台地主要分布在月海和月陆区域的交界区域;微小起伏丘陵主要分布在月溪和皱脊等构造单元区域;小起伏山地主要分布在撞击坑中央峰及坑底断裂区域;中起伏山地主要分布在撞击坑坑底和坑壁过渡区域、撞击坑坑壁和坑缘过渡区域、撞击盆地盆底与盆壁过渡区域以及盆壁与盆缘过渡区域;大起伏和极大起伏山地主要分布在撞击坑坑壁区域及撞击盆地盆壁区域。本文确定的月表起伏度分级标准可以对月表数字地貌分类体系的构建和月球地貌图集的编研提供定量标准和重要参考。     

Morphological differentiation characteristics and classification criteria of lunar surface relief amplitude

Lunar landforms are the results of geological and geomorphic processes on the lunar surface. It is very important to identify the types of lunar landforms. Geomorphology is the scientific study of the origin and evolution of morphological landforms on planetary surfaces. Elevation and relief amplitude are the most commonly used geomorphic indices in geomorphological classification studies. Previous studies have determined the elevation classification criteria of the lunar surface. In this paper,...     

Parallelization of the flow-path network model using a particle-set strategy

High-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS).     

Conservation-induced resettlement as a driver of land cover change in India: An object-based trend analysis

Located in the foothills of the Indian Himalaya, Rajaji National Park was established to protect and enhance the habitat of the Asian elephant (Elephas maximus) and tiger (Panthera tigris). In 2002 the Van Gujjars, indigenous forest pastoralists, were voluntarily resettled from the Chilla Range (an administrative unit of Rajaji National Park) to Gaindikhata, a nearby area where they were granted land for agriculture. In this study we used a variety of remote sensing approaches to identify changes in land cover associated with the resettlement. The methods comprise two main approaches. First, we used object-based image analysis (OBIA) to identify the pre-resettlement land cover classes of use areas (representing agricultural expansion and adjacent areas of grazing, and collection of fuelwood and fodder) and recovery areas (representing areas where settlements were removed, and the adjacent areas of resource use). Secondly, we used trend analysis to assess the gradual and abrupt changes in vegetation that took place in use and recovery areas. To conduct the trend analysis we used BFAST (Breaks For Additive Season and Trend), which separates seasonal variation from long-term trends, and identifies breaks that can be linked back to disturbances or land cover changes. We found that the OBIA classification yielded high average class accuracies, and we were able to make class distinctions that would have been difficult to make using a traditional pixel-based approach. Pre-resettlement, the recovery areas were classified as mixed forest and riparian vegetation. In contrast, the use areas were classified primarily as grass dominated, brush dominated, and plantation forest, and were located relatively far away from riparian areas. Following the resettlement, the trend analysis showed a sudden change in the seasonal variation of NDVI in areas converted to agriculture. Areas neighboring the new agricultural land experienced sudden decreases in NDVI, suggestive of disturbances, at a higher rate than the same land cover types elsewhere. At the same time, these neighboring areas experienced a gradual overall increase in NDVI which could be caused by an expansion of leafy invasive shrubs such as Lantana camara in areas heavily used for biomass collection. The recovery areas also experienced a gradual increase in NDVI as well as sudden breaks to this trend, but we lacked evidence to connect these changes to the resettlement. Our findings support the claim that the resettlement has shifted pressure from more ecologically valuable to less ecologically valuable land cover types, and suggest that to some degree resource use pressure has increased outside the park. The study employs a novel synthesis of OBIA and trend analysis that could be applied to land change studies more broadly.     

全月球撞击坑识别、分类及空间分布

撞击坑是月球表面分布广泛的地貌单元,是研究月球的最直接窗口。本文以嫦娥一号卫星获取的遥感影像和DEM,以及国际天文学联合会(IAU)公布的撞击坑名录为基础数据源,以全月球表面撞击坑为研究对象,采用遥感图像处理与专家知识融合的目视解译法确定撞击坑的边界,识别出全月球表面直径大于500 m的撞击坑共计106030个,采用累积频率和与IAU公布撞击坑对比两种方法对目视解译的撞击坑进行精度评价,其识别的总体误差率为10.97%;按照形态特征指标,将全月球撞击坑分为六大类,对比分析了不同类的撞击坑影像及形貌差异性;对全月球撞击坑分类进行统计分析,得出了不同类型撞击坑在月球表面的数量与密度特征及空间分布情况。     

An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.     

基于土壤数据和遥感影像的鄂尔多斯高原北部地貌分类(英文)

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 > 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 pre-sented by a geologically-based classification map.The comparison indicated that the geo-logically-based classification could not identify multiple landforms within a class that are de-pendent upon soil characteristics.     

Sensitivity analysis of a decision tree classification to input data errors using a general Monte Carlo error sensitivity model

We analysed the sensitivity of a decision tree derived forest type mapping to simulated data errors in input digital elevation model (DEM), geology and remotely sensed (Landsat Thematic Mapper) variables. We used a stochastic Monte Carlo simulation model coupled with a one‐at‐a‐time approach. The DEM error was assumed to be spatially autocorrelated with its magnitude being a percentage of the elevation value. The error of categorical geology data was assumed to be positional and limited to boundary areas. The Landsat data error was assumed to be spatially random following a Gaussian distribution. Each layer was perturbed using its error model with increasing levels of error, and the effect on the forest type mapping was assessed. The results of the three sensitivity analyses were markedly different, with the classification being most sensitive to the DEM error, than to the Landsat data errors, but with only a limited sensitivity to the geology data error used. A linear increase in error resulted in non‐linear increases in effect for the DEM and Landsat errors, while it was linear for geology. As an example, a DEM error of as small as ±2% reduced the overall test accuracy by more than 2%. More importantly, the same uncertainty level has caused nearly 10% of the study area to change its initial class assignment at each perturbation, on average. A spatial assessment of the sensitivities indicates that most of the pixel changes occurred within those forest classes expected to be more sensitive to data error. In addition to characterising the effect of errors on forest type mapping using decision trees, this study has demonstrated the generality of employing Monte Carlo analysis for the sensitivity and uncertainty analysis of categorical outputs that have distinctive characteristics from that of numerical outputs.     

基于重庆主城区居民感知的城市意象元素分析评价

城市意象是诠释一个城市环境的重要指标,对其分析研究,有助于解读及评估城市景观的优劣、城市生活环境品质的高低,也有助于把握城市空间结构。本文首先在回顾国内外相关研究进展的基础上,根据重庆城市特色,将Lynch K的5类城市意象要素进行细分,并转换为地点场所意象元素;其次采用问卷调查法,对城市意象因子和意象元素分别进行评价,并绘制城市意象元素分布图,以了解居民对城市意象元素的印象与品质之间的关系,从而为重庆城市规划建设提供有益的参考。     

近20 a塔城地区暖区暴雪环流分型及成因分析

选用2000—2019年11月—次年3月塔城地区7个国家气象观测站逐日降水、温度、常规地面和高空观测资料、美国国家环境预报中心（NCEP）再分析资料,确定近20 a塔城地区暖区暴雪天气过程并进行分析。结果表明：（1） 塔城地区暖区暴雪发生于塔额盆地的塔城站、裕民站和额敏站,塔城站出现频次最多;时间分布上,11月和12月出现暖区暴雪的频次最高,且主要集中在11月中旬—12月上旬,1月次之,2月最少。（2） 塔城地区暖区暴雪分为3类：低槽前部型、横槽底部型和西北急流型,地面低压为西方和西北路径。低槽前部型是最典型的暖区暴雪形势,主要出现在11月—12月上旬,发生在西西伯利亚低槽前部锋区与南支中纬度短波槽汇合区,地面低压为西北路径;横槽底部型主要出现在11月—次年1月,发生在极锋锋区底部偏西气流和中纬度暖湿西南气流汇合的强锋区中,地面低压为西方路径;西北急流型主要出现在11—12月,发生在极锋锋区西北气流中,地面低压为西北路径。（3） 500~300 hPa强西北或偏西急流、700 hPa偏西低空急流、850 hPa暖式切变的叠置区与暖区暴雪落区一致,低槽前部型和西北急流型为锋前暖区产生暴雪,横槽底部型为低压右前部暖锋锋生产生暴雪。（4） 低槽前部型和横槽底部型的水汽均为偏西路径,来自地中海、阿拉伯海的水汽经里海、咸海增强后向暴雪区输送;西北急流型有偏西和西北2条水汽输送路径,来自高纬度巴伦支海的水汽与来自中低纬度里海、咸海、地中海、阿拉伯海的水汽在巴尔喀什湖附近汇合后向暴雪区输送,较强的水汽输送伴随低层明显的水汽辐合,强辐合中心位于850~700 hPa之间。