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.     

Soil nutrients in relation to land use and landscape position in the semi-arid small catchment on the loess plateau in China

Characterizing spatial variability of soil nutrients in relation to site properties, including climate, land use, landscape position and other variables, is important for understanding how ecosystems work and assessing the effects of future land use change on soil nutrients. In order to assess the effects of land use and landscape position on soil nutrients consisting of soil organic matter (SOM), total N (TN), total P (TP), available N (AN) and available P (AP), soil samples were collected in August and October 1998 and July 1999 from three transects in a small catchment on the loess plateau, China. The three transects consisted of typical land use structure from the top to foot of hillslope in the study area: fallow land – cropland – woodland – orchard (T1), fallow land – shrub land – fallow land – cropland – woodland – orchard (T2) and intercropping land – woodland (T3). Significant differences among land uses were found for SOM, TN and AN. Woodland, shrub land and grassland had the higher levels for them compared to fallow land and cropland. Use of soil deterioration index showed that soils deteriorated moderately (−17·05%) under orchard and seriously (ranging from −29·91% to −20·32%) under fallow land, cropland and intercropping land, while soils had no deterioration (−0·74%) under shrubland and (−0·69%) grassland. This study indicated that the cultivated hilly lands must be abandoned before a critical minimum SOM of 0·492%. Soil nutrient responses to landscape positions were variable depending on transect and the location of land use types. The highest levels in SOM, TN and AN were observed at middle slope position on T1, while they occurred at foot slope position on T3. However, an increasing trend from upper slope to foot slope for five nutrients were found on T2.     

DEM resolution dependencies of terrain attributes across a landscape

This paper documents resolution dependencies in terrain analysis and describes how they vary across landform location. Six terrain attributes were evaluated as a function of DEM resolution—slope, plan curvature, profile curvature, north–south slope orientation, east–west slope orientation, and topographic wetness index. The research highlights the effect of varying spatial resolution through a spatial sampling/resampling scheme while maintaining sets of indexed sample points at various resolutions. Tested sample points therefore coincide exactly between two directly compared resolutions in terms of their location and elevation value. An unsupervised landform classification procedure based on statistical clustering algorithms was employed to define landform classes in a reproducible manner. Correlation and regression analyses identified sensitive and consistent responses for each attribute as resolution was changed, although the tested terrain attributes responded in characteristically different ways. These responses displayed distinguishable patterns among various landform classes, a conclusion that was further verified by a series of two‐sample, two‐tailed t‐tests.     

Identification of geomorphic process units in Kärkevagge, northern Sweden, by remote sensing and digital terrain analysis

Sediment transport processes in the Kärkevagge are investigated concerning their spatial and temporal characteristics due to long–term monitoring. Within this study remote sensing techniques and GIS modelling in connection with geomorphic mapping are applied for identification and characterization of geomorphic process units. Relationships between geomorphometric parameters and slope processes like solifluction, talus creep and rockfall have been analysed. Multitemporal Landsat–TM5 scenes are used as source for landcover characteristics (Normalized Difference Vegetation Index) after preprocessing involving orthorectification and topographic normalization in order to remove possible terrain–induced effects. Additionally, a digital elevation model with a resolution of 20 m for the Kärkevagge catchment is developed and parameters like slope gradient, slope aspect and profile curvature are extracted as input for the analysis of the sediment transport system. The combination of landcover information, geomorphometrical and topological features allows the definition of areas for single process activities. They show specific sediment displacement characteristics depending on material conditions, topological and geometrical features. Geomorphic process units, which show a homogenous composition, are extracted from these available layers.     

Identification of geomorphic process units in Kärkevagge, northern Sweden, by remote sensing and digital terrain analysis

Sediment transport processes in the Kärkevagge are investigated concerning their spatial and temporal characteristics due to long–term monitoring. Within this study remote sensing techniques and GIS modelling in connection with geomorphic mapping are applied for identification and characterization of geomorphic process units. Relationships between geomorphometric parameters and slope processes like solifluction, talus creep and rockfall have been analysed. Multitemporal Landsat–TM5 scenes are used as source for landcover characteristics (Normalized Difference Vegetation Index) after preprocessing involving orthorectification and topographic normalization in order to remove possible terrain–induced effects. Additionally, a digital elevation model with a resolution of 20 m for the Kärkevagge catchment is developed and parameters like slope gradient, slope aspect and profile curvature are extracted as input for the analysis of the sediment transport system. The combination of landcover information, geomorphometrical and topological features allows the definition of areas for single process activities. They show specific sediment displacement characteristics depending on material conditions, topological and geometrical features. Geomorphic process units, which show a homogenous composition, are extracted from these available layers.     

基于DEM和地貌计量的月球形貌类型自动制图（英文）

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology.In this paper,we outline an automated method for mapping lunar landforms that is based on digital terrain analysis.An iterative self-organizing(ISO)cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters.These parameters divide landforms into a number of spatially extended,topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties.To illustrate the applicability of our approach,we apply it to three representative test sites on the Moon,automatically presenting our results as a thematic landform map.We also quantitatively evaluated this approach using a series of confusion matrices,achieving overall accuracies as high as 83.34% and Kappa coefficients(K)as high as 0.77.An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.     

Third-order geomorphometric variables (derivatives): definition,computation and utilization of changes of curvatures

Third-order geomorphometric variables (based on third derivatives of the altitudinal field) have been neglected in geomorphometry, but their application to the delimitation of surface objects will lead to their increasing significance in future. New techniques of computation, presented and evaluated here, facilitate their use. This paper summarizes recent knowledge concerning definition, computation and geomorphologic interpretation of these variables. Formulae defining various third-order variables are unified based on the physical definition of slope gradient. Methods for their computation are compared from the point of view of method error and error generated by digital elevation model (DEM) inaccuracy. For exact mathematical test surfaces, the most natural and simple variant of the method of central differences (CD2) shows a method error 2–3 times smaller than the other methods used recently in geomorphometry. However, success in coping with DEM inaccuracy depends (for a given grid mesh) on the number and weighting of points from which the derivative is computed. This was tested for surfaces with varying degrees of random error. Here least squares-based methods are the most effective for mixed derivatives (especially for finer grids and less accurate DEMs), while a variant of the CD method, that repeats numerical evaluation of first derivatives (CD1), is the most successful for derivatives in cardinal directions. The CD2 method is generally the most successful for coarser grids where the method error is dominant.

Utilization of third-order variables is documented from examples of terrain feature (ridge, valley and edge) extraction and from a first statistical test of the hypothesis that real segments of the land surface have a tendency to a constant value of some morphometric variable. For detection of (sharp) ridges and valleys, it is shown that the rate of change of tangential curvature is inadequate: rate of change of normal curvature is also required. A basic confirmation of the constant-value tendency is provided.     

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.     

Comparison of polynomial models for land surface curvature calculation

Curvature is a fundamental surface property whose application, for example in geomorphology and hydrology, has long been recognised. Its measurement from Digital Terrain Models (DTMs) has received less attention than that of slope, and there is even disagreement about which definitions of curvature are most applicable. Here these problems are related to semantic problems in the definition of the land surface, and three distinct algorithms for the three main gravity-related components of curvature are compared for both artificial and real surfaces. Quadratic-based algorithms are shown to give more stable results. Higher-order local surfaces (e.g. partial quartic) can fit more complex landform features, but are reliable only for very accurate data.     

Automated classification of landform elements using object-based image analysis

This paper presents an automated classification system of landform elements based on object-oriented image analysis. First, several data layers are produced from Digital Terrain Models (DTM): elevation, profile curvature, plan curvature and slope gradient. Second, relatively homogenous objects are delineated at several levels through image segmentation. These object primatives are classified as landform elements using a relative classification model, built both on the surface shape and on the altitudinal position of objects. So far, slope aspect was not used in classification. The classification has nine classes: peaks and toe slopes (defined by the altitudinal position or the degree of dominance), steep slopes and flat/gentle slopes (defined by slope gradients), shoulders and negative contacts (defined by profile curvatures), head slopes, side slopes and nose slopes (defined by plan curvatures). Classes are defined using flexible fuzzy membership functions. Results are visually analyzed by draping them over DTMs. Specific fuzzy classification options were used to obtain an assessment of output accuracy. Two implementations of the methodology are compared using (1) Romanian datasets and (2) Berchtesgaden National Park, Germany. The methodology has proven to be reproducible; readily adaptable for diverse landscapes and datasets; and useful in respect to providing additional information for geomorphological and landscape studies. A major advantage of this new methodology is its transferability, given that it uses only relative values and relative positions to neighboring objects. The methodology introduced in this paper can be used for almost any application where relationships between topographic features and other components of landscapes are to be assessed.     

Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing (ISO) cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients (K) as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.     

The Flatness of U.S. States

Does perception match reality when people judge the flatness of large areas, such as U.S. states? The authors conducted a geomorphometric analysis of the contiguous United States, employing publicly available geographic software, Shuttle Radar Topography Mission (SRTM) elevation data, and a new algorithm for measuring flatness. Each 90‐meter cell was categorized as not flat, flat, flatter, or flattest, and each state was measured in terms of percentage flat, flatter, and flattest as well as absolute area in each category. Ultimately, forty‐eight states plus the District of Columbia were mapped and ranked according to these values. Keywords: flatness, U.S. states, slope, Kansas, Florida.     

Loess terrain segmentation from digital elevation models based on the region growth method

Regional automatic segmentation – automatic terrain segmentation according to terrain features – is significant for modern geographical analysis. We propose a new approach of terrain region segmentation based on the region growth method. This method features actual runoff nodes as seed points. The corresponding growth threshold is defined based on statistical analysis of quantitative indexes of topographic features. Terrain segmentation of some regions is completed using the growth threshold. The corresponding edge boundaries of different terrain regions are extracted by image processing. Thus, the automatic segmentation of the terrain region is realized by the edge boundary. The application of the method to a typical Chinese loess landform area and automatic segmentation of three types of terrain regions – gully interfluve land, gully slope land, and gully groove land – are achieved by analyzing characteristics of the curvature structure of surface profiles. Segmentation results, compared with results of visual interpretation from a high-precision digital orthophoto map, show an average accuracy of segmentation of 93.51%. Topographic factor features of segmentation results are statistically analyzed. This study presents an effective and practical approach for segmenting terrain regions. This approach may be incorporated into the theory and method system of digital terrain analysis.     

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.     

黄土高原小流域土壤养分的空间分布格局-Kriging插值分析

本文应用Kriging空间内插法,分析了黄土高原大南沟流域土壤有机质以及全N、全P、有效N和有效P等4种养分含量的空间分布格局。结果表明:土壤有机质呈现出坡上部低于坡下部的规律,其含量低于05%所占的面积最大,以耕地分布的区域为主,较高含量(06～08%)则分布在农果间作地和林地的区域;土壤全N的分布格局与土壤有机质具有相似性,只是坡下部的全N含量高于坡上部的趋势较为明显;土壤全P含量相差较小为138%,不同全P含量的空间分布面积基本相等;有效N和有效P并未表现出土地利用和景观位置控制的分布格局,有效P的空间分布较有效N更为复杂。     

关于农业地貌条件评价的研究

在农业资源、农业区划与国土整治的研究中,研究农业地貌条件的评价,是必不可少的基础作之一.本文讨论了农业地貌条件评价的内容和方法.     

深圳市地形对土地利用动态的影响

基于RS和GIS的技术支持,以深圳市为案例,利用多时段土地利用数据和地形数据,分析土地利用变化的时空分异,研究城市地域地形对土地利用动态的影响。分析表明,土地利用在空间上的变化引起其垂直重心的变化,耕地重心降低,林地和建设用地重心上升。深圳市地形对土地利用动态有明显的制约作用,主要表现为:随着高程和坡度的升高,土地利用动态度大致呈降低趋势。随着城市化的加深,同一高程带和坡度带上土地利用动态度呈加大趋势。土地利用动态度的情景分析指出,在自然景观时期、农业景观成熟期和后城市化时期,土地利用在高程带和坡度带上都比较稳定,动态度低;在这三个时期之间是农业化时期和快速城市化时期,土地利用动态度高。     

Dealing with double vagueness in DEM morphometric analysis

A method is presented to explicitly incorporate spatial and scale vagueness – double vagueness – into geomorphometric analyses. Known limitations of usual practices include using a single fixed set of crisp thresholds for morphometric classification and the imposition of a single arbitrary number of scales of analysis to the entire digital elevation model (DEM). Among the advantages of the proposed method are: fuzzification of morphometric classification rules, scale-dependent adaptive fuzzy set parametrization and an objective definition of maximum scale of analysis on a cell-by-cell basis. The method was applied to several DEMs ranging from the ocean floor to surface landscapes of both Earth and Mars. The result was evaluated with respect to modal morphometric features and to characteristic scales, suggesting a more robust method for deriving both morphometric classifications and terrain attributes. We argue that the method would be preferable to any single-scale crisp approach, at least in the context of preliminary hands-off morphometric analyses of DEMs.     

Hillslope-to-valley transition morphology: New opportunities from high resolution DTMs

The search for the optimal spatial scale for observing landforms to understand physical processes is a fundamental issue in geomorphology. Topographic attributes derived from Digital Terrain Models (DTMs) such as slope, curvature and drainage area provide a basis for topographic analyses. The slope–area relationship has been used to distinguish diffusive (hillslope) from linear (valley) processes, and to infer dominant sediment transport processes. In addition, curvature is also useful in distinguishing the dominant landform process. Recent topographic survey techniques such as LiDAR have permitted detailed topographic analysis by providing high-quality DTMs. This study uses LiDAR-derived DTMs with a spatial scale between 1 and 30 m in order to find the optimal scale for observation of dominant landform processes in a headwater basin in the eastern Italian Alps where shallow landsliding and debris flows are dominant. The analysis considered the scaling regimes of local slope versus drainage area, the spatial distribution of curvature, and field observations of channel head locations. The results indicate that: i) hillslope-to-valley transitions in slope–area diagrams become clearer as the DTM grid size decreases due to the better representation of hillslope morphology, and the topographic signature of valley incision by debris flows and landslides is also best displayed with finer DTMs; ii) regarding the channel head distribution in the slope–area diagrams, the scaling regimes of local slope versus drainage area obtained with grid sizes of 1, 3, and 5 m are more consistent with field data; and iii) the use of thresholds of standard deviation of curvature, particularly at the finest grid size, were proven as a useful and objective methodology for recognizing hollows and related channel heads.