Slope spectrum is a statistic model of slope distribution in a certain area. Previous researches display a potential importance of the slope spectrum in geomorphological studies. To quantitatively depict the slope spectrum, three indices (H, Td, S) were proposed, which can appropriately depict quantity features of slope distribution, but are difficult in depicting spatial structure of slope distribution. Hence, this paper suggests slope-landscape TUPU to quantitatively depict the spatial structure of slope distribution. The slope-landscape TUPU take each test area as an independent
landscape unit, and the slope class as patches constituting the landscape. So, theory and methodology of landscape ecology are applied to describe the spatial structure of slope distribution directly. Results show that the slope-landscape TUPU is capable of depicting spatial structure of slope spectrum. A continuous changes of the slope-landscape TUPU from south to north of the Loess Plateau shows an obvious spatial variation of surface roughness in the area, which is proved to be of great significance in describing the surface roughness. This research also suggests relationship between
digital terrain analysis and landscape ecology.
Slope spectrum is defined as a statistic model of slope distribution in a certain area. Previous researches mainly focus on
morphology depiction of the slope spectrum; its spatial distribution is unknown yet, especially in the Loess Plateau.
Theory and methodology of information entropy and statistics are applied for the objective of quantitatively analyzing
the slope spectrum and its spatial distribution in the Loess Plateau in North Shaanxi province. Experiment results show
that slope spectrum's information entropy (H), skewness of slope spectrum (S) and terrain driving force factor (Td) can
appropriately depict the slope spectrum and its spatial distribution from different points of view. Spatial distribution of
the slope spectrum represents spatial distribution of loess landform types, and it is correlatable with spatial distribution
of soil erosion intensity in the Loess Plateau. H, Td and gully density, surface incision depth show positive correlation: gully density and surface incision increase as H, Td increase. On the contrary, the S and gully density, surface incision depth show negative correlation. Lastly, spatial relationship between slope spectrum and loess landform types are qualitatively analyzed, and loess landform evolution as well.
DEM data availability and GIS-assisted processing of the data have extended the usage of DEM to a great extent. Taking 5 m grid resolution DEMs of 48 test areas in the Loess Plateau in north Shaanxi as test data, this paper introduces the definition, calculation, extraction and stable threshold area of terrain driving force (Td) for soil erosion. Then, spatial distribution of Td is investigated with a method of geostatistics analysis. Results show that spatial distribution of Td is correlatable with spatial distribution of the soil erosion intensity and Td can be taken as a regional terrain factor of regional soil erosion in the Loess Plateau. But Td is not exact enough in evaluation processing of regional soil erosion, an integrated analysis combining the climate, vegetation cover, soil and water conservation management is demanded. Then, some problems existing in the research of regional soil erosion are analyzed and a preliminary model of regional soil erosion contained Td is proposed. Subsequent research is focused on data collection and integration of regional soil erosion and its applicability in the Loess Plateau.
Landforms can be described and identified by parameterization of digital elevation model (DEM). This paper discusses the large-scale geomorphological characteristics of China based on numerical analysis of terrain parameters and develop a methodology for characterizing landforms from DEMs. The methodology is implemented as a two-step process. First, terrain variables are derived from a 1-km DEM in a given statistical unit including local relief, the earth's surface incision, elevation variance coefficient, roughness, mean slope and mean elevation. Second, every parameter regarded as a single-band image is combined into a multi-band image. Then ISODATA unsupervised classification and the Bayesian technique of Maximum Likelihood supervised classification are applied for landform classification. The resulting landforms are evaluated by the means of Stratified Sampling with respect to an existing map and the overall classification accuracy reaches to rather high value. It's shown that the derived parameters carry sufficient physiographic information and can be used for landform classification. Since the classification method integrates manifold terrain indexes, conquers the limitation of the subjective cognition, as well as a low cost, apparently it could represent an applied foreground in the classification of macroscopic relief forms. Furthermore, it exhibits significance in consummating the theory and the methodology of DEMs on digital terrain analysis.
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