11 November 2008 Research on the slope-landscape TUPU in northern Shaanxi Loess Plateau
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Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 71462Q (2008) https://doi.org/10.1117/12.813194
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fayuan Li, Fayuan Li, Guoan Tang, Guoan Tang, Youfu Dong, Youfu Dong, } "Research on the slope-landscape TUPU in northern Shaanxi Loess Plateau", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71462Q (11 November 2008); doi: 10.1117/12.813194; https://doi.org/10.1117/12.813194
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