Soil-landscape modelling using fuzzy c-means clustering of attribute data derived from a Digital Elevation Model (DEM)

Geoderma ◽  
1998 ◽  
Vol 83 (1-2) ◽  
pp. 17-33 ◽  
Author(s):  
S. de Bruin ◽  
A. Stein
Soil Research ◽  
1995 ◽  
Vol 33 (3) ◽  
pp. 381 ◽  
Author(s):  
M Mcleod ◽  
WC Rijkse ◽  
JR Dymond

A soil-landscape model, comprising 12 land components at a scale of 1 : 5000, has been developed in Neogene close-jointed mudstone in the Gisborne-East Cape region of the North Island, New Zealand. In a validation, soil order was predicted correctly in 81% of observations, soil group in 80%, soil subgroup in 63% and soilform in 60% of observations. A simplified model based on 11 land components for use at a scale of 1 : 50 000 has also been validated. Here soil order was predicted correctly in 71% of observations, soil group in 73% and soil subgroup in 49% of observations. For application with a digital elevation model (1 : 50 000), the number of land components was amalgamated to five. Here the soil order and soil group were predicted correctly in 63% of observations and soil subgroup in 40% of observations during validation. In all trials, the percentage of correct observations increased if a second choice or subdominant soil class was allowed. It took 2 person-weeks to produce a soil map from the 1 :50 000 form of the model over 400 km2 of steep and hilly country by photo interpretation of stereo aerial photographs, compared with 1 day of applying computer algorithms on the digital elevation model (DEM). The soil-landscape model succinctly relates soil class to land component and it enables improved targeting of farm and planning inputs by empowering existing research into soil fertilizer requirements and soil physical properties.


2018 ◽  
Vol 12 (5-6) ◽  
pp. 50-57 ◽  
Author(s):  
I. S. Voskresensky ◽  
A. A. Suchilin ◽  
L. A. Ushakova ◽  
V. M. Shaforostov ◽  
A. L. Entin ◽  
...  

To use unmanned aerial vehicles (UAVs) for obtaining digital elevation models (DEM) and digital terrain models (DTM) is currently actively practiced in scientific and practical purposes. This technology has many advantages: efficiency, ease of use, and the possibility of application on relatively small area. This allows us to perform qualitative and quantitative studies of the progress of dangerous relief-forming processes and to assess their consequences quickly. In this paper, we describe the process of obtaining a digital elevation model (DEM) of the relief of the slope located on the bank of the Protva River (Satino training site of the Faculty of Geography, Lomonosov Moscow State University). To obtain the digital elevation model, we created a temporary geodetic network. The coordinates of the points were measured by the satellite positioning method using a highprecision mobile complex. The aerial survey was carried out using an unmanned aerial vehicle from a low altitude (about 40–45 m). The processing of survey materials was performed via automatic photogrammetry (Structure-from-Motion method), and the digital elevation model of the landslide surface on the Protva River valley section was created. Remote sensing was supplemented by studying archival materials of aerial photography, as well as field survey conducted immediately after the landslide. The total amount of research results made it possible to establish the causes and character of the landslide process on the study site. According to the geomorphological conditions of formation, the landslide refers to a variety of landslideslides, which are formed when water is saturated with loose deposits. The landslide body was formed with the "collapse" of the blocks of turf and deluvial loams and their "destruction" as they shifted and accumulated at the foot of the slope.


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