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Geomorphometric Techniques for Landforms Analysis for Pedological Terrain Characterization

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Landforms, the result of erosion and other geomorphologic processes at the surface of Earth’s crust, have a significant control on soil cover forming and evolution. In the soil-forming equation of Jenny (Jenny, 1945) the “topography” is considered a independent soil system/terrain/environment property, becoming a soil-forming factor.

The control is either direct, by influence of geomorphometric variables or geomorphologic processes on soil properties (slope on erosion/deposition and drainage), either indirect, by the influence of landforms on the other environment conditions, which also control soil cover (slope and aspect on soil temperature and humidity).

In this context, delineation of landforms is used frequently in soil mapping, using soil land systems (Huggett, 1975), were topography has a strong control on soil cover. Geomorphometric variables strongly correlate with soil property, being very used as covariate in soil properties modelling (N. McKenzie, 1999; N. J. McKenzie, Gessler, Ryan, & O'Connell, 2000; Tanasa, Niculita, Rosca, & Pirnau, 2010).

We present here the results of several geomorphometric techniques which can be used to enhance the usability of landform indicators from Romanian Soil Study Methodology (Florea et. al., 1987a, 1987b, 1987c)

In Romanian soil study methodology (Florea, Bălăceanu, Răuță, Canarache, & (coords.), 1987a, 1987b, 1987c), landforms type are used for characterizing pedologically the terrain. For this purpose a set of indicators are used to classify the terrain in several classes. Landforms are used at macro-, meso- and micro-scale, for pedologic micro-zones characterization (indicator 1, 2, 6, 8), for delineating soil mapping units (indicator 20) and for delineating terrain mapping units (indicator 31, 32, 33, 34, 37, 38, 40).

For indicator 1, the pedologic micro-zones are delineated using some generalized types of landforms together with climatic zone and major soil. These generalized qualitative landforms are:

- floodplains;
- smooth (tabular) lowland;
- rolling relief;
- low roughness;
- mean roughness;
- high roughness.

Indicator 2 classify principal landforms as:
- mountains;
- hills, table lands and fragmented piedmonts;
- plains (including terraces), table lands and non- or weak fragmented piedmonts;
- terraces not in plain areas;
- floodplains, divagation plains, deltas, coastal plains.

For the delineation of generalized landforms of pedologic micro-zones from indicator 1 and of generalized small scale landforms from indicator 2, several approaches can be used. Floodplain can be delineated using altitude above channel network/available relief (Dury, 1951)/drainage relief (Glock, 1932), as the area which is vertically very close to channels. Roughness of the terrain surface can be assessed using vector ruggedness measure (Sappington, Longshore, & Thompson, 2007). Qualitative small scale classifications (Hammond, 1954, 1964; Iwahashi & Pike, 2007) can be used in this approach to obtain a complete series of general landform classes.

We propose the following data for indicator 1
We propose the following data for indicator 2

For indicator 6, the absolute altitude is classified in 100 m intervals from 0 to 1200 m, while for 1200 to 2000 m in 200 m intervals and the last class cumulate altitudes bigger than 2000 m.
An analysis of altitude distribution using SRTM3 data for entire Romania can tune the classes of absolute altitude from indicator 6 by applying Jenks algorithm (Jenks, 1967).

For indicator 9 the local micro-relief (altitude amplitude) which needs to be leveled is used to derive 6 classes of non-uniformity.
The local micro-relief altitude amplitude from indicator 9 needed to obtain non-uniformity classes can be modeled only using high resolution elevation data, like LIDAR, GPS or stereographic restitution. Image analysis of aerial ortorectified imagery can also be used to substitute altitude data, using the shading introduced by the non-uniform large scale topography.

Indicator 31 classify the shape elements of principal landform as:
- horizontal smooth areas (slope - horizontal rough areas (slope 20 cm and quasi-horizontal (slope 1-2 %) with relief 20 cm;
- slightly sloped (slope 2-5%);
- ridge: very large (width bigger than 100 m - plateaus), large (width 30-100 m), narrow (under 30 m width) and crests;
- smooth hillslope: long (length > 200 m) and short (length - rough hillslope: long, short, terraced;
- steep slope;
- floodplain: high floodplain, transitional floodplain and low floodplain.
For the surface shape elements classification of indicator 31 a mixed approach of (Schmidt & Hewitt, 2004) 15 classes classification, catena based classifications and geomorphometric variables threshold can be used.

Indicator 32 classify the meso- and microlandforms as:
- peak;
- saddle;
- aeolian landform: dune and interdune;
- mound;
- erosion remnants;
- glacial or nival cirque;
- karst landforms: lapiez, sinkholes, poljes;
- depressions: micro-depressions, closed loess depressions, inter-micro-depressions, large depressions
- fluvial morphology: alluvial cones, debris cones, glacis, natural levees, back-swamps, paleochannels, cut-offs, river channel, drainage channel, ephemeral channels, gullies, rills;
- badlands;
- landslide micro-landforms;
- gilgai micro-landforms;
- animal path;
- shape of the hillslope: upper hillslope (shoulder), median hillslope (backslope), toe slope, hollow, secondary rigdes on hillslopes;
- anthropogenic landforms.
The meso- and micro-landforms from indicator 32 can be delineated using geomorphometric techniques, but some landforms require extra information like geologic maps, aerial ortorectified images or other sources.
Peak and saddles can be identified using several neighborhood or multiscale approaches (Peucker & Douglas, 1975; Wang, Laffan, Liu, & Wu, 2010; Wood, 1996).
Shape of hillslope can be assessed using Pennock-Reuter classification (Reuter et al., 2006). Large scale depressions/hollows can be identified using a D8 flow algorithm, but there are serious problems, because in DEMs depression are considered artifacts, additional data being necessary to validate the depression presence. Only high resolution DEMs and karst areas can contain pure geomorphometric large scale depressions. On the other hand small scale depressions can be easily identified using geomorphometric criteria (Shary, Sharaya, & Mitusov, 2002).

Indicator 33 classify slope (in %) in the following categories: ≤ 2, 2-5, 5-10, 10-15, 15-20, 20-25, 25-35, 35-50, 50-100, > 100.
The 10 slope classes from indicator 33 can be somehow arbitrary as class limits, although in a previous study we have showed that for some areas in Iasi county, a histogram analysis of slope showed 9-10 classes, but with different thresholds (I. C. Niculita & Niculita, 2011). Jenks algorithm for example (Jenks, 1967) can be used to find the thresholds for a given areas considering 10 as the optimum number of classes.

Indicator 34 classify aspect (exposition) in the following categories: flat, N and NE, E and NW, S and SW, W and SE, all aspects.
Aspect classes from indicator 34 are based on climatic considerations, the single problem on computing this variable from DEMs, being the presence of flat areas in which the aspect cannot be computed. Real flat areas, especially in small scale DEMs like SRTM can be considered artifacts. Contour interpolated DEMs can present a large proportion of flat areas artifacts.

Indicator 37 classify rill erosion categories based on the depth of the rill or gully.
Indicator 38 classify the categories of landslides using the morphologic approach (the shape of the landslide body) and the actual rate of the process (stabilized, semi-stabilized, active).
Indicator 40 classify the categories of flooding activity using the temporal approach (yearly, 2 to 5 years and more than 5 years flooding frequency).


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