Scientific Papers, USAMV Bucharest, Series A, Vol. LII, 2009, ISSN 1222-5339
ANALYZING THE LANDFORMS - AGRICULTURAL LAND-USE TYPES
RELATIONSHIP USING A DTM-BASED INDICATOR
V. CHENDES*, SORINA DUMITRU**, C. SIMOTA**
*National Institute of Hydrology and Water Management
**National Research and Development Institute for Soil Science, Agrochemistry and
Environmental Protection of Bucharest
Keywords: DTM, landforms, TPI, SRTM, agricultural land-use
Abstract
The topographic complexity of hilly terrains and its influence on crop growth and soil
energy and water balance could be described using a comprehensiv system of AgroEnvironmental Indicators. These could be very useful in order to develop agricultural
politics in hilly regions. The derived parameters are the most helpful, by quatifying the
topography contribution to water redistribution in landscape, the changes in solar
radiation received at the Earth's surface, etc. One of them, the index of the landforms is
described in this paper, and a test for Bend Subcharpathians unit is performed. It could be
used in describing or assessing the variability induced by topography on spatial
distribution of the agricultural land-use types.
INTRODUCTION
In Romania, an important part of arable land is located on slopes of hilly regions,
the spatial variability of potential yields being function of soil properties, including
texture and drainage characteristics that control the available soil water. These
properties are depending on terrain characteristics. The topographic complexity of
these areas induces some particularities for water, temperature and radiation
regimes with direct effects on agri-environmental potential.
It seems that about 80% of yield variability is explained by a combination between
soil and terrain properties [1]. Therefore, a series of indicators derived from Digital
Terrain Models (DTM) could be very useful in assessing the variability induced by
topography on climatic conditions and crop management (as function of elevation,
slope, aspect etc.).
The digital terrain model is a digital representation of Earth surface, providing
different sets of basic data, the topographical parameters being the most important.
The watercourse on a surface is depending on the shape of the surface, therefore
the hydrological features are often extracted from the DTM. The flow direction
could be determinated as a function of slope. The upper slope could be used to
identify the ridges and valleys. Therefore, the DTMs represent an important data
source for several GIS-based applications.
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As a consequence, the objective of this paper is to discuss some aspects concerning
the identification of terrain characteristics and parameters that could contribute to
the development of several Agri-Environmental Indicators needed to assess the
vulnerability of farm systems in the hilly regions, and to support the politics for a
sustainable agriculture management in these regions.
MATERIAL AND METHODS
In order to analyse the landforms - agricultural land-use types relationship and to
develop Agri-Environmental Indicators used to characterise the vulnerability of
farm systems, a DTM for Romania at a medium resolution could be used. The
SRTM model has been chosen to be used for developing the indicators.
NASA Shuttle Radar Topographic Mission (SRTM) is a joint international
project developed by National Imagery and Mapping Agency (NIMA) and
National Aeronautics and Space Administration (NASA). The main objective of
this project is to produce elevation data in digital format (DEM) for about 80% of
the Earth surface. The original data have a resolution of 1’’ (approximately 30 m).
They have been post-processed and are available only for few countries, primarily
for the United States. The free online data have a resolution of about 90 m (3’’).
The SRTM model was developed through the use of “radar interferometry”
technique. A radar signal is transmitted to the ground (figure 1), being reflected
and captured at the same time in two points located not far from each other; thus
two radar images are captured. The differences between these two images permit
the estimation of the elevation of a
point.
Presently,
the
CGIAR-CSI
geoportal is authorized to provide
SRTM Digital Elevation Data [7].
The initial data being collected in
Geographical projection on a
WGS84 spheroid, a transformation
to Stereographic ’70 projection
Fig. 1. The achievement of the SRTM
(specific for Romania) has been model based on radar signals (source: [6])
necessary.
Although the initial resolution was approximately 90 m, it was required to adapt
and improve the SRTM digital terrain model so that to receive adequate resolutions
in order to use the model at detailed scales and in different applications. To do that,
the SRTM model was interpolated and exported with a 30 m resolution, fact that
helps to reach better results in DTM-based applications (figure 2).
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a)
b)
Fig. 2. SRTM model: a) 90 m resolution; b) 30 m resolution
In order to define new topographical parameters from the DTM, a series of spatial
analysis functions is called, which are usually included in most of the GIS software
packages (ArcView, Idrisi, GRASS, Surfer, MapInfo, Geomedia, Envi, SAGA,
LandSerf etc.). Many of the most well-known topographic parameters can be
derived from the elevation data according to the characteristics of the neighbouring
areas. There can be distinguished primary characteristics that can be calculated
directly from DTM, as well as derivative or secondary ones, which involve the
primary characteristics combination [4].
A more complex index, that uses several primary parameters could be considered
the index of the landforms. For the automatic extraction of some landforms, on the
basis of the Terrain Digital Model, it starts from a simple succession peak – slope –
horizontal surface – depression. This is an increasingly complicated process, as
these forms became more detalied, through the increasing of the DTM discretizing
step, or of the criteria that define the landforms.
One of the methods that provide satisfactory results in the process of landforms
extraction and that can be subsequently used in several GIS analysis is TPI
(Topographic Position Index) (figure 3). This represents the difference between a
cell elevation and the average of the vicinity cells elevation [3], being implemented
in an ArcView application. A main advantage is the fact that the classification
criteria definition can be modified by the user [2].
This index, along with the slope, allows the DTM classification and the forms
differentiation in six classes: ridge, upper slope, middle slope, lower slope, flat
slope, and valley. It depends on the chosen scale, therefore, using two calculation
algorithms, or two different diameters of the cell vicinity definition, Weiss [3]
obtained an increased accuracy of the landforms classification. Applying this
methodology, 10 categories have been generated.
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RESULTS AND DISCUSSION
For testing this index, the hilly region of Bend Subcarpathians has been chosen
(about 6,417 km2). The main characteristic of this area is an association and
alternation of large depresssions and hillsides, that shape distinctively different
slope and channel features. These two types of relief correspond to some unevenly
tectonised
geological
structures which account for
the main Subcarpathian traits.
For an optimal degree of
generalization
of
the
landforms, the circle radius
defining the neighborhood
area for each cell has been
considered 2000 m (figure 4).
Taking into account six (1-6)
values for the landforms, the
specific functions for regional
statistics could be applied.
Different
analysis
highlighting the homogenous
character of the two major
Fig. 3. The Topographic Position Index
relief types (hills, and valleys
Definition (after [3])
and depressions) could be
performed.
In Prahova Subcarpathians, the hilly regions are well individualized. This fact is
highlighted by the large percent of lower slope (11.8%) to the detriment of the
middle slope (32.2% relative to 40% for the other subunits). The middle slope
occupy the largest part from Subcarpathians, the total percent being 38.5%.
Important for agricultural purposes is the large percent of flat slope (generally
speaking, they define the horizontal surface of the terraces or the botom part of
large depresions) laying on about 19 – 20% from Prahova and Buzau
Subcarpathians areas, but decreasing to 16.8% in Vrancea Subcarpathians, due to
the relief of Vrancea Depression, very fragmented by hilly regions, with a clear
rippled and heterogen character.
From figure 5, one can notice that the agricultural lands (extracted from Corine
Land Cover [5]) lie on about 50% from lands defined as lower slope and flat slope
(48.3, respectively 45.7%), even that, due to their smaller area, only 30% from
agricultural lands are distributed on these landforms.
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Fig. 4. The main landforms generated on the DTM basis
Fig. 5. The percent of main agricultural land-use types on different landforms
Arable lands lie on about 16% from lower slope, having the greatest percent on this
landform. The same area (about 66 km2) from flat slope is occupied by arable
lands, but the percent is only 5.5%.
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CONCLUSIONS
1. There are several topographical parameters that could be derived from
elevation data as functions of the characteristics of the neighbouring areas.
In this paper a topographical parameter, the Index of the landforms, is
presented, as well as its assessment method based on TPI (Topographic
Position Index). The landforms are generated as function of slope and
relative position (the elevation of each DTM cell relative to the neighbouring
cells elevation). It allows the DTM classification and the forms
differentiation in six classes, depending on the chosen scale. This index was
tested for the Bend Subcarpathian unit.
2. The Index of the landforms, having values in the range 1-6, allows a series
of statistical analysis and correlations with agricultural land-use types.
Valleys and depressions relief types (including lower slope, flat slope and
valley landforms) represent about 36% from the studied area, flat slope lying
on about 19%. The agricultural lands lie on about 50% of lands defined as
lower slope and flat slope. About 70% of arable lands are situated on the two
landforms.
3. While a large part of crop yield variability is explained by the combination
between soil and terrain properties, the variability of agricultural land-use
types could be explained by analysing the Index of the landforms derived
from Digital Terrain Models. This index could be used in describing and
assessing the variability induced by topography on climatic conditions and
crop management.
REFERENCES
1. Iqbal J., J.J. Read, A.J. Thomasson and J.N. Jenkins, 2005. Relationships between
soillandscape and dryland cotton lint yield. Soil Science Society America Journal,
69 (pp. 872-882).
2. Jenness J., 2006. Topographic Position Index (TPI), extension for ArcView 3.x, v.
1.3a, http://www.jennessent.com.
3. Weiss A.D., 2001. Topographic Position and Landforms Analysis. Poster, ESRI
User Conference, San Diego, CA.
4. Wilson J. P., J.C. Gallant, 1998. Terrain Analysis: Principles and Applications. Ed.
John Wiley & Sons, ISBN-10: 0470244127.
5. ***, 2007. Corine Land Cover 2000. EEA, Copenhagen, http://www.eea.europa.eu,
The Ministery of Environment and Sustainable Development, and ‘Danube Delta’
National Institute for Research and Development.
6. http://www2.jpl.nasa.gov/srtm/.
7. http://srtm.csi.cgiar.org/.
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