ANALYZING THE LANDFORMS - AGRICULTURAL LAND-USE TYPES RELATIONSHIP USING A DTM-BASED INDICATOR
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1 Scientific Papers, USAMV Bucharest, Series A, Vol. LII, 2009, ISSN 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 Agro- Environmental 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. 135
2 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 (specific for Romania) has been necessary. Fig. 1. The achievement of the SRTM model based on radar signals (source: [6]) 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). 136
3 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. 137
4 RESULTS AND DISCUSSION For testing this index, the hilly region of Bend Subcarpathians has been chosen (about 6,417 km 2 ). 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 relief types (hills, and valleys and depressions) could be performed. Fig. 3. The Topographic Position Index Definition (after [3]) 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. 138
5 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 km 2 ) from flat slope is occupied by arable lands, but the percent is only 5.5%. 139
6 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, Relationships between soillandscape and dryland cotton lint yield. Soil Science Society America Journal, 69 (pp ). 2. Jenness J., Topographic Position Index (TPI), extension for ArcView 3.x, v. 1.3a, 3. Weiss A.D., Topographic Position and Landforms Analysis. Poster, ESRI User Conference, San Diego, CA. 4. Wilson J. P., J.C. Gallant, Terrain Analysis: Principles and Applications. Ed. John Wiley & Sons, ISBN-10: ***, Corine Land Cover EEA, Copenhagen, The Ministery of Environment and Sustainable Development, and Danube Delta National Institute for Research and Development
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