Control of real time GPS data to analyze the erosion in an olive farm

Control of real time GPS data to analyze the erosion in an olive farm M.I. RAMOS 1, A. L. GARCÍA 2, M.S. GARRIDO 1, F.R. FEITO 2, A.J. GIL 1 1 Department of Cartographic, Geodetic Engineering and Photogrammetry 2 Department of Informatic Campus Las Lagunillas s/n, 23071. Jaén SPAIN miramos@ujaen.es Abstract: - Erosion in agricultural areas is accentuated by the effects of meteorological factors, tillage practices and the slope of the land. This latter effect leads to surface runoff which in turn causes the soil erosion. To carry out erosion models is necessary to use spatial data, like DEM, slopes models or analysis of the 3D movements of the object points in order to better quantify the changes that take place. In this study we analyze spatio-temporal data in an agricultural zone acquired by using differentials corrections data for real time precise positioning (RTK). RTK corrections via internet/gprs from permanent reference GPS stations are used. The results provide information about the erosion that is occurring in the area. Key-Words: - Control, GPS-RTK, erosion, DEM, olive farm, spatial data. 1 Introduction Erosion is a problem that produces an important impact on the landscape and especially in agricultural areas. This process is accentuated by the effects of meteorological factors, agricultural machinery and the slope of the land. Many scientists have been involved in soil erosion research for years, and many models for the erosion process have been developed (Wischmeier and Smith, 1978; Nearing et al., 1989; Adinarayana et al., 1999; D Ambrosio et al., 2001; Veihe et al., 2001; Shen et al., 2003). There are several studies concerning the influence of soil management on soil erosion in olive orchards in different Mediterranean areas, but their conclusions on the impact of erosion are contradictory due to the diverse sets of environmental conditions considered. In this sense we consider that, regardless of the method used, it is essential to incorporate spatial data positioning tools in order to monitor and better quantify changes in the landscape. Three -dimensional variations of the terrain can be detected by means of comparison of sequential terrestrial measurements. This process requires spatial analysis; in particular the use of spatial data to better quantify the topographical changes (Ramos et al., 2007, 2008). Here the role of the DEMs and their grid sizes are very important. There is a plethora of studies which analyse at what DEM grid size it is appropriate to examine landscape features (Thieken et al., 1999; Zhang et al., 1999: Schoorl et al., 2000; Wolock and McCabe, 2000; Thompson et al., 2001; McMaster, 2002; Claessens et al., 2005; etc). The concluding remarks of some authors such as Hancock (2005), confirm that DEMs at grid scales greater than those appropriate for the analysis of the terrain may contain a considerable loss of detail. This means we need high resolution DEM and DEM-derived products. In addition, the accuracy of the elevation values also affects the quality of the DEM. Therefore, in order to obtain accurate results we must generate a precise DEM of small grid cell size from accurate ground surveys. In this sense, the use of GPS RTK receivers can achieve the level of precision required for the generation of highprecision DEM as the differential correction procedure, when performed in RTK GPS observations, improves the accuracy achieved until centimeter-level (Hofmann-Wellenhof, 2001). This study focuses on control, in detail, the spatial distribution of soil loss produced by erosion in an olive farm. We analyse the effects of erosion on a property of olive orchards located on variable sloping land. The study of the land displacement has its methodological base in the revision of the position of the object points throughout several campaigns. Two campaigns have been carried out. We have attempted to make each campaign coincide with the hardest ploughing periods, as this is when more erosion takes place. 2 Data and method ISBN: 978-1-61804-057-2 19

2.1 Study area In the initial phase of the study it is very important to select the experimental plot carefully. Here we deal with a particular plot of olive orchards located on variable sloping land. Our objective is to calculate 3D displacement with very high precision. The property selected is located in a village called Lahiguera in the province of Jaén, Fig 1. This is one of the main olive oil-producing regions in the country. In the province of Jaén olive orchards cover 589532Ha., representing 25% of the Spanish and 42% of the Andalusian surface. Fig. 1 Location of study area in Andalusia (Spain). In this article our study is of an unirrigated olive orchard where the slope gradient values are between 2% and 20%, Fig. 2. Fig. 2 Maximum sloped values in the olive orchard property. 2.2 High precision DEM In order to control variation in the topography and therefore evaluate the erosion of the terrain in the olive farm, we generate very accurate DEMs with high spatial resolution. Such DEMs require precise elevation data from field measurements. 2.1.1 GPS RTK measurements Precise GNSS-based differential positioning in Real Time or real-time kinematics (RTK) technique reduces the effects of orbit errors and ionospheric and tropospheric refraction. The use of corrections generated and transmitted from an RTK network allows these distance-dependent errors to be reduced (Euler et al., 2001). In Spain, the number of regions that have established active GNSS networks for accurate realtime positioning has increased significantly in recent years. In the Community of Andalusia (S Spain), the Andalusian Positioning Network (RAP) is an active GNSS network that provides either post-processing or real-time GNSS application data for accurate positioning. It currently consists of twenty two fully operational stations. All RAP reference stations are fitted with equipment from Leica Geosystems. Specifically 10 reference stations (first level stations) are fitted with GRX1230 Pro receivers and LEIAT504 LEIS antennas. They have a weather station and also include two radio-modems to broadcast the RTK corrections to the rover via radio-modem. The remaining 12 stations (second level stations) have GRX1200 Pro receivers and LEIAX1202 NONE antennas. RAP generates MAC-based network corrections (Max and i-max solutions) (Brown et al., 2005) in RTCM 3.0 format (RTCM, 2004) which can be accessed over the Internet using the NTRIP protocol (NTRIP, 2004). This network was calculated in ITRS (ITRF05) and then transformated into ETRS89 (ETRF05-mean observation epoch 2007.14). RAP network users can take advantage of accurate and continual positioning in the official geodetic reference system in Spain (ETRS89). The main objective of this study is to obtain information based on field data collected. The methodology based on the RAP active network has allowed taking the same cross sections along two observation campaigns undertaken in June 2010 and June 2011. For each point surveyed coordinates in the reference frame for the GNSS network under study (ETRF05) were obtained. Equipment and accessories used in RTK survey Dual-frequency GNSS receivers LEICA Viva GS10 equipped with individual Siemens MC75 GSM/GPRS, triple frequency antennas LEICA AS10 and radio field controllers CS10 are used. The receivers are configured to receive network corrections (Max solution) from the RAP network in RTCM 3.0 format via the Internet. Additional accessories are: telescopic carbon survey pole (2 m height) with tilting platform coupled and pole holder base plate for CS10 field controller. ISBN: 978-1-61804-057-2 20

2.1.2 DEM production The data collected from the GPS campaigns were interpolated using a Triangular Irregular Network (TIN), and from this the DEMs were built. This approach is the most appropriate one where data are known to have small margins of error. The characteristics of our data set: regular grid, dense sampling and precise measurements, made the use of the TIN interpolation technique particularly appropriate. We distinguish one DEM per per campaign. Next we generate the elevation grids of 0.5m cell size using the software Vertical Mapper (VERTICAL MAPPER v.3.1, 2002), an application that works within MapInfo Professional v.9 (MapInfo Cor., 2007). Fig. 3 Measured points. In Fig. 3 we can observe white holes in the zones measured. These correspond to the area occupied by each olive tree. In order to maintain the high precision of the DEM these holes are not taken into account in the data processing, meaning that instead they are considered as edges. The evaluation of DEM accuracy corresponds to the analysis of the vertical accuracy of the points measured. This is determined by the standard deviations of the coordinates from the covariance matrix of the coordinates measured. We compute the standard deviation, sigma h, of the h coordinate for each point measured. Finally, the accuracy of each DEM is calculated from the quadratic component of the two sources of error: the RMSE of the h values estimated from the interpolated surface model and the vertical inaccuracy of the GPS survey. The values of DEM accuracy are calculated from each control point. By introducing these values into a GIS we can map the spatial distribution of the elevation errors. Fig. 4 shows the variation of h error values inside the zone for one campaign and for a given grid cell size. Fig 4.- DEM accuracy of the zone for GPS campaign 1 considering grid cell sizes of 0,5 m. In this work the discussion is focused on the analysis of the changes detected in the terrain from campaign_1 to campaign_2. 3 Results In order to detect changes in the spatial distribution of topsoil we have to subtract the elevation grid values of Campaign_1 from those of Campaign_2. The nomenclature used to quote the elevation grids of Campaign_1 and 2 are GRID_h1 and GRID_h2 respectively. The results are calculated by means of a simple mathematical subtraction (GRID_h2 GRID_h1), using the application Grid Manager of the software Vertical Mapper. Fig 5.- DEM of the grids GRID_h2-GRID_h1. The grid of the Elevation Differences, Fig. 5, has positive and negative values. This means that some zones have lost top soil, which is erosion, while others have gained it, which is sediment deposition. The range of erosion/deposition values is from - 0.210 m to 0,197 m. Nevertheless, not all of these values are real because this range includes error values that must be removed from the grid. The real erosion/deposition values are those which were higher than the error. Therefore a grid which represents the spatial distribution of the error in the Elevation Differences must be calculated. At this ISBN: 978-1-61804-057-2 21

stage erosion/deposition values higher than this error would be considered as real values, that is significant values; and yet erosion/deposition values in the order of the errors are considered as nonsignificant. In this sense Fig. 6 shows distribution of significant values of Elevation diferences. Fig 6.- Significant values of GRID_h2-GRID_h1. The grid of the Elevation Elevation Differences error includes DEM error from both Campaign_1 and Campaign_2. Therefore the values of this grid correspond to the quadratic component of DEM accuracy from GRID_h1 and GRID_h2. DEM accuracy = ( GRID _ h2 GRID _ h1) 2 ( DEM accuracy ) + ( DEM accuracy ) 2 GRID _ h1 = GRID_h2 The Fig. 7 shows the spatial distribution of the error in the subtraction model of the grids. Fig 7.- DEM accuracy of the grids GRID_h1. GRID_h2-4 Conclusion The evolution of the topsoil in olive orchards is influenced by many factors, most importantly agricultural practices such as ploughing. Sediment transport is more rapid due to tillage using modern heavy machinery, which is the case study. Consequently tillage has direct and indirect impacts on the soil quality. Therefore tillage displaces the topsoil, causing sediment concentration (increase in the topsoil) in some places and soil loss in others. We conclude this from our analysis of the grids of high resolution. In order to analyse these changes accurately we have developed a DEM generated from field measurements taken by GPS. This positioning system provides planimetric and altimetric coordinates with centrimetric precision. The methodology used for taking the field measurements and generating the grids should be analysed carefully. In this study, by using GPS relative positioning receivers we obtained accuracy in the h coordinate from 0.004 to 0.08 m. This allowed us to detect deposition values of a few centimetres. These values have been small enough to detect a spatial redistribution of topsoil in this zone. For this reason it is essential that the accuracy of field measurements, the interpolation method and the phenomenon to analyse are of the same range. In conclusion, the results of our study confirm that in order to detect sediment movements in detail it is essential not only to use appropriate grid scales but also to analyse carefully the accuracy of the instrumentation for taking field measurements. Acknowledgements This work has been partially supported by the Spanish Ministry of Education and Science, the European Union (via ERDF funds), the Andalusia Junta, the Giennenes Studies Institute and The University of Jaén through research project TIN2007-67474-C03-03, P07-TIC-02773, RFC/IEG 2009 and UJA2010/13/08 (Caja de Jaén). References: [1] Adinarayana, J., Rao, K.G., Krishna, N.R., Venkatachalam, P., Suri, J.K., 1999. A rulebased soil erosion model form a hilly catchment. Catena 37, 309-318. [2] Brown, N., Keenan, R., Richter, B. and Troyer, L., 2005. Advances in Ambiguity Resolution for RTK Applications Using the New RTCM V3.0 Master-Auxiliari Messages. Proceedings of ION GNSS 2005. September 13-16, 2005, Long Beach, CA. [3] Claessens, L., Heuvelink, G.B.M., Schoorl, J.M., Veldkamp, A. 2005. DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surf. Process. Landf. 30, 461 477. ISBN: 978-1-61804-057-2 22

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