- Email: [email protected]
Using GPS and GIS tools to monitor olive tree movements
Computers and Electronics in Agriculture 57 (2007) 135–148
Using GPS and GIS tools to monitor olive tree movements M.I. Ramos a,∗ , A.J. Gil a,1 , F.R. Feito b,2 , A. Garc´ıa-Ferrer c,3 a
Dept. Ing. Cartogr´afica, Geod´esica y Fotogrametr´ıa, Universidad de Ja´en, Campus Las Lagunillas, Edif. A3, 23071 Ja´en, Spain b Dept. Inform´ atica, Universidad de Ja´en, Campus Las Lagunillas, Edif. A3, 23071 Ja´en, Spain c Dept. Ing. Gr´ afica y Sistemas de Informaci´on Geogr´afica. E.T.S. de Ingenieros Agr´onomos y Montes, Universidad de C´ordoba. Avda. Men´endez Pidal, s/n, 14004 C´ordoba, Spain Received 5 December 2006; received in revised form 2 March 2007; accepted 2 March 2007
Abstract Erosion is a problem that produces an important impact on the landscape, especially in agricultural areas. This process is accentuated by the effects of meteorological factors, tillage practices and the slope of the land. This latter effect is of greater importance because it leads to surface runoff which in turn causes the soil erosion. The properties of the land, such as the type of crop or the farm management practice, are also factors that determine the study of soil erosion. They must all be evaluated in order to obtain conclusions about the tillage erosion in a property. Nevertheless it is necessary to use spatial data to better quantify the changes that take place. In our study we quantify the eventual land movement and the subsequent displacement of olive trees produced by continuous tillage erosion. We analyse these movements on a property of olive orchards located on variable sloping land. Land movement monitoring has its methodological base in the repeated revision of the position of the object points, in this case the olive trees. The most suitable instrumentation for taking measurements in the zone we studied is GPS because of the lack of visibility through the trees. Data are integrated into GIS software in order to carry out a specific spatial analysis of this phenomenon. Our analysis provides accurate values of displacements which confirm that our olives trees have moved a few centimetres in a year. There is a relationship between olive tree movements and other spatial models such as elevation, slope or aspect. We have also observed that tillage practice causes complementary effects on tree movements. © 2007 Elsevier B.V. All rights reserved. Keywords: GPS; GIS; Olive trees; Deformation monitoring; Erosion; Precision agriculture
1. Introduction Soil erosion is the most important problem in Mediterranean olive orchards. Annually millions of tons of soil are swept away by runoff water, having a disastrous effect on the olive harvest and creating a gradual loss of farmland productivity. Southern Spain, Andalusia, contains the greatest concentration of olive cultivation, 75%. In particular, in the province of Jaen olive orchards cover 572,674 ha, representing 39% of the Andalusian olive cultivation (I.E.A., 2004). Many olive exploitations occupy large areas of mountains and hills (Romero, 1998). Land gradient and farming practices play important roles here. In these zones the major problem is the water erosion, wherein each year the rates ∗ 1 2 3
Corresponding author. Tel.: +34 953212470; fax: +34 953212854. E-mail addresses: [email protected] (M.I. Ramos), [email protected] (A.J. Gil), [email protected] (F.R. Feito), [email protected] (A. Garc´ıa-Ferrer). Tel.: +34 953212467; fax: +34 953212854. Tel.: +34 953212446; fax: +34 953212472. Tel.: +34 957218538; fax: +34 957218537.
0168-1699/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2007.03.003
136
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
of soil loss are between 60 and 105 t ha−1 . Here the steeper the slope of a field is, the greater the amount of soil loss produced by rain. In addition, in orchards where mechanized farming practices are used the erosion phenomenon is accelerated because agricultural machinery removes important surface vegetation which would otherwise reduce soil degradation caused by the rain (Francia et al., 2000). This latter is called tillage erosion, which is a form of erosion which is receiving increased attention. Tillage erosion delivers soil from upper slope positions to the lower parts of the slope. After many years of tillage, topsoil accumulates at the bottom of the slope. The negative effects of the erosion advance on crop yields are: decrease of organic matter, phosphorus levels, the potential planavailable water and aggregate stability (ECAF, 1999). Sometimes in olive orchards this erosion is so aggressive that it causes displacements of the olive trees themselves. These are progressive movements but their magnitude and direction are changeable. They should be directed downhill, but if the area is not very sloping they may occur at random because of other causes, especially the use of agricultural machinery. Other erosion factors such as meteorological ones are very aggressive too, but they are discontinuous in time. Nevertheless, tillage practices tend to be continuous throughout the year. There are several methods for the study of erosion (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). This is due to the large amount of factors which influence this phenomenon, hindering its estimation. Nevertheless, an accurate and timely estimation of soil erosion has become an urgent task (Lu et al., 2004). As has been mentioned, there are many factors which condition the methodology and instrumentation used in the study of erosion. 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. In this study we monitor displacements of olive trees of an orchard located on sloping terrain. We also establish the connection between such movements and slope. These movements are the main tillage erosion impact on this exploitation situated in south Spain. In order to carry out a thorough study of these movements it is necessary to analyse different spatial data: the exact position of the olive trees from three survey campaigns in 1 year, the digital elevation model (DEM) and the slopes model. Accuracy in spatial data is essential in order to provide reliable data of landscape changes (Ramos et al., 2004). Thus it is very important to use accurate techniques and instrumentation for taking field measurements, and an adequate data managing system (Nem´enyi et al., 2003). We use high precision techniques and geodesy instrumentation, such as the GPS system. This technique has the advantage that it does not require a direct line of sight between the moving receiver and the reference station. This is very useful considering the lack of visibility through the olive groves. We used dual-frequency GPS receivers to acquire data in the topographic surveys. The data collection techniques used to monitor olive tree movements was static rapid surveying, with observation periods of 10 min. The elevation accuracy of a DEM and actual points in a field are mainly related to the elevation accuracy of the measuring instrument, and the field procedure (Yao and Clark, 2000). In this sense, we used the stop-and-go technique to develop DEMs, stopping at a field point for about 3 s to collect data. Once the measurements are processed these data are integrated into a GIS in order to improve the spatial analysis of movements and therefore analyse the influence of erosion on the studied zone. 2. Methods and materials Our objective is to accurately measure olive trees displacement, and establish the connection between such movements and slope. In the first phase of our work we collect data from GPS field measurements from three survey campaigns in 1 year. Two kinds of field data are used: the position of olives trees for each campaign and the digital elevation model. During this phase we analyse the movements of the olive trees. In the second phase of the investigation data movements together with the digital elevation model are integrated into a GIS in order to perform the specific spatial analysis. We work with 52 olive trees that belong to the same orchard, an unirrigated olive orchard where the slope gradient varies between 2% and 25%. An agronomical classification of soils determines that mechanical tillage in such sloped zones produces quite important erosion damage to soil cover (C.A.P., 2003). The property is located in a village called Lahiguera in the province of Jaen (Andalusia, Spain). This is one of the main olive oil producing regions of the country; therefore it is the most representative area of this kind of exploitation. The olive orchard we studied is 1,19 ha with
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
137
Fig. 1. (a) Location of the study zone. (b) External control network designed to monitor the position of the local network. (c) Elevation of the olive orchard studied. The white circles are the olive trees monitored (object points). The zones A, B and C are the zones measured in order to obtain high precision DEMs. (d) The local control network is made up of three reinforced concrete pillars anchored to the ground.
120 olive trees of similar properties: they all have three trunks, are about 30 years old and their mean fruit harvest is 60 kg/year. The difference between them is the slope of the land where they are planted in the orchard. In this study the property is divided into three zones (Fig. 1(c)), following the criteria of the gradient of the slope of the terrain. Zones A and C have a higher gradient than B, this last being a flat zone. The classification is as follows: zone A: medium gradient: 15%, zone B: minimum gradient: 1%, zone C: highest gradient: 25%. We consider this classification useful for analysing zones with similar properties and isolating them from the property as a whole (Zhang et al., 2002). Also, this helps us to study how much slope does influence olive tree displacement. The plot of land selected is located next to a weather station. This station reports meteorological data of the zone like rainfall, minimum and maximum temperature, air humidity, windspeed, etc. In this study we did not use these variables in practice because there was no variation between the three zones selected. We employed them for planning field measurement surveys. Nevertheless the data would be useful in future studies in order to compare this plot with other ones with similar topographic properties but located in a different city. We took measurements after periods of hard farming practice or bad weather conditions in order to evaluate erosion damage.
138
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 2. (a) Leica SR9500 receiver collecting data. (b) Detail photography of nail embedded in the trunk of the olive tree. (c) GPS receiver and accessories used. Plates aligned to take measurements. (d) Measured points in zone A.
2.1. Information collected from GPS field measurements 2.1.1. Analysis of land displacements The methodology of this analysis consists of monitoring the position of several points in each survey. The research is carried out as follows: first we distinguish two groups of points, one, the local control network, consisting of control stations, which are points placed in a stable zone so their positions do not change (Fig. 1(d)); and the second, the object points, consisting of points which could have significant movements (Fig. 1(c)). Subsequently relative movements of object points from the local control network are analysed. This is a non-permanent GPS network that is observed periodically in GPS campaigns spaced over time to check its fixed position (Betti et al., 1999). The construction of the pillars of the local control network ensures that the GPS antennas will be placed exactly at the same position for different reoccupations; the pillars incorporate an embedded forced centring system (Gil et al., 2002). Despite the special care taken in the construction of the network, we assume that it is vulnerable. The pillars could be displaced due to farming machinery, tillage practices, human activity or subsoil movements. Therefore an external control network has been designed to monitor the position of the local one (Ramos et al., 2005a). The external control network is placed far from the study area in order to keep it away from the local movements (Fig. 1(b)). The three pillars meet the same constructive requirements as those of the local network. As we can see in Fig. 1(b) two pillars, “Salado” and “Cagancho”, are located in areas with a minimum slope gradient. The third one, “Terraza”, has been constructed on a rooftop in the urban area to avoid obstructions over 15◦ . Once we had designed control networks we marked the olive trees to be studied, the object points (Fig. 1(c)). We selected those ones located in the zones A, B and C; it was also considered useful to add others located outside these zones but sensitive to land movements as well. We hammered nails into the trunk to mark the trees (Fig. 2(b)). We took care to set the nails in the base and not in the branches in order to avoid movements coming from their growth. The first GPS survey was carried out in June 2004, the second in November 2004 and the third in June 2005. The equipment used was three Leica SR399, two Leica SR9500 and one Leica S500 dual frequency carrier phase GPS receivers, which belong to the University of Jaen. The networks have been tied into the European Terrestrial Reference
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
139
System 1989 (ETRS89), from which the coordinates of the “Terraza” point were computed. GPS observations data processing was performed using Leica Ski-Pro 2.5 software (Leica Geosystems, 2000). In each campaign we computed the baselines and their least squares adjustments. The coordinates of the networks which were obtained in June 2004 were considered as the reference frame (Crespi, 1996). This means that in the comparisons between the three campaigns the first one was fixed. To identify an olive tree whose coordinates had changed we performed an analysis of two kinds of data: firstly, the variation on planimetric position and secondly the altimetric one. The data used to study planimetry was the displacement vector defined by its module and geodetic azimuth. Module is the distance that separates the planimetric position of the point from one campaign to another; this parameter was computed from the coordinates, latitude and longitude, of the point. Geodetic azimuth is the orientation of vector displacement measured in degrees clockwise from North. The study of altimetric movements was based on the comparison of altitude values of a point during each campaign. These differences in altimetry were represented by vertical vectors whose starting point belongs to the first campaign, June 2004. In this field it is very important to discriminate between vector displacements and error values. We applied statistical tools in order to achieve accurate results of the movements. We worked with confidence regions from each point adjustment in the three campaigns. Planimetric error ellipses and altimetric error ratio were used. In the case of planimetric data, movements are significant if the error ellipses of each campaign do not intersect, but for altimetric data, changes in the positions are significant only if vector displacements are out of the error interval. Tables 1 and 2 show the numerical results of the analysis of the displacements of all the trees selected. An improvement of this analysis is shown in Fig. 3, where we have taken a piece of the property as an example; they show a spatial analysis of the results. To identify each object point we used a code that consists of three characters: the first is the zone of the property where the tree is placed, the second the number of olive tree rows inside the zone and the third is the column number. Note that code “d” corresponds to the trees outside the zones classified; these were listed in order from north to south. Fig. 3(a) and (b) corresponds to an example of the planimetric analysis where comparisons between the first campaign and the two others are represented. We can assume at 99% reliability that there were small displacements (2–4 cm) from June 2004 to June 2005 at several points. 2.1.2. Digital elevation model (DEM) In the previous section we calculated planimetric and altimetric displacement vectors of a few centimeters with high precision. At this stage the aim of our research is to monitor how land gradient influence such olive tree movements. Therefore, in order to obtain accurate results we must generate a precise slope model from a very accurate DEM with high spatial resolution (0.25 of cell size). The method of taking measurements in the field was considered carefully with the aim of providing precise coordinates of the land (Ramos et al., 2005b). One additional fact which must be considered is that the surface contains sand stones which break easily to the touch. The inconsistency of the sandy soil, into which the bar carrying the GPS antennae is driven, means that the bar sinks quite easily into the ground. This makes it difficult to measure the topography of the zone accurately. Therefore we adopted the following system: We use metacrylate plates of 0.5 m side length with a small hole in the centre where the bar, on which the GPS antenna is fitted, is inserted (Fig. 2(c)). The plates are dropped on the ground, then the bar is fitted in the orifice and measurements are taken. Each measurement is 0.5–1 m from the next. The distribution of these measurements follows a series of alignments that cover the area without taking points very close to the olive trees, because their branches interfere with the GPS signal reception (Fig. 2(d)). These zones without measurements are the white holes that appear in the figure. In order not to decrease the high precision of DEM these holes will not be taken into account in the data processing, meaning that instead they will be considered as edges. Finally, we used the software of treatment (interpolation, analysis and management) of grids Vertical Mapper v. 3.1 (Vertical Mapper v.3.1, 2001), an application that works within MapInfo Professional v.7.5 (MapInfo Cor., 2003), in order to generate the DEM. The interpolation method used was the triangulated irregular network (TIN). TIN involves a process whereby all original data points are connected in space by a network of triangular faces, as equilateral as possible, forming what is referred to as a triangular irregular network. This method is appropriate for generating the elevation grids since it creates an output surface which passes through the measured points. The features of the triangulation algorithm we chose were 2 m of maximum triangle side length and 0.25 m of cell size.
140
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Table 1 Planimetric displacement analysis of olive trees from June 2004 to November 2004 and June 2005 campaigns, ellipses at 99% reliability Olive trees, code
a11 a12 a13 a14 a21 a22 a23 a24 a31 a33 a34 b11 b12 b13 b14 b21 b23 b24 b31 b32 b33 b41 b42 b43 b44 b51 b52 b53 b54 b61 b62 b71 b72 b73 b74 b81 b82 b83 b84 d10 d11 d1 d2 d3 d4 d5 d6 d7 d9 m11 m12 m14
Planimetric movement
Ellipses error at 99% reliability
June 2004– November 2004
June 2004– June 2005
June 2004
November 2004
June 2005
Module (m)
Azimuth (◦ )
Module (m)
Azimuth (◦ )
a (m)
b (m)
Φ (◦ )
a (m)
b (m)
Φ (◦ )
a (m)
b (m)
Φ (◦ )
0.030 0.028 0.022 0.020 0.013 0.018 0.010 0.009 0.025 0.021 0.031 0.018 0.002 0.013 0.022 0.040 0.049 0.050 0.018 0.017 0.033 0.018 0.029 0.036 0.038 0.005 0.021 0.015 0.011 0.004 0.003 0.019 0.036 0.011 0.015 0.017 0.014 0.018 0.004 0.028 0.003 0.032 0.045 0.053 0.056 0.012 0.033 0.048 0.046 0.021 0.011 0.048
85 128 74 106 79 92 105 42 84 151 233 141 185 175 180 159 126 183 189 160 158 129 132 137 138 251 58 63 217 230 265 144 113 222 219 109 167 99 320 70 315 335 350 131 143 149 334 159 141 156 149 160
0.022 0.122 0.014 0.023 0.027 0.023 0.026 0.040 0.020 0.012 0.024 0.031 0.005 0.019 0.012 0.016 0.005 0.041 0.012 0.032 0.029 0.020 0.026 0.015 0.023 0.052 0.049 0.045 0.035 0.028 0.035 0.039 0.047 0.048 0.053 0.023 0.043 0.025 0.019 0.038 0.037 0.029 0.038 0.040 0.023 0.029 0.042 0.036 0.030 0.034 0.041 0.022
327 161 295 324 306 315 339 325 303 15 303 321 320 324 321 240 129 210 293 232 218 314 356 328 324 325 3 8 344 358 357 8 17 335 326 306 350 15 291 332 302 317 302 291 163 330 5 11 162 239 237 231
0.010 0.016 0.016 0.015 0.013 0.010 0.012 0.015 0.011 0.019 0.015 0.010 0.029 0.032 0.030 0.029 0.044 0.024 0.021 0.026 0.032 0.012 0.013 0.018 0.013 0.009 0.012 0.023 0.037 0.556 0.061 0.023 0.022 0.024 0.027 0.028 0.021 0.025 0.019 0.022 0.024 0.027 0.028 0.066 0.027 0.055 0.089 0.051 0.015 0.017 0.021 0.225
0.007 0.012 0.012 0.011 0.009 0.007 0.009 0.011 0.016 0.012 0.011 0.006 0.013 0.020 0.017 0.018 0.022 0.011 0.011 0.016 0.019 0.006 0.010 0.009 0.007 0.006 0.008 0.002 0.018 0.010 0.017 0.013 0.012 0.011 0.013 0.015 0.012 0.015 0.013 0.016 0.011 0.017 0.021 0.016 0.014 0.010 0.012 0.020 0.010 0.013 0.017 0.007
13 9 −1 −5 11 6 −8 −12 −18 −15 −17 −14 −6 −9 −5 0 −10 −5 19 14 11 20 31 2 5 −33 −27 −15 −9 −4 −18 −14 −17 −30 −28 −19 −17 −23 −24 −16 −6 −4 −6 −8 −5 −6 −6 −7 −19 −17 −13 −4
0.020 0.037 0.019 0.021 0.034 0.017 0.029 0.028 0.018 0.015 0.020 0.027 0.037 0.028 0.034 0.029 0.035 0.037 0.027 0.023 0.016 0.013 0.026 0.027 0.012 0.021 0.020 0.018 0.023 0.016 0.028 0.021 0.020 0.030 0.028 0.021 0.026 0.030 0.010 0.033 0.036 0.028 0.019 0.022 0.019 0.021 0.022 0.026 0.028 0.021 0.025 0.026
0.014 0.027 0.015 0.019 0.027 0.015 0.026 0.025 0.021 0.014 0.019 0.018 0.020 0.022 0.015 0.016 0.016 0.022 0.022 0.013 0.011 0.011 0.003 0.016 0.007 0.018 0.018 0.013 0.021 0.015 0.017 0.018 0.019 0.027 0.021 0.019 0.024 0.027 0.008 0.022 0.023 0.021 0.016 0.020 0.018 0.019 0.018 0.016 0.018 0.017 0.019 0.017
15 21 12 −2 18 7 −9 −27 2 4 −44 43 61 58 57 59 63 46 16 18 10 −3 4 7 0 10 3 11 22 17 19 62 70 19 39 65 60 19 100 50 50 48 50 80 36 67 −97 38 45 43 43 44
0.012 0.008 0.012 0.010 0.013 0.014 0.009 0.010 0.015 0.012 0,011 0.018 0.028 0.014 0.028 0.020 0.024 0.028 0.018 0.038 0.043 0.009 0.018 0.012 0.011 0.014 0.016 0.015 0.011 0.017 0.016 0.014 0.018 0.022 0.021 0.018 0.021 0.024 0.020 0.013 0.019 0.017 0.027 0.022 0.028 0.011 0.020 0.018 0.021 0.016 0.017 0.018
0.009 0.006 0.009 0.008 0.010 0.011 0.007 0.008 0.007 0.009 0.008 0.011 0.019 0.017 0.012 0.013 0.011 0.015 0.008 0.012 0.023 0.006 0.009 0.013 0.006 0.007 0.008 0.009 0.006 0.008 0.009 0.007 0.008 0.010 0.016 0.008 0.009 0.010 0.009 0.008 0.010 0.028 0.011 0.016 0.020 0.008 0.011 0.016 0.011 0.018 0.014 0.010
2 0 −1 −6 −5 −4 −10 −8 −11 −16 −13 −2 −3 −1 −3 −5 −5 −13 −7 16 27 10 12 15 18 20 21 16 19 20 22 21 21 21 18 20 18 19 18 −3 −5 −7 −9 −14 −18 −7 0 −3 −5 −7 −7 −5
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
141
Table 2 Altimetric displacement analysis of olive trees from June 2004 to June 2005 campaigns at 1% significance level Olive trees, code
a11 a12 a13 a14 a21 a22 a23 a24 a31 a33 a34 b11 b12 b13 b14 b21 b23 b24 b31 b32 b33 b41 b42 b43 b44 b51 b52 b53 b54 b61 b62 b71 b72 b73 b74 b81 b82 b83 b84 d10 d11 d1 d2 d3 d4 d5 d6 d7 d9 m11 m12 m14
Altimetric movement (m)
Error ratio at 1% significant level (m)
June 2004–November 2004
June 2004–June 2005
November 2004
June 2005
−0.012 0.017 −0.024 0.017 −0.039 −0.001 0.005 −0.011 0.012 0.016 0.010 0.011 0.004 −0.007 0.003 0.059 0.011 −0.019 0.057 0.075 0.040 0.025 0.044 0.080 0.019 −0.029 0.033 −0.030 0.005 0.001 −0.018 −0.004 0.046 −0.023 0.012 −0.007 0.015 0.041 0.024 0.017 0.030 0.038 0.033 0.030 0.045 0.005 −0.066 0.028 0.020 0.003 0.005 0.059
−0.024 −0.038 −0.093 −0.113 −0.067 −0.048 −0.102 −0.051 −0.026 −0.100 −0.046 −0.055 −0.020 −0.042 −0.136 0.002 −0.077 −0.078 −0.011 0.002 0.005 −0.069 −0.058 −0.061 −0.051 −0.127 −0.090 −0.082 −0.078 −0.120 −0.086 −0.083 −0.039 −0.090 −0.063 −0.036 −0.107 −0.022 −0.032 −0.051 −0.102 −0.065 0.001 −0.114 0.026 −0.036 −0.051 −0.123 −0.010 −0.044 −0.051 −0.051
±0.026 ±0.048 ±0.019 ±0.014 ±0.045 ±0.024 ±0.042 ±0.041 ±0.026 ±0.022 ±0.030 ±0.038 ±0.031 ±0.017 ±0.022 ±0.034 ±0.045 ±0.067 ±0.037 ±0.033 ±0.022 ±0.022 ±0.039 ±0.025 ±0.016 ±0.029 ±0.029 ±0.021 ±0.033 ±0.019 ±0.045 ±0.027 ±0.027 ±0.028 ±0.044 ±0.029 ±0.027 ±0.035 ±0.045 ±0.024 ±0.030 ±0.029 ±0.028 ±0.028 ±0.022 ±0.031 ±0.011 ±0.041 ±0.046 ±0.036 ±0.012 ±0.032
±0.025 ±0.016 ±0.025 ±0.020 ±0.025 ±0.027 ±0.016 ±0.019 ±0.065 ±0.020 ±0.018 ±0.023 ±0.041 ±0.030 ±0.020 ±0.030 ±0.035 ±0.036 ±0.027 ±0.021 ±0.058 ±0.013 ±0.018 ±0.015 ±0.017 ±0.022 ±0.027 ±0.012 ±0.017 ±0.031 ±0.021 ±0.028 ±0.022 ±0.030 ±0.039 ±0.032 ±0.033 ±0.040 ±0.044 ±0.029 ±0.002 ±0.031 ±0.039 ±0.016 ±0.028 ±0.020 ±0.023 ±0.028 ±0.017 ±0.024 ±0.022 ±0.033
142
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 3. (a) and (b) Planimetric displacement vectors of olive trees from June 2004 to November 2004 and June 2005 campaigns, ellipses at 99% reliability. (c) Altimetric displacement vectors of olive trees from June 2004 to November 2005 campaigns at 1% significance level. (d) Altimetric displacement vectors of olive trees from June 2004 to June 2005 campaigns at 1% significance level.
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
143
Table 3 Data classification Structure
Continuous variables DEM Slope model Aspect model Discrete variables Zones A, B, C Olive trees Points measured (to generate DEM) Displacements vectors of trees Planimetric error ellipses Altimetric error ratios
Nature
Raster
Vector
Spatial
Temporal
Grid Grid Grid
Contour Map Contour Map Contour Map
× × ×
Contour Map Points Points Lines Polygons Lines
× ×
×
× × ×
× × ×
2.2. Data integration into GIS tools 2.2.1. Precision agriculture and GIS Often the consequences of erosion are studied in terms of top soil loss, but these are at times less than useful for making correct decisions to solve erosion problems. For this reason the spatial analysis of erosion implies the use of visual information. In the introduction we mentioned tillage practices as one factor that exacerbates the erosion process in the olive orchards. It is therefore necessary to take into account all the information collected in order to make good agricultural management decisions. This is related to precision agriculture (Stafford, 2000), a new concept of farming in which spatial and temporal variability within a field are analysed in order to improve benefits. This concept is based on new positioning and representation technologies now available such as GPS and GIS. With these tools we can map our study area and extract and plot information with high variances within the field instead of estimating an average value for the entire field. GIS in particular provides a new point of view and more complete information in order to make valid technical agricultural decisions (Nem´enyi et al., 2003). Nowadays there is a considerable necessity of software GIS in precision agriculture but most of the current GIS tools are too expensive, complex or inappropriate for processing certain data at small scales (Runquist et al., 2001). Also, this software does not posses the accurate spatial analysis tools needed to solve spatial problems. Therefore, we have developed our own system from commercial GIS software, MapInfo v.7.5. Here we suggest a methodology which integrates vector and raster data in order to approach a visualization problem in agriculture. We use different kinds of data, from vector displacements obtained in the analysis of movements in the olive trees to the spatial parameters that influence the local erosion process. Some of these data are constant and others variable. In addition, they are different in nature or format and come from different sources, so we need a system able to process this data accurately. 2.2.2. Data management The variables that have significant influence on the agricultural harvest are categorized into six groups: yield variability, field variability, soil variability, crop variability, variability in anomalous factors and management variability (Zhang et al., 2002). In this article we focus on the field variability group, in particular the elevation and slope variables. Other data are added to these variables, such as the position of the olive trees during each campaign, the points measured to generate the DEM, the three zones selected in the property studied (Fig. 1(c)), the displacement vectors of the trees, the planimetric error ellipses and the altimetric error ratios (Fig. 3). All these variables are both spatial and temporal in nature and therefore our classification of them must also take into account the continuous and discrete nature of data. Continuous variables are spatial data and here it is considered that they do not change over time. Although discrete variables are spatial as well, most of them are temporal in nature (Table 3). Both continuous and discrete variables include raster and vector structure data.
144
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 4. Entity-relationship model data. Above is represented the interactions between olive entities and their attributes. At the bottom there are the results of a SQL query to monitor planimetric movements.
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
145
In the vector data system several stored attributes are assigned to one table. In this last there is one field per attribute and a record per value (Fig. 4). This structure allows constant availability and updating of data without needing a powerful computer (Feito et al., 1999). Raster structure data improves computations in spatial analysis. Here grid maps were used to perform accurate queries such as the connection, measured with high precision, between positional shifts of the olive trees and the gradient. With this kind of query the pixel size of grid maps is important in order to guarantee a detailed analysis. The pixel size depends on the resolution of DEM which was generated from an ASCII file of (x,y,h) coordinates points measured. Then the slope model was calculated using the Vertical Mapper v.3.1 software and the contour maps were generated as topologically constructed MapInfo regions from grid files. Finally, the lines and the polygons came from CAD files. The data were integrated into the GIS database so that many spatial operations, such as overlay and buffer, could be performed. In addition this system allowed us to establish a database of rapid numerical access and an adequate visualization of the results. The entity-relationship (ER) model was used to implement a data model in database management software. In Fig. 4, the ER model diagram represents the problem as entities and relationships. The tables resulting from the design of the ER model were the following: • ZONE (ID Zone, Description). This table is related to the polygons which represent the study zones chosen from the whole property. • CAMPAIGN (ID Campaign, Date, Description). This table distinguishes each field measuring campaign. • USE (ID Use, Description). The points measured could be used to generate the elevation model or for positioning the olive trees. Thus this table specifies whether the use of the point is the former or the latter. • POINT (ID Point, x, y, h, ID Zone, ID Campaign, ID Use). This is the field data measured. • ERROR ELLIPSE (ID Error Ellipse, a, b, Azimuth, ID Point). This table contains the parameters of the planimetric error ellipse of each measured point. • CONFIDENCE INTERVAL (ID Confidence Interval, Upper boundary, Lower boundary). This table contains the boundaries of the confidence interval of altimetric values measured for each point. 3. Results and discussion Visual spatial analysis allows the monitoring of olive tree movements from another point of view apart from the numerical calculations described in Section 2.1.1. With only one visual query we can evaluate planimetric or altimetric movements. For example, Fig. 4 shows a query which selects no significant planimetric movements; thus if the entity is selected it has no significant displacement. The results of this query show that some trees like a13 have no planimetric displacement, but others like a12 have planimetric displacement of a few centimeters, 6 cm, from June 2004 to June 2005. Points a11, a21, a22 and a23 have also moved 2–4 cm in the same period of time, but the direction and length of these movements are different from a12. Similar queries could be used to analyse altimetric movements. An example of the altimetric analysis is represented in Fig. 3(c) and (d): the graph presents vertical movements from the June 2004 campaign to November 2004 and the June 2004 campaign to June 2005, respectively. The error ratio at 1% significance level is represented for each point. We can see that most of the points have moved vertically from June 2004 to June 2005 except one, a11. The major vertical displacements have occurred in the space of a year, and they also tend to be greater than the horizontal ones, so we can conclude that in this zone the height of the hill is decreasing. This spatial analysis could be performed by inexperienced users of the theory of analysis of land movements. With this user-friendly system they would be able to understand and monitor these movements. In addition, spatial tools are especially useful for mapping results in different scales. We can study in detail the displacement of one olive tree in a horizontal or vertical direction and also analyse movements of all the trees with just one query. It is possible to know if displacements in a given area are due to the slope or the tillage practice. This issue is analysed in detail by overlaying movements data and the precision slope model (Fig. 5). A downhill movement means the effect of erosion is accentuated by slope values in that zone. When the planimetrical direction of the movement is not downhill we talk about an arbitrary movement, for example point a11 (Figs. 4 and 5). In this case we can confirm that machinery or tillage could counteract the influence of the slope. In Fig. 4, we can see that point a13 has no planimetrical shift because all error ellipses intersect. The integration of precision DEM into GIS provides a powerful tool for analysing spatial problems in high detail. We use the term “precision DEM” because of the specific instrumentation and methodology used to collect field
146
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 5. Altimetric displacements from June 2004 to November 2004 in zone A. Displacements vectors and slope and aspect model are overlaid.
measurements. There are several DEMs of the study area publicly available from government institutions. In particular we have an elevation model of 30 m of spatial resolution, but we considerer it inappropriate to this investigation. The advantage of using a DEM of small grid cell size is that, if we add to it precise data on olive tree movements obtained through the methodology described above, we are able to perform spatial analyses of high precision. The slope model and also the aspect model (Fig. 5) calculated from the precision DEM help us to analyse the direction of displacements. If such displacements are directed downhill then we can confirm the high influence of topographic parameters. Sometimes the tree movement is counteracted by tillage practices and the result is a vector displacement in an arbitrary direction. In this case an elevation model of very high detail is essential to analyse this phenomenon because the movements are in the order of a few centimeters. An example of this analysis is the one we carried out to monitor the altimetric shift of a13 olive tree. Fig. 3 shows that this altimetric movement is significant but that the planimetric one is not. This case could be analysed in detail combining slope and aspect models with the vector displacement (Fig. 5). This tree is situated on a zone whose slope values are 20–29%. The aspect model shows that the area around the tree a13 faces from 135 to 225 degrees from the north. In addition, during mechanized tillage, the tractor drives in the direction of maximum slope and moves soil cover in an angular direction and at a deep of 20–40 cm. This could counteract the planimetric movement of some trees. In addition, the slope and aspect models help us to develop advanced queries in which all the data are overlaid. These results are therefore more reliable because all the data are taken into account. As an example, we are able to see which zone is more affected by the erosion phenomenon, that is, which movements are greater; or it also interesting to see which zone has been less affected by the tree movements, or which one has not changed from June 2004 to June 2005. 4. Conclusions The spatial analysis system presented allows users to monitor in detail olive tree movements caused by erosion agents. Our analysis has provided accurate values of displacements which confirm that our olives trees have moved
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
147
a few centimetres in 1 year. High precision DEM have been incorporate into our GIS in order to carry out advanced spatial queries. We have detected that there is a relationship between these movements and other spatial models such as elevation, slope or aspect. Also we have confirmed that tillage practice causes complementary effects on tree movements. In some cases tillage practices counteract displacements exacerbated by slope gradient, restraining soil cover movements. In other cases, both movements occur in the same direction and produces important changes in the position of the trees. These analyses are very useful for enhancing the management of olive tree plantations or other fruit trees, because this system makes possible analyse results in detail or map them at small scales. Once the consequences of soil erosion are analysed it is necessary to contemplate a change in farming techniques in order to reduce the advance of the erosion process. Acknowledgements We wish to thank the reviewers for their helpful comments. This work has been partially supported by the Spanish Ministry of Education and Science and the European Union (via ERDF funds) through research projects TIN200406326-C03-03 and ESP2006-10113. References Adinarayana, J., Rao, K.G., Krishna, N.R., Venkatachalam, P., Suri, J.K., 1999. A rule-based soil erosion model form a hilly catchment. Catena 37, 309–318. Betti, B., Biagi, L., Crespi, M., Riguzzi, F., 1999. GPS sensitivity analysis applied to non-permanent deformation control networks. J. Geodesy 73, 158–167. C.A.P. (Consejer´ıa de Agricultura y Pesca). Junta de Andaluc´ıa (Eds.), 2003. El Olivar Andaluz. Viceconsejer´ıa. Servicio de Publicaciones y Divulgaci´on. Sevilla, Spain. Crespi, M., 1996. A Software Package for the Adjustment and the Analysis of GPS Control Networks In: Unguendoli, M. (Ed.), Reports on Survey and Geodesy in memoria of Prof. A. Gubellini and G. Folloni. Ed. Nautilus, Bologna, pp. 237–264. D’Ambrosio, D., di Gregorio, S., Gabriela, S., Gaudio, R., 2001. A cellular automata model for soil erosion by water. Phys. Chem. Earth. Part B: Hydrol. Oceans Atmosphere 26, 33–39. ECAF, European Conservation Agriculture Federation, 1999. Conservation Agriculture in Europe: environmental, economic and EU policy perspectives. European Conservation Agriculture First Report. Available at: http://www.ecaf.org/. Francia, J.R., Mart´ınez, A., Ruiz, S., 2000. Erosi´on en suelos de olivar en fuertes pendientes. Comportamiento de distintos manejos de suelo. S. E. C. S. Edafolog´ıa 7–2, 147–155. Feito, F.R., Garrido, A., Ogayar, B., Molina, A., 1999. GISELEC’2000. Proceedings 7a Jornadas Hispano-Lusas de Ingenier´ıa El´ectrica, Lisboa Portugal. Gil, A.J., Rodr´ıguez-Caderot, G., Lacy, M.C., Sanz de Galdeano, C., Alfaro, P., 2002. Establishment a non-permanent GPS Network to monitor the deformation in the Granada Basin (Betic Cordillera, South Spain). Stud. Geophys. Geod., 46, 395–410. StudiaGeo s.r.o., Prague. I.E.A. (Instituto Estad´ıstico de Andaluc´ıa), 2004. Anuario Estad´ıstico de Andaluc´ıa 2004. Junta de Andaluc´ıa. Spain. http://www.juntadeandalucia. es/iea/anuario/anuario04/index.htm. Leica Geosystems, A.G. (Ed.), 2000. CH-9435 Heerbrugg, Switzerland. Lu, D., Li, G., Valladares, G.S., Batistella, M., 2004. Mapping soil erosion risk in Rondonia, brazilian amazonia: using RUSLE. Rem. Sens. GIS. Land Degrad. Dev. 15, 499–512. 2003. MapInfo Professional v 7. 5. Reference Guide. MapInfo Coorporation, New York. ´ Pecze, Zs., St´ep´an, 2003. The role of GIS and GPS in precision farming. Comput. Electron Agric. 40, 45–55. Nem´enyi, M., Mesterh´azi, P.A., Nearing, M.A., Foster, G.R., Lane, L.J., Finkner, S.C., 1989. A process-based soil erosion model form USDA-Water Erosion Prediction Project Technology. Trans. ASAE 32, 1587–1593. Ramos, M.I., Feito, F.R., Gil, A.J., 2004. Towards a high precision digital elevation model. In: UNION, Agency Science Press, WSCG POSTER proceedings, WSCG’ 2004: The 12th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision’2004, Plzen, Czech Republic. Ramos, M.I., Gil, A.J., Feito, F.R., 2005a. Deformation analysis to study erosion in sloped olive orchards. In: Sans´o, F., Gil, A. (Eds.), Geodetic Deformation Monitoring: from Geophysical to Engineering Roles. IAG Symposium, vol. 131. Springer, Ja´en, Spain, pp. 265– 269. Ramos, M.I., Feito, F., Gil, R.A.J., 2005b. Analysis of slope to study erosion. In: Proceedings of the XXII International Cartographic Conference (ICC2005), A Coru˜na, Spain. Romero, L.R., 1998. Olive forming in the age of science innovation. Olivae 72, 42–51. Runquist, S., Zhang, N., Taylor, R.K., 2001. Development of a field-level geographic information system. Comput. Electron Agric. 31, 201– 209. Shen, D.Y., Ma, A.N., Lin, H., Nie, X.H., Mao, S.J., Zhang, B., Shi, J.J., 2003. A new approach for simulating water erosion on hillslopes. Int. J. Remote Sens. 24, 2819–2835.
148
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Stafford, J.V., 2000. Implementing precision agriculture in the 21st century. J. Agric. Eng. Rest. 76 (3), 267–275. Veihe, A., Rey, J., Quinton, J.N., Strauss, P., Sancho, F.M., Somarraba, M., 2001. Modelling of event-based soil erosion in Costa Rica Nicaragua and Mexico: evaluation of the EUROSEM model. Catena 44, 187–203. VERTICAL MAPPER v.3.1, 2001. Contour modeling & display software for MapInfo Professional. Version 3.1. User guide. Canada. Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses. Agriculture Handbook no. 537. United States Department of Agriculture, Washington, DC, p. 58. Yao, H., Clark, R.L., 2000. Evaluation of sub-meter and 2 to 5 meter accuracy GPS receivers to develop digital elevation models. Precision Agric. 2, 189–200. Zhang, N., Wangb, M., Wanga, N., 2002. Precision agriculture—a worldwide overview. Comput. Electron Agric. 36, 113–132.
Using GPS and GIS tools to monitor olive tree movements M.I. Ramos a,∗ , A.J. Gil a,1 , F.R. Feito b,2 , A. Garc´ıa-Ferrer c,3 a
Dept. Ing. Cartogr´afica, Geod´esica y Fotogrametr´ıa, Universidad de Ja´en, Campus Las Lagunillas, Edif. A3, 23071 Ja´en, Spain b Dept. Inform´ atica, Universidad de Ja´en, Campus Las Lagunillas, Edif. A3, 23071 Ja´en, Spain c Dept. Ing. Gr´ afica y Sistemas de Informaci´on Geogr´afica. E.T.S. de Ingenieros Agr´onomos y Montes, Universidad de C´ordoba. Avda. Men´endez Pidal, s/n, 14004 C´ordoba, Spain Received 5 December 2006; received in revised form 2 March 2007; accepted 2 March 2007
Abstract Erosion is a problem that produces an important impact on the landscape, especially in agricultural areas. This process is accentuated by the effects of meteorological factors, tillage practices and the slope of the land. This latter effect is of greater importance because it leads to surface runoff which in turn causes the soil erosion. The properties of the land, such as the type of crop or the farm management practice, are also factors that determine the study of soil erosion. They must all be evaluated in order to obtain conclusions about the tillage erosion in a property. Nevertheless it is necessary to use spatial data to better quantify the changes that take place. In our study we quantify the eventual land movement and the subsequent displacement of olive trees produced by continuous tillage erosion. We analyse these movements on a property of olive orchards located on variable sloping land. Land movement monitoring has its methodological base in the repeated revision of the position of the object points, in this case the olive trees. The most suitable instrumentation for taking measurements in the zone we studied is GPS because of the lack of visibility through the trees. Data are integrated into GIS software in order to carry out a specific spatial analysis of this phenomenon. Our analysis provides accurate values of displacements which confirm that our olives trees have moved a few centimetres in a year. There is a relationship between olive tree movements and other spatial models such as elevation, slope or aspect. We have also observed that tillage practice causes complementary effects on tree movements. © 2007 Elsevier B.V. All rights reserved. Keywords: GPS; GIS; Olive trees; Deformation monitoring; Erosion; Precision agriculture
1. Introduction Soil erosion is the most important problem in Mediterranean olive orchards. Annually millions of tons of soil are swept away by runoff water, having a disastrous effect on the olive harvest and creating a gradual loss of farmland productivity. Southern Spain, Andalusia, contains the greatest concentration of olive cultivation, 75%. In particular, in the province of Jaen olive orchards cover 572,674 ha, representing 39% of the Andalusian olive cultivation (I.E.A., 2004). Many olive exploitations occupy large areas of mountains and hills (Romero, 1998). Land gradient and farming practices play important roles here. In these zones the major problem is the water erosion, wherein each year the rates ∗ 1 2 3
Corresponding author. Tel.: +34 953212470; fax: +34 953212854. E-mail addresses: [email protected] (M.I. Ramos), [email protected] (A.J. Gil), [email protected] (F.R. Feito), [email protected] (A. Garc´ıa-Ferrer). Tel.: +34 953212467; fax: +34 953212854. Tel.: +34 953212446; fax: +34 953212472. Tel.: +34 957218538; fax: +34 957218537.
0168-1699/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2007.03.003
136
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
of soil loss are between 60 and 105 t ha−1 . Here the steeper the slope of a field is, the greater the amount of soil loss produced by rain. In addition, in orchards where mechanized farming practices are used the erosion phenomenon is accelerated because agricultural machinery removes important surface vegetation which would otherwise reduce soil degradation caused by the rain (Francia et al., 2000). This latter is called tillage erosion, which is a form of erosion which is receiving increased attention. Tillage erosion delivers soil from upper slope positions to the lower parts of the slope. After many years of tillage, topsoil accumulates at the bottom of the slope. The negative effects of the erosion advance on crop yields are: decrease of organic matter, phosphorus levels, the potential planavailable water and aggregate stability (ECAF, 1999). Sometimes in olive orchards this erosion is so aggressive that it causes displacements of the olive trees themselves. These are progressive movements but their magnitude and direction are changeable. They should be directed downhill, but if the area is not very sloping they may occur at random because of other causes, especially the use of agricultural machinery. Other erosion factors such as meteorological ones are very aggressive too, but they are discontinuous in time. Nevertheless, tillage practices tend to be continuous throughout the year. There are several methods for the study of erosion (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). This is due to the large amount of factors which influence this phenomenon, hindering its estimation. Nevertheless, an accurate and timely estimation of soil erosion has become an urgent task (Lu et al., 2004). As has been mentioned, there are many factors which condition the methodology and instrumentation used in the study of erosion. 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. In this study we monitor displacements of olive trees of an orchard located on sloping terrain. We also establish the connection between such movements and slope. These movements are the main tillage erosion impact on this exploitation situated in south Spain. In order to carry out a thorough study of these movements it is necessary to analyse different spatial data: the exact position of the olive trees from three survey campaigns in 1 year, the digital elevation model (DEM) and the slopes model. Accuracy in spatial data is essential in order to provide reliable data of landscape changes (Ramos et al., 2004). Thus it is very important to use accurate techniques and instrumentation for taking field measurements, and an adequate data managing system (Nem´enyi et al., 2003). We use high precision techniques and geodesy instrumentation, such as the GPS system. This technique has the advantage that it does not require a direct line of sight between the moving receiver and the reference station. This is very useful considering the lack of visibility through the olive groves. We used dual-frequency GPS receivers to acquire data in the topographic surveys. The data collection techniques used to monitor olive tree movements was static rapid surveying, with observation periods of 10 min. The elevation accuracy of a DEM and actual points in a field are mainly related to the elevation accuracy of the measuring instrument, and the field procedure (Yao and Clark, 2000). In this sense, we used the stop-and-go technique to develop DEMs, stopping at a field point for about 3 s to collect data. Once the measurements are processed these data are integrated into a GIS in order to improve the spatial analysis of movements and therefore analyse the influence of erosion on the studied zone. 2. Methods and materials Our objective is to accurately measure olive trees displacement, and establish the connection between such movements and slope. In the first phase of our work we collect data from GPS field measurements from three survey campaigns in 1 year. Two kinds of field data are used: the position of olives trees for each campaign and the digital elevation model. During this phase we analyse the movements of the olive trees. In the second phase of the investigation data movements together with the digital elevation model are integrated into a GIS in order to perform the specific spatial analysis. We work with 52 olive trees that belong to the same orchard, an unirrigated olive orchard where the slope gradient varies between 2% and 25%. An agronomical classification of soils determines that mechanical tillage in such sloped zones produces quite important erosion damage to soil cover (C.A.P., 2003). The property is located in a village called Lahiguera in the province of Jaen (Andalusia, Spain). This is one of the main olive oil producing regions of the country; therefore it is the most representative area of this kind of exploitation. The olive orchard we studied is 1,19 ha with
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
137
Fig. 1. (a) Location of the study zone. (b) External control network designed to monitor the position of the local network. (c) Elevation of the olive orchard studied. The white circles are the olive trees monitored (object points). The zones A, B and C are the zones measured in order to obtain high precision DEMs. (d) The local control network is made up of three reinforced concrete pillars anchored to the ground.
120 olive trees of similar properties: they all have three trunks, are about 30 years old and their mean fruit harvest is 60 kg/year. The difference between them is the slope of the land where they are planted in the orchard. In this study the property is divided into three zones (Fig. 1(c)), following the criteria of the gradient of the slope of the terrain. Zones A and C have a higher gradient than B, this last being a flat zone. The classification is as follows: zone A: medium gradient: 15%, zone B: minimum gradient: 1%, zone C: highest gradient: 25%. We consider this classification useful for analysing zones with similar properties and isolating them from the property as a whole (Zhang et al., 2002). Also, this helps us to study how much slope does influence olive tree displacement. The plot of land selected is located next to a weather station. This station reports meteorological data of the zone like rainfall, minimum and maximum temperature, air humidity, windspeed, etc. In this study we did not use these variables in practice because there was no variation between the three zones selected. We employed them for planning field measurement surveys. Nevertheless the data would be useful in future studies in order to compare this plot with other ones with similar topographic properties but located in a different city. We took measurements after periods of hard farming practice or bad weather conditions in order to evaluate erosion damage.
138
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 2. (a) Leica SR9500 receiver collecting data. (b) Detail photography of nail embedded in the trunk of the olive tree. (c) GPS receiver and accessories used. Plates aligned to take measurements. (d) Measured points in zone A.
2.1. Information collected from GPS field measurements 2.1.1. Analysis of land displacements The methodology of this analysis consists of monitoring the position of several points in each survey. The research is carried out as follows: first we distinguish two groups of points, one, the local control network, consisting of control stations, which are points placed in a stable zone so their positions do not change (Fig. 1(d)); and the second, the object points, consisting of points which could have significant movements (Fig. 1(c)). Subsequently relative movements of object points from the local control network are analysed. This is a non-permanent GPS network that is observed periodically in GPS campaigns spaced over time to check its fixed position (Betti et al., 1999). The construction of the pillars of the local control network ensures that the GPS antennas will be placed exactly at the same position for different reoccupations; the pillars incorporate an embedded forced centring system (Gil et al., 2002). Despite the special care taken in the construction of the network, we assume that it is vulnerable. The pillars could be displaced due to farming machinery, tillage practices, human activity or subsoil movements. Therefore an external control network has been designed to monitor the position of the local one (Ramos et al., 2005a). The external control network is placed far from the study area in order to keep it away from the local movements (Fig. 1(b)). The three pillars meet the same constructive requirements as those of the local network. As we can see in Fig. 1(b) two pillars, “Salado” and “Cagancho”, are located in areas with a minimum slope gradient. The third one, “Terraza”, has been constructed on a rooftop in the urban area to avoid obstructions over 15◦ . Once we had designed control networks we marked the olive trees to be studied, the object points (Fig. 1(c)). We selected those ones located in the zones A, B and C; it was also considered useful to add others located outside these zones but sensitive to land movements as well. We hammered nails into the trunk to mark the trees (Fig. 2(b)). We took care to set the nails in the base and not in the branches in order to avoid movements coming from their growth. The first GPS survey was carried out in June 2004, the second in November 2004 and the third in June 2005. The equipment used was three Leica SR399, two Leica SR9500 and one Leica S500 dual frequency carrier phase GPS receivers, which belong to the University of Jaen. The networks have been tied into the European Terrestrial Reference
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
139
System 1989 (ETRS89), from which the coordinates of the “Terraza” point were computed. GPS observations data processing was performed using Leica Ski-Pro 2.5 software (Leica Geosystems, 2000). In each campaign we computed the baselines and their least squares adjustments. The coordinates of the networks which were obtained in June 2004 were considered as the reference frame (Crespi, 1996). This means that in the comparisons between the three campaigns the first one was fixed. To identify an olive tree whose coordinates had changed we performed an analysis of two kinds of data: firstly, the variation on planimetric position and secondly the altimetric one. The data used to study planimetry was the displacement vector defined by its module and geodetic azimuth. Module is the distance that separates the planimetric position of the point from one campaign to another; this parameter was computed from the coordinates, latitude and longitude, of the point. Geodetic azimuth is the orientation of vector displacement measured in degrees clockwise from North. The study of altimetric movements was based on the comparison of altitude values of a point during each campaign. These differences in altimetry were represented by vertical vectors whose starting point belongs to the first campaign, June 2004. In this field it is very important to discriminate between vector displacements and error values. We applied statistical tools in order to achieve accurate results of the movements. We worked with confidence regions from each point adjustment in the three campaigns. Planimetric error ellipses and altimetric error ratio were used. In the case of planimetric data, movements are significant if the error ellipses of each campaign do not intersect, but for altimetric data, changes in the positions are significant only if vector displacements are out of the error interval. Tables 1 and 2 show the numerical results of the analysis of the displacements of all the trees selected. An improvement of this analysis is shown in Fig. 3, where we have taken a piece of the property as an example; they show a spatial analysis of the results. To identify each object point we used a code that consists of three characters: the first is the zone of the property where the tree is placed, the second the number of olive tree rows inside the zone and the third is the column number. Note that code “d” corresponds to the trees outside the zones classified; these were listed in order from north to south. Fig. 3(a) and (b) corresponds to an example of the planimetric analysis where comparisons between the first campaign and the two others are represented. We can assume at 99% reliability that there were small displacements (2–4 cm) from June 2004 to June 2005 at several points. 2.1.2. Digital elevation model (DEM) In the previous section we calculated planimetric and altimetric displacement vectors of a few centimeters with high precision. At this stage the aim of our research is to monitor how land gradient influence such olive tree movements. Therefore, in order to obtain accurate results we must generate a precise slope model from a very accurate DEM with high spatial resolution (0.25 of cell size). The method of taking measurements in the field was considered carefully with the aim of providing precise coordinates of the land (Ramos et al., 2005b). One additional fact which must be considered is that the surface contains sand stones which break easily to the touch. The inconsistency of the sandy soil, into which the bar carrying the GPS antennae is driven, means that the bar sinks quite easily into the ground. This makes it difficult to measure the topography of the zone accurately. Therefore we adopted the following system: We use metacrylate plates of 0.5 m side length with a small hole in the centre where the bar, on which the GPS antenna is fitted, is inserted (Fig. 2(c)). The plates are dropped on the ground, then the bar is fitted in the orifice and measurements are taken. Each measurement is 0.5–1 m from the next. The distribution of these measurements follows a series of alignments that cover the area without taking points very close to the olive trees, because their branches interfere with the GPS signal reception (Fig. 2(d)). These zones without measurements are the white holes that appear in the figure. In order not to decrease the high precision of DEM these holes will not be taken into account in the data processing, meaning that instead they will be considered as edges. Finally, we used the software of treatment (interpolation, analysis and management) of grids Vertical Mapper v. 3.1 (Vertical Mapper v.3.1, 2001), an application that works within MapInfo Professional v.7.5 (MapInfo Cor., 2003), in order to generate the DEM. The interpolation method used was the triangulated irregular network (TIN). TIN involves a process whereby all original data points are connected in space by a network of triangular faces, as equilateral as possible, forming what is referred to as a triangular irregular network. This method is appropriate for generating the elevation grids since it creates an output surface which passes through the measured points. The features of the triangulation algorithm we chose were 2 m of maximum triangle side length and 0.25 m of cell size.
140
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Table 1 Planimetric displacement analysis of olive trees from June 2004 to November 2004 and June 2005 campaigns, ellipses at 99% reliability Olive trees, code
a11 a12 a13 a14 a21 a22 a23 a24 a31 a33 a34 b11 b12 b13 b14 b21 b23 b24 b31 b32 b33 b41 b42 b43 b44 b51 b52 b53 b54 b61 b62 b71 b72 b73 b74 b81 b82 b83 b84 d10 d11 d1 d2 d3 d4 d5 d6 d7 d9 m11 m12 m14
Planimetric movement
Ellipses error at 99% reliability
June 2004– November 2004
June 2004– June 2005
June 2004
November 2004
June 2005
Module (m)
Azimuth (◦ )
Module (m)
Azimuth (◦ )
a (m)
b (m)
Φ (◦ )
a (m)
b (m)
Φ (◦ )
a (m)
b (m)
Φ (◦ )
0.030 0.028 0.022 0.020 0.013 0.018 0.010 0.009 0.025 0.021 0.031 0.018 0.002 0.013 0.022 0.040 0.049 0.050 0.018 0.017 0.033 0.018 0.029 0.036 0.038 0.005 0.021 0.015 0.011 0.004 0.003 0.019 0.036 0.011 0.015 0.017 0.014 0.018 0.004 0.028 0.003 0.032 0.045 0.053 0.056 0.012 0.033 0.048 0.046 0.021 0.011 0.048
85 128 74 106 79 92 105 42 84 151 233 141 185 175 180 159 126 183 189 160 158 129 132 137 138 251 58 63 217 230 265 144 113 222 219 109 167 99 320 70 315 335 350 131 143 149 334 159 141 156 149 160
0.022 0.122 0.014 0.023 0.027 0.023 0.026 0.040 0.020 0.012 0.024 0.031 0.005 0.019 0.012 0.016 0.005 0.041 0.012 0.032 0.029 0.020 0.026 0.015 0.023 0.052 0.049 0.045 0.035 0.028 0.035 0.039 0.047 0.048 0.053 0.023 0.043 0.025 0.019 0.038 0.037 0.029 0.038 0.040 0.023 0.029 0.042 0.036 0.030 0.034 0.041 0.022
327 161 295 324 306 315 339 325 303 15 303 321 320 324 321 240 129 210 293 232 218 314 356 328 324 325 3 8 344 358 357 8 17 335 326 306 350 15 291 332 302 317 302 291 163 330 5 11 162 239 237 231
0.010 0.016 0.016 0.015 0.013 0.010 0.012 0.015 0.011 0.019 0.015 0.010 0.029 0.032 0.030 0.029 0.044 0.024 0.021 0.026 0.032 0.012 0.013 0.018 0.013 0.009 0.012 0.023 0.037 0.556 0.061 0.023 0.022 0.024 0.027 0.028 0.021 0.025 0.019 0.022 0.024 0.027 0.028 0.066 0.027 0.055 0.089 0.051 0.015 0.017 0.021 0.225
0.007 0.012 0.012 0.011 0.009 0.007 0.009 0.011 0.016 0.012 0.011 0.006 0.013 0.020 0.017 0.018 0.022 0.011 0.011 0.016 0.019 0.006 0.010 0.009 0.007 0.006 0.008 0.002 0.018 0.010 0.017 0.013 0.012 0.011 0.013 0.015 0.012 0.015 0.013 0.016 0.011 0.017 0.021 0.016 0.014 0.010 0.012 0.020 0.010 0.013 0.017 0.007
13 9 −1 −5 11 6 −8 −12 −18 −15 −17 −14 −6 −9 −5 0 −10 −5 19 14 11 20 31 2 5 −33 −27 −15 −9 −4 −18 −14 −17 −30 −28 −19 −17 −23 −24 −16 −6 −4 −6 −8 −5 −6 −6 −7 −19 −17 −13 −4
0.020 0.037 0.019 0.021 0.034 0.017 0.029 0.028 0.018 0.015 0.020 0.027 0.037 0.028 0.034 0.029 0.035 0.037 0.027 0.023 0.016 0.013 0.026 0.027 0.012 0.021 0.020 0.018 0.023 0.016 0.028 0.021 0.020 0.030 0.028 0.021 0.026 0.030 0.010 0.033 0.036 0.028 0.019 0.022 0.019 0.021 0.022 0.026 0.028 0.021 0.025 0.026
0.014 0.027 0.015 0.019 0.027 0.015 0.026 0.025 0.021 0.014 0.019 0.018 0.020 0.022 0.015 0.016 0.016 0.022 0.022 0.013 0.011 0.011 0.003 0.016 0.007 0.018 0.018 0.013 0.021 0.015 0.017 0.018 0.019 0.027 0.021 0.019 0.024 0.027 0.008 0.022 0.023 0.021 0.016 0.020 0.018 0.019 0.018 0.016 0.018 0.017 0.019 0.017
15 21 12 −2 18 7 −9 −27 2 4 −44 43 61 58 57 59 63 46 16 18 10 −3 4 7 0 10 3 11 22 17 19 62 70 19 39 65 60 19 100 50 50 48 50 80 36 67 −97 38 45 43 43 44
0.012 0.008 0.012 0.010 0.013 0.014 0.009 0.010 0.015 0.012 0,011 0.018 0.028 0.014 0.028 0.020 0.024 0.028 0.018 0.038 0.043 0.009 0.018 0.012 0.011 0.014 0.016 0.015 0.011 0.017 0.016 0.014 0.018 0.022 0.021 0.018 0.021 0.024 0.020 0.013 0.019 0.017 0.027 0.022 0.028 0.011 0.020 0.018 0.021 0.016 0.017 0.018
0.009 0.006 0.009 0.008 0.010 0.011 0.007 0.008 0.007 0.009 0.008 0.011 0.019 0.017 0.012 0.013 0.011 0.015 0.008 0.012 0.023 0.006 0.009 0.013 0.006 0.007 0.008 0.009 0.006 0.008 0.009 0.007 0.008 0.010 0.016 0.008 0.009 0.010 0.009 0.008 0.010 0.028 0.011 0.016 0.020 0.008 0.011 0.016 0.011 0.018 0.014 0.010
2 0 −1 −6 −5 −4 −10 −8 −11 −16 −13 −2 −3 −1 −3 −5 −5 −13 −7 16 27 10 12 15 18 20 21 16 19 20 22 21 21 21 18 20 18 19 18 −3 −5 −7 −9 −14 −18 −7 0 −3 −5 −7 −7 −5
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
141
Table 2 Altimetric displacement analysis of olive trees from June 2004 to June 2005 campaigns at 1% significance level Olive trees, code
a11 a12 a13 a14 a21 a22 a23 a24 a31 a33 a34 b11 b12 b13 b14 b21 b23 b24 b31 b32 b33 b41 b42 b43 b44 b51 b52 b53 b54 b61 b62 b71 b72 b73 b74 b81 b82 b83 b84 d10 d11 d1 d2 d3 d4 d5 d6 d7 d9 m11 m12 m14
Altimetric movement (m)
Error ratio at 1% significant level (m)
June 2004–November 2004
June 2004–June 2005
November 2004
June 2005
−0.012 0.017 −0.024 0.017 −0.039 −0.001 0.005 −0.011 0.012 0.016 0.010 0.011 0.004 −0.007 0.003 0.059 0.011 −0.019 0.057 0.075 0.040 0.025 0.044 0.080 0.019 −0.029 0.033 −0.030 0.005 0.001 −0.018 −0.004 0.046 −0.023 0.012 −0.007 0.015 0.041 0.024 0.017 0.030 0.038 0.033 0.030 0.045 0.005 −0.066 0.028 0.020 0.003 0.005 0.059
−0.024 −0.038 −0.093 −0.113 −0.067 −0.048 −0.102 −0.051 −0.026 −0.100 −0.046 −0.055 −0.020 −0.042 −0.136 0.002 −0.077 −0.078 −0.011 0.002 0.005 −0.069 −0.058 −0.061 −0.051 −0.127 −0.090 −0.082 −0.078 −0.120 −0.086 −0.083 −0.039 −0.090 −0.063 −0.036 −0.107 −0.022 −0.032 −0.051 −0.102 −0.065 0.001 −0.114 0.026 −0.036 −0.051 −0.123 −0.010 −0.044 −0.051 −0.051
±0.026 ±0.048 ±0.019 ±0.014 ±0.045 ±0.024 ±0.042 ±0.041 ±0.026 ±0.022 ±0.030 ±0.038 ±0.031 ±0.017 ±0.022 ±0.034 ±0.045 ±0.067 ±0.037 ±0.033 ±0.022 ±0.022 ±0.039 ±0.025 ±0.016 ±0.029 ±0.029 ±0.021 ±0.033 ±0.019 ±0.045 ±0.027 ±0.027 ±0.028 ±0.044 ±0.029 ±0.027 ±0.035 ±0.045 ±0.024 ±0.030 ±0.029 ±0.028 ±0.028 ±0.022 ±0.031 ±0.011 ±0.041 ±0.046 ±0.036 ±0.012 ±0.032
±0.025 ±0.016 ±0.025 ±0.020 ±0.025 ±0.027 ±0.016 ±0.019 ±0.065 ±0.020 ±0.018 ±0.023 ±0.041 ±0.030 ±0.020 ±0.030 ±0.035 ±0.036 ±0.027 ±0.021 ±0.058 ±0.013 ±0.018 ±0.015 ±0.017 ±0.022 ±0.027 ±0.012 ±0.017 ±0.031 ±0.021 ±0.028 ±0.022 ±0.030 ±0.039 ±0.032 ±0.033 ±0.040 ±0.044 ±0.029 ±0.002 ±0.031 ±0.039 ±0.016 ±0.028 ±0.020 ±0.023 ±0.028 ±0.017 ±0.024 ±0.022 ±0.033
142
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 3. (a) and (b) Planimetric displacement vectors of olive trees from June 2004 to November 2004 and June 2005 campaigns, ellipses at 99% reliability. (c) Altimetric displacement vectors of olive trees from June 2004 to November 2005 campaigns at 1% significance level. (d) Altimetric displacement vectors of olive trees from June 2004 to June 2005 campaigns at 1% significance level.
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
143
Table 3 Data classification Structure
Continuous variables DEM Slope model Aspect model Discrete variables Zones A, B, C Olive trees Points measured (to generate DEM) Displacements vectors of trees Planimetric error ellipses Altimetric error ratios
Nature
Raster
Vector
Spatial
Temporal
Grid Grid Grid
Contour Map Contour Map Contour Map
× × ×
Contour Map Points Points Lines Polygons Lines
× ×
×
× × ×
× × ×
2.2. Data integration into GIS tools 2.2.1. Precision agriculture and GIS Often the consequences of erosion are studied in terms of top soil loss, but these are at times less than useful for making correct decisions to solve erosion problems. For this reason the spatial analysis of erosion implies the use of visual information. In the introduction we mentioned tillage practices as one factor that exacerbates the erosion process in the olive orchards. It is therefore necessary to take into account all the information collected in order to make good agricultural management decisions. This is related to precision agriculture (Stafford, 2000), a new concept of farming in which spatial and temporal variability within a field are analysed in order to improve benefits. This concept is based on new positioning and representation technologies now available such as GPS and GIS. With these tools we can map our study area and extract and plot information with high variances within the field instead of estimating an average value for the entire field. GIS in particular provides a new point of view and more complete information in order to make valid technical agricultural decisions (Nem´enyi et al., 2003). Nowadays there is a considerable necessity of software GIS in precision agriculture but most of the current GIS tools are too expensive, complex or inappropriate for processing certain data at small scales (Runquist et al., 2001). Also, this software does not posses the accurate spatial analysis tools needed to solve spatial problems. Therefore, we have developed our own system from commercial GIS software, MapInfo v.7.5. Here we suggest a methodology which integrates vector and raster data in order to approach a visualization problem in agriculture. We use different kinds of data, from vector displacements obtained in the analysis of movements in the olive trees to the spatial parameters that influence the local erosion process. Some of these data are constant and others variable. In addition, they are different in nature or format and come from different sources, so we need a system able to process this data accurately. 2.2.2. Data management The variables that have significant influence on the agricultural harvest are categorized into six groups: yield variability, field variability, soil variability, crop variability, variability in anomalous factors and management variability (Zhang et al., 2002). In this article we focus on the field variability group, in particular the elevation and slope variables. Other data are added to these variables, such as the position of the olive trees during each campaign, the points measured to generate the DEM, the three zones selected in the property studied (Fig. 1(c)), the displacement vectors of the trees, the planimetric error ellipses and the altimetric error ratios (Fig. 3). All these variables are both spatial and temporal in nature and therefore our classification of them must also take into account the continuous and discrete nature of data. Continuous variables are spatial data and here it is considered that they do not change over time. Although discrete variables are spatial as well, most of them are temporal in nature (Table 3). Both continuous and discrete variables include raster and vector structure data.
144
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 4. Entity-relationship model data. Above is represented the interactions between olive entities and their attributes. At the bottom there are the results of a SQL query to monitor planimetric movements.
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
145
In the vector data system several stored attributes are assigned to one table. In this last there is one field per attribute and a record per value (Fig. 4). This structure allows constant availability and updating of data without needing a powerful computer (Feito et al., 1999). Raster structure data improves computations in spatial analysis. Here grid maps were used to perform accurate queries such as the connection, measured with high precision, between positional shifts of the olive trees and the gradient. With this kind of query the pixel size of grid maps is important in order to guarantee a detailed analysis. The pixel size depends on the resolution of DEM which was generated from an ASCII file of (x,y,h) coordinates points measured. Then the slope model was calculated using the Vertical Mapper v.3.1 software and the contour maps were generated as topologically constructed MapInfo regions from grid files. Finally, the lines and the polygons came from CAD files. The data were integrated into the GIS database so that many spatial operations, such as overlay and buffer, could be performed. In addition this system allowed us to establish a database of rapid numerical access and an adequate visualization of the results. The entity-relationship (ER) model was used to implement a data model in database management software. In Fig. 4, the ER model diagram represents the problem as entities and relationships. The tables resulting from the design of the ER model were the following: • ZONE (ID Zone, Description). This table is related to the polygons which represent the study zones chosen from the whole property. • CAMPAIGN (ID Campaign, Date, Description). This table distinguishes each field measuring campaign. • USE (ID Use, Description). The points measured could be used to generate the elevation model or for positioning the olive trees. Thus this table specifies whether the use of the point is the former or the latter. • POINT (ID Point, x, y, h, ID Zone, ID Campaign, ID Use). This is the field data measured. • ERROR ELLIPSE (ID Error Ellipse, a, b, Azimuth, ID Point). This table contains the parameters of the planimetric error ellipse of each measured point. • CONFIDENCE INTERVAL (ID Confidence Interval, Upper boundary, Lower boundary). This table contains the boundaries of the confidence interval of altimetric values measured for each point. 3. Results and discussion Visual spatial analysis allows the monitoring of olive tree movements from another point of view apart from the numerical calculations described in Section 2.1.1. With only one visual query we can evaluate planimetric or altimetric movements. For example, Fig. 4 shows a query which selects no significant planimetric movements; thus if the entity is selected it has no significant displacement. The results of this query show that some trees like a13 have no planimetric displacement, but others like a12 have planimetric displacement of a few centimeters, 6 cm, from June 2004 to June 2005. Points a11, a21, a22 and a23 have also moved 2–4 cm in the same period of time, but the direction and length of these movements are different from a12. Similar queries could be used to analyse altimetric movements. An example of the altimetric analysis is represented in Fig. 3(c) and (d): the graph presents vertical movements from the June 2004 campaign to November 2004 and the June 2004 campaign to June 2005, respectively. The error ratio at 1% significance level is represented for each point. We can see that most of the points have moved vertically from June 2004 to June 2005 except one, a11. The major vertical displacements have occurred in the space of a year, and they also tend to be greater than the horizontal ones, so we can conclude that in this zone the height of the hill is decreasing. This spatial analysis could be performed by inexperienced users of the theory of analysis of land movements. With this user-friendly system they would be able to understand and monitor these movements. In addition, spatial tools are especially useful for mapping results in different scales. We can study in detail the displacement of one olive tree in a horizontal or vertical direction and also analyse movements of all the trees with just one query. It is possible to know if displacements in a given area are due to the slope or the tillage practice. This issue is analysed in detail by overlaying movements data and the precision slope model (Fig. 5). A downhill movement means the effect of erosion is accentuated by slope values in that zone. When the planimetrical direction of the movement is not downhill we talk about an arbitrary movement, for example point a11 (Figs. 4 and 5). In this case we can confirm that machinery or tillage could counteract the influence of the slope. In Fig. 4, we can see that point a13 has no planimetrical shift because all error ellipses intersect. The integration of precision DEM into GIS provides a powerful tool for analysing spatial problems in high detail. We use the term “precision DEM” because of the specific instrumentation and methodology used to collect field
146
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Fig. 5. Altimetric displacements from June 2004 to November 2004 in zone A. Displacements vectors and slope and aspect model are overlaid.
measurements. There are several DEMs of the study area publicly available from government institutions. In particular we have an elevation model of 30 m of spatial resolution, but we considerer it inappropriate to this investigation. The advantage of using a DEM of small grid cell size is that, if we add to it precise data on olive tree movements obtained through the methodology described above, we are able to perform spatial analyses of high precision. The slope model and also the aspect model (Fig. 5) calculated from the precision DEM help us to analyse the direction of displacements. If such displacements are directed downhill then we can confirm the high influence of topographic parameters. Sometimes the tree movement is counteracted by tillage practices and the result is a vector displacement in an arbitrary direction. In this case an elevation model of very high detail is essential to analyse this phenomenon because the movements are in the order of a few centimeters. An example of this analysis is the one we carried out to monitor the altimetric shift of a13 olive tree. Fig. 3 shows that this altimetric movement is significant but that the planimetric one is not. This case could be analysed in detail combining slope and aspect models with the vector displacement (Fig. 5). This tree is situated on a zone whose slope values are 20–29%. The aspect model shows that the area around the tree a13 faces from 135 to 225 degrees from the north. In addition, during mechanized tillage, the tractor drives in the direction of maximum slope and moves soil cover in an angular direction and at a deep of 20–40 cm. This could counteract the planimetric movement of some trees. In addition, the slope and aspect models help us to develop advanced queries in which all the data are overlaid. These results are therefore more reliable because all the data are taken into account. As an example, we are able to see which zone is more affected by the erosion phenomenon, that is, which movements are greater; or it also interesting to see which zone has been less affected by the tree movements, or which one has not changed from June 2004 to June 2005. 4. Conclusions The spatial analysis system presented allows users to monitor in detail olive tree movements caused by erosion agents. Our analysis has provided accurate values of displacements which confirm that our olives trees have moved
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
147
a few centimetres in 1 year. High precision DEM have been incorporate into our GIS in order to carry out advanced spatial queries. We have detected that there is a relationship between these movements and other spatial models such as elevation, slope or aspect. Also we have confirmed that tillage practice causes complementary effects on tree movements. In some cases tillage practices counteract displacements exacerbated by slope gradient, restraining soil cover movements. In other cases, both movements occur in the same direction and produces important changes in the position of the trees. These analyses are very useful for enhancing the management of olive tree plantations or other fruit trees, because this system makes possible analyse results in detail or map them at small scales. Once the consequences of soil erosion are analysed it is necessary to contemplate a change in farming techniques in order to reduce the advance of the erosion process. Acknowledgements We wish to thank the reviewers for their helpful comments. This work has been partially supported by the Spanish Ministry of Education and Science and the European Union (via ERDF funds) through research projects TIN200406326-C03-03 and ESP2006-10113. References Adinarayana, J., Rao, K.G., Krishna, N.R., Venkatachalam, P., Suri, J.K., 1999. A rule-based soil erosion model form a hilly catchment. Catena 37, 309–318. Betti, B., Biagi, L., Crespi, M., Riguzzi, F., 1999. GPS sensitivity analysis applied to non-permanent deformation control networks. J. Geodesy 73, 158–167. C.A.P. (Consejer´ıa de Agricultura y Pesca). Junta de Andaluc´ıa (Eds.), 2003. El Olivar Andaluz. Viceconsejer´ıa. Servicio de Publicaciones y Divulgaci´on. Sevilla, Spain. Crespi, M., 1996. A Software Package for the Adjustment and the Analysis of GPS Control Networks In: Unguendoli, M. (Ed.), Reports on Survey and Geodesy in memoria of Prof. A. Gubellini and G. Folloni. Ed. Nautilus, Bologna, pp. 237–264. D’Ambrosio, D., di Gregorio, S., Gabriela, S., Gaudio, R., 2001. A cellular automata model for soil erosion by water. Phys. Chem. Earth. Part B: Hydrol. Oceans Atmosphere 26, 33–39. ECAF, European Conservation Agriculture Federation, 1999. Conservation Agriculture in Europe: environmental, economic and EU policy perspectives. European Conservation Agriculture First Report. Available at: http://www.ecaf.org/. Francia, J.R., Mart´ınez, A., Ruiz, S., 2000. Erosi´on en suelos de olivar en fuertes pendientes. Comportamiento de distintos manejos de suelo. S. E. C. S. Edafolog´ıa 7–2, 147–155. Feito, F.R., Garrido, A., Ogayar, B., Molina, A., 1999. GISELEC’2000. Proceedings 7a Jornadas Hispano-Lusas de Ingenier´ıa El´ectrica, Lisboa Portugal. Gil, A.J., Rodr´ıguez-Caderot, G., Lacy, M.C., Sanz de Galdeano, C., Alfaro, P., 2002. Establishment a non-permanent GPS Network to monitor the deformation in the Granada Basin (Betic Cordillera, South Spain). Stud. Geophys. Geod., 46, 395–410. StudiaGeo s.r.o., Prague. I.E.A. (Instituto Estad´ıstico de Andaluc´ıa), 2004. Anuario Estad´ıstico de Andaluc´ıa 2004. Junta de Andaluc´ıa. Spain. http://www.juntadeandalucia. es/iea/anuario/anuario04/index.htm. Leica Geosystems, A.G. (Ed.), 2000. CH-9435 Heerbrugg, Switzerland. Lu, D., Li, G., Valladares, G.S., Batistella, M., 2004. Mapping soil erosion risk in Rondonia, brazilian amazonia: using RUSLE. Rem. Sens. GIS. Land Degrad. Dev. 15, 499–512. 2003. MapInfo Professional v 7. 5. Reference Guide. MapInfo Coorporation, New York. ´ Pecze, Zs., St´ep´an, 2003. The role of GIS and GPS in precision farming. Comput. Electron Agric. 40, 45–55. Nem´enyi, M., Mesterh´azi, P.A., Nearing, M.A., Foster, G.R., Lane, L.J., Finkner, S.C., 1989. A process-based soil erosion model form USDA-Water Erosion Prediction Project Technology. Trans. ASAE 32, 1587–1593. Ramos, M.I., Feito, F.R., Gil, A.J., 2004. Towards a high precision digital elevation model. In: UNION, Agency Science Press, WSCG POSTER proceedings, WSCG’ 2004: The 12th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision’2004, Plzen, Czech Republic. Ramos, M.I., Gil, A.J., Feito, F.R., 2005a. Deformation analysis to study erosion in sloped olive orchards. In: Sans´o, F., Gil, A. (Eds.), Geodetic Deformation Monitoring: from Geophysical to Engineering Roles. IAG Symposium, vol. 131. Springer, Ja´en, Spain, pp. 265– 269. Ramos, M.I., Feito, F., Gil, R.A.J., 2005b. Analysis of slope to study erosion. In: Proceedings of the XXII International Cartographic Conference (ICC2005), A Coru˜na, Spain. Romero, L.R., 1998. Olive forming in the age of science innovation. Olivae 72, 42–51. Runquist, S., Zhang, N., Taylor, R.K., 2001. Development of a field-level geographic information system. Comput. Electron Agric. 31, 201– 209. Shen, D.Y., Ma, A.N., Lin, H., Nie, X.H., Mao, S.J., Zhang, B., Shi, J.J., 2003. A new approach for simulating water erosion on hillslopes. Int. J. Remote Sens. 24, 2819–2835.
148
M.I. Ramos et al. / Computers and Electronics in Agriculture 57 (2007) 135–148
Stafford, J.V., 2000. Implementing precision agriculture in the 21st century. J. Agric. Eng. Rest. 76 (3), 267–275. Veihe, A., Rey, J., Quinton, J.N., Strauss, P., Sancho, F.M., Somarraba, M., 2001. Modelling of event-based soil erosion in Costa Rica Nicaragua and Mexico: evaluation of the EUROSEM model. Catena 44, 187–203. VERTICAL MAPPER v.3.1, 2001. Contour modeling & display software for MapInfo Professional. Version 3.1. User guide. Canada. Wischmeier, W.H., Smith, D.D., 1978. Predicting rainfall erosion losses. Agriculture Handbook no. 537. United States Department of Agriculture, Washington, DC, p. 58. Yao, H., Clark, R.L., 2000. Evaluation of sub-meter and 2 to 5 meter accuracy GPS receivers to develop digital elevation models. Precision Agric. 2, 189–200. Zhang, N., Wangb, M., Wanga, N., 2002. Precision agriculture—a worldwide overview. Comput. Electron Agric. 36, 113–132.