Philippe LAGACHERIE, Jean Claude FAVROT et Marc VOLTZ. UFR science du sol INRA, 2 place Viala Montpellier cedex 1

Transcription

1 Scientific registration n : 461 Symposium n : 17 Presentation : poster Using information from reference areas for mapping soils and their properties over physiographic regions Utilisations de secteurs de référence représentatifs pour cartographier les sols et leurs propriétés sur des petites régions naturelles Philippe LAGACHERIE, Jean Claude FAVROT et Marc VOLTZ UFR science du sol INRA, 2 place Viala Montpellier cedex 1 Introduction To apply sustainable farming techniques requires to precisely know the nature, distribution and behaviour of soils at the farm level which is beyond the possibilities of national inventories. To overcome this problem Favrot (1981) proposed to identify small natural regions over which soil distribution follows constant patterns, and then to survey in detail reference areas chosen to be representative of the entire regions. The survey of a reference area provide the definition of the main soil classes of the region with their discriminating criteria, their properties, and their specific recommendations ensuring the maintenance of soil fertility and water quality. The soil survey is then extended to the whole region by allocating any site to the pre-defined soil classes on the basis of a low cost survey wich uses simple soil observations and mapping rules established during the previous reference area survey (Favrot, 1989). The method has been applied between 1980 and 1996 to 117 small natural region which cover together 44,000 km² i.e. 8% of the french territory. More details on application of the reference area method is provided in Favrot et al (1997). The application of the reference area method over the french territory have shown that the extension of the reference area survey to the whole region was not always straightforward. This was mainly because the information collected in the reference area was underexploited which raised the need of mapping procedures able to use this information. In this paper, we summarize three of these procedures, each of them exploiting for a specific purpose a part of the soil knowledge collected in the reference area: i) drawing a medium scale soil map of the region from easily available 1

2 Using soil landscape mapping rules built from a reference area to draw a mediumscale soil map Soil surveyors tend to limit their ground observations by using the rules which predict soils from external data (e.g. topography, geology) that were previously established in the reference area soil survey. These soil landscape mapping rules are based on strong and stable deterministic relations linking soils with topographical or geological criteria which were studied by various authors (Huggett, 1975 ; Legros, 1975 ; Conacher and Darlympe, 1977; Shovic and Montagne, 1985; Skidmore et al., 1991). Unfortunately, these rules are not enough formalized by the authors of reference area soil surveys for being exploited in the entire region. Thus our objective was to formalize the soil landscape mapping rules so that they can be automatically applied for predicting soil mapping units external to a reference area perimeter. Soil landscape rules were built using a classification tree method (Lagacherie and Holmes, 1997). This method uses a learning set of points included in the reference area for which are known both the soil mapping units and the external data which are supposed to be in relation with soils. An estimate of the probability of occurrence of the soil mapping unit is derived by selecting the most discriminative external data. The result takes the form of a decision tree in which each branch corresponds to a mapping rule expressed as follows: «If the elevation at x 0 is greater than 20.5 meters and if x 0 is on the left bank of the river and if x 0 is included in geological unit n 1,3, or 4 Then estimate of probability of presence of soil classes 11,12,13,...,3 at x 0 are respectively 37%, 33%,23%,...,1%» The main difficulty of the classification tree method is to to adjust the complexity of the tree. Too small a tree provides very simple rules (i.e. with few conditions) having a weak discriminative power ( i.e. its estimates of probability does not reveal any dominant soil classes). In this case a part of the information available in the learning set is lost and the precision of soil predictions is artificially low. Conversely, a decision tree can be much larger than the data warrant. It produces complexe and apparently precise rules which are not sufficiently robust to be employed for prediction. A method which takes into account geographical errors for choosing the right-sized tree was developed ( Lagacherie and Holmes,1997). 2

3 Figure 1: The predictive soil map using soil landscape rules derived from the reference area of the Central Valley of Herault (in Lagacherie et al, 1997) The classification tree method was applied to the reference area of the Central Valley of Herault in which 17 soil classes were delineated. The external data used in this case were a set of topographical data derived from the 1:25,000 scale topographical map of the region and the 1:50,000 scale geological map. Because of the complexity of the soil patterns the right sized tree selected in this case could not discriminate all the 17 soil classes. However, it was possible to defined 7 prediction groups of soil classes from the probability distributions provided by each rules. The rules produced by the classification tree method was directly applied within a GIS in view to map the soil class groups (figure 1). The level of detail of the resulting map was shown to be intermediate between the existing soil maps of the region at scale 1:100,000 and 1:250,000. On the other hand, mapping errors were estimated by measuring the purity of the mapping units in three test areas covering 131ha. This demonstrated that the map units of the predictive mapping using classification tree method have a good purity (74%) similar to the one measured in 1:250,000 soil map (69%). A soil survey procedure using the knowledge of soil pattern established on a reference area The knowledge of the soil pattern is an important part of the more or less intuitive knowledge which helps the soil surveyor to deal with the scarcity of observation points for delineating soil classes. It is based on the fact that the soil classes are not randomly distributed in space but follow identifiable spatial relations. According to these relations, two soil classes can be said to be systematically adjacent. Conversely, the presence of a given soil class can exclude the existence of another unit in the immediate neighbourhood. Such situations especially occur in regions where the soil classes form identifiable patterns, and are common: they have been often described in the literature (Fridland, 1972; Hole, 1978; Burgess and Webster, 1984). Catenas are probably the best known example of such patterns, but others exist. 3

4 In regions characterized by a reference area, the knowledge of the soil pattern previously observed in the reference area can produce mapping rules to predict the soil classes at unvisited places between the observation points. For example, if the soil surveyor knows that in the reference area the soil classes A and B are systematically separated by soil class C, he will predict unit C to be somewhere between the two observation points where, respectively, A and B are identified. This knowledge will also influence his decision about the need for new observation points. For example, if a soil pattern is known to be very homogeneous, the surveyor will accept that the soil class remains unchanged between similar observation points, even if these points are far from each other. Thus, he will not select new observation points. Conversely, if the soil pattern is known to be very complex, the surveyor will have to verify that no additional soil classes occur between the two observed points. All this knowledge implicitely lies in soil maps of reference areas. Our objective was to formulate this knowledge through mapping rules which can predict soils over the region represented by the reference area. The soil survey procedure we proposed (Lagacherie et al., 1995) follows four main steps: i) formalizing the soil pattern mapping rules from the soil map of a reference area ii) identifying soil mapping units of the reference area at observed sites located in unmapped areas iii) predicting soil mapping units at unvisited sites from information collected in the first two steps and iv) selecting new observed sites if required. The three last steps can be repeated within an iterative process which ends when the soil predictions are judged accurate enough. In the following we summarize these steps. More details can be found in the reference cited above. The soil pattern mapping rules provide a set of conditional probabilities of occurrence for each soil mapping units of the reference area. An example of such rule is given hereafter: x i,, «If the soil mapping unit s 5 is identified at the observed point x i, if the point x 0, is located between d k meters and d k +50 meters from and if the point x 0, is higher than x i, then estimate of probability of presence of soil classes s 1, s 2,..., s j,,..., s p are respectively 3%, 69%,23%,...,1%» The set of rules is obtained by changing the soil mapping unit, the distance and the relative elevation («higher», «lower» or «same elevation»). The estimates of probability are obtained from a set of points included in the reference area i.e. for which the soil mapping unit and elevation are known. Each estimate is the proportion of points of the considered soil mapping units among the set of points which meet the conditions of the rule. Applying such rules needs to identify the soil mapping units at a limited set of observed sites located out of the reference area. In practice, this identification is usually carried out by trained soil surveyors from auger hole observations. Several techniques have been proposed by various authors for automating this operation (Legros, 1996). Some of these are based on the calculation of a mathematical distance between a soil mapping unit description and a soil observation. Others use an expert system approach. The set of observed sites with soil mapping unit identification is then used with the soil pattern mapping rules for estimating the probability of occurrence of soil mapping unit at 4

5 unvisited sites. Each observation point surrounding a given unvisited site activates a rule at a given site according to the identified soil mapping unit and the relative position from the considered site (i.e. distance and elevation). Consequently, the different soil probability estimates which are induced by each rule must be combined so that a single probability can be finally assigned to the predicted site. This is done by averaging the soil probability estimates given by the different observation points. This average is weighed in order to enhance the influence of the nearest observations points as soil surveyors generally do in practice. Figure 2: An example of use of the soil survey procedure using the knowledge of soil pattern of a reference area (in Lagacherie et al, 1995). a: actual map, b: predicted map It is now possible to predict a soil class from the set of soil probability estimates calculated at a given site. This is logically the one for which the probability of presence is maximum. Simultaneously, the sum of the probabilities of presence of the non-selected soil classes is considered as an estimate of the prediction error. This error estimates can be used for selecting new observation points in view to improve the predictions. As the general purpose is to minimize prediction errors, it seems logical to select the new observation points among those having a high error estimate. Practically, the user defines an error threshold below which prediction are judged satisfactory. New sets of observation points are then added according to a nested sampling untill all the predicted points exhibits an error estimates lower than the selected threshold. At each new addition, the probability estimates and the soil class predictions are revised which simulates the reasonning of a soil surveyor in the field. The soil survey procedure using the knowledge of soil pattern was tested in the Central Valley of Herault (south of France) for which we had the required data i.e. a reference area and a DTM on the whole region for elevation estimates. The predictive maps produced by this procedure were compared with three conventional maps of test areas. The levels of purity obtained by the procedure globally ranged between 55% and 90% according to the density of observed sites and to the complexity of the soil patterns. These results proved to be at least equal if not better than those of conventional soil 5

6 maps using similar density of observations which can be found in the litterature. Figure 2 gives an example of predictive maps obtained by the soil survey procedure described above. In this example, four steps are considered, each of them corresponding to the addition of new observation sites after error estimation. The predictive maps of the third and fourth step show a good adequation with the real map whereas the ones of the two first step must be considered as good sketches. Furthermore, the new observation points selected by the procedure are preferentially located in the areas which exhibit the highest level of complexity. Therefore, the procedure is a good formulation of a purposive sampling strategy which use the knowledge of the soil pattern for limiting the observation points without reducing the precision of the final map. Mapping soil properties over a region using sample information from a reference area. The soil information previously collected during a soil survey of a reference area can also be used for mapping soil properties at a regional scale with acceptable precision and cost (Voltz et al., 1997). The mapping procedure we investigated follows two stages: i) assigning a value of soil property to a set of observed sites located in the region represented by a reference area and ii) interpolating these values at unvisited sites. First, the soil is observed at a set of sites distributed over the region and each site is assigned to one of the soil classes identified in the reference area. The value of a property at an observation site is then classically predicted by the mean of the soil class identified at the considered site, soil class means being estimated by sampling at representative profiles chosen in the reference area. In view to estimate soil properties at unvisited sites the values of soil properties assigned to the observation sites are then interpolated by kriging. This classification-kriging procedure was tested in the Central Valley of Herault for predicting water content at wilting point over a test area surrounding the reference area and covering 1736 ha. It was compared with two alternate procedures. The first one, which is currently applied in regional studies, uses a medium scale (1:100,000) soil map. The prediction of a given soil property consists in assigning to all sites included in a given soil mapping unit the value of soil property measured at its representative profile. The second one is the ideal procedure which could be applied if no limitations of cost would exist. It consists in kriging a set of measured values at observation sites which cover the whole region (i.e. ordinary kriging). The results are presented in table1. The precision of the classification kriging procedure using information from a reference area was clearly superior to the precision of the predictions from the 1: soil maps for all the tested densities of observation sites and all the classes of distances from observation sites. This precision was also close to that of ordinary kriging with actual data when predictions points were at small distance from the observation sites. However the precision of predictions using our procedure tended to decrease when the distance from observation sites increased. To reduce this drawback requires to investigate alternatives to kriging for interpolating between observation points. We have recently tested such a procedure which uses the probabilities of occurrence of reference area soil mapping unit determined with the soil survey procedure presented in the previous section. This new procedure seems to 6

7 provide better results than the classification kriging procedure especially for points located far from observed sites (paper in progress). observation prediction distance between prediction sites grid method and closest observation sites m m 282 m 200 m number 52 ordinary kriging 447 classification-kriging 621 classification at 1: m number ordinary kriging classification-kriging classification at 1: m number ordinary kriging classification-kriging classification at 1: Table 1. Prediction mean square errors of water content at wilting point in g kg -1) (modified after Voltz et al, 1997) Conclusion Three mapping procedures using information from a representative reference area were investigated. The results we obtained in our study region demonstrated that these procedures can potentially be useful for mapping soil classes and soil properties over a wide region with acceptable precisions and costs. However extending the use of such procedures to various small natural regions requires to explore some critical points in greater details, namely the optimal selection of the reference area(s) within a given region and the influence of the complexity of the regional soil pattern on the quality of the results. Research works have been intitiated on these two last points (Nguyen The, 1997, Salvador et al, 1997). On the other hand, the application of the last presented procedure is limited to soil properties that can be effectively classified and have small within-class variance in the reference areas. Bibliography 7

8 Burgess T.M. and Webster R., (1984). Optimal sampling strategies for mapping soil types. I. Distribution of boundary spacings. Journal of Soil Science, 35: Conacher, A. J., and Darlympe, J. B. (1977). The nine unit land surface model : an approach to pedogeomorphic research. Geoderma,18, Favrot J.C. (1981). Pour une approche raisonnée du drainage agricole en France: La méthode des secteurs de référence. C.R. Académie d Agriculture de France, séance du 6 mai 1981, pp Favrot, J. C. (1989). Une stratégie d'inventaire cartographique à grande échelle : la méthode des secteurs de référence. Science du sol 27, Favrot, J.C., Bouzigues R., Lagacherie P., Legros J.P., Métral R., Yhorette, J. (1997) The small natural region, a suitable area for the establishment of agronomic and pedological references for systematic soil management at farm and field level. XVIth World Congress of Soil Science, Montpellier France august Fridland, (1972). Pattern of the soil cover. Israel Programm for Scientific Translations. Jerusalem pp Hole F. D., (1978). An approach to landscape analysis with emphasis on soils. Geoderma, 21: 1-23Lagacherie, P., and Holmes, S. (1997). Adressing geographical data errors in a classification tree for soil class predictions. Int. J. Geographical Information Science 11, Huggett, R. J. (1975). Soil lanscape system : a model of soil genesis. Geoderma,13, jan- 22. Lagacherie, P., Legros, J. P., and Burrough, P. A. (1995). A soil survey procedure using the knowledge on soil pattern of a previously mapped reference area. Geoderma 65, Legros, J. P. (1975). Occurrence des podzols dans l'est du Massif Central. Science du sol,1, Legros, J. P. (1996). Cartographie des Sols. de l'analyse spatiale à la gestion des territoires, Presses Polytechniques et Universitaires Romandes, Lausanne. Nguyen The, N. (1997). Formalisation du modèle d'organisation des sols d'une région naturelle à partir de cartes pédologiques existantes., DEA fédéral de Science du sol. UFR ENSAM.INRA Montpellier. Salvador, S., Lagacherie, P., Morlat, R. (1997) Zonage prédictif des terroirs viticoles à partir de secteurs pris comme référence. Etude et Gestion des Sols, 4, 3, Shovic, H. F., and Montagne, C. (1985). Application of a statistical soil-landscape model to an order III Wildland soil survey. Soil science society of America Journal,49, Skidmore, A. K., Ryan, P. J., Dawes, W., Short, D., and O'Loughlin, E. (1991). Use of expert system to map forest soil from geographical information system. Int.J. Geographical Information Systems,5, Voltz M., Lagacherie P., Louchart X., (1997) Predicting soil properties over a region using sample information from a mapped reference area. European Journal of Soil Science, 48,