- Software name: GenExP-LandSiTes

- contact: florence.leber (a) engees.unistra.fr, jfmari (a) loria.fr

- Year first available: 2006

- Hardware required: Java platform edition 6, http://java.sun.com/

- Library required:

- package JRI, http://www.rforge.net/JRI

- software R, http://cran.r-project.org/

- spatial statistic library spastat, http://cran.r-project.org/web/packages/spatstat

- package JRI, http://www.rforge.net/JRI
- Program language: Java

- Software and supplementary material are downloadable free to use under the GPL, http://www.gnu.org/licenses/gpl.txt

Download (2011 release for windows: R and JRI required)

Download (2009 release for windows: R and JRI required)

Spatially explicit landscape models have been widely used for ecological or forestry studies (Turner and Gardner, 1991). They would be very useful for studying several processes occurring in an agricultural landscape, such as gene flow, erosion or spread of plant diseases. Most of the existing models are raster-based and thus are rather not adapted for agronomic purposes at a landscape scale. Indeed, agricultural models have to represent and manage geometrical patches and thus should rely on tessellation methods (Gaucherel *et al.*, 2006). We developed GenExP-LandSiTes to fulfill this need. GenExP-LandSiTes allows to simulate various landscapes configurations and compositions, controlling the shape, area or spatial distribution of the fields.

- Stochastic field patterns generation: this includes the simulation of spatial point processes, the generation of the field patterns with tessellation methods, and the stochastic simulation of land-use;
- Post-processing: this transforms the mosaic of polygons into a more real istic mosaic of agricultural fields and collects statistics on the simulated landscapes.

Figure 1: Area selection (red): the menu allows to first parametrise the selected area, then to calculate the chosen tessellation, and finally to visualise the different elements (fields, land cover, centroids,etc.) of the resulting landscape.

Figure 2: Voronoi tessellation to represent a landscape composed of agricultural (colors) and non agricultural areas (white). Each area can be visualised in its own way:

- Seeds are displayed in the west area (blue);
- Plot vertices are displayed in the north-east area (orange);
- Plot centroids are displayed in the

south-east area (red).

Two tessellation methods have been implemented so far.

- The Voronoi tessellation, using the "3D Hull" algorithm for Delaunay triangulation (O'Rourke, 1998): given a set of points in the Euclidean plan, the set of seeds, a Voronoi tessellation (or Voronoi diagram) is a covering of the Euclidean plan with non-overlapping convex polygons, each of them surrounding a seed. The points in a polygon are closer to its seed than to any other seed located in any other polygon. Therefore the edges consist in the points located at an equal distance from two seeds.
- The rectangular tessellation, using the Mackisack algorithm (Mackisack and Miles, 1996). The basic principle of the algorithm is to generate the edges of the rectangles by crossing two-directional rays starting from a set of points. The area is filled in with non-overlapping rectangles sharing T-vertices.

The generation of points that serve as seeds in the tessellation process is performed under R by the routines of the spatial statistic library spatstat (Baddeley and Turner, 2005). The JRI package2 was used, allowing communications between the Java methods and the R routines. In order to generate seeds, several options are possible.

- Generation following a Poisson process, the polygon centroids are generated assuming a Poisson process whose parameters are arbitrary set by the user;
- Generation based on a real landscape, the points are sampled in a real landscape (cf. Figure 3-left) on a one point per parcel basis (Adamczyk
*et al.*, 2007). These points serve as seeds to simulate virtual landscapes (cf. Figure 3-center). Assuming this set of points, GenExP-LandSiTes can also train stochastic point generation models by means of R routines to simulate new landscapes with similar configurations of seeds or with controlled variable configurations (cf. Figure 3-right).

Figure 3: Two types of tessellations: real field pattern (left), simulated field pattern --Voronoi tessellation-- around real centroids (center), simulated field pattern based on simulated centroids (right). The original map was provided by Institute for the Protection and Security of the Citizen (Joint Research Centre of the European Union) or AUP (Agence Unique de Paiement / French Paiment Agency CAP Support).

Once the landscape configuration is simulated, the user can allocate crops
to the fields according to various methods. As a starting point, the user chooses a distribution of land-uses. Afterwards, the land-use mosaic is built randomly according to this distribution.
GenExP-LandSiTes can also manage stochastic rules of land cover successions. These rules are carried out by a data-mining software (CarrotAge) that treats the crops successions as high-order Markov chains generated by a second-order Hidden Markov Models (HMM2) (Le Ber *et al.*, 2006b). At time 0, GenExP-LandSiTes allocates land covers to the fields according to a distribution provided by the user (for example: 50% maize, 30% wheat, 20% others); then, at successive time slots, crops are allocated to the fields following the crop successions, e.g. (maize / wheat) or (maize / maize), according to the transition probabilities given by the HMM2.

- Deletion of polygons exceeding landscape limits (clipping);
- Deletion of polygon sides lower than a given threshold (merging of vertices) in order to simulate realistic agricultural landscapes;
- Selection of the polygons for a statistical study;

Adamczyk, K., Angevin, F., Colbach, N., Lavigne, C., Le Ber, F., Mari, J.-F., 2007. GenExP, un logiciel simulateur de paysages agricoles pour l'étude de la diffusion de transgènes. *Revue Internationale de Géomatique* 17 (3-4), 469-487.

Baddeley, A., Turner, R., 2005. Spatstat: an R package for analyzing spatial point patterns. *Journal of Statistical Software* 12 (6), 1-42.

Colbach, N., Monod, H., Lavigne, C., 2009. A simulation study of the medium-term effects of field patterns on cross-pollination rates in oilseed rape (*Brassica napus L.*). *Ecological Modelling* 220 (5), 662-672.

Gaucherel, C., Fleury, D., Auclair, D., Dreyfus, P., 2006. Neutral models for
patchy landscapes. Ecological Modelling 197 (1-2), 159--170.

Lavigne, C., Klein, E. K., Mari, J.-F., Le Ber, F., Adamczyk, K., Monod, H., Angevin, F., 2008. How do genetically modified (GM) crops contribute to background levels of GM pollen in an agricultural landscape? *Journal of Applied Ecology* 45 (4), 1104-1113.

Le Ber, F., Lavigne, C., Mari, J.-F., Adamczyk, K., Angevin, F., 2006. GenExP, un logiciel pour simuler des paysages agricoles, en vue de l'étude de la diffusion de transgènes. *In: Actes du Colloque International de Géomatique et d'Analyse Spatiale (SAGEO 2006)*, Strasbourg.

Le Ber, F., Benoît, M., Schott, C., Mari, J.-F., Mignolet, C., 2006. Studying crop sequences with CarrotAge, a HMM-based data mining software. *Ecological Modelling 191* (1), 170-185.

Le Ber, F., Lavigne, C., Adamczyk, K., Angevin, F., Colbach, N., Mari, J.-F., Monod, H., 2009. Neutral modelling of agricultural landscapes by tessellation methods -- application for gene flow simulation. *Ecological Modelling* 220, 3536-3545.

Mackisack, M., Miles, R., 1996. Homogeneous rectangular tessellations. *Advances in Applied Probability* 28, 993-1013.

O'Rourke, J., 1998. *Computational Geometry in C*. Cambridge University Press, 2nd edition.

Turner, M. G., Gardner, R. H. (Eds.), 1991. *Quantitative Methods in Landscape Ecology*. Vol. 82 of Ecological Studies. Springer.