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Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Analysis.

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Presentation on theme: "Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Analysis."— Presentation transcript:

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2 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Analysis of Landscape Patterns for Environmental Issues in Agricultural regions El Ghali Lazrak, Marc Benoît, Jean-François Mari INRA, UR 055, SAD_ASTER, Mirecourt

3 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Marc Benoît, Ghali Lazark, Jean-François Mari, Introduction Example 1: Seine basin and CarroTage modelling pro cess : Example 2: Chizé plain and ArpentAg mdelling process: Conclusions:

4 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 How to caracterize landscape regularities to: 1.Create new images for scenarios building? 2.Link these landscapes regularities with water and biodiversity issues? 3. Use available informations ? 4. Test stochastics methods? How can we created new knowledges on cropping systems dynamics in landscapes? Our questions How these landscape regularities determine biodiversity evaluations?

5 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Marc Benoît, Ghali Lazark, Jean-François Mari, Introduction Example 1: Seine basin and CarroTage modelling process : Example 2: Chizé plain and ArpentAg mdelling process: Conclusions:

6 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Example 1: Agricultural landscapes … for water Spatialization of cropping systems ( crop sequences) in Seine watershed ( 97 000 km²) to implement a simulation model of nitrate and pesticids transfert (Mignolet et al., 2004 ; Ledoux et al., 2007) Birth of CarrotAge: a new « Markov Son » for temporal data mining

7 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Data base on crop sequences: Annual national survey Teruti (SCEES) Land use on 550000 points (1982 à 2003), on 155000 points since 2004

8 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Diversity of Crop Sequences in 1990s and their location in Seine basin ( 100000 km²), 147 Agricultural Regions (Le Ber et al., 2006; Mignolet et al., 2007) Ø Identification of regions caracterized by crop sequences landcsapes pp pa+pt maïs pois colza orge blé ? 92939495969798 Livestock regions pp pa+pt maïs pois colza orge blé ? 92939495969798 Barrois plateau pp pa+pt maïs pois colza orge blé ? 9293949596979899 Picardie plateaux pp pa+pt maïs pois colza orge blé ? 9293949596979899 Champagne

9 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Marc Benoît, Ghali Lazark, Jean-François Mari, Introduction Example 1: Seine basin and CarroTage modelling process : Example 2: Chizé plain and ArpentAg mdelling process: Conclusions:

10 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Example 2: Agricultural landscapes … for Birds Spatialization of crops sequences for species richness protection (birds cases) ArpentAge building process for time-space data mining (ANR Project « BiodivAgrim »: 2007-2011)

11 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 350 km², 11000 fields since 1994 for : land cover, fields limits ( changing each year), birds species, … Busard cendré Outarde canepetière The study zone Source and nature of data for data mining

12 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Data spatial resolution choice : Method : Grid sampling for space data mining For temporal data mining, no problem with spatial sampling. This sampling is a necessity for spatial analysis

13 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Data spatial resolution choice : Problem : lose of spatial information due to data density With a high spatial resolution, the bigger fields are « over – represented » With a low spatial density, the smallest fields are forgoten Method : Grid sampling for space data mining For temporal data mining, no problem with spatial sampling. This sampling is a necessity for spatial analysis

14 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Choosing the spatial resolution Quantifing the loose of spatial information in relationship with the spatial resolution level Finding a solution …to explain our choice !

15 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 example of Small Agricultural Region « Saint-Quentinois et du Laonnois » (02) Markov diagram « One Crop" Probabilities of main crops froma 1992 to 1999 Sugar Beets Peas Grassalnds Barley 20.5% 11.3% 7% 4.9% Fallow L. Maïze Oil rapes Potatoes 4% 3.7% 2.6% 1.9% Probabilities of main « tri-crops » from 1992 to 1999 Markov diagram "Three crops sequences » 1992-94 1993-95 1994-96 1995-97 1996-98 1997- 99 TIME-SPACE modelling Step1: Extraction of crop sequences

16 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Step1: Extraction of time regularities through crop sequences dynamics Evolution of CS (rotations) in SAR Beauce Evolution of CS (rotations) in Chizé zone « Crazy » evolution from 1992 to 1996: adaptation to new CAP rules Rotations Stabilization in 1996 (end of «farmer learned the CAP rules ») homogeneous evolution : no CAP adaptation TIME-SPACE modelling

17 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 TIME-SPACE modelling Step 2: Spatialization of time regularities Rotations type 1 Rotations type 2 Rotations type 3 Rotations type 4 … Classes defined from CarrotAge

18 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 TIME-SPACE modelling Step3: Relationships with biodiversity patches : birds location

19 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères pour lEnvironnement dans les Territoires Agricoles Marc Benoît, Ghali Lazark, Jean-François Mari, Introduction Example 1: Seine basin and CarroTage modelling process : Example 2: Chizé plain and ArpentAg mdelling process: Conclusions

20 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Conclusion (1) Modelling data on Time-Space dynamics of crop sequences: -To produce new methods of modelling of landscape regularities (LANDSCAPE MODALITIES). - To model impacts of agricultural practices on environmental issues ( LANDSCAPE IMPACTS) … To contribute to LANDSCAPE AGRONOMY through stochastic modelling processes.

21 Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 Conclusion (2) Creating new knowledge management procedures: Community Information System on Agricultural Practices (CISAP) : ( ANR-ADD- COPT: 2006-2008, then RMT OAAT: 2008-2013) Inter-operating of two models : land cover are organized in crop sequences ( TIME dimension of CarrotAge), and these crop sequences are organised in SPACE (ArpentAge) : A TIME … then SPACE modelling

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