Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères...

Spatial Landscape Modelling;Toulouse; 3-5 juin 2008

ArpentAge :

Analyse de Régularités Paysagères pour l’Environnement dans les Territoires

Agricoles

Analysis of Landscape Patterns for Environmental Issues in Agricultural regions

El Ghali Lazrak, Marc Benoît, Jean-François MariINRA, UR 055, SAD_ASTER, Mirecourt


ArpentAge :

Analyse de Régularités Paysagères pour l’Environnement 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:


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?


ArpentAge :




Introduction

Example 1: Seine basin and CarroTage modelling process :


Conclusions:


Example 1: Agricultural landscapes … for Example 1: Agricultural landscapes … for waterwater

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 Birth of CarrotAge: a new « Markov Son » for temporal data miningmining


Data base on crop sequences:

Annual national survey “Teruti” (SCEES)Annual national survey “Teruti” (SCEES)

Land use on 550000 points (1982 à 2003), on 155000 points since 2004


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)

Identification of regions caracterized by crop sequences landcsapes

pp

pa+pt

maïs

pois

colza

orge

blé

?

92 93 94 95 96 97 98

Livestock regions

pp

pa+pt

maïs

pois

colza

orge

blé

?

92 93 94 95 96 97 98

Barrois plateau

pp

pa+pt

maïs

pois

colza

orge

blé

?

92 93 94 95 96 97 98 99

Picardie plateaux

pp

pa+pt

maïs

pois

colza

orge

blé

?

92 93 94 95 96 97 98 99

Champagne


ArpentAge :




Introduction



Conclusions:


Example 2: Agricultural landscapes … for BirdsExample 2: Agricultural landscapes … for Birds

Spatialization of crops sequences

for species richness protection (birds cases)

ArpentAge building process for time-space data miningArpentAge building process for time-space data mining

(ANR Project « BiodivAgrim »: 2007-2011)


350 km²,

11000 fields since 1994 for :

land cover,

fields limits ( changing each year),

birds species, …

Busard cendréBusard cendré

Outarde canepetièreOutarde canepetière

The study zone

Source and nature of data for data miningSource and nature of data for data mining


Data spatial resolution choice :Method : Grid sampling for space data miningMethod : Grid sampling for space data mining

For temporal data mining, no problem with spatial sampling. This sampling is a necessity for spatial analysis


Data spatial resolution choice :

Problem : lose of spatial information due to data densityProblem : 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 miningMethod : Grid sampling for space data mining

For temporal data mining, no problem with spatial sampling. This sampling is a necessity for spatial analysis


Choosing the spatial resolution

Quantifing the loose of spatial information in relationship with the spatial resolution level

Finding a solution …to explain our choice !Finding a solution …to explain our choice !


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 PeasGrassalndsBarley

20.5%11.3%7%4.9%

Fallow L.MaïzeOil rapesPotatoes

4%3.7%2.6%1.9%

blé + betterave + blé 14.5% blé + pois + blé 7.0% betterave + blé + betterave 6.9% pr. perm. + pr. perm. + pr. perm. 6.8% pois + blé + betterave 5.3% betterave + blé + pois 4.5% betterave + blé + blé 1.9% blé + blé + blé 1.6% betterave + blé + orge 1.6% blé + blé + betterave 1.5% orge + betterave + blé 1.5% blé + orge + betterave 1.5% blé + colza + blé 1.4% blé + maïs + blé 1.1% betterave + pois + blé 1.0% pois + blé + orge 1.0% blé + jacheres + blé 0.9% pois + blé + blé 0.9% p.de t. + blé + betterave 0.9% orge + pois + blé 0.9% blé + betterave + pois 0.8% blé + p.de t. + blé 0.8% jacheres + jacheres + jacheres 0.8% blé + blé + pois 0.8% pois + betterave + blé 0.7% potagers + potagers + potagers 0.7% blé + orge + pois 0.6% pois + blé + pois 0.6% colza + blé + pois 0.6% pois + blé + colza 0.6% betterave + blé + colza 0.6% betterave + blé + maïs 0.6% betterave + blé + p.de t. 0.5% pois + blé + maïs 0.5% blé + pois + betterave 0.5% maïs + blé + betterave 0.4%

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 modellingStep1: Extraction of crop sequencesStep1: Extraction of crop sequences


Step1: Extraction of time regularities through crop Step1: Extraction of time regularities through crop sequences dynamicssequences 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



Step 2: Spatialization of time regularitiesStep 2: Spatialization of time regularities

Rotations type 1

Rotations type 2Rotations type 3

Rotations type 4……

Classes defined from CarrotAge



Step3: Relationships with biodiversity patches : birds locationStep3: Relationships with biodiversity patches : birds location


ArpentAge :




Introduction



Conclusions


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.


Conclusion (2)

Creating new knowledge management procedures: Community Information System on Agricultural Practices (CISAP) : ( ANR-ADD-COPT: 2006-2008, then RMT OAAT: 2008-2013) O

pé

rati

on

na

lis

ati

on

Formalisation

Co

nc

ep

tio

n

Socialisation

ImplémentationUtilisation

Négociation

Expression des besoins

Mobilisation

ReprésentationModélisation

Identification

Valider la faisabilité

Valider le logiciel

Valider le cahier des charges

Evaluer l’Observatoire

Analyse

Spécificationdu SI

Institutionnalisationde l’observatoire

Développementdu SI

Acquisitiondes données

Organisation de l’observatoire

Construction de connaissance

Appropriation de l’observatoire

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

Spatial Landscape Modelling;Toulouse; 3-5 juin 2008 ArpentAge : Analyse de Régularités Paysagères...

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