AGRICULTURAL
LANDSCAPE
SEGMENTATION

a stochastic method to map heterogeneous variables

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# Agricultural landscape segmentation: a stochastic method to map heterogeneous variables

Davide Rizzo, Jean-François Mari, Elisa Marraccini, El Ghali Lazrak

► To cite this version:

Davide Rizzo, Jean-François Mari, Elisa Marraccini, El Ghali Lazrak. Agricultural landscape segmentation: a stochastic method to map heterogeneous variables. Advances in Spatial Typologies: How to move from concepts to practice?, IALE-Europe Thematic Workshop 2014:, Jul 2014, Lisbon, Portugal. hal-01098402

HAL Id: hal-01098402

https://inria.hal.science/hal-01098402

Submitted on 24 Dec 2014

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Research group

RIZZO Davide

INRA SAD-ASTER
Mirecourt (France)

MARI Jean-François

Université de Lorraine, LORIA & CNRS
UMR 7503
Vandœuvre-lès-Nancy F-54506, France

INRIA

Villers-lès-Nancy, F-54600 France

MARRACCINI Elisa

Scuola Superiore Sant'Anna
Institute of Life Sciences
Pisa (Italy)

LAZRAK El Ghali

INRA SAD-ASTER
Mirecourt (France)

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INRA
SCIENCE & IMPACT

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UNIVERSITÉ
DE LORRAINE

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INSTITUTE
OF LIFE
SCIENCES

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Scuola Superiore
Sant'Anna

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INRA
SCIENCE & IMPACT

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Inria
INVENTEURS DU MONDE NUMÉRIQUE
**Symposium 1 – Models and methods:** What are the most recent methodological developments?
What methods and tools are available? How to define categories for multiple purposes – linking
social, economic and environmental processes? How much “simplification” is acceptable? How to
best integrate different types of knowledge?

Agricultural landscape segmentation: a stochastic method to
map heterogeneous variables

Rizzo D<fnref n="1" />,*, Mari J-F<fnref n="2" />,<fnref n="3" />,<fnref n="4" />, Marraccini E <fnref n="5" />/ Bonari E<fnref n="5" />, Lazrak EG<fnref n="1" />

Agricultural landscapes are segmented into land management units, whose definition
changes according to the concerned stakeholders or the research focus (Rizzo et al.,
2013). Innovative approaches are needed to describe and model time-space dynamics
of these units by using multipledata sources. Various methods proposed so far in
literature mainly differed for disciplinary backgrounds and study targets(e.g.
environmental protection, conservation of cultural features, etc.). In this context,
agronomy appears to have a marginal role because of a small interest in spatially-
explicit and context-wise issues of agriculture. Accordingly, the landscape agronomy
perspective claims for an improved understanding of the interaction between farming
practices and natural resources at the landscape level (Benoît, Rizzo et al., 2012). Our
aim in this paper is to presenta method capable to define land management units by
handling heterogeneous spatial data. We tested a stochastic data mining methodthat
was originally developed for the time-space modelling of agricultural land uses(Mari,
Lazrak, &Benoît, 2013). We stressed the Markov random field(MRF) assumption of this

<footnote n="1">¹ INRA SAD-ASTER, Mirecourt (France)</footnote>

<footnote n="2">² Université de Lorraine, LORIA, UMR 7503, Vandœuvre-lès-Nancy F-54506, France</footnote>

<footnote n="3">³CNRS, LORIA, UMR 7503, Vandœuvre-lès-Nancy F-54506, France</footnote>

<footnote n="4">⁴ INRIA, Villers-lès-Nancy, F-54600 France</footnote>

<footnote n="5">⁵ (to be confirmed) ScuolaSuperioreSant'Anna, Institute of Life Sciences, Land Lab, Pisa (Italy)</footnote>

<footnote>* Corresponding author, e-mail: ridavide@gmail.com</footnote>
# Introduction #1 | Agricultural landscape management

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Agricultural landscapes are composed of many land management units.

Involved stakeholders or specific research foci can define these units differently

Straume, 2014
Zanten et al., 2013

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Innovative approaches to address temporal and spatial dynamics using multiple data sources

Brown et al., 2013
Rizzo et al., 2013

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biblio at http://bit.ly/IALE2014
method assuming that the characteristics of a spatial unit depend on the
characteristics of the neighbouring ones. The study was carried out on the Monte
Pisano, a Mediterranean terraced system (62 km², central Italy). Different sets and
classification of variables were tested stressing natural and management issues.
Finally, the landscape was segmented using 6 variables: geology, aspect, morphology,
land cover, terrace type and proximity to road. The layers were sampled on a regular
point grid then the MRF was approximated to a hidden Markov model by means of
a space-filling curve. The results consisted in a set of maps of agro-environmental
management units and a hierarchy of related landscape characteristics. This
exploratory method can improve the landscape assessment by providing a rapid
appraisal of heterogeneous data in a spatially-explicit way.

REFERENCES

Benoît, M., Rizzo, D., Marraccini, E., Moonen, A. C., Galli, M., Lardon, S., ... Bonari, E. (2012). Landscape agronomy: a new field for addressing agricultural landscape dynamics. *Landscape Ecology*, *10*(27), 1385-1394. doi:10.1007/s10980-012-9802-8

Mari, J. F., Lazrak, E. G., &Benoît, M. (2013). Time space stochastic modelling of agricultural landscapes for environmental issues. *Environmental Modelling & Software*, *46*, 219-227. doi:10.1016/j.envsoft.2013.03.014

Rizzo, D., Marraccini, E., Lardon, S., Rapey, H., Debolini, M., Benoît, M., &Thenail, C. (2013). Farming systems designing landscapes: land management units at the interface between agronomy and geography. *Geografisk Tidsskrift-Danish Journal of Geography*, **113**(2), 71-86. doi:10.1080/00167223.2013.849391

KEYWORDS (maximum 3)

Landscape agronomy, data mining, terraced landscape
Introduction #2| Landscape agronomy

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**Agricultural land uses:**
randomly distributed among fields
managed by farmers ?

Understanding how
**spatial organization & temporal evolution**
of farming practices
reveal logical processes and driving forces

a major challenge for landscape agronomists

Benoît, Rizzo et al., 2012

biblio at http://bit.ly/IALE2014
# Aim | context-wise landscape segmentation

## Temporospatial method...

Mari et al. 2013

Arpentage is a HMM based software specifically developed to perform (un)supervised segmentations of agricultural landscapes

## ...to map landscape regularities

Test this method in new contexts with an explicit landscape agronomic approach

Profile of **heterogeneous spatial data** instead of (crop) time sequences

Focus on abandoned areas in a Mediterranean terraced landscape dominated by olive groves

Developed to map **crop sequences** as support to evaluate agricultural impact on water quality and on biodiversity

Mignolet 2008
Lazrak et al. 2009
# Methods #1 | Landscape segmentation

Space
dominant

sequence of
images

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Software for LUCC analysis generally
implement unsupervised clustering on spatial
entities based on space-dominant attributes

Time
dominant

image of
sequences

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Data mining approaches involving clustering of sequences
allowed knowledge extraction to get a landscape
description in terms of land-use patterns
# Methods #2 | ArpentAge

It relies on a stochastic principle to
model time space landscape regularities
on 2 Markovian assumptions

**Markov chain**

land use of a given field in a year
depends only on the land-use of the
recent previous years in the same field.

**Markov random field**

land use of a given field depends only
on the land-use of the neighboring
fields

free download: http://www.loria.fr/~jfmari/App

French acronym for: Landscape Regularities
Analysis: Environment, Territory, Agronomy

Arpenter is a French verb meaning *to survey*

*probably the 1st software in the agronomic area
implementing a time-dominant model and processing
time-space data.*

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Linear
temporal segmentation

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Ergodic*

spatial segmentation
(and transition analysis)

*all states are inter connected
# Methods #3 | Approximating a MRF by a HMM

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$n=1$

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$n=2$

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$n=3$

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$n=4$

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the Hilbert-Peano fractal curve is used to approximate the Markov Random Field (MRF) assumption through a Hidden Markov Model (HMM)

a space filling curve allows to approximate the 2D as a simple chain

Lazrak 2012, PhD thesis

biblio at http://bit.ly/IALE2014
# Methods #4 | hierarchical model

Two stochastic processes can be represented, the first (hidden) driving the second (known and visible)

Each "super state" of a "super model" is a HMM (generally linear) to describe the (time) sequences

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Hierarchical HMM is strictly ergodic to segment the landscape into patches of homogeneous time sequences

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Combining heterogeneous data to deal with drivers of management dynamics (instead of pixels)

ArpentAge instead of clustering because "context-wise" (cf. fractal curve)

Context-wise landscape segmentation focused on management related variables

HMM = Hidden Markov Model
Materials #1 | study area: Monte Pisano

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Mediterranean terraced olive grove farming system (62 km²)
Materials #2 | input data

<table><tr><td>Geology</td><td>5 cl.</td><td>• anthropic • alluvial &amp; sand-loam sediments<br/>• quartzes • limestone • schist</td></tr><tr><td>Orientation</td><td>4 cl.</td><td>N, S, E, W</td></tr><tr><td>Morphology</td><td>4 cl.</td><td>• Plains • valleys • slopes • ridges</td></tr><tr><td>Land cover</td><td>14 cl.</td><td>• Urban • garden<br/>• cultivated olive groves • abandoned olive groves • vineyards • orchards • arable lands<br/>• fallows • garigues • outcrops • wetland vegetation • water • pine wood • wood</td></tr><tr><td>Terrace type</td><td>5 cl.</td><td>(bench) terraces • talwegs • pocket terraces<br/>• broad field terraces • extensive</td></tr><tr><td>Dry-stone wall density</td><td>6 cl.</td><td>From 0 to 44.5 m/ha (natural breaks)</td></tr></table>

Layers were sampled on a 10m regular point grid

Markov random field approximated to a hidden Markov model using a space-filling curve
Results #1 | an output map

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# Results #2 | variable segmentation

*The increasing segmentation allows to define a hierarchy of the variables*

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Three major management regions:

* Terraced groves
  medium-high density DSW on quartz, E-S oriented with some garigues and pine woods

* Woods
  may differ for morphology or exposition

* Foothills
  may be segmented for specific terrace types, mix with urban land cover or plain morphology

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Especially relevant for management issues
# Results #3 | location of abandoned olive groves

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West exposed terraces

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periurban

Western side

prevalently associated to the cluster grouping the foothills, with the prevalence of the anthropic bedrock and S-W exposition

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C

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foothills

Eastern side

prevalently associated to sand-loam sediment area, with low density terraced in valleys.
# Conclusions and perspectives

## Leverages...

**variable choice**
e.g., what if we introduce proximity to road?

to
further
explore

input variable **classification**
type and number of classes

variable input **quality**
different geometric scales

**HHMM parameterization**
space filling curve, number of states

## ...and potentials

**Flexibility** of variables treated
remote sensing map, census data, etc.

**Flexibility** of scale of analysis
potentially no limit if input data are defined coherently to the research question

Future applications could address the temporospatial characterization of **agricultural practices** at the **watershed scale**
9th IALE World Congress

July 5-10, 2015

Portland, Oregon

Participate !

RIZZO Davide,
FERGUSON Rafter Sass,
BENOIT Marc,
PADOA-SCHIOPPA Emilio

Landscape agronomy: advances and
challenges of a new field addressing the
management of rural land systems

contact & info
[ridavide@gmail.com](mailto:ridavide@gmail.com)

read more at
http://bit.ly/IALE2015_Symposium_LA

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