>> more information HERE http://bit.ly/1VBEFbX << Several interdisciplinary perspectives (e.g., landscape agronomy, land system science, ecoagriculture) urge agronomy to contextualize the characterization of agricultural activities within the land management system. This challenges the discipline to scale up the analysis of agricultural dynamics from farm to landscape levels, where conflicting choices may emerge about the management of natural resources. Shortcoming of data covering large areas, especially about farming practices and decision-making processes, is a major constraint. Nevertheless, we consider that segmenting the land according to the observed land cover sequences can eventually incorporate a relevant part of the farmers' medium-term decision-making processes, influenced for instance by climate changes or territorial conflicts related to local resources. Furthermore, focusing on land cover sequences may provide a consistent target of analysis across multiple scales. Our aim is to discuss the relevance of a data-mining method to handle remotely sensed data and analyze temporal and spatial agricultural dynamics in a landscape perspective. The method, originally developed to handle large and labor-demanding survey datasets, is based on the stochastic segmentation with Hidden Markov Models. It firstly identifies temporal regularities of the crop/grassland sequences, then use them to segment into homogeneous patches the study area (potentially ranging from farmland to region). Starting from a case study carried out on a 12-year time series of satellite images for the Yar watershed (Brittany, France) we address the potential contribution of this kind of approach to improve the dynamic analysis of farming systems. [...]

1. Davide Rizzo*
EG Lazrak ¹
JF Mari ¹ ²
L Hubert‐Moy ³
M Benoît ²
* UMR AGIR INRA‐INPT
¹ INRA SAD‐ASTER
² LORIA, UMR 7503 & INRIA
³ LETG‐Rennes‐COSTEL, UMR CNRS &
Université de Rennes 2
Opportunities for
scaling up research on
agricultural dynamics
Use of crop sequences for
data mining of remotely
sensed time series across
multiple scales

2. Can maps of land
cover sequences
improve cross-scale
understanding of
farming systems?
T7 . Scaling up from farm to landscape multiscale
scenario analysis of agricultural systems
…will make possible to build farming system
design from a landscape agronomy perspective
Understanding how
land patterns, farming
practices and natural
resources are related

3. aim
Discussing a time dominant and
context wise data-mining method

4.  watershed of 61.5 km² (Brittany, FRA)
 mainly maize, wheat and grasslands related
to industrial breeding.
12 year time series of satellite images was
classified into 6 land cover classes for individual
field polygons– then sampled with a 7-m
regular point grid
We distinguished permanent from
temporary grassland and identified
and localized the type of rotations in
which temporary grasslands were
involved.
Yar case study

5. Lazrak et al., (submitted)
Mapping agricultural land use dynamics with
remotely sensed series: a generic data mining
approach at a watershed scale
want to know more?
ridavide+fsd@gmail.com Davide Rizzo, PhD
It’s ok to tweet
download the poster
http://bit.ly/FSD5_Rizzo_poster
…and come discussing it !
Thanks for your
attention
More information upcoming