Stochastic data-mining for understanding time and space dynamics of agricultural landscapes. Contribution to numerical methods for landscape agronomy.
type: Dataset
description: Agriculture is the human activity that uses and transforms most of the Earth's surface. Agricultural intensification and simplification have created numerous ecological and environmental problems. To better manage the future development of agricultural landscapes, it is important to understand the past and current dynamics of agricultural landscapes at regional scales compatible with the scales where environmental and ecological services manifest themselves. Yet, most studies on agricultural dynamics at regional scales do not distinguish between the dynamics related to a steady-state farming activity and the dynamics related to changes in the mechanisms of farming activity. Meanwhile, studies reported in the literature that make this distinction have the disadvantage of being difficult to duplicate. The purpose of this thesis is to develop a generic method for modelling the past and current dynamics of Landscape Organization of Farming Activity (LOFA). We developed a stochastic modelling method based on Hidden Markov Models that allows data mining within a corpus of spatio-temporal land use data to segment the corpus and reveal hidden agricultural dynamics. We applied this method to land use corpora from various sources (field surveys, remote sensing) belonging to two agricultural landscapes of regional dimension : the study site of Chizé (430 km², Poitou-Charentes, France) and the catchment area of Yar (60 km², Brittany, France). This method provides three contributions to the modeling of LOFA : (i) LOFA description following a temporo-spatial approach that first identifies temporal regularities and then localizes them by segmenting the agricultural landscape into compact areas having similar temporal regularities ; (ii) data mining of the neighborhood of land use successions and their dynamics ; (iii) combining of the regularities revealed by our data mining approach at the regional level with rules identified by agronomy and ecology experts at more local scales t…
landing page: https://theses.hal.science/tel-00782768
identifier: NNT: 2012LORR0159
keyword: agronomie-des-territoires
keyword: crop-successions
keyword: cropping-system
keyword: hierarchical-hidden-makov-model-hhmm
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-aicomputer-science-csartificial-intelligence-csai
keyword: landscape-agronomy
keyword: modele-de-markov-cache-hierarchique
keyword: sdebeenvironmental-sciencesbiodiversity-and-ecology
keyword: sdvsaagrolife-sciences-q-bioagricultural-sciencesagronomy
keyword: succession-de-cultures
keyword: systeme-de-culture
keyword: theses
issued: 2026-05-14T19:48:02.084692
modified: 2026-05-14T19:48:02.084698
language: fr
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Stochastic data-mining for understanding time and space dynamics of agricultural landscapes. Contribution to numerical methods for landscape agronomy.
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00782768
issued: 2026-05-14T19:48:02.097436
modified: 2026-05-14T19:48:02.059810