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, La garrigue, mosaïque de paysages, tend à s'uniformiser face à l'avancée progressive de la forêt
, Mais contrairement aux idées reçues, le regain de la forêt n'est pas toujours positif. C'est le cas pour la garrigue où la fermeture des milieux est problématique. Des méthodes basées sur la reconnaissance de motifs à partir d'images satellites sont développées pour comprendre et cartographier le paysage et aider les acteurs concernés par le devenir de la garrigue
se promène dans la garrigue à la recherche de ses chèvres encore égarées -décidément, c'est de famille -il y passe parfois des heures. Pas facile en e et de s'y retrouver dans ce dédale de végétation : ça pique, ça gratte et puis on n'y voit pas grand-chose avec cette mosaïque de grands chênes verts ,
, de l'herbe. Seulement, depuis que les collègues de Jean-René ont arrêté leur activité pastorale à cause d'une augmentation de la compétition économique [1], #crisedel'emploi, l'herbe et les buissons ne sont plus broutés, ils poussent en paix et le paysage tend à se fermer pour laisser place à une forêt dense, La garrigue, c'est un gigantesque puzzle composé de plusieurs pièces imbriquées : des arbres, des buissons
, Mais alors pourquoi c'est pas cool le regain de la forêt ?
, En premier lieu pour Jean-René lui-même qui a de plus en plus de mal à savoir où faire pâturer ses chèvres : où peuvent-elles circuler dans ce labyrinthe ? Où est la boustifaille ? Mais surtout où diable se sont-elles encore fourrées ? Et puis ça dégrade aussi la qualité du paysage : au grand dam d'Isodore De Lahaute, citadin en mal de nature qui ne verra plus que des arbres à perte de vue lors de ses sorties champêtres, Cette fermeture des milieux pose quelques problèmes
, ce serait d'avoir une carte pour s'orienter, ou bien un (très grand) escabeau pour y voir un peu plus clair. Jean-René a bien pensé à acheter une montgol ère, comme à l'époque de la grande guerre où des clichés étaient pris depuis des ballons pour aider les poilus à s'y
emploi n°2) et puis maintenant il y a bien mieux : les images satellites. Elles ont maintenant une telle précision qu ,
, Les motifs dessinés par les arbres et par les buissons sont maintenant bien apparents pour Jean-René muni de telles images. C'est facile à reconnaître
avons pas tous les mêmes perceptions. Selon que ce soit Jean-René ou son lleul qui essaie de déterminer la structure de la garrigue, l'interprétation ne sera pas tout à fait la même, d'autant ANNEXE A que Jean-René ,
, Heureusement pour lui, les maths associés à l'informatique parviennent à traduire la perception humaine des textures
, Si les algorithmes de reconnaissance de motifs ne détectent pas aussi bien les textures que l'oeil humain (#manisstillthebest), ils présentent l'avantage d'être automa
, Une approche possible en détection automatique de texture est l'approche fréquentielle. Le principe est de repérer des structures qui se répètent dans l'espace. Par exemple, Jean-René, adepte de la pizza, a bien remarqué que dans celle qu'il a ectionne tout particulièrement, la pizza reine, il y a des motifs qui se répètent
Une reine de 33 cm, cuisinée selon les principes évoqués par Giovanni présente donc un motif d'une fréquence de 6,5 champignons par largeur de pizza. Grâce à cette approche, on peut traduire l'aspect visuel d'une pizza en termes fréquentiels ,
bien beau tout ça, l'approche de fréquentielle, les motifs, mais il fait comment le Jean-René pour retrouver ses chèvres avec ça ? ,
, On peut par exemple en déduire à quel point les buissons et les arbres sont connectés entre eux et en déduire le chemin emprunté par les chèvres. On peut également connaître le pourcentage d'herbe dans une partie de la garrigue, ou autrement dit la part de gueuleton potentiel pour une chèvre. Tout ça automatiquement, ces motifs peuvent directement être liés à d'autres mesures qui nous intéressent
Ils sont friands d'outils qui leur permettent d'avoir une vision globale du territoire. Ainsi, l'analyse des motifs dans une image satellitaire permet de connaître le taux de fermeture d'une garrigue. A fortiori, on peut suivre l'évolution de la fermeture des milieux avec plusieurs images acquises à des dates di érentes. Cette approche synthétique du territoire n'en est qu'à ses débuts et pourrait permettre de comprendre le comportement de certains animaux : quel type de paysage l'Outarde canepetière préfère-t-elle pour procréer [5] ? Elle permettrait également d'étudier l'impact de mesures de gestion : faut-il brûler les broussailles pour maintenir les garrigues ouvertes ? Ou bien faut-il les girobroyer ? Quelle sont les pratiques les plus e caces à longs termes ? Celles qui respectent le mieux l'environnement ? humaines (agriculture, pastoralisme, débroussaillage, etc.) si bien que l'avenir de la diversité biologique dans ces milieux ne peut être déconnecté de celui des activités humaines ,
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2 Valeurs optimales des paramètres de segmentation pour chaque année. Année d'acquisition Échelle Homogénéité ,
, des di érences de dates d'acquisition entre ces deux zones. Ces deux zones sont donc échantillonnées et classées séparément. Pour résumer : ? deux segmentations sont réalisées
, ? des échantillons sont pris pour chaque couple année/causse
, ? les sous classes échantillonnées peuvent être di érentes selon le couple année/causse étudié (la notion de sous classes sera présentée dans la sous-section 3
Le choix des paramètres de cet algorithme est basé sur une analyse visuelle des résultats obtenues sur une sélection d'imagette (voir Figure B.4), pour di érentes valeurs des paramètres d'échelle et d'homogénéité. Ces paramètres contrôlent la taille et la régularité des contours des régions obtenues. La segmentation est jugée satisfaisante lorsque les petits buissons (quelques pixels) sont correctement délimités tandis que la sur-segmentation de larges zones continues de buissons ou d'arbres est évitée autant que possible, Generic Region Merging, 2015. ,
En e et, le modèle de classi cation utilise la radiométrie des échantillons pour pouvoir classer l'ensemble des objets de l'image. Or, une classe thématique peut correspondre à plusieurs signatures radiométriques distinctes. Par exemple, la classe SN peut correspondre à la fois à de la roche nue et du sol nu. Cependant, ces deux sous-classes n'ont pas exactement la même signature spectrale. Il est par exemple possible de voir sur les imagettes que le sol nu apparaît plutôt en marron tandis que la roche nue apparaît plutôt en blanc. Dans ce cas, les résultats de classi cation peuvent être améliorés en e ectuant d'abord un premier niveau de classi cation avec l'ensemble des sous classes, puis en regroupant les sous REFERENCES Amano, A n d'optimiser les performances de la classi cation, il est parfois nécessaire de réaliser un échantillonnage avec un nombre de classes supérieur à celui souhaité, 2016. ,
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