Reference : Classification of riparian forest species and health condition using multi-temporal a...
Scientific journals : Article
Life sciences : Environmental sciences & ecology
http://hdl.handle.net/2268/193070
Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system
English
[fr] Classification de la composition spécifique et de l'état sanitaire des forêts riveraines à partir de séries temporelles d'images hyperspatialles collectées à l'aide d'un drone
Michez, Adrien mailto [Université de Liège - ULiège > > > Form. doct. sc. agro. & ingé. biol.]
Piégay, Hervé [> >]
Lisein, Jonathan mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
Claessens, Hugues mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
Lejeune, Philippe mailto [Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels >]
2016
Environmental Monitoring and Assessment
Springer
188
3
Yes (verified by ORBi)
International
0167-6369
1573-2959
[en] Hyperspatial imagery ; Unmanned aerial system ; UAS ; Random forests ; Riparian forest ; Forest health condition ; Unmanned Aerial Vehicle ; UAV ; Multi-temporal remote sensing
[en] Riparian forests are critically endangered many anthropogenic pressures and natural hazards. The importance of riparian zones has been acknowledged by European Directives, involving multi-scale monitoring. The use of this very high resolution and hyperspatial imagery in a multi-temporal approach is an emerging topic. The trend is reinforced by the recent and rapid growth of the use of the unmanned aerial system (UAS), which has prompted the development of innovative methodology. Our study proposes a methodological framework to explore how a set of multi-temporal images acquired during a vegetative period can differentiate some of the deciduous riparian forest species and their health conditions. More specifically, the developed approach intends to identify, through a process of variable selection, which variables derived from UAS imagery and which scale of image analysis are the most relevant to our objectives.
The methodological framework is applied to two study sites to describe the riparian forest through two fundamental characteristics: the species composition and the health condition. These characteristics were selected not only because of their use as proxies for the riparian zone ecological integrity but also because of their use for river management.
The comparison of various scales of image analysis identified the smallest OBIA objects (ca. 1 m²) as the most relevant scale. Variables derived from spectral information (bands ratio's) were identified as the most appropriate, followed by variables related to the vertical structure of the forest. Classification results show good overall accuracies for the species composition of the riparian forest (five classes, 79.5 and 84.1 % for Site 1 and Site 2). The classification scenario regarding the health condition of the black alders of the Site 1 performed the best (90.6 %).
The quality of the classification models developed with a UAS-based, cost-effective, and semi-automatic approach competes successfully with those developed using more expensive imagery, such as multispectral and hyperspectral airborne imagery. The high overall accuracy results obtained by the classification of the diseased alders open the door to applications dedicated to monitoring of the health conditions of riparian forest. Our methodological framework will allow UAS users to manage large imagery metrics datasets derived from those dense time series.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/193070
10.1007/s10661-015-4996-2
http://link.springer.com/10.1007/s10661-015-4996-2

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