DOI: 10.1016/j.jag.2015.06.014
Scopus记录号: 2-s2.0-85015614511
论文题名: Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery
作者: Michez A ; , Piégay H ; , Jonathan L ; , Claessens H ; , Lejeune P
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 44 起始页码: 88
结束页码: 94
语种: 英语
英文关键词: Mapping of invasive species
; Random forests
; Supervised classification
; UAS
; Unmanned aerial system
Scopus关键词: algorithm
; forest dynamics
; image classification
; imaging method
; invasive species
; mapping
; riparian zone
; unmanned vehicle
; Fallopia sachalinensis
; Heracleum mantegazzianum
; Impatiens glandulifera
; Polygonum cuspidatum
英文摘要: Riparian zones are key landscape features, representing the interface between terrestrial and aquatic ecosystems. Although they have been influenced by human activities for centuries, their degradation has increased during the 20th century. Concomitant with (or as consequences of) these disturbances, the invasion of exotic species has increased throughout the world's riparian zones. In our study, we propose a easily reproducible methodological framework to map three riparian invasive taxa using Unmanned Aerial Systems (UAS) imagery: Impatiens glandulifera Royle, Heracleum mantegazzianum Sommier and Levier, and Japanese knotweed (Fallopia sachalinensis (F. Schmidt Petrop.), Fallopia japonica (Houtt.) and hybrids). Based on visible and near-infrared UAS orthophoto, we derived simple spectral and texture image metrics computed at various scales of image segmentation (10, 30, 45, 60 using eCognition software). Supervised classification based on the random forests algorithm was used to identify the most relevant variable (or combination of variables) derived from UAS imagery for mapping riparian invasive plant species. The models were built using 20% of the dataset, the rest of the dataset being used as a test set (80%). Except for H. mantegazzianum, the best results in terms of global accuracy were achieved with the finest scale of analysis (segmentation scale parameter = 10). The best values of overall accuracies reached 72%, 68%, and 97% for I. glandulifera, Japanese knotweed, and H. mantegazzianum respectively. In terms of selected metrics, simple spectral metrics (layer mean/camera brightness) were the most used. Our results also confirm the added value of texture metrics (GLCM derivatives) for mapping riparian invasive species. The results obtained for I. glandulifera and Japanese knotweed do not reach sufficient accuracies for operational applications. However, the results achieved for H. mantegazzianum are encouraging. The high accuracies values combined to relatively light model-inputs needed (delineation of a few umbels) make our approach a serious contender as a cost-effective tool to improve the field management of H. mantegazzianum. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80098
Appears in Collections: 气候变化事实与影响
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作者单位: University of Liege, Gembloux Agro-Bio Tech, Biosystem Engineering Department, Forest Management, 2 Passage des Déportés, Gembloux, Belgium; University of Lyon, UMR CNRS EVS, Site ENS, 15 Parvis R. Descartes, Lyon Cedex, France
Recommended Citation:
Michez A,, Piégay H,, Jonathan L,et al. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,44