DOI: 10.1016/j.jag.2016.09.009
Scopus记录号: 2-s2.0-85018650582
论文题名: Combining machine learning and ontological data handling for multi-source classification of nature conservation areas
作者: Moran N ; , Nieland S ; , Tintrup gen ; Suntrup G ; , Kleinschmit B
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 54 起始页码: 124
结束页码: 133
语种: 英语
英文关键词: Biotope classification
; EUNIS
; GEOBIA
; Grasslands
; Machine learning
; Nature conservation
; Ontology
; OWL
; Remote sensing
Scopus关键词: artificial intelligence
; biotope
; conservation management
; data processing
; grassland
; image classification
; nature conservation
; remote sensing
; Germany
; Rhineland-Palatinate
英文摘要: Manual field surveys for nature conservation management are expensive and time-consuming and could be supplemented and streamlined by using Remote Sensing (RS). RS is critical to meet requirements of existing laws such as the EU Habitats Directive (HabDir) and more importantly to meet future challenges. The full potential of RS has yet to be harnessed as different nomenclatures and procedures hinder interoperability, comparison and provenance. Therefore, automated tools are needed to use RS data to produce comparable, empirical data outputs that lend themselves to data discovery and provenance. These issues are addressed by a novel, semi-automatic ontology-based classification method that uses machine learning algorithms and Web Ontology Language (OWL) ontologies that yields traceable, interoperable and observation-based classification outputs. The method was tested on European Union Nature Information System (EUNIS) grasslands in Rheinland-Palatinate, Germany. The developed methodology is a first step in developing observation-based ontologies in the field of nature conservation. The tests show promising results for the determination of the grassland indicators wetness and alkalinity with an overall accuracy of 85% for alkalinity and 76% for wetness. © 2016 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79943
Appears in Collections: 气候变化事实与影响
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作者单位: Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, Berlin, Germany; Dept. Environmental Systems, RLP AgroScience – Institute for Agroecology, Breitenweg 71, Neustadt, Germany
Recommended Citation:
Moran N,, Nieland S,, Tintrup gen,et al. Combining machine learning and ontological data handling for multi-source classification of nature conservation areas[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,54