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Title: Review of native vegetation condition assessment concepts, methods and future trends
Contributor(s): Tehrany, Mahyat Shafapour (author); Kumar, Lalit (author)orcid ; Drielsma, Michael (author)
Publication Date: 2017
DOI: 10.1016/j.jnc.2017.08.004
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Abstract: The main aim of this review paper is to evaluate and make recommendations on how current and emerging remote sensing (RS) technology might be best used to improve vegetation condition assessment and monitoring. This research reviews the vegetation attributes used in various approaches to vegetation condition assessment, the most efficient and rapid techniques to assess those attributes, and proposes applicable suggestions for future vegetation condition assessment using fusion and ensemble techniques. The attributes are those that have strong correlations with components of vegetation condition and are expected to produce trustable indications of change. Further to this, it aims to identify those vegetation attributes that can be best assessed using field survey and those that can be remotely measured world-wide. Vegetation has various structural, functional and compositional characteristics. To measure specific vegetation characteristics, the suitable type of RS sensor is required. Multi-spectral, hyperspectral, Radio Detection And Ranging (RADAR) and Light Detection And Ranging (LiDAR) are the main types of RS sensors, and each type has a range of applications. A variety of automated and repeatable methods are provided by RS technology to monitor the indicators of vegetation condition. However, dependency on site-based data remains. Further work is essential to find a rapid, cost effective and transferable RS method to map and monitor vegetation condition. Moreover, near future improvements in RS, such as Sentinel products, are expected to ease the process of vegetation condition assessment and enhance the outcomes.
Publication Type: Journal Article
Source of Publication: Journal for Nature Conservation, v.40, p. 12-23
Publisher: Elsevier GmbH - Urban und Fischer
Place of Publication: Germany
ISSN: 1618-1093
Field of Research (FOR): 050206 Environmental Monitoring
050104 Landscape Ecology
090905 Photogrammetry and Remote Sensing
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
School of Environmental and Rural Science

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