University of Oulu

Nasim, S., Oussalah, M., Haghighi, A. T., Klove, B., Monitoring vegetation height using data acquisition from ubiquitous multi-sensor’s platform, Proceedings of the FRUCT’25, Helsinki, Finland, 5-8 November 2019, ISSN: 2305-7254, p. 539-545

Monitoring vegetation height using data acquisition from ubiquitous multi-sensor’s platform

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Author: Nasim, Sofeem1; Oussalah, Mourad1; Haghighi, Ali Torabi2;
Organizations: 1Centre for Machine Vision and Signal Processing, University of Oulu, Finland
2Water Resources Environmental Engineering, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: FRUCT, 2019
Publish Date: 2020-01-20


Vegetation height plays a crucial role in various ecological and environmental applications, such as biodiversity assessment and monitoring, landscape characterization, conservation planning and disaster management. Its estimation is traditionally based on in situ measurements or airborne Light Detection and Ranging sensors. However, such methods are often proven insufficient in covering large area landscapes due to high demands in cost, labor and time. Since, the emergence of wearable technology, ubiquitous sensors and Internet of Things offers an appealing framework for monitoring environmental parameters at extremely low cost, which, in turn, contributes to the development of affordable real-time vegetation monitoring system. This is especially relevant to rural environments and underdeveloped countries. We proposed a methodology for data acquisition from a ubiquitous sensor wearable platform and developed a machine-learning model to learn vegetation height on the basis attribute associated with pressure sensor. The proposed methods are proven particularly effective in a region where the land has forestry structure. The results of linear regression model (r2 = 0.81 and RSME = 16.73 cm) and multi-regression model (r2= 0.83 and RSME = 15.73 cm), indicate a promising alternative in vegetation height estimation when in situ or Light Detection and Ranging data or wireless sensor network are not available or affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.

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Series: Proceedings of Conference of Open Innovations Association FRUCT
ISSN: 2305-7254
ISSN-E: 2343-0737
ISSN-L: 2305-7254
ISBN: 978-952-69244-0-3
Pages: 539 - 545
Host publication: Proceedings of the FRUCT’25, Helsinki, Finland, 5-8 November 2019
Host publication editor: Balandin, S.
Niemi, V.
Tuytina, T.
Conference: Conference of Open Innovations Association FRUCT
Type of Publication: A4 Article in conference proceedings
Field of Science: 114 Physical sciences
Funding: This work is partially supported by CBC Karelia IoT Business Creation project (2018-2020). The authors would also like to thank members of FabLab at University for their help in designing the ubiquitous platform.
Copyright information: © The Authors 2019.