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Vegetation height estimation using ubiquitous foot-based wearable platform

Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among...

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Autores principales: Nasim, Sofeem, Oussalah, Mourad, Klöve, Bjorn, Haghighi, Ali Torabi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680353/
https://www.ncbi.nlm.nih.gov/pubmed/33219863
http://dx.doi.org/10.1007/s10661-020-08712-5
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author Nasim, Sofeem
Oussalah, Mourad
Klöve, Bjorn
Haghighi, Ali Torabi
author_facet Nasim, Sofeem
Oussalah, Mourad
Klöve, Bjorn
Haghighi, Ali Torabi
author_sort Nasim, Sofeem
collection PubMed
description Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among the commonly employed methods for vegetation height estimation. However, such methods are known for their high incurred labor, time, and infrastructure cost. The emergence of wearable technology offers a promising alternative, especially in rural environments and underdeveloped countries. A method for a locally designed data acquisition ubiquitous wearable platform has been put forward and implemented. Next, a regression model to learn vegetation height on the basis of attributes associated with a pressure sensor has been developed and tested. The proposed method has been tested in Oulu region. The results have proven particularly effective in a region where the land has a forestry structure. The linear regression model yields (r(2) = 0.81 and RSME = 16.73 cm), while the use of a multi-regression model yields (r(2) = 0.82 and RSME = 15.73 cm). The developed approach indicates a promising alternative in vegetation height estimation where in situ measurement, LiDAR data, or wireless sensor network is either not available or not affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks.
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spelling pubmed-76803532020-11-23 Vegetation height estimation using ubiquitous foot-based wearable platform Nasim, Sofeem Oussalah, Mourad Klöve, Bjorn Haghighi, Ali Torabi Environ Monit Assess Article Vegetation height plays a key role in many environmental applications such as landscape characterization, conservation planning and disaster management, and biodiversity assessment and monitoring. Traditionally, in situ measurements and airborne Light Detection and Ranging (LiDAR) sensors are among the commonly employed methods for vegetation height estimation. However, such methods are known for their high incurred labor, time, and infrastructure cost. The emergence of wearable technology offers a promising alternative, especially in rural environments and underdeveloped countries. A method for a locally designed data acquisition ubiquitous wearable platform has been put forward and implemented. Next, a regression model to learn vegetation height on the basis of attributes associated with a pressure sensor has been developed and tested. The proposed method has been tested in Oulu region. The results have proven particularly effective in a region where the land has a forestry structure. The linear regression model yields (r(2) = 0.81 and RSME = 16.73 cm), while the use of a multi-regression model yields (r(2) = 0.82 and RSME = 15.73 cm). The developed approach indicates a promising alternative in vegetation height estimation where in situ measurement, LiDAR data, or wireless sensor network is either not available or not affordable, thus facilitating and reducing the cost of ecological monitoring and environmental sustainability planning tasks. Springer International Publishing 2020-11-21 2020 /pmc/articles/PMC7680353/ /pubmed/33219863 http://dx.doi.org/10.1007/s10661-020-08712-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Nasim, Sofeem
Oussalah, Mourad
Klöve, Bjorn
Haghighi, Ali Torabi
Vegetation height estimation using ubiquitous foot-based wearable platform
title Vegetation height estimation using ubiquitous foot-based wearable platform
title_full Vegetation height estimation using ubiquitous foot-based wearable platform
title_fullStr Vegetation height estimation using ubiquitous foot-based wearable platform
title_full_unstemmed Vegetation height estimation using ubiquitous foot-based wearable platform
title_short Vegetation height estimation using ubiquitous foot-based wearable platform
title_sort vegetation height estimation using ubiquitous foot-based wearable platform
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7680353/
https://www.ncbi.nlm.nih.gov/pubmed/33219863
http://dx.doi.org/10.1007/s10661-020-08712-5
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