GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep

Traditional knowledge about the behavior of grazing livestock is about to disappear. Shepherds well know that sheep behavior follows non-random patterns. As a novel alternative to seeking behavioral patterns, this study quantified the grazing activities of two sheep flocks of Churra breed (both in t...

Descripción completa

Detalles Bibliográficos
Autores: Plaza, J., Sánchez, N., Palacios, C., Sánchez-García, M., Abecia Martínez, J. A., Criado, M., Nieto, J.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Zaragoza
Repositorio:Zaguán. Repositorio Digital de la Universidad de Zaragoza
OAI Identifier:oai:zaguan.unizar.es:112096
Acceso en línea:http://zaguan.unizar.es/record/112096
Access Level:acceso abierto
id ES_defa5280e72d9bebeffcd787e6dc3f8a
oai_identifier_str oai:zaguan.unizar.es:112096
network_acronym_str ES
network_name_str España
repository_id_str
spelling GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheepPlaza, J.Sánchez, N.Palacios, C.Sánchez-García, M.Abecia Martínez, J. A.Criado, M.Nieto, J.Traditional knowledge about the behavior of grazing livestock is about to disappear. Shepherds well know that sheep behavior follows non-random patterns. As a novel alternative to seeking behavioral patterns, this study quantified the grazing activities of two sheep flocks of Churra breed (both in the same area but separated by 10 years) based on Global Position System (GPS) monitoring and remote monitoring sensing techniques. In the first monitoring period (2009-10), geolocations were recorded every 5 min (4, 240 records), while in the second one (2018-20), records were taken every 30 min (7, 636 records). The data were clustered based on the day/night and the activity (resting, moving, or grazing). An airborne LiDAR dataset was used to study the slope, aspect, and vegetation height. Four visible-infrared orthophotographs were mosaicked and classified to obtain the land use/land cover (LU/LC) map. Then, GPS locations were overlain on the terrain features, and a Chi-square test evaluated the relationships between locations and terrain features. Three spatial statistics (directional distribution, Kernel density, and Hot Spot analysis) were also calculated. Results in both monitoring periods suggested that the spatial distribution of free-grazing ewes was non-random. The flocks showed strong preferences for grazing areas with gentle north-facing slopes, where the herbaceous layer formed by pasture predominates. The geostatistical analyses of the sheep locations corroborated those preferences. Geotechnologies have emerged as a potent tool to demonstrate the influence of environmental and terrain attributes on the non-random spatial behavior of grazing sheep. © 2022 Malque Publishing. All rights reserved.2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://zaguan.unizar.es/record/112096reponame:Zaguán. Repositorio Digital de la Universidad de Zaragozainstname:Universidad de ZaragozaInglésinfo:eu-repo/semantics/openAccessoai:zaguan.unizar.es:1120962026-05-29T13:59:51Z
dc.title.none.fl_str_mv GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
title GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
spellingShingle GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
Plaza, J.
title_short GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
title_full GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
title_fullStr GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
title_full_unstemmed GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
title_sort GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
dc.creator.none.fl_str_mv Plaza, J.
Sánchez, N.
Palacios, C.
Sánchez-García, M.
Abecia Martínez, J. A.
Criado, M.
Nieto, J.
author Plaza, J.
author_facet Plaza, J.
Sánchez, N.
Palacios, C.
Sánchez-García, M.
Abecia Martínez, J. A.
Criado, M.
Nieto, J.
author_role author
author2 Sánchez, N.
Palacios, C.
Sánchez-García, M.
Abecia Martínez, J. A.
Criado, M.
Nieto, J.
author2_role author
author
author
author
author
author
description Traditional knowledge about the behavior of grazing livestock is about to disappear. Shepherds well know that sheep behavior follows non-random patterns. As a novel alternative to seeking behavioral patterns, this study quantified the grazing activities of two sheep flocks of Churra breed (both in the same area but separated by 10 years) based on Global Position System (GPS) monitoring and remote monitoring sensing techniques. In the first monitoring period (2009-10), geolocations were recorded every 5 min (4, 240 records), while in the second one (2018-20), records were taken every 30 min (7, 636 records). The data were clustered based on the day/night and the activity (resting, moving, or grazing). An airborne LiDAR dataset was used to study the slope, aspect, and vegetation height. Four visible-infrared orthophotographs were mosaicked and classified to obtain the land use/land cover (LU/LC) map. Then, GPS locations were overlain on the terrain features, and a Chi-square test evaluated the relationships between locations and terrain features. Three spatial statistics (directional distribution, Kernel density, and Hot Spot analysis) were also calculated. Results in both monitoring periods suggested that the spatial distribution of free-grazing ewes was non-random. The flocks showed strong preferences for grazing areas with gentle north-facing slopes, where the herbaceous layer formed by pasture predominates. The geostatistical analyses of the sheep locations corroborated those preferences. Geotechnologies have emerged as a potent tool to demonstrate the influence of environmental and terrain attributes on the non-random spatial behavior of grazing sheep. © 2022 Malque Publishing. All rights reserved.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://zaguan.unizar.es/record/112096
url http://zaguan.unizar.es/record/112096
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv
publisher.none.fl_str_mv
dc.source.none.fl_str_mv reponame:Zaguán. Repositorio Digital de la Universidad de Zaragoza
instname:Universidad de Zaragoza
instname_str Universidad de Zaragoza
reponame_str Zaguán. Repositorio Digital de la Universidad de Zaragoza
collection Zaguán. Repositorio Digital de la Universidad de Zaragoza
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422023296417792
score 15,300724

Similares en LA Referencia