GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Journal of Animal Behaviour and Biometeorology |
Texto Completo: | https://malque.pub/ojs/index.php/jabb/article/view/223 |
Resumo: | 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. |
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Journal of Animal Behaviour and Biometeorology |
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GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheepBehavioural patternsPastoralismGeolocationsRemote sensingTopographic attributesTraditional 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.Malque Publishing2022-01-25info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionResearch Articlesapplication/pdfhttps://malque.pub/ojs/index.php/jabb/article/view/22310.31893/jabb.22014Journal of Animal Behaviour and Biometeorology; Vol. 10 No. 2 (2022): April; 22142318-12652318-1265reponame:Journal of Animal Behaviour and Biometeorologyinstname:Universidade Federal Rural do Semi-Árido (UFERSA)instacron:UFERSAenghttps://malque.pub/ojs/index.php/jabb/article/view/223/204Copyright (c) 2022 Journal of Animal Behaviour and Biometeorologyhttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessPlaza, JavierSánchez, NildaPalacios, CarlosSánchez-García, MarioAbecia, José AlfonsoCriado, MarcoNieto, Jaime2023-05-20T20:19:48Zoai:ojs2.malque.pub:article/223Revistahttps://periodicos.ufersa.edu.br/index.php/jabbPUBhttp://periodicos.ufersa.edu.br/revistas/index.php/jabb/oai||souza.jr@ufersa.edu.br2318-12652318-1265opendoar:2023-05-20T20:19:48Journal of Animal Behaviour and Biometeorology - Universidade Federal Rural do Semi-Árido (UFERSA)false |
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, Javier Behavioural patterns Pastoralism Geolocations Remote sensing Topographic attributes |
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 |
author |
Plaza, Javier |
author_facet |
Plaza, Javier Sánchez, Nilda Palacios, Carlos Sánchez-García, Mario Abecia, José Alfonso Criado, Marco Nieto, Jaime |
author_role |
author |
author2 |
Sánchez, Nilda Palacios, Carlos Sánchez-García, Mario Abecia, José Alfonso Criado, Marco Nieto, Jaime |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Plaza, Javier Sánchez, Nilda Palacios, Carlos Sánchez-García, Mario Abecia, José Alfonso Criado, Marco Nieto, Jaime |
dc.subject.por.fl_str_mv |
Behavioural patterns Pastoralism Geolocations Remote sensing Topographic attributes |
topic |
Behavioural patterns Pastoralism Geolocations Remote sensing Topographic attributes |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-25 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Research Articles |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://malque.pub/ojs/index.php/jabb/article/view/223 10.31893/jabb.22014 |
url |
https://malque.pub/ojs/index.php/jabb/article/view/223 |
identifier_str_mv |
10.31893/jabb.22014 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://malque.pub/ojs/index.php/jabb/article/view/223/204 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Journal of Animal Behaviour and Biometeorology https://creativecommons.org/licenses/by-nc-nd/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Journal of Animal Behaviour and Biometeorology https://creativecommons.org/licenses/by-nc-nd/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Malque Publishing |
publisher.none.fl_str_mv |
Malque Publishing |
dc.source.none.fl_str_mv |
Journal of Animal Behaviour and Biometeorology; Vol. 10 No. 2 (2022): April; 2214 2318-1265 2318-1265 reponame:Journal of Animal Behaviour and Biometeorology instname:Universidade Federal Rural do Semi-Árido (UFERSA) instacron:UFERSA |
instname_str |
Universidade Federal Rural do Semi-Árido (UFERSA) |
instacron_str |
UFERSA |
institution |
UFERSA |
reponame_str |
Journal of Animal Behaviour and Biometeorology |
collection |
Journal of Animal Behaviour and Biometeorology |
repository.name.fl_str_mv |
Journal of Animal Behaviour and Biometeorology - Universidade Federal Rural do Semi-Árido (UFERSA) |
repository.mail.fl_str_mv |
||souza.jr@ufersa.edu.br |
_version_ |
1799319802139901952 |