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.
Assuntos
Animais , Ovinos/psicologia , Pastagens , Criação de Animais Domésticos/métodos , Dinâmica Populacional , Tecnologia de Sensoriamento Remoto/métodos , Tecnologia de Sensoriamento Remoto/veterináriaRESUMO
Saprolegniasis is one of the most economical and ecologically harmful diseases in different species of fish. Low water temperature is one of the most important factors which increases stress and creates favourable conditions for the proliferation of Saprolegniasis. Therefore, the monitoring of water surface temperature (WST) is fundamental for a better understanding of Saprolegniasis. The objective of this study was to develop a predictive algorithm to estimate the probability of fish kills caused by Saprolegniasis in Río Tercero reservoir (Argentina). WST was estimated by Landsat 7 and 8 imagery using the Single-Channel method. Logistic regression was used to relate WST estimated from 2007 to 2017 with different episodes of fish kills by Saprolegniasis registered in the reservoir during this period of time. Results showed that the algorithm created with the first quartile (25th percentile) of the WST values estimated by Landsat sensors was the most suitable model to estimate Saprolegniasis in the studied reservoir.
Assuntos
Characidae , Monitoramento Ambiental , Doenças dos Peixes/mortalidade , Infecções/veterinária , Tecnologia de Sensoriamento Remoto/veterinária , Saprolegnia/fisiologia , Animais , Argentina , Doenças dos Peixes/etiologia , Infecções/etiologia , Infecções/mortalidade , LagosRESUMO
Google Earth(®) provides free access to satellite images and has been used in several areas that require cartographic information. The present study assessed the inconsistencies between geo-referencing of livestock premises by GPS and the acquisition of geographic coordinates by remote sensing (RS) images provided by Google Earth(®) in the Brazilian states of Bahia, Distrito Federal, Minas Gerais and Parana. The overall mean and standard deviation of the distances in the study were 30.98±19.89 m. The mean distance differences between the two techniques were, for these states, 37.20±19.75 m, 28.38±17.38 m, 31.61±15.72 m, 28.43±24.30 m, respectively. Despite the fact that there is variation between the geo-referencing points using GPS and RS, geographical localization of health inspections should be useful as long as the errors between the results of the two methodologies are considered.