Study on Grassland Dynamics and Livestock Behavior Changes Under Different Grazing Strategies
Description
Dataset Description This dataset consists of GPS tracking data and remote sensing imagery used to assess the impact of grazing strategies on grassland vegetation and cattle behavior. The data was collected during a fenced grazing experiment conducted in Jinhua Village, Jinshan Town, Wenchang City, Hainan Province, China. GPS Data The GPS data includes the location trajectories of cattle tracked using GPS collars, recorded at regular intervals. These data provide precise spatial information on the cattle's movement within the grazing plots and are essential for understanding their foraging behavior, walking, standing, ruminating, and resting patterns. The dataset includes timestamps for each location point, along with GPS coordinates (latitude and longitude). Additionally,cattle behavior was observed and recorded in the field, with GPS-collected activity data integrated to develop a behavioral classification model. Remote Sensing Data The remote sensing data includes high-resolution imagery captured using unmanned aerial systems (UAS) to assess vegetation changes over time in the grazing plots. The images were processed to calculate the Normalized Difference Vegetation Index (NDVI), a key indicator of vegetation health. NDVI data were obtained before and after grazing activities to monitor vegetation recovery and degradation under different grazing intensities and regimes. Data Analysis The dataset was used to classify cattle behaviors using machine learning models, such as XGBoost, Random Forest, Decision Tree, Extra Trees, and CatBoost. The models were trained using GPS and behavioral data, providing insights into the effects of grazing strategies on cattle behavior. NDVI data were analyzed to determine the relationship between cattle behavior and vegetation changes. Purpose This dataset is intended to support the study of sustainable grassland management and grazing strategies. It provides valuable insights into the interaction between cattle behavior and grassland vegetation under different grazing pressures. The data can be used to refine grazing management practices and inform the development of more sustainable land management strategies.