Contain exciting Google earth engine notebooks for geospatial analysis
Analyzing Normalized Difference Vegetation Index (NDVI) time series in Python is a common and essential task in remote sensing and environmental monitoring. NDVI, a vegetation index derived from satellite imagery, provides valuable insights into vegetation health and dynamics over time. To work with NDVI time series data in Python, one typically leverages Earth observation data sources such as Landsat or Sentinel-2 through libraries like Google Earth Engine or open-source packages like rasterio and geopandas. Python offers powerful data analysis and visualization tools, including matplotlib, pandas, and seaborn, which enable users to process, explore, and plot NDVI time series data efficiently. By plotting NDVI values over time, researchers, land managers, and environmentalists can track changes in vegetation, monitor seasonal patterns, detect anomalies, and assess the impact of factors like climate, land use, and disturbance events on ecosystems, contributing to informed decision-making and ecosystem management.
