The goal of this project is to develop a Python library to exploit multitemporal land-cover fraction layers in data-cube formats, derived from global land-cover products and/or local or photo-interpreted land-cover datasets. The toolbox will support correlation analysis, quantification, and validation of multi-temporal land-cover patterns (with a focus on forest and related classes). In addition to analytical routines, the toolbox will include interactive query and visualisation tools to explore spatial and temporal patterns in land cover fractions and relative changes.
Languages: Python
Suggested SW/Libraries: Jupyter Notebook, pandas, NumPy, xarray, SciPy, GeoPandas, rasterio, matplotlib, (optional) plotly or bokeh