MSc Thesis: Sentinel-1 Coherence Processing & Analysis pipeline, using Snappy, Rasterio & Xarray.
Original proposal for this thesis, which outlines the goals, objectives, and methodology of the research.
Python module contained in src/sentinel1slc.py that provides functions that interface to the Sentinel-1 preprocessing tools provided by the SNAP software. uses the SNAPPY interface to perform preprocessing.
bin/seninel1slc_bsc_coh_preprocessing.py contains a script for processing SLC and ASF SBAS pairs to generate coherence and backscatter data.
src/coherence_time_series.py contains a Python class that provides functionality for combining polarisations and varying window sizes to generate Xarray data cubes from the coherence and backscatter data generated in the previous step. This class includes a range of methods for manipulating and visualising the data.
The bin/coherence_time_series_analysis.py script provides interface examples to the CoherenceTimeSeries class used to performs data analysis and to generate final data products.
This directory contains works in progress and a range of utility functions. These include functions for animating a coherence stack, calculating coherence change and coherence change detection.
This study focuses on the Central Kalimantan region of Indonesia on the island of Borneo, which is known for its extensive logging activities. The study area consists of a number of SAR acquisition tiles and was selected using the Global Forest Watch platform due to its clear-cut forested areas, which were detected by the RADD layer of the integrated deforestation alert system. The study examines specific events that occurred between February 2021 and March 2022, which are of particular interest.
example acquisition over Borneo.