In-situ-Water-quality-monitoring-in-Oil-and-Gas-operations

Salinity and turbidity detection of water ponds using satellite imagery. This is a statistical algorithm to analyze water quality in lakes/ponds/deltas using LandSat8 satellite time series data. The repo contains methods for detection on 2 types of datasets, the Commercial dataset (includes all the bands in a single .TIF file) and LandSat8 sensor dataset (Contains individual .TIF files for each band)

Our Method

Published: Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

Satish Kumar*, Rui Kou*, Vikram Jayaram

This repository includes:

• Source code for Water Quality detection algorithm
• Dataloader for LandSat8 imagery and Satelytics imager
• Python code for Pansharpening multispectral image using panchromatic band
• Example datasample for generating color coded output, histogram, volume of water in pond/lake/delta etc

The repo structure follows standard dataloader and utility function imported into the main code. Please consider citing our work if it is useful to you

Requirements

• Python ≥ 3.5
• pip ≥ 21.1.0
• Virtualenv

Installation

1. Clone this repository
2. Create a python (3.6 or greater) virtualenv
3. Activate the virtualenv
4. Install dependencies

pip install -r requirements.txt

5. cd to rio-pansharpen directory and run the following commands

cd rio-pansharpen
rm -rf build/*
python setup.py install

This finishes the environment setup for water quality detection

Getting started

main.py is the primary file to run the code. To see the list of arguments run

python main.py --help

It dumps the following output

usage: main.py [-h] [-d DATA_DIR] [-r REPORT_PATH] [-vo VISUAL_OUT]
               [-ho HIST_OUT] [-ph PLOT_HIST] [-vt VOLUME]

optional arguments:
  -h, --help            show this help message and exit
  -d DATA_DIR, --data_dir DATA_DIR
                        path to data directory
  -r REPORT_PATH, --report_path REPORT_PATH
                        file to get location of ponds
  -vo VISUAL_OUT, --visual_out VISUAL_OUT
                        path to dir for qualitative output
  -ho HIST_OUT, --hist_out HIST_OUT
                        path to directory to dump histogram data
  -ph PLOT_HIST, --plot_hist PLOT_HIST
                        enable histogram plot
  -vt VOLUME, --volume VOLUME
                        enable volume and top 10 average csv file

Sample commands to run the code

python main.py -d <path to data directory> -ph <True/False> -vt <True/False>

The output of above command will generate color coded output of water quality of each pond/lake in visual_output directory and histogram plot in histogram directory. Along with that, it will generate 2 .csv files with names vol.csv and top5avg.csv, which contains the relative volumne of water in each pond and expected value of top-10 pixel of water quality output respectively