The FLOod Mapping PYthon toolbox is a free and open-source python toolbox for mapping of floodwater. It exploits the dense Sentinel-1 GRD intensity time series and is based on four processing steps. In the first step, a selection of Sentinel-1 images related to pre-flood (baseline) state and flood state is performed. In the second step, the preprocessing of the selected images is performed in order to create a co-registered stack with all the pre-flood and flood images. In the third step, a statistical temporal analysis is performed and a t-score map that represents the changes due to flood event is calculated. Finally, in the fourth step, a multi-scale iterative thresholding algorithm based on t-score map is performed to extract the final flood map. We believe that the end-user community can benefit by exploiting the FLOODPY's floodwater maps.
This is research code provided to you "as is" with NO WARRANTIES OF CORRECTNESS. Use at your own risk.
The installation notes below are tested only on Linux. We provide some notes for Windows. Recommended setup: Python 3.9, SNAP 9.0
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Option 1: You can either run the automated script aux/install_snap.sh for downloading and installing the official Linux installer from the official ESA repository.
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Option 2: You can download SNAP manually from here and install it using the following commands:
chmod +x install_snap.sh
./install_snap.sh- Please also install aria using the following command:
sudo apt-get install aria2
Even though we offer credentials (for demonstration reasons), we encourage you to create your own account in order to not encounter any problems due to traffic.
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Please create an account at: ESA-scihub.
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Please create an account at: NASA-earthdata
Currently, FloodPy is based on ERA-5 data. ERA-5 data set is redistributed over the Copernicus Climate Data Store (CDS). You have to create a new account here if you don't own a user account yet. After the creation of your profile, you will find your user id (UID) and your personal API Key on your User profile page.
- Option 1: create manually a
.cdsapircfile under yourHOMEdirectory with the following information:
url: https://cds.climate.copernicus.eu/api/v2
key: UID:personal API Key
- Option 2: Run aux/install_CDS_key.sh script as follows:
chmod +x install_CDS_key.sh
./install_CDS_key.shMore details for CDS API for windows can be found here.
You can download FLOODPY toolbox using the following command:
git clone https://github.com/kleok/FLOODPY.git
FLOODPY is written in Python3 and relies on several Python modules. You can install them by using conda or pip.
- Using conda Create a new conda environement with required packages using the the file FLOODPY_env.yml.
conda env create -f path_to_FLOODPY/FLOODPY_env.yml
- Using pip You can install python packages using setup.py
cd path_to_FLOODPY
pip install .
Append to .bashrc file
export FLOODPY_HOME= path_of_the_FLOODPY_folder
export PYTHONPATH=${PYTHONPATH}:${FLOODPY_HOME}
export PATH=${PATH}:${FLOODPY_HOME}/floodpy
FLOODPY generates a floodwater map based on Sentinel-1 GRD products and meteorological data. You can run FLOODPY via jupyter notebook or via command line.
Option 1: Run FloodPy via Jupyter Notebook. See example here
Option 2: Run FloodPy via command line. FLOODPYapp.py
FLOODPYapp.py includes the functionalities for FLOODPY's routine processing for generating floodwater maps. User should provide the following information at configuration file FLOODPYapp_template.cfg
We suggest you to can have a look at the plots for each Sentinel-1 image (located at projectfolder) to find out if you have a considerable decrease of backscatter in the flood image with respect to the baseline images. If you are able to identify a decrease of backscatter in the flood image (darker tones), then you can expect that FLOODPY will generate a useful floodwater map. In cases that you have similar or bigger backscatter values of flood image with respect to baseline images (due to complex backscatter mechanisms) FLOODPY`s results cannot be trusted.
#######################################
# CONFIGURATION FILE #
#######################################
# A. Project Definition
#-------------------------
#A1. The name of your project withough special characters.
Projectname = Palamas
#A2. The location that everything is going to be saved. Make sure
# you have enough free space disk on the specific location.
projectfolder = /home/kleanthis/Palamas
#A3. The location of floodpy code
src_dir = /home/kleanthis/Projects/FLOODPY/floodpy/
#A4. SNAP ORBIT DIRECTORY
snap_dir = /home/kleanthis/.snap/auxdata/Orbits/Sentinel-1
#A5. SNAP GPT full path
GPTBIN_PATH = /home/kleanthis/snap9/bin/gpt
# B. Flood event temporal information
#-------------------------------------------------------------
# Your have to provide the datetime of your flood event. Make sure that
# a flood event took place at your provided datetime.
# Based on your knowledge you can change [before_flood_days] in order
# to create a biggest
# Sentinel-1 image that is going to be used to extract flood information
# will be between Flood_datetime and Flood_datetime+after_flood_days
# the closest Sentinel-1 to the Flood_datetime is picked
#-------------------------------------------------------------
# B1. The datetime (time in UTC) of flood event (Format is YYYYMMDDTHHMMSS)
Flood_datetime = 20200921T030000
# B2. Days before flood event for baseline stack construction
before_flood_days = 20
# B3. Days after flood event
after_flood_days = 3
# C. Flood event spatial information
#-------------------------------------------------------------
# You can provide AOI VECTOR FILE or AOI BBOX.
# Please ensure that your AOI BBOX has dimensions smaller than 100km x 100km
# If you provide AOI VECTOR, AOI BBOX parameters will be ommited
#-In case you provide AOI BBOX coordinates, set AOI_File = None
#--------------------------------------------------------
# C1. AOI VECTOR FILE (if given AOI BBOX parameters can be ommited)
AOI_File = None
# C2. AOI BBOX (WGS84)
LONMIN=22.02
LATMIN=39.46
LONMAX=22.17
LATMAX=39.518
# D. Precipitation information
#-------------------------------------------------------------
# Based on your knowledge, provide information related to the
# accumulated precipitation that is required in order a flooding to occur.
# These particular values will be used to classify Sentinel-1 images
# which images correspond to flood and non-flood conditions.
#--------------------------------------------------------
# D1. number of consequent days that precipitation will be accumulated.
# before each Sentinel-1 acquisition datetime
days_back = 12
# D2. The threshold of acculated precipitation [mm]
accumulated_precipitation_threshold = 120
########################################
# E. Data access and processing #
########################################
#E1. The number of Sentinel-1 relative orbit. The default
# value is Auto. Auto means that the relative orbit that has
# the Sentinel-1 image closer to the Flood_datetime is selected.
# S1_type can be GRD or SLC.
S1_type = GRD
relOrbit = Auto
#E3. The minimum mapping unit area in square meters
minimum_mapping_unit_area_m2=4000
#E4. Computing resources to employ
CPU=8
RAM=20G
#E5. Credentials for Sentinel-1/2 downloading
scihub_username = flompy
scihub_password = rslab2022
aria_username = floodpy
aria_password = RSlab2022
After the setup of the configuration file you can use the default recipe script FLOODPYapp.py to run the following individual steps for you case study:
FLOODPYapp.py FLOODPYapp_template.cfg --dostep Download_Precipitation_data
FLOODPYapp.py FLOODPYapp_template.cfg --dostep Download_S1_data
FLOODPYapp.py FLOODPYapp_template.cfg --dostep Preprocessing_S1_data
FLOODPYapp.py FLOODPYapp_template.cfg --dostep Statistical_analysis
FLOODPYapp.py FLOODPYapp_template.cfg --dostep Floodwater_classification
Algorithms implemented in the software are described in detail at our publications. If FLOODPY was useful for you, we encourage you to cite the following work:
- Karamvasis K, Karathanassi V. FLOMPY: An Open-Source Toolbox for Floodwater Mapping Using Sentinel-1 Intensity Time Series. Water. 2021; 13(21):2943. https://doi.org/10.3390/w13212943
You can also have a look at other works that are using FLOODPY:
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Gounari 0., Falagas A., Karamvasis K., Tsironis V., Karathanassi V., Karantzalos K.: Floodwater Mapping & Extraction of Flood-Affected Agricultural Fields. Living Planet Symposium Bonn 23-27 May 2022.
https://drive.google.com/file/d/1HiGkep3wx45gAQT6Kq34CdECMpQc8GUV/view?usp=sharing -
Zotou I., Karamvasis K., Karathanassi V., Tsihrintzis V.: Sensitivity of a coupled 1D/2D model in input parameter variation exploiting Sentinel-1-derived flood map. 7th IAHR Europe Congress. September 7-9, 2022. Page 247 at https://www.iahreuropecongress.org/PDF/IAHR2022_ABSTRACT_BOOK.pdf
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Zotou I, Karamvasis K, Karathanassi V, Tsihrintzis VA. Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model. Water. 2022; 14(24):4020. https://doi.org/10.3390/w14244020
Feel free to open an issue, comment or pull request. We would like to listen to your thoughts and your recommendations. Any help is very welcome! ❤️
FLOODPY Team: Kleanthis Karamvasis, Ioanna Zotou, Alekos Falagas, Olympia Gounari, Vasileios Tsironis, Markos Mylonas, Pavlos Alexantonakis