OBIA4RTM

The development of OBIA4RTM is discontinued in favor of the Earth Observation Data Analysis Library EOdal.

OBIA4RTM

An open-source tool for object-based image analysis for radiative transfer modeling using ProSAIL (Prospect5 + 4SAIL) free for non-commercial applications (research and education) under Creative Commons Attribution-NonCommercial 4.0 International Public License.

IMPORTANT OBIA4RTM is currently just a first prototype and will continously updated. Therefore, contributions (see below) are welcomed!

If you'd like to see how OBIA4RTM works in practice have a look at this new repository that provides some insights into and results from an agricultural use case.

About OBIA4RTM

OBIA4RTM aims for plant parameter retrieval - relevant in smart farming applications - by using radiative transfer models (RTM) and object-based image analysis (OBIA) that direclty addresses actual user needs and policy demands in a highly efficient, flexible and scalable way. It uses optical satellite data (concurrently Sentinel-2) as input.

The RTM approach makes the tool transferable and nearly globally applicable to a broad range of different crop types, while OBIA accounts for producing results on a per-object rather than per pixel-base. Image objects have the distinct advantage of being directly related to real-world entities such as single field parcels. Furthermore, results on a per-object base can be easily managed and shared via geospatial databases and web-interfaces and refer also to the requirements of the Big-Data era.

The basis idea of OBIA4RTM is to combine to widely used Remote Sensing analysis techniques:

The biophysical parameter retrieval from optical imagery by means of radiative transfer modelling (RTM) and the object-based image analysis (OBIA) concept. While RTM accounts for retrieving the most relevant plant parameters relevant in farming context (Leaf Area Index, Leaf Chlorophyll Content, etc.), the OBIA approach allows for semantic enrichment of spectral data by means of incorporating expert knowledge and advanced spatial analysis techniques.

OBIA4RTM relies therefore on two main pillars: It describes plant spectra by means of physical equations that are universally applicable by using RTM and it introduces the concept of spatial autocorrelation to reduce redundancies and provide more meaningful image objects by means of OBIA. It is thereby capable to provide vegetation parameter retrieval techniques that are not bound by temporal or geographic restrictions. Furthermore, OBIA4RTM directly addresses objects and, thus, human needs as humans tend not to think in artificial spatial units (i.e. pixels) but in terms of tangible entities such as single field patches or individual trees in an orchard.

Contributing

Contribution to OBIA4RTM is more than welcomed! My time resources are limited and in case you are interested in the topic and you have ideas about

• improving the code (since it is not always implemented very efficiently ;) )
• adding additional features
• testing the functionalities and reporting bugs
• improving the usability
• etc.

you are invited to join!

Workflow

The overall workflow implemented in OBIA4RTM is shown below.

Inputs

OBIA4RTM currently expects Sentinel-2 imagery in Level-2A (i.e. corrected for atmospheric effects). Per default it is assumed that Sen2Core was used for generating the Level-2A data (see: http://step.esa.int/main/third-party-plugins-2/sen2cor/) or data was almost downloaded in this processing level from Copernicus or any other (national) data hub. OBIA4RTM offers a convenient wrapper around the Sen2Core command line tool that takes about all the preprocessing and formatting steps. This wrapper works independently of OBIA4RTM in the sense that it does not interact with the OBIA4RTM database. In can be therefore also used for other Sentinel-2 preprocessing workflows.

Optionally, preprocessing can be also done using Google Earth Engine (optionally) which makes OBIA4RTM a zero-download program as not imagery is downloaded expect the object spectra that are directly written to the OBIA4RTM database.

It should be noted, that:

• OBIA4RTM expects stacked Sentinel-2 (A and B) image data as GeoTiff-File. Only the following Sentinel-2 bands (wavelengths and bandwidths shown for Sentinel-2A only) are used:

Band Number | Central wavelength (nm) | Bandwidth (nm) | Spatial resolution (m) ----------- | ----------------------- | -------------- | ---------------------- 2 | 492.4 | 66 | 10 3 | 559.8 | 36 | 10 4 | 664.6 | 31 | 10 5 | 704.1 | 15 | 20 6 | 740.5 | 15 | 20 7 | 782.8 | 20 | 20 8a | 864.7 | 21 | 20 11 | 1613.7 | 91 | 20 12 | 2202.4 | 175 | 20

• Scene metadata is read directly from either the Sen2Core provided metadata file (or in case you have already acquired Level-2A data the equivalent xml file) or by using the Google-Earth-Engine derived scene metadata. Without this metadata information, OBIA4RTM cannot be executed!

• the use of Copernicus land monitoring data is just a suggestion. Users can use also different land cover/ use classification or can even provide no classification at all (then all objects will be treated equally, otherwise, vegetation parameters have to be set per land use class).

• OBIA4RTM does not provide any image segementation facilities as there are lots of freely available image segmentation algorithms available (e.g. within the Orfeo-Toolbox).

• concurrently, OBIA4RTM uses the Root Mean Squared Error (RMSE) as cost function within the inversion strategy. Users can specify their own cost-functions. It is planned to add more cost functions in the future.

• all image objects are stored in a PostgreSQL database with PostGIS extension. Make sure to have PostgreSQL and PostGIS installed on your machine or where-ever you want to run OBIA4RTM.

Installation

The installation of OBIA4RTM is multi-step procedure. It is suggested to start installing all dependencies first and then building the OBIA4RTM package using the setup.py file.

PostgreSQL and PostGIS installation

The OBIA4RTM backend requires PostgreSQL for storing the information in a hybrid database (i.e. mainly relational with some object-oriented features). OBIA4RTM was mainly developed under PostgreSQL 10.9 but it is assumed that higher versions (>11.0) should work as well. For installing PostgreSQL you can refer this tutorial for Windows or use this instructions under Ubuntu.

After having installed PostgreSQL, PostGIS is required to make PostgreSQL become a spatial database. For installation instructions, please see here.

Python Packages

OBIA4RTM depends on a set of non-standard Python-3 packages that are required to make the software running. NOTE that Python 2.x is not supported by OBIA4RTM as Python2 will reach its end of lifecycle in the near future!

Only the Google Earth Engine Python-API as well as Py6S are optional packages that users might skip. It is recommended to use Anaconda for the Python package management especially if you are planning to deploy OBIA4RTM under Windows. Especially the Setup of GDAL and the required RTM modules worked out smoothly under Windows when using Anaconda and installling directly from the Anaconda cloud.

You can install the following packages from the Anaconda command prompt:

$ conda install -c conda-forge spectral
$ conda install -c anaconda scipy
$ conda install -c anaconda psycopg2
$ conda install -c conda-forge gdal
$ conda install -c jgomezdans prosail
$ conda install -c anaconda numpy

In case your planning to use Google Earth Engine (GEE), make sure to also install

$ conda install -c conda-forge earthengine-api
$ conda install -c conda-forge Py6S

See also the instructions how to use GEE Python API.

OBIA4RTM setup

First, clone or download OBIA4RTM from Github (as long as no PyPi/ Anaconda package is available):

$ git clone https://github.com/lukasValentin/OBIA4RTM.git

Then go into the OBIA4RTM directory (cd ./OBIA4RTM) and run either

$ python3 -m pip install .

or

$ python3 setup.py install

to use either pip or the more advanced egg-installation.

After that, OBIA4RTM is installed but not ready to use. Therefore, open a Python3 session and type in

from OBIA4RTM import install
install.install()

This installation script will take care about the database setup