PYCOLD

A Python library for COntinuous monitoring of Land Disturbance (COLD) and its extension algorithms at the speed of C language

The base algorithms were mostly written using C wrapped in Python, and have been verified with MATLAB version. The C codes of the package were partially modified from C-CCDC developed by USGS.

This library provides:

1. Original COntinuous monitoring of Land Disturbance (COLD): a upgraded CCDC algorithm proposed by Dr.Zhe Zhu for offline satellite-based time-series analysis
2. Stochastic Continuous Change Detection (S-CCD, a near real-time and short-memory implementation of COLD)
3. Object-based COLD (OB-COLD), integrating spatial information into COLD by using a ‘change object’ view

The recent applications of S-CCD and OB-COLD could be found in CONUS Land Watcher

1. Pre-work

Clone github repo to your local code directory for the first use:

git clone https://github.com/GERSL/pycold.git

Or you call pull the recent repo if you want to update the existing pycold repo:

git pull origin devel:devel

2. Installation

The steps to install this library in development mode are consolidated into a single script: run_developer_setup.sh. On debian-based systems, this will install all of the developer requirements and ensure you are setup with a working opencv-python-headless and gdal Python modules, as well as other requirements and then it will compile and install pycold in editable development mode.

The following is an overview of these details and alternative choices that could be made.

2.1 Install Required Libraries

The ZLIB, GSL libraries are required.

For Ubuntu/Debian systems, they can be installed via:

sudo apt-get update
sudo apt-get install build-essential  -y
sudo apt-get install zlib1g-dev -y
sudo apt-get install gfortran -y
sudo apt-get install libgsl-dev -y

On CentOS systems run:

sudo apt-get install gcc gcc-c++ make  -y
sudo apt-get install zlib-devel -y
sudo apt-get install gcc-gfortran -y
# Yum provides an gsl 1.5, but we need 2.7
# sudo apt-get install gsl-devel -y
curl https://ftp.gnu.org/gnu/gsl/gsl-2.7.1.tar.gz  > gsl.tar.gz && tar xfv gsl.tar.gz && cd gsl-2.7.1 && ./configure --prefix=/usr --disable-static && make && make install

2.2 Compile and Install PYCOLD

The following instructure assume you are inside a Python virtual environment (e.g. via conda or pyenv).

# Install required packages
pip install -r requirements.txt

Note that in all cases gdal will need to be manually installed. The following step will install GDAL from a custom pypi server containing precompiled wheels.

# Install GDAL (note-this must be done manually)
pip install -r requirements/gdal.txt

Additionally, to access the cv2 module, pycold will require either opencv-python or opencv-python-headless, which are mutually exclusive. This is exposed as optional dependencies in the package via either "graphics" or "headless" extras. Headless mode is recommended as it is more compatible with other libraries. These can be obtained manually via:

pip install -r requirements/headless.txt

# XOR (choose only one!)

pip install -r requirements/graphics.txt

Option 1: Install in development mode

For details on installing in development mode see the developer install instructions.

We note that all steps in the above document and other minor details are consolidated in the run_developer_setup.sh script.

Option 2: Build and install a wheel

Scikit-build will invoke CMake and build everything. (you may need to remove any existing _skbuild directory).

python -m build --wheel .

Then you can pip install the wheel (the exact path will depend on your system and version of python).

pip install dist/pycold-0.1.0-cp38-cp38-linux_x86_64.whl

You can also use the build_wheels.sh script to invoke cibuildwheel to produce portable wheels that can be installed on different than they were built on. You must have docker and cibuildwheel installed to use this.

Option 3: build standalone binaries with CMake by itself (recommended for C development)

mkdir -p build
cd build
cmake ..
make

Option 4: Use a docker image.

This repo provides dockerfiles that illustrate a reproduceable method for compling and installing PYCOLD. See dockerfiles/README.rst for details.

3. Using pycold for pixel-based processing (more see jupyter examples <tool/notebook/pycold_example.ipynb>)

COLD:

from pycold import cold_detect
cold_result = cold_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)

COLD algorithm for any combination of band inputs from any sensor:

from pycold import cold_detect
# input a user-defined array instead of multiple lists
cold_result = cold_detect_flex(dates, np.stack((band1, band2, band3), axis=1), qas, tmask_b1=1, tmask_b2=2)

S-CCD:

# require offline processing for the first time
from pycold import sccd_detect, sccd_update
sccd_pack = sccd_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)

# then use sccd_pack to do recursive and short-memory NRT update
sccd_pack_new = sccd_update(sccd_pack, dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)

Q&A

Q1: Has pycold been verified with original Matlab codes?

Re: yes, multiple rounds of verification have been done. Comparison based on two testing tiles shows that pycold and Matlab version have smaller than <2% differences for breakpoint detection and <2% differences for harmonic coefficients; the accuracy of pycold was also tested against the same reference dataset used in the original COLD paper (Zhu et al., 2020), and pycold reached the same accuracy (27% omission and 28% commission) showing that the discrepancy doesn’t hurt accuracy. The primary source for the discrepancy is mainly from the rounding: MATLAB uses float64 precision, while pycold chose float32 to save the run-time computing memory and boost efficiency.

Q2: how much time for production of a tile-based disturbance map (5000*5000 pixels) using pycold?

Re: I tested it in UCONN HPC environment (200 EPYC7452 cores): for processing a 40-year Landsat ARD tile (1982-2021), the stacking typically takes 15 mins; per-pixel COLD processing costs averagely 1 hour; exporting maps needs 7 mins.

4. Citations

If you make use of the algorithms in this repo (or to read more about them), please cite (/see) the relevant publications from the following list:

[COLD] Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., & Zhou, C. (2020). Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238, 111116.

[S-CCD] Ye, S., Rogan, J., Zhu, Z., & Eastman, J. R. (2021). A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment, 252, 112167.

[OB-COLD] Ye, S., Zhu, Z., & Cao, G., (2022). Object-based continuous monitoring of land disturbance from dense Landsat time series. Remote Sensing of Environment, 287, 113462.