pyxccd 0.0.1

A PYthon library for basic and eXtended COntinuous Change Detection algorithms

PYXCCD

A PYthon library for basic and eXtended COntinuous Change Detection

Author: Su Ye ([e-mail])

The Continuous Change Detection and Classification (CCDC) algorithm has been popular for processing satellite-based time series datasets, particularly for Landsat-based datasets. As a CCDC user, you may already be familiar with the existing CCDC tools such as pyccd and gee ccdc.

Wait.. so why does the pyxccd package still exist?

I developed pyxccd mainly for the below purposes:

1. Near real-time monitoring: This package provides the unique S-CCD algorithm to recursively update model coefficients and detect changes;

2. The latest version of CCDC (COLD) with the highest breakpoint detection accuracy: The COLD algorithm has been verified with Zhe’s MATLAB version;

3. Large-scale time-series processing in the desktop environment: the core of xccd was coded in C with the superior computing efficiency and small memory usage;

4. Using dataset other than Landsat (such as Sentinel-2, modis): pyxccd supports the use of any band combination from any sensor (the flexible mode);

5. Decomposing time-series signals to unveil inter-season/inter-annual variation (such as phenological shifts): S-CCD allows continuously outputting trend and seasonal signal components as “states”;

1. Installation

It only supports windows and linux system so far. Please contact the author if you wish to install it in the macOS system.

pip install pyxccd

2. Using pyxccd for pixel-based processing (more see jupyter examples)

COLD:

from pyxccd 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 pyxccd 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 pyxccd import sccd_detect, sccd_update
sccd_pack = sccd_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, 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, qas)

S-CCD for outputting continuous seasonal and trend states:

# open state output by setting state_intervaldays as a non-zero value
sccd_result, state_ensemble = sccd_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, qas, state_intervaldays=1)

3. Documentation

Tutorial: under development

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:

[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.

[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.

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

Q&A

Q1: Has pyxccd been verified with original Matlab codes?

Re: yes, multiple rounds of verification have been done. Comparison based on two testing tiles shows that pyxccd and Matlab version have smaller than <2% differences for breakpoint detection and <2% differences for harmonic coefficients; the accuracy of pyxccd was also tested against the same reference dataset used in the original COLD paper (Zhu et al., 2020), and pyxccd 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 pyxccd 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 pyxccd?

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, per-pixel S-CCD processing costs averagely 0.5 hour; exporting maps needs 7 mins.