px39n / GreenBrown

GreenBrown python

GreenBrown

Introduction

This code is a python reimplementation of R package GreenBrown. Detailed API/tutorial please see Example.ipynb . It contains a range of R packages and methods reimplement like GeenBrown bfast, Strucchange.

For now, It could be used to

1. Detect Annual, Seasonal, and Short Structure change and Breakpoints based on OLS-SUM/differential method (or Differential or Recursive)
2. Execute trend test like MK. Sctest
3. Seasonal Decomposition: 1.Yt=Tt+An+Tm+St+rem 2. Yt=Tt+St+Res
4. Preprocess and Spatial Statistics according to Annual Data.

In the Future

1. Finished some works in library
2. More functions, More Trend Tests
3. Design for Anomaly Classification

The package has been developed at the Max Planck Institute for Biogeochemistry, Jena, Germany in order to distribute the code of the following publications:

• Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. DOI: 10.1016/j.rse.2009.08.014. DownLoad Paper
• Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010). Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114, 2970-2980. DOI: 10.1016/j.rse.2010.08.003. DownLoad Paper
• Verbesselt, J., Zeileis, A., & Herold, M. (2013). Near real-time disturbance detection using satellite image time series, Remote Sensing of Environment. DOI: 10.1016/j.rse.2012.02.022. DownLoad Paper
• Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M., Neigh, C., Reichstein, M., 2013. Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. Remote Sensing 5, 2113–2144. doi:10.3390/rs5052113
• Forkel, M., Migliavacca, M., Thonicke, K., Reichstein, M., Schaphoff, S., Weber, U., Carvalhais, N., 2015. Codominant water control on global interannual variability and trends in land surface phenology and greenness. Glob Change Biol 21, 3414–3435. doi:10.1111/gcb.12950

Spatial Statistics and Trend Analysis

1.Load Example Data

 import numpy as np 
 import matplotlib.pyplot as plt 
 from greenbrown.utils import * 
 import greenbrown as gb 
 Yt = load_example() Tt = Yt.index  

2. Cell-based Trend Analysis

 trd3=gb.AnomalyDetection(Yt,Tt) 
 trd3.summary() 
 trd3.plot() 
 ATs,Am,An,Sts,St_bp,Rem=trd3.values  

3. Spatial Statistics

 tif = load_tif_sample()[20:24,20:24,:] 
 tif_index = pd.date_range('2000', periods=16, freq='Y') 
 tif = interpolate_3d(tif) 
 Ss = gb.Spatial_Statistcs(tif,tif_index) 
 Ss.excute() 
 Ss.plot() 
 # Ss.summary()  

Other Methods

1. BFAST Season Decomposition by OLS-MoSUM

 bst=gb.bfast.BFAST(Yt,Tt,max_iter=1) 
 bst.plot() 
 bst.summary() 
 yt,tt,st,rt=bst.composition  

Output: See Example.juynb

2. Annual Trend and anomaly Detection

 trd=gb.TrendAAT(Yt,Tt,0.15,0.05) 
 trd.summary() 
 trd.plot() 
 ATs,Am,An=trd.values  

Output: See Example.juynb

3. Seasonal component extraction

 trd2=gb.TrendSST(Yt,Tt) 
 trd2.summary() 
 trd2.plot() 
 STs,St_bp=trd2.values

Output: See Example.juynb