Yet Another Time Series Model (YATSM)

About

Yet Another Timeseries Model (YATSM) is a Python package for utilizing a collection of timeseries algorithms and methods designed to monitor the land surface using remotely sensed imagery.

YATSM can be thought of as three components:

1. A library of functions and other objects useful for performing time series analysis, including various statistics and input/output helpers.
2. Time series analysis algorithm implementations
3. The command line interface (CLI) provides a set of programs that facilitate the running of time series algorithms on “time series stacks” and the analysis of the results.

User Guide

To get started with YATSM, please follow this user guide:

• Installation
  • Dependencies
  • Conda
  • virtualenv and pip
  • Developer Installation
  • Platform Support
• Dataset Preparation
• Model Parameter Exploration
• Timeseries Model Specification
• Science Models
  • Models
• Model Configuration
  • Configuration File
  • Dataset Parameters
  • YATSM Analysis Parameters
  • YATSM Algorithm Parameters
  • Phenology
  • Classification
  • Example
• Batch Interface
  • Sun Grid Engine
• Mapping Derived Information
  • Missing Values
  • Examples
  • Docs TODO
• Mapping Changes
  • Examples
  • Docs TODO
• Classification
• Long Term Mean Phenology
  • Configuration
• Integration of user-provided time series algorithms
  • Behind the scenes
  • References:

Command Line Interface Utilities

The Command Line Interface (CLI) for YATSM is built using click and is accessed using the yatsm command. Documentation about the CLI is available below:

• Command Line Utilities
  • yatsm
  • yatsm pixel
  • yatsm line
  • yatsm cache
  • yatsm train
  • yatsm classify
  • yatsm changemap
  • yatsm map
  • gen_date_file.sh

Indices and tables

• Index
• Module Index
• Search Page

References

[1]

Zhe Zhu and Curtis E. Woodcock. Continuous change detection and classification of land cover using all available landsat data. Remote Sensing of Environment, 144(0):152 – 171, 2014. URL: http://www.sciencedirect.com/science/article/pii/S0034425714000248, doi:http://dx.doi.org/10.1016/j.rse.2014.01.011.