README.md

In SandroGroth/phenoRS: Preprocessing and Extraction of phenological parameters from Remote Sensing Data.

phenoRS

R package for automated phenology metrics extraction from remotely sensed data.

:warning: This package is still in an early state of development and not yet ready to use.

Project Status:

• [ ] Data Aquisition:

  • [ ] Automatized download of remote sensing products (based on getSpatialData [4] package):
    • [x] MODIS VI Composites
    • [ ] Landsat 4, 5, 7, 8
    • [ ] Sentinel 2
  • [x] Downloading options:
    • [x] aria2c parallelized bulk download
    • [x] R parallelized download (using doSNOW)
    • [x] standard download

• [ ] Data Extraction and Preparation:

  • [x] Supported products:
    • [x] MOD13Q1 v6
    • [ ] Landsat 4, 5, 7, 8
    • [ ] Sentinel 2
  • Preparation functions (based on GDAL):
    • [ ] Data extraction:
      • [x] .hdf -> .tif
      • [ ] .tar.gz -> .tif
      • [ ] SAFE -> .tif
    • [ ] Vegetation Index calculation
    • [x] Tile mosaicking
    • [x] Reprojection
    • [x] AOI Cropping
    • [x] AOI Masking
    • [x] Conversion to .envi binary files (for faster processing)
  • [ ] Reading optional Land Use Information to improve Curve fitting (weight assignment)

• [ ] Time Series Preprocessing / Curve Fitting:

  • [ ] Shiny App for interactive selection of fine tuning parameters (in Progress)
  • [ ] Settings-Management: Reding/Wrtiting settings as .json files. (in Progress)
  • [ ] Initial weight assignment based on QA Band for:
    • [x] MOD13Q1 v6 Summary QA
    • [ ] MOD13Q1 v6 Detailed QA
    • [ ] Landsat QA
    • [ ] Sentinel 2 QA
  • [ ] Outilier-Detection:
    • [x] QA-Band
    • [x] Modified Hampel Median Filter [3]
    • [ ] STL decomposition [1]
    • [ ] STL * initial weights [2]
  • [ ] Seasonalty extraction
  • [ ] Fitting methods:
    • [ ] Adaptive Savitzky-Golay Filter
    • [ ] Asymetric Gaussian
    • [ ] Double logistic functions
    • [ ] Harmonic modeling [5]
  • [ ] Upper envelope adaption

• [ ] Phenology Metrics Extraction:

  • [ ] Start of season (SOS)
  • [ ] End of season (EOS)
  • [ ] Length of season (LOF)
  • [ ] Base value
  • [ ] time of middle season
  • [ ] Maximum value
  • [ ] Amplitude
  • [ ] Small integrated value
  • [ ] Large integrated value

• [ ] Change Detection:

  • tbd

• [ ] Output Visualisations:

  • tbd

References

[1] Cleveland, R.B., Cleveland, W.S., McRae, J.E., and Terpenning, I., 1990, STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, 6, 3-73.

[2] Eklundh, L., and Jönsson, P., 2017, TIMESAT 3.3 with seasonal trend decomposition and parallel processing - Software Manual. Lund University, 92 pp. http://web.nateko.lu.se/timesat/docs/TIMESAT33_SoftwareManual.pdf.

[3] Hampel F. R.,1974, The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69, 382–393.

[4] Schwalb-Willmann, J., 2018, getSpatialData - Get different kinds of freely available spatial datasets. R package version 0.0.4. http://www.github.com/16eagle/getSpatialData/.

[5] Philipp, M, 2020, rHarmonics- R package for harmonic modelling of time-series data. R package version 0.1.0. http://www.github.com/MBalthasar/rHarmonics/.

SandroGroth/phenoRS documentation built on July 12, 2020, 4:33 a.m.