Mapping of rice growth phases and bare land using Landsat-8 OLI and machine learning algorithms Please cite this work with: Ramadhani, F., Pullanagari, R., Kereszturi, G., & Procter, J. (2020). Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning. International Journal of Remote Sensing, 41(21), 8428-8452. https://doi.org/10.1080/01431161.2020.1779378
This is the steps to recreate the mapping. Please change the path of folder accordingly.
Folder SR_output_model_LS8_2018_indramayu_edit contains the results of the classification model
Folder SR-LS8-indramayu-utm contains the images of the classification results from the model
A. Download Data
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Install Python and GEE login. Please refer to https://developers.google.com/earth-engine/python_install
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Download data using dl_SR_LS8_indramayu_complete_utm.py and dl_SR_LS8_indramayu_fmask_complete_utm.py
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Downloading LS8 values based on CCTV locations dl_SR_LS8_cctv_complete_utm.py and dl_SR_LS8_cctv_fmask_complete_utm.py
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Download CCTV images from http://katam.litbang.pertanian.go.id/
B. Building the model
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Synchronize with according images and its interpretations of rice growth stages and bare land.
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Create tabulation based CCTV images and LS8 value. Erase all FMASK<>322
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Building the model using several classifiers and tuning it using SR_Landsat8_ML_2018_indramayu_EDIT.R
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Recap the classifier's result using recap_SR_output_model_LS8_2018_indramayu_edit.R
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Copy the best model for each classifier into MODEL folder
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Change RDS path file to best model on SR_Landsat8_indramayu_ML_classify2.R
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Run SR_Landsat8_indramayu_ML_classify2.R for classifying.R
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Run SR_Landsat8_indramayu_ML_mask_clip_region_paddy_utm2.R and SR_Landsat8_indramayu_ML_merge_fmask_utm2.R for merging into one image
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Run SR_Landsat8_indramayu_ML_change_detection_paddy_utm2.R for detecting change classes
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Run SR_Landsat8_indramayu_ML_change_detection_paddy_utm_reclass2.R for reclass of change detection