Pasture Mapping Codes

This repository organizes the pasture mapping codes developed by Laboratório de Processamento de Imagens e Geoprocessamento (LAPIG/UFG). The methology used by LAPIG team is avaliable in the paper of PARENTE et al. (2017)

Requisites:

• Python 3.9 or above

• Gdal python package and Gdal Binaries

• scipy python package

• joblib python package

• Earth Engine python library

• An folder synchronization with Google Drive (For Windows | For Unix)

Recommendations for Windows:

• Install [Miniconda - Python > 3.9](https://python-poetry.org/docs/#windows-powershell-install-instructions or above and the Gdal package. For Windows users, we need to add some system variables like:

• PATH = C:\ProgramData\Miniconda3\Library\bin;

• GDAL_DATA = C:\ProgramData\Miniconda3\Library\share\gdal

Recommendations for Unix:

• Install Python-Gdal and Gdal Binaries (sudo apt-get install -y python-gdal; sudo apt-get install -y gdal)

• Install Earth Engine python library. Click here to see how to install and configure with Python PIP.

How to use

1. Run classification in Google Earth Engine (GEE)

You have 2 options for make your classification:

Using Python with GEE

First download/clone the in this Github repository, then acess the 1_gee_processing folder through the system terminal/prompt and execute the command bellow:

python LANDSAT_COL9_1985_2023_justRun_v2.py

Using JavaScript in GEE

• Access this link and, if desired, change the parameters of year, landsatWRSPath, landsatWRSRow, my_folder. After that you can click in Run and export your result in Task.

Also, you can change the training dataset (cultivated and natural) by changing the variable TRAIN_DATA (line 9).

2. Prepare the data for Multidimensional Median Filter

Merge the classifications files by year using the binaries gdalbuildvrt and *gdal_translate. E.g.:

• gdalbuildvrt lapig_pasture_map_|year xxxx|.vrt |year xxxx|.tif
• gdal_translate lapig_pasture_map_|year xxxx|.vrt lapig_pasture_map_|year xxxx|.tif -co COMPRESS=LZW -co BIGTIFF=YES

In addition, if you want to view a file in a GIS like QGIS, just add a pyramid to your data using:

• gdaladdo -ro lapig_pasture_map_.tif 2 4 8 --config COMPRESS_OVERVIEW LZW --config USE_RRD YES

3. Applying the multidimensional median filter (3 x 3 x 5)

This code need 2 arguments to run, the and the (e.g. python 2_Multidimensional_median_filter prob_rasters_dir filtered_rasters_dir).

python 2_Multidimensional_median_filter_parallel.py <input_dir_name> <output_dir_name>

4. Merging the files... again.

Like in the section 2, we will use the gdalbuildvrt and gdal_translate to merge the result files by year.

Changelog

* Version 3.0 released (Github version)