How can I efficiently handle and analyze large remote sensing datasets in Python, specifically for vegetation indices calculation?
I am currently working with a dataset containing multiple years of Landsat imagery, and I want to calculate various vegetation indices such as NDVI, EVI, SAVI, etc. However, my code is running extremely slow and I keep running out of memory.
How can I optimize my code and handle this large dataset in a more efficient manner?
[Important] I don't want to use Google Earth Engine or any online platforms such as Google Colab.
--- Here is my sample code ---
import numpy as np
import rasterio
from rasterio.plot import show
from rasterio.windows import Window
# Open raster file
with rasterio.open("landsat.tif") as src:
# Define window size for processing
win_height, win_width = 256, 256
# Loop through windows to process data
for i, j, window in src.block_windows(1, height=win_height, width=win_width):
# Read data
data = src.read(window=window, out_shape=(src.count, win_height, win_width))
# Calculate vegetation indices
ndvi = (data[3] - data[2]) / (data[3] + data[2])
evi = 2.5 * (data[3] - data[2]) / (data[3] + 6 * data[2] - 7.5 * data[0] + 1)
savi = ((data[3] - data[2]) / (data[3] + data[2] + 0.5)) * 1.5
# Write indices to output file
with rasterio.open("output.tif", 'w', driver='GTiff',
width=win_width, height=win_height, count=3,
crs=src.crs, transform=src.transform,
dtype=np.float32) as dst:
dst.write(ndvi, 1)
dst.write(evi, 2)
dst.write(savi, 3)
Hello Mahdi,
Have you tried xarray, dask, or tsai?
These are some libraries which can almost efficiently handle large amounts of data (usually larger than RAM capacity).
Trying to manipulate data types, reading data directly from your disk drive, and using vectorized operations are other techniques that I can think of right now.
Maybe you could find following links useful.
https://docs.dask.org/en/stable/array.html
https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/00_How_to_efficiently_work_with_very_large_numpy_arrays.ipynb
https://docs.xarray.dev/en/stable/
Handling large remote sensing datasets in Python can be challenging, especially when calculating vegetation indices that involve processing multiple layers of imagery. Here are a few tips to optimize your code and handle the dataset efficiently:
Use a rasterio package: Rasterio is a Python library that allows you to read and write geospatial raster data in a variety of formats. It is designed to be fast and efficient, making it an excellent choice for handling large remote sensing datasets. Rasterio can also help you extract the necessary data from each image, such as the red and NIR bands required for calculating NDVI, and perform other preprocessing steps such as resampling or cropping.
Use a generator function: Instead of loading all of your images into memory at once, you can use a generator function to load them one at a time as needed. This can significantly reduce memory usage and speed up your code. You can use the yield keyword in Python to create a generator function that reads images from disk, performs the necessary calculations, and yields the result.
Parallelize your code: You can also speed up your code by parallelizing it across multiple CPU cores. The multiprocessing package in Python allows you to run multiple processes in parallel, which can significantly speed up computations. You can use the Pool class to distribute the work across multiple processes.
Use a spatial index: If you are working with a large number of images, it can be helpful to use a spatial index to quickly locate images that overlap with a specific area of interest. The rtree package in Python provides a spatial index implementation that can be used to efficiently search for images that intersect with a given bounding box.
By combining these techniques, you should be able to handle and analyze large remote sensing datasets in Python efficiently.
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