Geoprocessing is a framework and set of tools for processing geographic and related data. The comprehensive suite of geoprocessing tools can be used to perform spatial analysis or manage GIS data in an automated way. Geoprocessing is for everyone that uses ArcGIS Pro or any GIS software suite. Whether you are a new or advanced user, geoprocessing will likely be an essential part of your day-to-day work.

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Python is the scripting language of ArcGIS. ArcGIS includes a Python API, ArcPy, that gives users access to all geoprocessing tools as well as scripting functions and specialized modules that help them automate GIS tasks. Users can write scripts that use ArcPy in conjunction with a diverse array of functions provided through Python's standard and third-party libraries. They can execute Python commands and scripts with ArcGIS Notebooks, in the Python window, through script tools, or run Python outside of ArcGIS. However Python is run, the commands work the same way and use the same geoprocessing tools.
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Geoprocessing with Python

https://pro.arcgis.com/en/pro-app/latest/help/analysis/geoprocessing/basics/python-and-geoprocessing.htm

https://data-flair.training/blogs/python-libraries/

https://gisgeography.com/python-libraries-gis-mapping/

https://www.rsgislib.org/

https://pro.arcgis.com/en/pro-app/2.7/help/analysis/geostatistical-analyst/how-inverse-distance-weighted-interpolation-works.htm

https://gisgeography.com/inverse-distance-weighting-idw-interpolation/

Python and geoprocessing

What is Geoprocessing?

Python has over 200 standard libraries and thousands of third-party libraries. Here are some of the frequently used libraries for GIS purposes:

1. ArcPy: ESRI's ArcGIS Python library for performing geoprocessing operations.  It is a useful and productive way to perform geographic data analysis, data conversion, data management, and map automation with Python.

2. Geopandas: Python library capable of adding geographic components to tabular analysis, thereby make working with geospatial data in python easier.

3. GDAL: Python library used for translating between GIS data formats and extensions.

4. RSGISLib: It is a Remote Sensing and GIS software Library for raster processing and analysis. It is a collection of tools for processing remote sensing and GIS datasets.

5. PyProj: Library for performing GIS data projection and datum transformation within a range of geographic referencing systems. It can also be used to perform geodetic calculations and distance measurements for any datum.

6. LiDAR: Python library used for processing and visualizing Light Detection and Ranging (LiDAR) dataset. It is a Python package for delineating the nested hierarchy of surface depressions in Digital Elevation Models (DEMs). It is particularly useful for analyzing high-resolution topographic data, such as DEMs derived from LiDAR data.

Some Python Libraries for GIS and Mapping

The Normalized Difference Vegetation Index (NDVI) is a standard band-ratio calculation frequently used to analyze ecological remote sensing data. NDVI indicates whether the remotely-sensed target contains live green vegetation. When sunlight strikes objects, certain wavelengths of this spectrum are absorbed and other wavelengths are reflected. The pigment chlorophyll in plant leaves strongly absorbs visible light (with wavelengths in the range of 400-700 nm) for use in photosynthesis. However, the cell structure of the leaves strongly reflects near-infrared light (wavelengths ranging from 700 - 1100 nm). Plants reflect up to 60% more light in the near-infrared portion of the spectrum than they do in the green portion of the spectrum. By comparing the ratio of Near Infrared (NIR) to Visible (VIS) bands in hyperspectral data, we can obtain a quick look at the vegetation in the region of interest. NDVI is a normalized measure of the difference between reflectance at near-infrared and visible bands of the electromagnetic spectrum.
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Use this tutorial (https://www.battelleecology.org/calc-ndvi-py) to demonstrate how this process can be executed in Python.

Calculate NDVI and Extract Spectra using Masks in Python

https://eos.com/make-an-analysis/ndvi/

https://www.earthdatascience.org/courses/use-data-open-source-python/multispectral-remote-sensing/vegetation-indices-in-python/calculate-NDVI-python/

https://developers.planet.com/planetschool/calculate-an-ndvi-in-python/

https://www.battelleecology.org/calc-ndvi-py

Calculate NDVI in Python

IDW interpolation method is used to predict numerical values of a variable at areas with unknown values. The algorithm uses known variable values surrounding the unknown location to predict the numerical attribute value of that location. The known values closest to the prediction (unknown value) location have more influence on the predicted value than those farther away. IDW assumes that local (surrounding) influence on the predicted value diminishes with distance raised to the power of p. It gives greater weights to points closest to the prediction location, and the weights diminish as a function of distance, hence the name inverse distance weighted.
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The code below demonstrates how the IDW algorithm can be run in Python.

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Description: Interpolationing Unknown Elevation from Known Elevations of Neighbouring Points by IDW

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Variable description: z  = elevation; d = distance; p = power

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print ("Welcome to the IDW Interpolation Method Program")
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print ("===============================================")
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print ("")
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Entering input parameters
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z1 = float (input ("Input the known elevation value of the first location and press ENTER:   "))
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d1 = float (input ("Input the known distance value from the first location and press ENTER:  "))
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print ("")
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z2 = float (input ("Input the known elevation value of the second location and press ENTER:  "))
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d2 = float (input ("Input the known distance value from the second location and press ENTER:  "))
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print ("")
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z3 = float (input ("Input the known elevation value of the third location and press ENTER:   "))
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d3 = float (input ("Input the known distance value from the third location and press ENTER:  "))
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print ("")
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z4 = float (input ("Input the known elevation value of the fourth location and press ENTER:  "))
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d4 = float (input ("Input the known distance value from the fourth location and press ENTER: "))
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print ("")
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p = float(input ("Input the value of power (p) and press ENTER. For optimal results, p usually ranges between 1 and 2:  "))
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print ("")
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Simplifying variables
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A1 = z1/(d1**p)

A2 = z2/(d2**p)

A3 = z3/(d3**p)

A4 = z4/(d4**p)

B1 = 1/(d1**p)

B2 = 1/(d2**p)

B3 = 1/(d3**p)

B4 = 1/(d4**p)
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Calculting unknown elevation from known elevation points
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Z = (A1 + A2 + A3 + A4)/(B1 + B2 + B3 + B4)
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Printing the result rounded up to 3 decimal places
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print ("The Inverse Distance Weighted (IDW) elevation value of the unknown location is", round (Z,3))
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print ("======================================================================================")
print ("")
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End of program
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print ("End of the program ...")


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