Python coding that takes images acquired using a Near-Infrared (NIR) converted camera and generates a modified Normalized Differential Vegetation Index (NDVI). Contains standalone with colorbar legend and batch versions. ENDVI and SAVI Indexes also available and with greyscale options.

This repository contains a study how we can examine the vegetation cover of a region with the help of satellite data. The notebook in this repository aims to familiarise with the concept of satellite imagery data and how it can be analyzed to investigate real-world environmental and humanitarian challenges.

Gaussian Processes for Vegetation Parameter Estimation from Hyperspectral Data with Limited Ground Truth

Crop classification of Krishna and Godavari delta

Predicting how Actic vegetation types distribution in 2050 based on CAVM and CMIP6 climate models

pan-Arctic albedo trend analysis based on MODIS satellite and CMIP6 climate models data

Regression Analysis of Normalized Difference Vegetation Index (NDVI) to Compare Seasonal Patterns and Vegetation Trend of Nepal (2021)

Analysis of vegetation data from a student excursion to Gschnitz (Austria)

Data and code of Bauer et al. (2018) Riv Res Appl

Data and code of Bauer et al. (2018) Tuexenia