Calculating Spectral Indices

Spectral indices constitute an essential image processing technique for remote sensing applications. An index combines pixel intensity values across defined bands to accentuate or filter specific ground components based on their spectral reflectance signatures. For instance, common vegetation indices leverage contrast between near infrared brightness and visible wavelength absorption to highlight photosynthetically active regions. Other indices accentuate burn scars, soil exposures, water bodies and snow cover. Carefully constructed spectral indices thus generate derived image layers emphasizing the relative abundance of targeted land cover categories pertinent to sustainability research questions. Over the time series, their quantified pixel values track spatiotemporal dynamics far more specifically than raw broadband intensity alone.

Normalized Difference Vegetation Index - NDVI

The normalized difference vegetation index (NDVI) constitutes a widely used spectral index in remote sensing research for quantitative characterization of live green vegetation coverage and condition. Its formulation exploits the distinct reflectance signatures of chlorophyll absorption in the visible red wavelengths compared to high near-infrared reflectivity by leaf mesophyll structures. By difference ratios of these spectral bands, the resulting NDVI values indicate relative density and health status of vegetation canopies imaged by satellite sensors. Values range from -1 to 1 based on the vegetation contrasts, with higher positive indices corresponding to more extensive photosynthesizing foliage. Spatially and temporally, NDVI enables scientific monitoring of subtle changes in crop vigor, forest phenology, drought impacts on landscapes, and numerous other dynamics relevant to ecological sustainability and land use management. The sensitivity yet computational simplicity underpinning spectral indices makes them vital tools for policy-relevant Earth observation analytics. When validated against ground measurements, they provide robust quantifiable indicators of vegetation shifts essential for data-driven environmental decision making over time.
The formula for the normalized difference vegetation index is (B8-B4)/(B8+B4). While high values suggest dense canopy, low or negative values indicate urban and water features. It is calculated as NDVI = (B8 - B4) /(B8 + B4) or (NIR – Red) / (NIR + Red)

Moisture Index

The moisture stress index derived from remote sensing constitutes a useful indicator for detecting vegetation water deficits and associated health impacts. Its formulation incorporates contrasting sensitivities in the shortwave infrared domain associated with leaf liquid water absorption features and the moisture-insensitive near-infrared reflectance plateau. Higher index values represent higher inferred moisture content within the imaged vegetation canopies. Conversely, lower index values suggest plants under duress from insufficient available soil and atmospheric moisture, vulnerable to drought impacts. Spatiotemporal tracking enables quantification of moisture stress emergence and recovery cycles across landscapes. When validated against field measurements or climate reanalysis data, the trends quantify changing exposure risks over time that can inform proactive resource sustainability planning and climate adaptation policies specific to affected ecosystems and human communities dependent upon them. Derivation of biophysical variables like moisture stress facilitates translation of raw spectral observations into scientifically actionable intelligence for addressing pressing sustainability challenges.
It is calculated as (B8A - B11) / (B8A + B11). We have processed B4 and B8 bands so now we can calculate Normalized Difference Vegetation Index
#It calculated as NDVI = (B8 - B4) /(B8 + B4) or (NIR – Red) / (NIR + Red)
ndvisample = (nir.astype(float)-red.astype(float))/(nir.astype(float)+red.astype(float))
#we are using earthpy to visualize NDVI
ep.plot_bands(ndvisample, cmap="RdYlGn", vmin=-1, vmax=1);