Index processing algorithms:

Red-edge Chlorophyll Index, ReCL

Description
The Red-edge Chlorophyll Index of photosynthetic activity of the vegetation cover is most often used when assessing the chlorophyll content in plant leaves by multispectral data, which has an extreme red-edge channel.

The red edge is a region in the red-NIR transition zone of vegetation reflectance spectrum and marks the boundary between absorption by chlorophyll in the red visible region, and scattering due to leaf internal structure in the NIR region. This transition zone is in the basis of several vegetation indices like NDVI which is the normalized difference between the reflectance in the red visible (0.6µm) and the NIR (0.8µm) reflectance. Also the red edge position (REP) is used to estimate the chlorophyll content of leaves or over a canopy.

Input data
A multichannel raster image in GeoTIFF format after radiometric calibration


Output data
A single-channel raster image in GeoTIFF format


Source data
Satellite systems: Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: NIR, RED

Applied fields
The detection of leaf chlorophyll content (LCC) is important for monitoring the physiological status of plants, assessing plant health, and estimating photosynthetic potential. It is also helpful for understanding light acclimation mechanisms in higher plants, and furthermore, provides an indication of plant stress and senescence.

Normalized Burn Ratio (NBR)

Description
The Normalized burn ratio (NBR) is used to identify burned areas and estimate fire severity. The formula is similar to a normalized difference vegetation index (NDVI), except that it uses near-infrared (NIR) and shortwave-infrared (SWIR) portions of the electromagnetic spectrum. The NIR and SWIR parts of the electromagnetic spectrum are a powerful combination of bands to use for this index given vegetation reflects strongly in the NIR region of the electromagnetic spectrum and weakly in the SWIR.

Input data
A multichannel raster image in GeoTIFF format after radiometric calibration


Output data
A single-channel raster image in GeoTIFF format


Source data
Satellite systems: Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB.
Channels: NIR, SWIR2

Applied fileds
The Normalized Burn Ratio is employed to understand the impacts of fire on the landscape. It is most powerful as a tool to better understand fire extent and severity when used after calculating the difference between pre and post fire conditions. This difference is best measured using data collected immediately before the fire and then immediately after the fire. NBR is less effective if time has passed and vegetation regrowth / regeneration has begun after the fire. Once vegetation regeneration has begun, the fire scar will begin to reflect a stronger signal in the NIR portion of the spectrum because healthy plants reflect strongly in the NIR portion due to the properties of chlorophyll.

The normalized Difference Water Index (NDWI)

Description
The Normalized Difference Water Index (NDWI) is an approach that has been employed to delineate open water features and enhance their presence (while eliminating the presence of soil and terrestrial vegetation features) in remotely-sensed digital imagery. It makes use of reflected near-infrared radiation and visible green light and may also provide researchers with turbidity estimations of water bodies.

The index is strongly related to the plant water content and therefore it’s a very good proxy for plant water stress.  Early recognition of plant water stress can be critical to prevent crop failure or lower crop production in agricultural areas. 

NDWI is computed using the near infrared (NIR – MODIS band 2) and the short wave infrared (SWIR – MODIS band 6) reflectance’s: [NDWI = (Xgreen – Xnir)/(Xgreen + Xnir)].  This formulation of NDWI produces an image in which the positive data values are typically open water areas; while the negative values are typically non-water features (i.e. terrestrial vegetation and bare soil dominated cover types).

Input data
A multichannel raster image in GeoTIFF format after radiometric calibration


Output data
A single-channel raster image in GeoTIFF format

Source data
Satellite systems: Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: NIR, GREEN

Applied fileds
Agriculture: NDWI and NDWI anomalies can be presented in the form of maps and graphs, providing information both on the spatial distribution of the vegetation water stress and its temporal evolution over longer time periods. Gridded data can easily be aggregated over administrative or natural entities such as hydrological watersheds. This allows for the qualitative and quantitative comparison of the intensity and duration of the NDWI anomalies with recorded impacts such as yield reductions, low flows, lowering of groundwater levels, to cite but a few.

The normalized Difference Vegetation Index (NDVI)

Description

To determine the density of green on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants. Many different wavelengths make up the spectrum of sunlight. When sunlight strikes objects, certain wavelengths of this spectrum are absorbed and other wavelengths are reflected. The pigment in plant leaves, chlorophyll, strongly absorbs visible light (from 0.4 to 0.7 µm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 µm). The more leaves a plant has, the more these wavelengths of light are affected, respectively.

In general, if there is much more reflected radiation in near-infrared wavelengths than in visible wavelengths, then the vegetation in that pixel is likely to be dense and may contain some type of forest. If there is very little difference in the intensity of visible and near-infrared wavelengths reflected, then the vegetation is probably sparse and may consist of grassland, tundra, or desert.

Nearly all satellite Vegetation Indices employ this difference formula to quantify the density of plant growth on the Earth — near-infrared radiation minus visible radiation divided by near-infrared radiation plus visible radiation. The result of this formula is called the Normalized Difference Vegetation Index (NDVI). Written mathematically, the formula is:

NDVI = (NIR — VIS)/(NIR + VIS)

Calculations of NDVI for a given pixel always result in a number that ranges from minus one (-1) to plus one (+1); however, no green leaves gives a value close to zero. A zero means no vegetation and close to +1 (0.8 – 0.9) indicates the highest possible density of green leaves.

Input data
A multichannel raster image in GeoTIFF format after radiometric calibration


Output data
A single-channel raster image in GeoTIFF format

Source data
Satellite systems: Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: NIR, RED

The Green Normalized Difference Vegetation Index (GNDVI)

Description

The Green Normalized Difference Vegetation Index (GNDVI) is an index of plant “greenness” or photosynthetic activity. It is one of the most commonly used vegetation indices to determine water and nitrogen uptake into the crop canopy. GNDVI basically replaces the red piece of standard NDVI collection with a very specific band of light in the green range to obtain different useful information.
Water must be used effectively. Frequent GNDVI allows for irrigation optimization and indicates when water isolation occurs or varies throughout the field in a glance. Water is also a main factor required for photosynthesis. Using GNDVI images you can allocate water efficiently to the areas that need it most.
The formula to calculate the GNDVI index is as follows:

GNDVI = (NIR – Green) / (NIR + Green)

Water is also a main factor required for photosynthesis. Using GNDVI images you can allocate water efficiently to the areas that need it most.

  • By monitoring the proper grade of the field to improve flood row irrigation.
  • With drip irrigation, clogged lines can be found, optimizing how the drip irrigation is set up.

Input data
Multichannel raster image in GeoTIFF format after radiometric calibration


Output data
Single-channel raster image in GeoTIFF format


Source data
Satellite systems Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB. Index
Channels: GREEN, NIR

Applied fileds
Agromonitoring, Forestry:
The index is applicable in assessing depressed and aging vegetation. GNDVI algorithm is most commonly used to determine water and fertilizer uptake across a field. With good source data and GNDVI results you will be able to allocate your water and fertilizer in a more effective way.

The relative index of chlorophyll (Green Chlorophyll Index, GCI)

Description
The Green Chlorophyll Index (GCI) is used to estimate leaf chlorophyll content across a wide range of plant species. Having broad NIR and green wavelengths provides a better prediction of chlorophyll content while allowing for more sensitivity and a higher signal-to-noise ratio. The CIgreen and CIred-edge values are sensitive to small variations in the chlorophyll content and consistent across most species.

The total chlorophyll content is linearly correlated with the difference between the reciprocal reflectance of green/ red-edge bands and the NIR band. Hence, a CIgreen- calculated using the observation in the green region (570 nm) and a CIred-edge – using observations in the red-edge (730 nm) are widely used.

Increases in leaf chlorophyll concentration or leaf area, decreases in foliage clumping, and changes in canopy architecture each can contribute to decreases in the NIR wavelengths and increases in the red wavelengths, thereby causing an increase in the broadband greenness value.

Input data
A multichannel raster image in GeoTIFF format after radiometric calibration


Output data
A single-channel raster image in GeoTIFF format


Source data
Satellite systems Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: NIR, GREEN

Applied fields
Vegetation phenology (growth) studies, land-use and climatological impact assessments, and vegetation productivity modeling.

Land classification

Description

Creation of a fake-color map of land cover types from the data of red and near infrared channels of an aerospace image of high spatial resolution. Identification of natural and anthropogenic objects (pools and reservoirs, buildings and structures, lawns, trees, etc.) on the image through neural network classification. The main advantage of using neural networks is their noise resistance and high computational speed due to parallel processing of data

Input data
A raster image in GeoTIFF format


Output data
A raster image in GeoTIFF format


Data Requirements
Spatial resolution of the original data – at least 15 cm
The results of the classification were obtained using aerial photography data with a spatial resolution of 15 cm

Source data
Google Aerial NRGB data. Channels: BLUE, RED, GREEN, NIR

Applied fields

Urban Planning and Management:
Calculation of the characteristics of urbanization, such as the percentage of area occupied by objects of the classes “vegetation”, “buildings and structures”, etc. Preparation of a raster basis for creating a vector map.

Online monitoring:
Comparative analysis of different maps of the earth cover. Regular observation of changes on the surface of the earth. For example, monitoring of construction sites, forest cuttings, consequences of emergency situations of natural and technogenic origin.

Control of water consumption:
Identification of water bodies and calculation of their surface area. Monitoring of temporary changes in the area of ​​water bodies.

The normalized Difference Salinity Index (NDSI)

Description
Soil salinization occurs when water-soluble salts accumulate in the soil to a level that impacts on agricultural production, environmental health, and economics. In the early stages, salinity affects the metabolism of soil organisms and reduces soil productivity, but in advanced stages it destroys all vegetation and other organisms living in the soil, consequently transforming fertile and productive land into barren and desertified lands. It has resulted in limiting agricultural land-use patterns and become a severe environmental hazard that impacts the growth of many crops.

The Normalized Difference Salinity Index and Salinity Index (NDSI) were designed to monitor and map soil salinity at an early stage to enact effective soil reclamation program that helps lessen or prevent increase of soil salinity.

Input data
A multichannel raster image in GeoTIFF format after radiometric calibration


Output data
A single-channel raster image in GeoTIFF format


Source data
Satellite systems Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB.
Channels: RED, NIR

Applied fields
Soil salinization is a major form of land degradation in agricultural areas, where information on the extent and magnitude of soil salinity is needed for better planning and implementation of effective soil reclamation programs. The results are expected to have great significance in maintaining ecological stability and agricultural output, and to formulate better management strategies in irrigated areas.

Enhanced Vegetation Index (EVI)

Description
This index was developed as a standard.
In areas of dense canopy, where the leaf area index (LAI) is high, the NDVI values ​​can be improved by leveraging information in the blue wavelength. Information in this portion of the spectrum can help correct for soil background signals and atmospheric influences
Input data
Multichannel raster image in GeoTIFF format – image after radiometric calibration (reflectance)
Output data
Single-channel raster image in GeoTIFF format
Source data
Satellite systems Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: NIR, RED, BLUE

The normalized Difference Snow and Ice Index (NDSII)

Description
The Normalized Difference The Normalized Difference The Snow and Ice Index is a spectral band that takes advantage of the spectral differences of the snow in short-wave infrared and visible spectral bands to identify snow versus other features in a scene. At visible wavelengths, snow cover is just as bright as clouds. However, at 1.6 microns, snow cover absorbs the sunlight, and therefore shows much darker than clouds. This allows the effective discrimination between snow cover and clouds. Values ​​of NDSII> 0.4 usually indicate the presence of snow.

Input data
Multichannel raster image in GeoTIFF format – image after radiometric calibration (reflectance)


Output data
Single-channel raster image in GeoTIFF format


Source data
Satellite systems Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: GREEN, SWIR1

The Atmospherically Resistant Vegetation Index (ARVI)

Description
This index is an enhancement to the NDVI that is relatively resistant to atmospheric factors (for example, aerosol). It uses blue reflectance to correct red reflectance for atmospheric scattering. It is the most useful in the regions of high atmospheric aerosol content, including the tropical regions contaminated by soot from slash-and-burn agriculture. The gamma constant depends on the aerosol type.

Input data
Multichannel raster image in GeoTIFF format – image after radiometric calibration (reflectance)


Output data
Single-channel raster image in GeoTIFF format


Source data
Satellite systems Landsat 8, Sentinel-2, RapidEye, PlanetScope, WorldView-2, WorldView-3, Google Aerial NRGB
Channels: NIR, RED, BLUE

Applied fields
Agromonitoring, forestry