MultiCAST - SPECTRAL VEGETATION INDICES

SPECTRAL VEGETATION INDICES

GENERAL INFORMATION

Remote sensing spectral Vegetation Indices (VIs) are mathematical algorithms applied to remotely sensed data, primarily obtained from satellite or airborne sensors, to quantify and analyze vegetation characteristics. VIs are derived from the spectral reflectance properties of vegetation, particularly in the visible and near-infrared (NIR) regions of the electromagnetic spectrum. These indices provide valuable information about vegetation health, biomass, and various biophysical parameters, aiding in the monitoring and assessing of ecosystems, crops, and environmental conditions. VIs exploit the unique spectral response of vegetation, which is influenced by its biochemical and structural properties. Chlorophyll, the primary pigment responsible for photosynthesis, strongly absorbs light in the blue and red regions while it reflects light in the green and NIR regions. This distinctive reflectance pattern allows for the calculation of various SVIs, which can be indicative of vegetation vigor, density, stress, and other important characteristics.

MAIN REMOTE SENSING VEGETATION INDICES

Several remote-sensing vegetation indices have been developed and widely used to analyze vegetation properties. Here are some of the main VIs:

Normalized Difference Vegetation Index (NDVI): NDVI is one of the most widely used and robust VIs. It calculates the difference between the reflectance in the NIR and red spectral bands, normalized by their sum. NDVI values range from -1 to +1, with higher values indicating healthier, more vigorous and dense vegetation.
Difference Vegetation Index (DVI): DVI is a simple index that measures the difference between the reflectance in the green and red spectral bands. It provides a measure of the “greenness” of vegetation and can be used to assess vegetation stress and vitality.
Ratio Vegetation Index (RVI): RVI is calculated as the ratio between the reflectance in the NIR and red spectral bands. It is similar to NDVI but does not normalize the values. RVI can be used to monitor vegetation changes and estimate vegetation biomass.
Enhanced Vegetation Index (EVI): EVI is an improved version of NDVI that corrects for atmospheric influences and the saturation of the red reflectance signal. EVI incorporates the blue and NIR spectral bands in its calculation, providing enhanced sensitivity to vegetation changes and reducing the impact of atmospheric effects.
Soil-Adjusted Vegetation Index (SAVI): SAVI is another variant of NDVI that addresses the influence of bare soil on vegetation indices. By introducing a soil adjustment factor, SAVI reduces the background noise caused by exposed soil and enhances the sensitivity to vegetation cover.
Transformed Soil-Adjusted Vegetation Index (TSAVI): TSAVI is a modified version of SAVI that incorporates a transformation parameter to improve the discrimination of vegetation and reduce soil background effects. TSAVI is useful for assessing vegetation cover and biomass in heterogeneous landscapes.
Perpendicular Vegetation Index (PVI): PVI is a distance-based vegetation index that considers the perpendicular distance of a pixel’s reflectance values from a reference point. It is particularly useful for distinguishing between different types of vegetation and assessing vegetation health.
Leaf Area Index (LAI): LAI represents the total leaf area per unit ground area and is an essential parameter for characterizing vegetation structure and productivity. LAI can be estimated using SVIs by relating vegetation indices to LAI measurements obtained from field observations or indirect methods.
Fraction of Photosynthetically Active Radiation (FPAR): FPAR estimates the proportion of incoming solar radiation absorbed by the vegetation canopy for photosynthesis. It is a critical parameter for modeling ecosystem productivity, carbon fluxes, and vegetation growth.

TECHNIQUES TO RETRIEVE VEGETATION INDICES

The retrieval of vegetation indices from remote sensing data involves several techniques and algorithms. These techniques vary based on the sensor characteristics, data preprocessing steps, and the specific vegetation index being calculated. Here are some common techniques used to retrieve vegetation indices:

Band Ratio Method: Band Ratio Method involves calculating the ratio between reflectance values in specific spectral bands. This method is used for indices such as NDVI, RVI, and TSAVI, which rely on the ratio of NIR to red reflectance. The band ratio method enhances the contrast between features by dividing the digital number (DN) for pixels in one image band by the DN of pixels in another image band.
Spectral Transformation: Spectral Transformation involves transforming the original reflectance values using mathematical functions or indices. This transformation enhances certain spectral features and suppresses others, improving the sensitivity to vegetation properties. Spectral transformation techniques can include principal component analysis (PCA) and minimum noise fraction (MNF) transformations.
Distance-Based Techniques: Distance-Based Techniques, such as the Perpendicular Vegetation Index (PVI), involve calculating the perpendicular distance of a pixel’s reflectance values from a reference point or line in spectral space. Spectral space refers to a multi-dimensional space where each dimension represents a different spectral band of the remote sensing data. In this space, each pixel is represented by a point whose coordinates correspond to the reflectance values in each spectral band. The reference point or line is typically chosen to represent bare soil or non-vegetated areas, allowing the PVI to distinguish between vegetated and non-vegetated pixels. By measuring the perpendicular distance between a pixel’s reflectance values and the reference point or line, the PVI provides information about vegetation type, health, and spatial distribution.
Slope-Based Techniques: Slope-Based Techniques, such as the Difference Vegetation Index (DVI) and Ratio Vegetation Index (RVI), calculate the slope or difference between reflectance values in specific spectral bands. These techniques are useful for assessing vegetation vigor, stress, and biomass by measuring the difference or ratio between reflectance values in the red and near-infrared (NIR) spectral bands. Vegetation typically reflects more NIR light than red light, so healthy vegetation will have a higher slope or ratio value than stressed or sparse vegetation.

MAIN SATELLITES USED FOR VEGETATION INDICES

Several satellites are used to acquire remote sensing data for vegetation indices. Here are some of the main satellites used in vegetation studies:

Landsat series: The Landsat satellites, such as Landsat 8 and Landsat 9, provide multispectral data at moderate spatial resolution. They have a rich history of data acquisition, allowing for long-term monitoring and analysis of vegetation dynamics, land cover changes, and ecosystem health.
Sentinel-2: The Sentinel-2 satellites, operated by the European Space Agency (ESA), offer high-resolution multispectral data with a revisit frequency of a few days. These satellites provide detailed monitoring of vegetation at regional and global scales, enabling the assessment of vegetation health, land use changes, and ecosystem monitoring.
MODIS: The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are onboard the Terra and Aqua satellites. MODIS data provide daily global coverage at a moderate spatial resolution. They are widely used for monitoring vegetation phenology (seasonal changes) and productivity, as well as other environmental parameters such as land surface temperature, fire occurrence, and atmospheric composition.
SPOT: The SPOT satellites, such as SPOT 6 and SPOT 7, provide high-resolution multispectral data. With their superior spatial resolution, these satellites allow for detailed mapping and analysis of vegetation at the local scale. SPOT data are often used for precision agriculture, forestry management, and land cover mapping.
PlanetScope: PlanetScope is a constellation of small satellites that offers high-resolution multispectral data with daily revisits. These satellites provide a unique opportunity for near-real-time monitoring of vegetation dynamics, land use changes, and other applications that require frequent observations.
ASTER: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument is onboard the Terra satellite. While primarily designed for high-resolution land surface temperature measurements, ASTER also provides multispectral data, including visible, NIR, and SWIR bands. ASTER data are used for vegetation mapping, land cover classification, and geological studies.

These satellites, with their diverse spatial and temporal capabilities, contribute to the availability of remote sensing data for vegetation indices, supporting a wide range of applications in agriculture, forestry, environmental monitoring, and ecosystem studies.

MAIN TECHNICAL FEATURES

The technical features of remote sensing data used for vegetation indices depend on the satellite or sensor characteristics. Some of the main technical features and factors to consider when selecting remote sensing data for vegetation analysis include:

Spectral Bands: The number and position of spectral bands determine the information content and sensitivity of the remote sensing data for vegetation analysis. Sensors with spectral bands in the visible, NIR, and SWIR regions are preferred for vegetation studies.
Spatial Resolution: Spatial resolution refers to the size of the smallest detectable feature in the imagery. Higher spatial resolution enables the identification and characterization of smaller vegetation units and individual plants.
Radiometric Resolution: Radiometric resolution refers to the ability of a sensor to distinguish between subtle differences in radiance or reflectance values. Higher radiometric resolution improves the accuracy and precision of vegetation indices.
Temporal Resolution: Temporal resolution indicates how frequently a satellite revisits a specific location. Higher temporal resolution allows for more frequent monitoring of vegetation changes and phenology.

In addition to these technical features, there are a few other factors that can be considered when selecting remote sensing data for vegetation analysis:

Atmospheric Correction: The accuracy of vegetation indices can be affected by atmospheric conditions, such as the presence of clouds, aerosols, and water vapor. Some remote sensing data products include atmospheric correction to account for these effects and improve the accuracy of vegetation indices.
Calibration: The radiometric calibration of remote sensing data is important for ensuring the accuracy and consistency of vegetation indices over time. Calibration involves adjusting the sensor measurements to account for changes in sensor performance and other factors that can affect the radiometric accuracy of the data.
Data Processing: The processing and analysis of remote sensing data can involve several steps, such as geometric correction, radiometric correction, and atmospheric correction. The availability of pre-processed data products can simplify the analysis of vegetation indices and improve their accuracy.

MAIN APPLICATIONS

Remote sensing spectral vegetation indices play a crucial role in monitoring and analyzing vegetation characteristics across various applications. These indices provide valuable insights into vegetation health, biomass, and other biophysical parameters, aiding in the understanding and management of terrestrial ecosystems and agricultural systems. Some of the main applications include:

Vegetation Monitoring and Mapping: VIs are used to monitor and map vegetation cover, dynamics, and changes over time. They are valuable tools for assessing deforestation, land degradation, and ecosystem health.
Agriculture and Crop Management: VIs provide critical information for agricultural practices, such as crop health monitoring, yield estimation, irrigation management, and disease detection.
Forestry and Biodiversity Assessment: VIs aid in forest inventory, species classification, monitoring of forest disturbances (e.g., fire and insect outbreaks), and assessing biodiversity patterns.
Environmental Monitoring: VIs are used for monitoring and assessing environmental parameters, including land surface temperature, water quality, and habitat suitability.
Climate Change Studies: VIs contribute to climate change studies by monitoring vegetation responses to climate variations, estimating carbon sequestration, and modeling ecosystem processes.