Frequently Asked Questions - Indices

FAQs - Indices

OSAVI (Optimized Soil-Adjusted Vegetation Index)

      Uses

  • differentiate soil pixels 
  • related to LAI at some levels where NDVI saturates
  • accounts for non-linear interactions of light between soil and vegetation 
  • used as a structural index for some combined indices designed for chlorophyll detection

 

Description

OSAVI maps variability in canopy density. In addition, it is not sensitive to soil brightness (when different soil types are present). It is robust to variability in soil brightness and has enhanced sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy and where NDVI saturates (high plant density).

OSAVI is a special case of the Soil Adjusted Vegetation Index (SAVI). OSAVI was developed by Rondeaux et al. in 1996 using the reflectance in the near-infrared (nir) and red (r) bands with an optimized soil adjustment coefficient. The soil adjustment coefficient (0.16) was selected as the optimal value to minimize NDVI's sensitivity to variation in soil background under a wide range of environmental conditions. OSAVI is a hybrid between ratio-based indices such as NDVI and orthogonal indices such as PVI. SAVI has a default soil-adjustment factor of 0.5; however, it is recommended to use 0.16 as implemented in OSAVI. Like any normalized difference index, OSAVI values can range from -1 to 1. High OSAVI values indicate denser, healthier vegetation whereas lower values indicate less vigor.

 

© Information courtesy of http://micasense.com

TAGS: Indices, OSAVI

CIR Composite (Color Infrared) 

Uses

  • assessing plant health 
  • identifying water bodies 
  • variability in soil moisture
  • assessing soil composition

Description

This layer is a color composite and not an Index. It is referred to as a Color Infrared Composite because instead of combining Red, Green, and Blue bands (which is the standard image display method you are accustomed to) we are combining NIR, Red, and Green bands. NIR light is displayed as red, red light is displayed as green, and green light is displayed as blue (R: NIR, G: RED, B: GREEN). This color composite highlights the response of the Near-infrared band to crop health and water bodies.

Healthy vegetation reflects a high level of NIR and appears red in CIR layers. Unhealthy vegetation will reflect less in the NIR and appear as washed out pink tones, very sick or dormant vegetation is often green or tan, and man-made structures are light blue-green. Soils may also appear light blue, green, or tan depending on how sandy it is, with sandiest soil appearing light tan and clay soils as dark tan or bluish green. This is also highly useful in identifying water bodies in the imagery, which absorb NIR wavelengths and appear black when water is clear. Since this is not an index, as stated above, there is no color palette to select. The colors you see are a result of additive mixture of NIR, Red, and Green wavelengths at each image pixel.

 

© Information courtesy of http://micasense.com

Chlorophyll Map

      Uses

  • detect chlorotic crops 
  • stress detection
  • identify vigorous, healthy crops 
  • estimate chlorophyll content 
  • estimate N content if you know that N is limiting 

 

Description

The Chlorophyll Map is a layer that is less sensitive to leaf area than NDRE. This layer isolates the chlorophyll signal from variability in leaf area as a function of changes in canopy cover. It has a physiological basis which takes into account the relationship between canopy cover and canopy nutrient content.

The Chlorophyll Map is especially sensitive to well gathered and well calibrated data.  Non-plant pixels are excluded and shown as transparent, which in some cases results in plant pixels also being omitted.  This layer is less useful for row crops and more useful for vineyards and orchards, as the dense canopy is better at differentiating the Chlorophyll signal.

 

© Information courtesy of http://micasense.com

 

DSM (Digital surface Model)

      Uses

  • estimate relative crop volume 
  • identify surface properties 
  • model water flow & accumulation

 

Description

DSM is a digital model representation of a terrain's surface. DSM represents the elevations above sea level of the ground and all features on it. A DSM is a gridded array of elevations. it is a layer symbolized by a gray color ramp, special effects such as hill-shading may be used to simulate relief. DSMs can be used to study surface properties and water flow.

A digital surface model (DSM) is usually constructed using automatic extraction algorithms (i.e. image correlation in stereo photogrammetry). DSM resembles laying a blanket on your imagery. It represents top faces of all objects on the terrain, including vegetation and man-made features, and highlights the different elevations of the features

 

© Information courtesy of http://micasense.com

TAGS: DSM