page_title: Normalized difference vegetation index
section_title: Performance and limitations
hierarchical_section_title: Normalized difference vegetation index / Performance and limitations
caption_attribution_description: English: NDVI applied to the urban area of Ponta Grossa, southern Brazil during a drought at the end of winter 2017
context_page_description: The normalized difference vegetation index is a simple graphical indicator that can be used to analyze remote sensing measurements, often from a space platform, assessing whether or not the target being observed contains live green vegetation.
context_section_description: It can be seen from its mathematical definition that the NDVI of an area containing a dense vegetation canopy will tend to positive values (say 0.3 to 0.8) while clouds and snow fields will be characterized by negative values of this index. Other targets on Earth visible from space include:
  free standing water (e.g., oceans, seas, lakes and rivers) which have a rather low reflectance in both spectral bands (at least away from shores) and thus result in very low positive or even slightly negative NDVI values,
  soils which generally exhibit a near-infrared spectral reflectance somewhat larger than the red, and thus tend to also generate rather small positive NDVI values (say 0.1 to 0.2).
  In addition to the simplicity of the algorithm and its capacity to broadly distinguish vegetated areas from other surface types, the NDVI also has the advantage of compressing the size of the data to be manipulated by a factor 2 (or more), since it replaces the two spectral bands by a single new field (eventually coded on 8 bits instead of the 10 or more bits of the original data).
  The NDVI has been widely used in applications for which it was not originally designed. Using the NDVI for quantitative assessments (as opposed to qualitative surveys as indicated above) raises a number of issues that may seriously limit the actual usefulness of this index if they are not properly addressed. The following subsections review some of these issues.
  Mathematically, the sum and the difference of the two spectral channels contain the same information as the original data, but the difference alone (or the normalized difference) carries only part of the initial information. Whether the missing information is relevant or valuable is for the user to judge, but it is important to understand that an NDVI product carries only a fraction of the information available in the original spectral reflectance data.
  Users of NDVI have tended to estimate a large number of vegetation properties from the value of this index. Typical examples include the Leaf Area Index, biomass, chlorophyll concentration in leaves, plant productivity, fractional vegetation cover, accumulated rainfall, etc. Such relations are often derived by correlating space-derived NDVI values with ground-measured values of these variables. This approach raises further issues related to the spatial scale associated with the measurements, as satellite sensors always measure radiation quantities for areas substantially larger than those sampled by field instruments. Furthermore, it is of course illogical to claim that all these relations hold at once, because that would imply that all of these environmental properties would be directly and unequivocally related between themselves.
  The reflectance measurements should be relative to the same area and be acquired simultaneously. This may not be easy to achieve with instruments that acquire different spectral channels through different cameras or focal planes. Mis-registration of the spectral images may lead to substantial errors and unusable results.
  Also, the calculation of the NDVI value turns out to be sensitive to a number of perturbing factors including
  Atmospheric effects: The actual composition of the atmosphere (in particular with respect to water vapor and aerosols) can significantly affect the measurements made in space. Hence, the latter may be misinterpreted if these effects are not properly taken into account (as is the case when the NDVI is calculated directly on the basis of raw measurements).
  Clouds: Deep (optically thick) clouds may be quite noticeable in satellite imagery and yield characteristic NDVI values that ease their screening. However, thin clouds (such as the ubiquitous cirrus), or small clouds with typical linear dimensions smaller than the diameter of the area actually sampled by the sensors, can significantly contaminate the measurements. Similarly, cloud shadows in areas that appear clear can affect NDVI values and lead to misinterpretations.  These considerations are minimized by forming composite images from daily or near-da
caption: The surrounding fields and water bodies like the dam to the northeast help to mask with high values in Ponta Grossa, southern Brazil
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