Measuring vegetation from satellite imagery with NDVI

The measurement of vegetation signatures using remote sensing sources has become a critical way to measure the effects of regional and global-scale drought and agricultural production. It is also useful in distinguishing vegetation in regions, including wild and cultivated varieties. The most common method for this is the normalized difference vegetation index (NDVI) technique.

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What is Normalized Difference Vegetation Index (NDVI)?

The concept of normalized difference vegetation index (NDVI) lies with the fact that plants have evolved to reflect near-infrared light, where visible light is strongly absorbed by almost all plants in photosynthesis. The measure is thus an index of these two aspects, helping to show how vigorous plant growth is happening in regions. Calibrated results can then be used to estimate vegetation growth and overall biomass, as more vegetation growth will affect the ratio of visible light absorbed and near-infrared light reflected.

How NDVI is Used to Understand Vegetation Growth

The method of NDVI has existed since the early 1970s, where today it has become the standard in using multispectral satellite data to measure plant growth. For instance, in Europe, abandonment and cultivation of farmland has become a major topic of focus. Some abandoned farmland are reused as habitat for native species, while re-cultivation is also occurring as faming benefits are provided to crop lands once abandoned. Measuring this has policy potential, allowing decision-makers to determine where to focus agricultural efforts while also balancing potentially what areas might be best abandoned or used for conservation purposes .

One of the biggest areas for NDVI has been measuring agricultural yield, as this allows national and international organizations to assess what yield outputs will be and potentially to monitor if likely areas may experience drought and possible famine due to environmental conditions. The use of NDVI has been used to forecast in advance of yield production by looking at the stage of development for agricultural vegetation and comparing it to the past. The utility of this allows adequate time for drought-related decisions to be made by relevant agencies and governments. 

Overall, it is the simplicity of the NDVI technique and its applicability to vegetation-base studies that have helped to make it perhaps the most extensively used in categories of remote sensing techniques used to monitor agriculture and plant growth.

MapMyApple gives satellite vegetation orchard images (up to 10 images/m)

MapMyApple gives satellite vegetation orchard images (up to 10 images/m)

MapMyApple advises apple growers about current level of orchard vegetation and allows them to monitor orchard vegetation using high-resolution satellite images. This is one of the values that MapMyApple brings to its users: it is giving them abbility to use one of the most extensively used remote sensing techniques in 21. century.