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      What is NDVI?

      What does the NDVI layer tell me and how can I use it best?

      NDVI (Normalised Difference Vegetation Index) is a remote sensing index commonly used to estimate the health and abundance of vegetation in a given area. It is calculated by subtracting the red band of a remote sensing image from the near-infrared (NIR) band, and then dividing this difference by the sum of the two. The resulting NDVI values range from -1 to 1, with higher values indicating healthier vegetation.

      The data used in FarmLab to calculate NDVI is from the Landsat 8 and 9 satellites and is collected in visible and infrared regions of the electromagnetic spectrum. That data is collected at a resolution of 30m and is processed within the FarmLab software. This data can be provided anywhere in the world covered the Landsat satellites every 16 days.   

      The NDVI layer can provide valuable information about vegetation in a particular area. Here are a few examples:

      1. Vegetation cover: NDVI can be used to estimate the amount of vegetation cover in a given area. Higher NDVI values generally indicate more vegetation, while lower values indicate less vegetation.

      2. Vegetation health: NDVI can be used to monitor changes in vegetation health over time. Sudden drops in NDVI values can indicate stress or damage to vegetation, such as drought, disease, or insect infestation.

      3. Crop monitoring: NDVI is often used to monitor crop health and productivity. By analysing NDVI values over time, farmers and researchers can identify areas of their fields that may be underperforming and adjust their management practices accordingly.

      4. Land cover classification: NDVI can also be used as a tool for classifying land cover types. For example, areas with high NDVI values are often associated with forests or grasslands, while areas with low NDVI values may be water bodies or bare soil.

      Overall, the NDVI layer is a useful tool for analysing vegetation patterns and changes in the environment over time. It can be applied to a variety of fields, including agriculture, forestry, and land management.