Index layers

This article covers the following index layers:

In addition to the red, green, and blue bands used to create the truecolor layers, we also pull other bands from our public imagery sources. These bands, or wavelengths, can provide additional insights into changing conditions on the ground. We provide four seven layers in Lens, meaning that they are calculated from remote sensing data reflectance values and can be quantified. Sentinel-2 layers use Level-2A ortho-rectified atmospherically corrected surface reflectance. These indices are available on a weekly time scale in the case of Sentinel-2 data, about every 16 days in the case of Landsat 8, and less frequently for NAIP data. Selecting an index layer from the Layer dropdown will display the corresponding captures in the Date dropdown. 

Built-up index

Built-up Index is derived from the Normalized Difference Built-up Index (NDBI). NDBI uses SWIR and NIR bands to highlight built-up areas. Areas with buildings and a lack of vegetation will show higher values in this layer and areas of healthy vegetation will show lower values in this layer. Please note that water and sediment can cause false positive values with this layer, so it is always recommended to compare truecolor imagery to the Built-Up Index to contextualize the data.

Burn index

Available for Sentinel-2 and Landsat 8 sensings, the Normalized Burn Ratio can provide insight into the timing and severity in burned areas. This can be used by organizations assessing pre- and post-fire conditions or monitoring vegetation recovery following a fire. NBR is an index calculated using near-infrared (NIR) and shortwave infrared (SWIR) values derived from NASA/USGS Landsat 8 and ESA Sentinel-2. 

Burned areas display high SWIR and low NIR reflectance, which is the opposite of healthy vegetation. Values in Lens range from -0.1 to 0.4, where lower values denote healthy vegetation and higher values correspond to more intense burns. Though NBR traditionally ranges from 0 to 1, this range was reduced in Lens to make it easier to distinguish burned areas. Because index values may vary based on the landscape, pre-fire vegetation, presence of smoke or clouds, it’s useful to view alongside truecolor, vegetation, and other datasets. For example, a desert landscape in the hot summer will show higher burn index values than a forested area, even without any fire event. Climate and phenological cycles are important to keep in mind when viewing this dataset, and it’s intended to support assessment in areas where known fires have occurred.

Chlorophyll

Chlorophyll (S2) is derived from Normalized Difference Chlorophyll Index. This dataset uses public sensor data to assess the concentration of chlorophyll in water bodies – a clear indicator for algal blooms. The calculation uses red and red edge sensor bands from the European Space Agency Sentinel-2 constellation. Values range from -0.2 to 0.5 with higher values indicating higher presence of chlorophyll. Areas with high chlorophyll levels appear in bright green and yellow, and water appears blue. 

Enhanced vegetation

Enhanced vegetation is derived from the Enhanced Vegetation Index (EVI). EVI is a vegetation index that was developed to improve upon the Normalized Difference Vegetation Index (NDVI), particularly in areas with dense vegetation cover or in situations where atmospheric conditions may affect the quality of satellite imagery. EVI uses blue light to correct for the atmospheric influences and the canopy background signal, which can lead to more accurate assessments of vegetation health and density. Higher values indicate healthier and more dense vegetation. See Huete et al. (2002) and the USGS webpage on EVI for more details. Depending on the location and ecosystem of your property, you may find that EVI shows slightly less seasonal variation than NDVI or that EVI is more accurate in areas of dense vegetation. We recommend doing some testing and comparing both NDVI and EVI to determine which one may be most appropriate for your site (or continue to use both for further confirmation of vegetation trends). 

Phycocyanin

Phycocyanin is a natural pigment found in blue-green algae and some species of cyanobacteria. High concentrations of phycocyanin can be an indicator for algal bloom events which can impact people and aquatic ecosystems alike. This phycocyanin index dataset utilizes a remote sensing formula tuned for phycocyanin pigment reflectance for inland water bodies. This is an experimental layer.  See this paper for more information.

Snow

The snow layer users the Normalized Difference Snow Index (NDSI) to highlight areas on a property where snow is present. NDSI is calculated from a satellite image's green and short-wave infrared bands, using public satellite data inputs to derive a value between 0 and 1. Lower values indicate a lack of snow, while higher values denote areas with snow presence and are shown in white.

Surface moisture

The Surface moisture layer uses infrared and visible spectrum public satellite data to identify moist bare soil and very shallow water. Derived from the Normalized Difference Water Index (NDWI), this layer ranges from -0.6 to 0, where higher values indicate more moisture and saturated bare ground in darker blues, and lower values indicate variations in dry land. The moisture layer is best used in areas with bare ground or minimal vegetation when some context on ground conditions is known. Since the layer is heavily influenced by shadow, it is less ideal for areas with lots of topography. This layer provides more detail on areas that appear dry in the Surface Water layer, such as saturated land in wetland environments. Small and narrow streams or water bodies are also more easily picked up with this layer. 

In regions where vegetation is blocking a top-down view of the soil or water surface, this surface moisture layer will display values indicating no water present. We, therefore, recommend using the Vegetation layer to assess moisture in vegetated areas, where darker greens indicate plants that are not experiencing water stress. Note that buildings and roads reflect light in a similar way to water, so we recommend that you refer to other layers to ensure that areas appearing blue in this layer are in fact moist, rather than developed. In some cases, shadows may also be present based on the angle the imagery was taken. We recommend taking a look at the Truecolor layer first to get oriented before utilizing this layer.

Surface water

Sentinel-2 surface water

Sentinel-2 surface water is derived from the Normalized Difference Water Index (NDWI) and shows which parts of a property have standing water present. Values range from -0.1 to 0.4, where low values show dry land and higher values in blue show areas with water, such as rivers or ponds. 

Sentinel-1 surface water (Radar)

This surface water dataset is derived from ESA Sentinel-1, launched in 2014 and captured approximately every 12 days. Sentinel-1 uses C-band synthetic aperture radar to collect data in any conditions, regardless of weather or time of day. Surface water is displayed on a spectrum from dry to water, or white to blue respectively, and can be viewed visually or as a time-series in Analysis. This dataset is ideal for assessing water conditions in areas with significant cloud cover or storms, and applications include monitoring changes in bodies of water or post-disaster assessments.

Turbidity

The Normalized Difference Turbidity Index (NDTI) is useful for assessing water quality. By comparing the reflectance of specific wavelengths of light, typically in the visible and near-infrared spectrum, NDTI quantifies the turbidity level of water bodies. Turbidity is an indicator of suspended particles in water which may be related to pollution or sedimentation, which can impact aquatic species and ecosystems. This layer is derived from ESA Sentinel-2 going back to late 2015 with frequent captures.

Vegetation

Vegetation vigor is a measure of photosynthetic activity, or how much plant growth is occurring. It's derived from the Normalized Difference Vegetation Index (NDVI) using visible and near-infrared reflectance detected by satellite or aerial sensors. Specifically, we display L2A surface reflectance data. The vegetation layer ranges from 0 to 1, with low values indicating no vegetation present in white or yellow and high values showing areas with vigorous vegetation in dark green. This data provides a reliable way to evaluate vegetation health and changes over time. When using Analysis on the S2 vegetation layer, sometimes shadows in areas with hills and mountains can read as higher NDVI in the winter given the lower sun angle, so always cross-reference with truecolor imagery when in doubt.

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All images on this page are Copernicus Sentinel data