Public Imagery and Data Layers
In this article:
- Public Truecolor imagery in Lens
- Index layers
- Color Infrared layers
- Landcover data from NLCD
Landcover data from ESA WorldCover
Public Truecolor imagery in Lens
We automatically provide available public remote sensing data to all Lens users at no cost. This includes 1-meter resolution USDA NAIP aerial flyover data going back to approximately 2003, as well as 10-meter resolution Sentinel-2 imagery from the European Space Agency. Like the imagery you can order through the Lens Library, this is Truecolor imagery, which means that it displays ground conditions in a natural color palette, similar to what humans observe. Selecting Higher Resolution or Higher Frequency Truecolor from the Layer dropdown will display the corresponding images in the Date dropdown.
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 insight into changing ecological conditions on the ground. We provide four index layers in Lens, meaning that they are calculated from remote sensing data reflectance values and can be quantified. These relate to vegetation vigor and water presence on the landscape. These indices are available on a weekly time scale, in the case of Sentinel-2 data, and less frequently for NAIP data. Selecting an index layer from the Layer dropdown will display the corresponding captures in the Date dropdown.
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. 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.
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.
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. 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.
The snow layer uses 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.
Color Infrared Layers
We also provide a few color infrared data layers in Lens to help identify certain features or conditions on your property. Unlike Index layers, Color infrared layers display bands from the infrared spectrum of light to visible light bands, rather than quantifying reflectance and calculating an output. Additionally, we don't mask clouds for Color Infrared imagery the way we do for Index layers. For more information, we’d recommend reading NASA Earth Observatory's wonderful write-up on falsecolor imagery, another term for color infrared.
Near-infrared (NIR), red, green
This layer shows near-infrared (NIR) light as red, red light as green, and green light as blue. Plants reflect near-infrared and green light, while absorbing red. Since they reflect more near-infrared than green, plant-covered land appears deep red. Denser plant growth is darker red. Cities and exposed ground are gray or tan, and clear water is black. Similar to NDVI, this band combination is valuable for assessing plant health. Where NDVI is a calculated index, NIR is closer to a raw value.
One interesting use for this layer is distinguishing hardwood (deciduous) from softwood (coniferous) trees. The image below shows a mixed stand of hardwood and softwood trees. The left truecolor image displays what the environment naturally looks like to the human eye. While there is clearly some variation in tree types, it is difficult to discern. The right image displays the same forest in Lens’ infrared data layer. We can more easily identify conifers as they appear more grey, while deciduous trees - which have more photosynthetic activity - are bright red.
Blue, short wave infrared (SWIR)
This layer shows blue light as red, and two different short-wave infrared (SWIR) bands as green and blue. This band combination is valuable in distinguishing snow, ice, and clouds. Ice reflects more blue light than snow or clouds. Ice on the ground will be bright red in this color infrared layer, while snow is orange, and clouds range from white to dark peach.
Short wave infrared, near-infrared, green
This layer shows near short-wave infrared (SWIR) light as red, NIR as green, and green light as blue. Because water and wet soil stand out in this band combination, it is valuable for monitoring floods. Saturated soil and sediment-laden water will appear blue. Ice clouds, snow, and ice are bright blue, since ice reflects visible light and absorbs infrared. Clear water will show as black. This helps distinguish water from snow and ice; it also distinguishes clouds made up mostly of liquid water or ice crystals.
Newly burned land reflects SWIR light and appears red in this combination. Hot areas like lava flows or fires are also bright red or orange. Exposed, bare earth generally reflects SWIR light and tends to have a red or pink tone. Urban areas are usually silver or purple, depending on the building material and how dense the area is.
Since plants reflect near-infrared light very strongly, vegetated areas are bright green. The signal is so strong that green often dominates the scene.
Landcover data from NLCD
This layer shows the National Land Cover Database (NLCD) classifications of land cover at 30m resolution based on a 16-class legend. The NLCD is developed by a partnership of multiple United States Federal agencies and the most recent available layer in Lens is from 2016. You can visualize this public dataset and landcover classifications for properties in Lens but Upstream Tech is not responsible for the accuracy of the classifications.
Landcover data from ESA WorldCover
The ESA WorldCover global landcover layer from 2020 is based on data from Sentinel-1 and 2, and provides land cover classifications at 10m resolution globally. This data layer from the European Space Agency was developed by a consortium major European service providers and research organizations and includes 12 categories of land cover. You can visualize this public dataset and landcover classifications for properties in Lens but Upstream Tech is not responsible for the accuracy of the classifications.