Modeled dataset layers
Via the Lens Library, you have access to several modeled datasets that can be added to your account as layers. These are layers derived from remotely sensed images using various methods to classify or quantify for a specific purpose (examples: land use, tree canopy height, etc.). This article will summarize the available modeled dataset layers in Lens. To learn more about the Lens Library, see our article here.
This article covers:
- Landcover data from NLCD
- Landcover data from ESA WorldCover
- Impact Observatory's Land Use Land Cover
- NatureServe's Map of Biodiversity Importance (MoBI)
- Biodiversity Intactness
- Global Forest Loss
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. The legend for this layer will appear on your screen when the layer is selected.
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. The legend for this layer will appear on your screen when the layer is selected.
Impact Observatory's Land Use Land Cover
This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution for 2017 - 2022. Each year is generated from Impact Observatory’s deep learning AI land classification model using a massive training dataset of billions of human-labeled image pixels. The global maps were produced by applying this model to every Sentinel-2 scene, processing over 400,000 Earth observations per year. The algorithm generates LULC predictions for 9 classes globally. These classifications include Built, Crops, Trees, Water, Rangeland, Flooded Vegetation, Snow/Ice, Bare Ground, and Clouds. Additional information about Impact Observatory's global maps is available here and you can reach out to their team directly with any questions about the data and methodology. The legend for this layer will appear on your screen when the layer is selected.
Custom land cover data from Impact Observatory
In addition to the free Impact Observatory Landcover dataset, we also offer a premium version with more classifications and recent data. Impact Observatory's innovative AI-powered methods categorize land cover into 14 classes: Water Channel Extent, Variable Water, Persistent Water, Dense Trees, Sparse Trees, Dense Rangeland, Sparse Rangeland, Flooded Vegetation, Crops, High Density Built, Low Density Built, Bare Ground, Snow/Ice, Clouds. This custom data can be requested directly through Lens and costs $.02/acre, with a minimum spend of $500. The data will include the time period requested and one year prior for baseline comparison. To initiate a data request, click the "Request" button on the Custom Landcover tile in the Layers Library and fill out the linked form to get a quote for your area and time frame.
NatureServe's Map of Biodiversity Importance (MoBI)
The Map of Biodiversity Importance (MoBI) Layer provides information about the number of species in the continental US that are protected by the Endangered Species Act and/or considered to be in danger of extinction. This dataset is free and available to all Lens customers, and will be displayed beyond the property boundaries to enable you to contextualize your site within a larger area. The MoBI layer visualizes NatureServe's All Species Richness dataset which covers the continental United States - it's useful to understand the richness of species including vertebrates, freshwater invertebrates, pollinators, and plants. The richness value represents the number of species with habitat overlapping a given 990 meter cell. As a synthesis of predictive models, MoBI cannot guarantee either the presence or absence of imperiled species at a given location.
NatureServe has been an authoritative source for biodiversity data over many decades, collecting, analyzing, and delivering biodiversity knowledge that informs conservation action. To learn more about their MoBI dataset, see here.
Created by Impact Observatory and Vizzuality, this data layer depicts the intactness of biodiversity globally for the years 2017-2020 at a 100-meter resolution. The intactness of biodiversity is based both on the abundance of individual species as well the species composition compared to an intact baseline. This dataset can help identify areas of critical remaining intact habitat and help support spatial planning and management. This layer is terrestrial and won't include data for areas of water. Like the MoBI layer, this will load beyond property boundaries so you can see the context of the larger area. Learn more about the methodology in this white paper.
Global Forest Loss
The University of Maryland Global Forest Change layer displays 30m data showing gross forest loss for each year from 2001 to 2022, derived from Landsat imagery. Areas where forest has transitioned to non-forest are during a given year are displayed in red on the map. Forest loss could be a result of natural or human-induced causes. This dataset makes it possible to assess the timing and annual extent of forest loss over the last two decades, which can support site assessments and baselining. Data and methodology were originally published by Hansen et. al and this work is a collaboration between the Global Land Analysis & Discovery Lab at the University of Maryland, Google, USGS, and NASA.