LandViewer offers long-term observations, enhanced vegetation analysis

LandViewer, a cloud service developed by EOS Data Analytics, provides access to satellite data and fast-paced analytics. In recent months, it has undergone numerous updates, which have expanded the existing catalogue of satellite imagery, introduced more tools for analysis and added other new features.

By the end of 2018, free space and airborne data available for browsing, analysis and download via LandViewer included imagery from the European Space Agency’s (ESA’s) Sentinel-2 and Sentinel-1, NASA-USGS’s Landsat 8 and previous missions, MODIS, CBERS-4 and NAIP.

This broad selection of Earth observation data has grown even larger with the addition of high-resolution commercial imagery from Airbus, SpaceWill and SI Imaging Services.

LandViewer has evolved into a single platform. On top of open-source data, users can freely explore the potential of commercial data with global coverage, short revisit periods, and spatial resolution up to 40 centimeters.

The current catalogue includes imagery from Pléiades 1a/1b, SPOT 5, SPOT 6 and SPOT 7, along with KOMPSAT-2, 3, 3A and SuperView. The high-resolution imagery browser offers free preview, automatic price calculation by selected area, and fast image delivery within three business days via cloud EOS Storage.

Preview of KOMPSAT-3A image collected over Shanghai Hongqiao International Airport on Oct. 29, 2018. (Photo: EOS)

Long-term observations. An abundance of available data, such as weekly updated Sentinel-2 imagery and historical Landsat data, has made it much easier to monitor changes over long time spans. Rather than taking a long time to select and process years of satellite data to get a multitemporal perspective, the LandViewer’s new Time Series Analysis will crunch the remote sensing data and deliver the results in an easily interpretable graph.

Sentinel-2 time series graph generated for agricultural fields in Kansas state. (Screenshot: EOS)

Users can select an area of interest (AOI), and a satellite dataset and a time period between 1 month and 10 years. The algorithm can then pick all imagery with minimum cloudiness and calculate NDVI, NDWI or NDSI in just a few moments. By default, the generated Time Series graph contains lines (representing the min, max, mean and std values) that can be hidden or displayed for convenience; whenever an unusual spike or drop in values is noticed, a satellite scene that represents that part of the curve can be visualized to establish the cause. The results can be downloaded either as an image (.png), or a .csv file for working in Excel.

Enhanced vegetation analysis. Users searching for an in-depth look at vegetation cover can use LandViewer’s new spectral indexes: SAVI, EVI, ARVI, GCI, SIPI and NBR. These indexes complement generalized NDVI analysis by making corrections for atmospheric and topographic effects or soil brightness influences, depending on vegetation density, climate and elevation in the area of interest.

The NBR index is designed to highlight burned areas against healthy vegetation; the difference between pre-fire NBR and post-fire NBR values can be applied to estimate the severity of burn.

The use of several indexes simultaneously enables better insight into plant health and helps to identify stressed or infected vegetation at an early stage.

Sentinel-2-derived SAVI analysis of an arid agricultural region in Saudi Arabia. (Screenshot: EOS)

User-friendly legend and area calculation. Another new LandViewer feature, the index legend, is designed to solve the problem of interpreting the index results, a common issue for new users. Now when a spectral index is applied over the selected territory, the user can view a detailed legend, where each color-marked class contains a short description.

For example, calculation of NDVI will identify and highlight areas with “dense”, “moderate”, “sparse vegetation”, “open soil” or “no vegetation”.

Screenshot: EOS

Another new time-saving functionality is that the area of each class within the spectral index legend is calculated automatically, in both square meters and by percentage.

Also, the expanded Area of Interest (AOI) tool enables bulk uploading of AOIs and speeds up work by allowing simultaneous visualization and fast switching of all AOIs on a map for imagery searches or new scene subscription.

Advanced zone analytics. By introducing the clustering function, EOS’ remote sensing experts and software developers have taken LandViewer’s spatio-temporal analytics to the next level. With this function, users can run unsupervised satellite data-based classification of an area up to 200 square kilometers into as many as 19 clusters (or zones). This process involves setting custom parameters (size/number of zones) and waiting a few moments for LandViewer to build a raster image of the area with color-marked zones, and a vector layer outlining the boundaries. Both outputs can be downloaded.

This scalable analysis can provide various insights across agriculture, forestry, coastal monitoring and other industries. For example, a farmer can make use of convenient color mapping of zones within the field based on NDVI values for precise in-field navigation and crop management.

Engaging animations. With the informative spectral data contained in satellite image pixels, LandViewer has introduced a time-lapse animation feature allowing journalists and active social media users to create engaging animated stories and share them on the internet. Each GIF can contain up to 300 scenes, with indexes or band combinations applied. From calving of glaciers to construction of new stadiums,  satellite imagery is full of information that’s worth watching and sharing with the world.