LandViewer launched by the California-based company, EOS Data Analytics, has been widely popular as a service granting access to collections of satellite imagery and analytical tools. With the recent updates it increased both the imagery catalogue and the selection of analytics for remote sensing data analysis.
High-resolution imagery for enhanced remote sensing data analysis
At the end of 2018, EOS has become a reseller of high-resolution commercial imagery provided by world’s top imaging companies, Airbus, SpaceWill, and SI IS. The optical satellite imagery with spatial resolution up to 40 cm/pixel is now available via the same LandViewer interface that has been long used by Earth observation data analysts to access, analyze, and download the inclusively open-source satellite data from Copernicus and Landsat missions, along with MODIS, CBERS-4, NAIP.
LandViewer platform provides quick search across collections of Pléiades 1a/1b, SPOT 5, SPOT 6 and SPOT 7, KOMPSAT-2, 3, 3A, SuperView, Gaofen 1, 2 and Ziyuan-3 imagery with a convenient preview option and instant price quoting. High-resolution satellite imagery creates new opportunities for multiple industries, where there’s need for a highly detailed view of the territory enabling remote sensing-based detection and recognition of small-sized objects (vehicles, buildings, roads, etc.). Global coverage and short revisiting period of commercial imaging satellites add up to the list of advantages. LandViewer gives customers an ability to find the exact satellite scene, order and receive the image product within 3 business days directly to their personal cloud storage.
Satellite time-lapse animations in few easy steps
In order to contribute to the remote sensing shift from ‘space scientists-only domain’ to ‘a thing for everyone’, LandViewer developers introduced the Time-Lapse animation feature that allows making beautiful animated stories in less than five minutes. Journalists, data visualization savvies, or remote sensing bloggers may strengthen their story’s impact by adding amazing satellite image GIFs and videos created in LandViewer. Each animation can include up to 300 images, from one or different satellites, with spectral indexes/band combinations applied, and be instantly shared on social media.
From volcano eruptions and massive Amazon rainforest logging to the construction of new skyscrapers – satellite image pixels are packed full of events that must be shared with the world.
Time Series Analysis
Petabytes of analysis-ready GIS data like Sentinel-2 imagery and historical Landsat imagery collections going back till 1980s made spatiotemporal analysis much easier. However, going through all these datasets to pick cloud-free scenes taken by passive satellite sensors and analyze a year or two would take hours. Thanks to the new Time Series analysis function available in LandViewer, this can be done in several minutes and the results are displayed in a convenient, customizable graph.
What you need to do is determine your area of interest (AOI), pick a satellite dataset to obtain remote sensing data from, and a period of time ranging from one month to ten years. The system will automatically find all relevant satellite data with minimum cloud cover percentage and calculate NDVI, NDWI or NDSI in a couple of moments. For convenience, the resulting graph contains curves representing Min., Max., Mean and Std. values that can be either hidden or viewed again with a click. Additionally, several years of data can be shown on a split-by-years graph, on which unusual spikes or declines are much easier to identify. You can instantly visualize any part of the curve: viewing the image it represents may provide more details in order to establish the possible cause. The results of Time Series analysis are available for download as a .png image or .csv file.
New vegetation indexes for extracting insights from remote sensing bands
In view of the growing demand for agriculture and forestry applications of remote sensing data, LandViewer introduced several new spectral indexes. They are: SAVI, ARVI, EVI, SIPI, GCI, and NBR. It’s a known fact that NDVI, the most widely used index for vegetation analysis, is sensitive to soil brightness influences, atmospheric and topographic effects. To reduce this impact and maximize the accuracy of vegetation cover analysis in problematic areas, users can complement their satellite data-based research with these new LandViewer indexes calculated on the fly. NBR index, in its turn, is meant for highlighting the burnt areas; and the most popular workflow for estimating how severe the burn was is to calculate the difference between before-fire NBR and after-fire NBR index values.
Using more than one remote sensing index at a time increases the accuracy and provides better insight into crop or plant health, aids in early identification of infected or stressed vegetation areas.
New detailed remote sensing index legend with area calculation
Interpreting the index results may be quite a challenge for non-expert users and beginner GIS analysts alike. This is when the new LandViewer’s legend comes in handy: once a spectral index is calculated for a selected area, a detailed legend is displayed with every class containing a brief description and respective values. For instance, NDVI legend will tell you which parts of your area contain dense, moderate or sparse vegetation, as well as those containing no vegetation or bare soil.
Another useful addition that simplifies remote sensing data analysis is the automatic calculation of each class within the Area of Interest, by percentage and in square meters.
If you use satellite data to monitor several areas on regular basis, make sure to take advantage of the optimized AOI tool which now supports bulk uploading of KML, GeoJSON, Shp archives containing all necessary areas. It accelerates work and allows viewing all AOIs on a map, quick switching between them and convenient search across satellite datasets, along with new scenes subscription.
Satellite image segmentation for effective management of zones
Clustering, the newly added analytical function in LandViewer, consists in unsupervised satellite data classification of area under 200 square km, which transforms a raster image into a color marked map of zones that have similar spectral index values. Users may choose both the number of zones (up to 19) and their size. Along with a raster image, they can also process a vector layer that outlines zone boundaries and download both output files.
Clustering analysis is scalable and can be applied by both remote sensing specialists and non-GIS experts in a variety of industries, including forestry, agriculture, coastal monitoring, geology. A farmer, for instance, can use NDVI-based clustering to color-map zones within the croplands and increase precision of in-field navigation and crop management.
You can freely explore LandViewer’s new satellite data and analytics at https://eos.com/landviewer