BARE EARTH COMPOSITE (BEC)

In areas covered by vegetation or transformed by human activity, direct exposure of soil and rocks is often obscured, limiting the potential for detailed mineral mapping. To overcome this challenge, we have developed advanced methodologies for generating Bare Earth Composites (BECs) using satellite data from Sentinel-2, ASTER, or a combination of both sensors (resulting in a 21-band composite). These methodologies allow us to find uncovered pixels and reveal the underlying mineral features, thus increasing the area available for mapping.

Our approach leverages the spectral and spatial capabilities of Sentinel-2 and ASTER data to extract surface information, which is otherwise masked by vegetation. This results in high-quality composites that provide insights into the mineral structure with a spatial resolution of 10–30 meters. By incorporating this methodology, we estimate that the total area available for accurate mapping could increase by as much as 20%. This substantial improvement makes it possible to map regions previously inaccessible due to vegetation cover, enhancing both the accuracy and coverage of survey.

Each BEC dataset is created upon the request it means that You are working on latest data (regarding Sentinel-2 Data)

Moreover, the fusion of multi-spectral data from these two satellites gives more data for analysis of surface materials, offering a more detailed and reliable insight. This composite can be especially useful in mineral prospecting and land use planning, where precise identification of surface features is critical.

Differences in data coverage when using a traditional masking method, based on the detection and elimination of pixels representing vegetation in single image (on the left), compared to data coverage after generating a bare earth composite (on the right). The bare earth composite method provides more comprehensive coverage by isolating periods of minimal vegetation presence, allowing for more accurate mapping.

The schematic workflow for BEC calculation based on the Sentinel-2 data


For more information on how TerraEye’s spectral indices and AI solutions can support your exploration projects, contact us today or visit:

🌐 app.terraeye.co

🌐 terraeye.co

Let's revolutionize mineral exploration together! 🌍✨

Popular posts from this blog

Unlock the Power of Satellite Data, Remote Sensing, and Artificial Intelligence with TerraEye

Image
TerraEye revolutionizes mineral exploration and environmental monitoring by integrating advanced artificial intelligence (AI) into its platform. By transforming vast amounts of raw satellite data into actionable insights, TerraEye enables faster, more accurate, and cost-effective decision-making processes. Decoding AI in Geology At TerraEye, we believe that AI should be a transparent tool, not a black box. Our AI solutions are designed to help uncover insights that might have been missed, providing clarity rather than confusion. We collaborate closely with expert exploration geologists who contribute their knowledge to train and refine our algorithms. This partnership ensures that our AI is grounded in real-world expertise, enhancing its effectiveness and reliability. AI Clustering for Geological Analysis One of the major AI applications in TerraEye’s platform is its use of AI-based clustering algorithms, such as K-means , for lithological mapping. This process involves applying machin...
Read more

Lineament Maps – What They Are and How They Are Created

Image
TerraEye introduces a lineament detection algorithm based on Digital Elevation Model (DEM) data. This approach allows for the identification of surface expressions of linear structures, which can be part of local geological formations significant for mineralization. As such, lineament data serves as an important input in the analysis of areas for potential mineral resources. The detection process is carried out using the Canny edge detection algorithm (Mohammadpour et al., 2020), which has proven to be effective for this purpose. In this version, the results are generated from a DEM derived from the global Copernicus GLO-30 dataset. The Canny algorithm operates in several stages: first, it filters noise to eliminate irrelevant details, then identifies and selects pixels where DEM values change sharply. From these selected pixels, lines are created, and in the final step, thresholding allows for the extraction of strong and clear edges or more subtle ones depending on the settings. The ...
Read more

Navigating TerraEye: An In-Depth Guide to Mineral Exploration Using Sentinel-2 and EMIT Data

Image
  In the complex field of mineral exploration, the ability to analyze vast amounts of satellite data accurately and efficiently is a key competitive advantage. TerraEye, a cloud-based application, provides advanced tools to process and interpret satellite data, particularly from Sentinel-2 and EMIT sensors. This blog post offers a detailed guide on how to navigate TerraEye, focusing on data processing and how to interpret the results of automated analyses. Additionally, we'll cover how to order additional datasets and request further processing of your Area of Interest (AOI) directly within the app. How to Get Started with TerraEye Getting started with TerraEye is straightforward: Sign Up: Create an account on the TerraEye website by entering your details and agreeing to the terms of service. Log In: Use your credentials to access the TerraEye dashboard. Explore Demo Sites: Familiarize yourself with TerraEye’s features by exploring the demo sites available on your dashboard. Cre...
Read more