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Updated: May 14, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

A Web-Based Platform for Quantitative Assessment of Change Detection Using Rao's Q Index in Remote Multispectral

Rafaela Tiengo¹, Silvia Merino-De-Miguel¹, Jéssica Uchôa²

¹Departamento de Ingeniería y Gestión Forestal y Ambiental, ETSIMFMN-Escuela Técnica Superior de Ingeniería de Montes, Forestal y del Medio Natural, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

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A new web platform uses spectral diversity metrics to quantify land use and land cover change (LULCC) from satellite images. It effectively detected lava flows from a volcanic eruption, showcasing its utility for environmental monitoring.

Area of Science:

Geospatial analysis
Remote sensing
Environmental science

Background:

Land use and land cover change (LULCC) impacts ecosystems and requires effective monitoring tools.
Quantitative assessment of LULCC is crucial for understanding environmental dynamics.

Purpose of the Study:

To develop and implement a web-based geospatial platform for quantitative LULCC assessment.
To operationalize the Rao spectral diversity metric (Rao's Q) for detecting and quantifying LULCC.
To support ecosystem monitoring and Earth's surface characterization through spectral indices.

Main Methods:

Development of a web-based geospatial platform supporting single-band and multi-band processing.
Implementation of interactive Area of Interest (AOI) delimitation and calendar-based temporal selection.

Keywords:

Rao’s Q index change detection land cover land use remote sensing web-based geospatial analysis

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Last Updated: May 14, 2026

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

Automated calculation of Rao's Q surfaces and change maps, integration of spectral indices.

Main Results:

The platform successfully detected lava flows from the 2024 Reykjanes Peninsula volcanic eruption using Sentinel-2 SWIR imagery.
The multidimensional approach (bands 11 + 12) provided balanced results (OA=83.0%, PA=84.0%, UA=82.4%) for lava flow detection.
Single-band 11 processing achieved the highest precision (UA=97.4%) for lava flow detection.

Conclusions:

The developed platform is a robust tool for monitoring LULCC and assessing environmental impacts.
The Rao's Q metric, particularly with multidimensional spectral data, shows strong performance in detecting rapid environmental changes like volcanic eruptions.
The integration of spatiotemporal analysis, spectral diversity, and indices enhances the platform's utility for Earth observation.