Seismology
Seismologists worldwide rely on MATLAB to investigate tectonic and anthropogenic seismicity, monitor volcanic activity, and analyze waveform data from various sensor networks. Experts have developed toolboxes that customize MATLAB for seismological data access and analysis.
Discover how you can:
- Use MATLAB and Signal Processing Toolbox to read, analyze, and compare seismic waveforms (Signal Processing Onramp)
- Interactively analyze waveforms and automatically generate code with the Signal Analyzer app (Documentation)
- Read and write miniSEED files with RDMSEED and MKMSEED functions (Toolbox)
- Download and process earthquake data from the ISC Bulletin with ISC Earthquake Toolbox for MATLAB (Toolbox and API)
- Use the MATLAB app ZMAP7 GUI for seismic data visualization, statistical analysis, and earthquake catalog data research (Toolbox)
- Explore the MATLAB geodetic toolboxes Stavel and Gridstrain to derive velocity and strain rate fields from GNSS data (Toolboxes)
Learn More
- Community Toolboxes for Seismology
- Signal Processing Toolbox and Wavelet Toolbox to analyze seismic signals, filter noise, and detect patterns
Coulomb is an open-source MATLAB toolbox for earthquake, tectonic, and volcano research and teaching. In Toda et al., 2005 (600 citations), researchers use the Coulomb toolbox to simulate stress changes, deformation patterns, and earthquake triggering in Southern California.
Ocean and Climate
MATLAB enables researchers to analyze and model complex oceanic and atmospheric systems, providing insights into climate change and environmental impacts.
- Climate Data Toolbox: Explore historical data and analyse the temperature trends and spatio-temporal climate patterns (Paper, G3)
- Tide Model Driver 3.0: Predict tides based on tide model data (TMD Getting Started)
- Ocean Data Tools: Access data via APIs at popular oceanographic data sites (Toolbox)
- jLab: Perform big data analysis, signal processing, wavelet analysis, and mapping for oceanographic applications (Toolbox)
Learn More
- cmocean: Perceptually-uniform colormaps for commonly-used oceanographic variables
- July 2023 was “the hottest” month since records have been kept (Blog Post, Published Paper)
- Community toolboxes for:
Hydrology
MATLAB enables complex simulations, statistical analysis, and graphical representations of hydrological data, aiding in tasks such as watershed modeling, flood and landslide prediction, and water quality assessment.
- Map Flood Areas Using Sentinel-1 SAR Imagery: Map flooded areas using Sentinel-1 SAR images using the Hyperspectral Imaging Library for Image Processing Toolbox (Support Package)
- CUAHSI Hydroshare: Access and analyze hydrology data with MATLAB code repos and MATLAB Online on HydroShare (Online Platform)
- Wadenow Toolbox: Forecast the velocity trend of rainfall-triggered landslides by means of continuous wavelet transform and deep learning (Paper, Geosciences)
Topotoolbox is an open-source toolbox for analyzing topographic data. It provides tools and an interface for processing digital elevation models (DEMs), enabling the study of landscape evolution, hydrological modeling, and geomorphological analysis.
Agriculture
MATLAB has tools for data analysis, image processing, and smart farming. It enables crop yield prediction, soil moisture analysis, and advanced image-based monitoring. You can use:
- Hyperspectral image processing functions to detect changes in land cover (Code Example)
- ThingSpeak and IoT sensors to collect and analyze data for early detection of plant diseases using machine learning models (Case Study)
- MATLAB to analyse image signals from different parts of the electromagnetic spectrum for vegetation detection and mapping (Case Study)
Learn More
- MATLAB for Agriculture Technology (5 videos) - Video Series
- Community Toolboxes for Agriculture
- ThingSpeak for Smart Farming
Li et al., 2020, measured plant growth implementing MATLAB for automated image processing. Images collected by smartphones from different harvest days were stored and analyzed on a local computer using the MATLAB mobile app on a smartphone. Then, MATLAB scripts extracted images from the shared folder, processed the images, and estimated the leaf area. Data were stored in the cloud and visualized on the smartphone.
