학술논문

Time Series Scattering Power Decomposition Using Ensemble Average in Temporal–Spatial Domains: Application to Forest Disturbance Detection
Document Type
Periodical
Source
IEEE Geoscience and Remote Sensing Letters IEEE Geosci. Remote Sensing Lett. Geoscience and Remote Sensing Letters, IEEE. 21:1-5 2024
Subject
Geoscience
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Scattering
Time series analysis
Satellite constellations
European Space Agency
Vegetation mapping
Synthetic aperture radar
Deforestation
Dual polarization
forest disturbance detection
scattering power decomposition
Sentinel-1
time series analysis
tropical forests
Language
ISSN
1545-598X
1558-0571
Abstract
This letter proposes a novel synthetic aperture radar (SAR) time series analysis method based on the scattering power decomposition algorithm with a reasonable ensemble average in both temporal and spatial domains. We reveal that the ensemble average is effective not only in the spatial domain but also in the temporal–spatial domains in the scattering power decomposition. That is, if we extend the ensemble average window in the temporal domain, the proposed method can accurately achieve volume scattering power with a higher spatial resolution than conventional approaches. The precise volume scattering power serves accurate forest monitoring. As an application, we performed forest disturbance detection in the Amazon rainforest using Sentinel-1 time series data. The proposed method detected the disturbances earlier, in less than 2 months, compared to other methods that take about 3 months.