English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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&#x2013;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.

Time Series Scattering Power Decomposition Using Ensemble Average in Temporal–Spatial Domains: Application to Forest Disturbance Detection