Identification of optimal Sentinel-1 SAR polarimetric parameters for forest monitoring in Czechia

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F25%3A10496163" target="_blank" >RIV/00216208:11310/25:10496163 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=R5eGGSloT4" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=R5eGGSloT4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14712/23361980.2024.18" target="_blank" >10.14712/23361980.2024.18</a>

Result language

angličtina

Original language name

Identification of optimal Sentinel-1 SAR polarimetric parameters for forest monitoring in Czechia

Original language description

Time series analysis of synthetic aperture radar data (SAR) offers a systematic, dynamic and comprehensive way to monitor forests. The main emphasis of this study is on the identification of the most suitable and best performing Sentinel-1 SAR polarimetric parameters for forest monitoring. This is accomplished through: 1) a pairwise correlation analysis of SAR polarimetric parameters, multispectral optical vegetation indices and ancillary data, 2) a univariate binary time series classification for differentiation between forest types and 3) a visual exploration of time series. For this purpose, 600 validated broad-leaved and 600 coniferous forest areas in Czechia were used. Nine different SAR polarimetric parameters were examined, including VH and VV polarizations, VV/VH and VH/VV polarization ratios, the Radar Vegetation Index, Radar Forest Degradation Index, polarimetric radar vegetation index and the original and modified versions of the dual polarimetric SAR vegetation index. The pairwise correlation analysis revealed that most of the derived SAR polarimetric parameters were functions of each other with nearly identical behavior (r &gt; vertical bar 0.96 vertical bar). The strongest correlation of r (similar to)0.50 between SAR and optical features was found for broad-leaved forest for VV/VH and VH/VV. The highest overall accuracy in the time series classification of forest types was achieved by VH (76%), while for VV, VV/VH and VH/VV it was higher than 60%. Furthermore, the time series analysis of these parameters showed seasonal behaviors of the SAR features in both forest types. These results demonstrated the high relevance of using VH, VV, VV/VH and VH/VV time series in forest monitoring compared to other SAR polarimetric parameters. This study also introduces a novel pipeline to generate multi-modal time series datasets in Google Earth Engine (MMTS-GEE), used to generate data for the analysis. MMTS-GEE combines spatially and temporally aligned SAR and multispectral data, extended with topographic and weather data, and a land cover class label. Its high versatility enables its use in time series analyses, intercomparisons and in machine learning applications for tabular time series data. The GEE code for the proposed tool and analysis is freely available to the research community.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10508 - Physical geography

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Acta Universitatis Carolinae. Geographica

ISSN

0300-5402

e-ISSN

2336-1980

Volume of the periodical

60

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

15

Pages from-to

46-60

UT code for WoS article

001436847200004

EID of the result in the Scopus database

2-s2.0-105004947280