Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Integration of Multi‐Sensor Remote Sensing for Environmental Monitoring

Assessment of Wetland Degradation in Semi‐Arid Ecosystems

Bibliographic Data

ID21648985
AuthorsRana Waqar Aslam (0000-0002-8711-8700, School of Geography and Tourism Anhui Normal University Wuhu China, corresponding author), Hong Shu (0000-0003-2108-1797, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Wuhan University Wuhan China), Iram Naz (0000-0002-7330-0288, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Wuhan University Wuhan China), Aqil Tariq (0000-0003-1196-1248, Regional Centre for Space Science and Technology Education in Asia and the Pacific (China) (Affiliated to the United Nations), International Innovation and Research Centre Institute of Beihang University Hangzhou China), Jianzhong Lu (0000-0002-6432-8481, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Wuhan University Wuhan China, corresponding author), Abdul Quddoos (0009-0007-3493-9920, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Wuhan University Wuhan China), Aleksandra O Utkina (Institute of Environmental Engineering RUDN University Moscow Russia)
Year2026
Publication date2026-06-23
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLand Degradation and Development (JOURNAL)
Journal identifiersISSN: 1085-3278 • E-ISSN: 1099-145X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ldr.70746
OpenAlexW7165688663
LanguageEN
References cited39

Environmental monitoring of wetland ecosystems using conventional single‐sensor approaches faces significant limitations in assessment accuracy and temporal consistency. This study develops and validates an integrated environmental assessment framework combining optical and radar remote sensing for monitoring critical wetland degradation in semi‐arid ecosystems. The assessment methodology integrates Sentinel‐1 SAR data (VV and VH polarizations in both ascending and descending modes) with multiple Sentinel‐2 derived environmental indices, including water indices (AWEI, MNDWI, SWI, SMBWI, NDWI), vegetation indices (SAVI, NDVI, GDVI), and built‐up indices (SMI, NDBI). Environmental assessment of two critical wetlands in Pakistan's Soan Valley (2016–2019) reveals significant ecosystem degradation, with wetland extent diminishing from 24.1 km 2 (4% of the 959.1 km 2 study area) to 15.8 km 2 (2%), representing a critical loss of 8.3 km 2 of vital wetland habitat. The integrated assessment approach provided robust environmental monitoring capabilities, validated through strong correlations between MNDWI and SAR water masks (correlation coefficients: 0.47–0.68). Analysis of land use transitions reveals significant ecosystem transformation, with rangeland expansion from 349.1 km 2 (36%) to 420.1 km 2 (44%) replacing former wetland areas. The framework demonstrates enhanced capability in environmental change detection and ecosystem boundary delineation compared to single‐sensor optical or SAR approaches, achieving 15%–23% improvement in wetland boundary detection accuracy and maintaining consistent monitoring effectiveness under varying environmental conditions including cloud cover where optical‐only methods fail. This study provides both critical evidence of ecosystem degradation and establishes a robust environmental assessment methodology applicable to wetland management globally, particularly in semi‐arid regions facing similar environmental challenges

Ecosystem · Environmental change · Environmental degradation · Environmental impact assessment · Environmental Monitoring · Land cover · Wetland · Flood Risk Assessment and Management · Remote Sensing in Agriculture · Remote-Sensing Image Classification

  • Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery

    Hanqiu Xu•International Journal of Remote…•2006

  • Use of normalized difference built-up index in automatically mapping urban areas from TM imagery

    Yong Zha, Jay Gao et al.•International Journal of Remote…•2003

Citation velocityhistorical
Highly citedNo

Tools

Open DOI
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae