Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China) DOI Open Access
Polina Lemenkova

Transylvanian Review of Systematical and Ecological Research, Journal Year: 2025, Volume and Issue: 27(1), P. 1 - 16

Published: April 1, 2025

Abstract This study presents environmental analysis of the Yangtze River Basin, Wuhan region central China, performed using machine learning (ML) methods Remote Sensing (RS) data classification. The workflow is Geographic Resources Analysis Support System (GRASS) Information (GIS) scripting software for processing Landsat images by two approaches: unsupervised clustering and supervised ML algorithms. Six were taken biennially in autumn from 2013 to 2023 detect wetland changes area. article demonstrates application GIS landscape dynamics riverine lacustrine areas around River.

Language: Английский

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique DOI Creative Commons
Polina Lemenkova

Coasts, Journal Year: 2024, Volume and Issue: 4(1), P. 127 - 149

Published: Feb. 26, 2024

Mapping coastal regions is important for environmental assessment and monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) present more advantageous solutions pattern-finding tasks such as the automated detection of landscape patches heterogeneous landscapes. This study aimed to discriminate patterns along eastern coasts Mozambique ML modules Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm module ‘r.learn.train’ was used map landscapes shoreline Bight Sofala, remote sensing (RS) data at multiple temporal scales. dataset included Landsat 8-9 OLI/TIRS imagery collected dry period during 2015, 2018, 2023, which enabled evaluation dynamics. supervised classification RS rasters supported by Scikit-Learn package Python embedded GRASS Sofala characterized diverse marine ecosystems dominated swamp wetlands mangrove forests located mixed saline–fresh waters coast Mozambique. paper demonstrates advantages areas. integration Earth Observation data, processed decision tree classifier land cover characteristics recent changes ecosystem Mozambique, East Africa.

Language: Английский

Citations

9

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh DOI Open Access
Polina Lemenkova

Water, Journal Year: 2024, Volume and Issue: 16(8), P. 1141 - 1141

Published: April 17, 2024

Mapping spatial data is essential for the monitoring of flooded areas, prognosis hazards and prevention flood risks. The Ganges River Delta, Bangladesh, world’s largest river delta prone to floods that impact social–natural systems through losses lives damage infrastructure landscapes. Millions people living in this region are vulnerable repetitive due exposure, high susceptibility low resilience. Cumulative effects monsoon climate, rainfall, tropical cyclones hydrogeologic setting Delta increase probability floods. While engineering methods mitigation include practical solutions (technical construction dams, bridges hydraulic drains), regulation traffic land planning support systems, geoinformation rely on modelling remote sensing (RS) evaluate dynamics hazards. Geoinformation indispensable mapping catchments areas visualization affected regions real-time monitoring, addition implementing developing emergency plans vulnerability assessment warning supported by RS data. In regard, study used monitor southern segment Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated (March) post-flood (November) periods analysis extent landscape changes. Deep Learning (DL) algorithms GRASS GIS modules qualitative quantitative as advanced image processing. results constitute a series maps based classified

Language: Английский

Citations

6

Evaluating the Potential of Landsat 8/9 and Sentinel 2 Data and Different Spectral and Spatial Indices for Segment Extraction in Large Watersheds for OBIA Approach in Remote Sensing: A Case Study of the Sebou Watershed DOI
Badia Ez-zahouani, Ana Cláudia Teodoro,

Abdelhak El Kharki

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: 38, P. 101575 - 101575

Published: April 1, 2025

Language: Английский

Citations

0

Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China) DOI
Polina Lemenkova

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0

Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy DOI Creative Commons
Polina Lemenkova

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(5), P. 153 - 153

Published: May 12, 2025

This work presents the use of remote sensing data for land cover mapping with a case Central Apennines, Italy. The include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018-2024). operational workflow included image processing which were classified into raster maps automatically detected 10 classes types over tested study. approach was implemented by using set modules Geographic Resources Analysis Support System (GRASS) Information (GIS). To classify (RS) data, two approaches carried out. first is unsupervised classification based on MaxLike and clustering extracted Digital Numbers (DN) landscape feature spectral reflectance signals, second supervised performed several methods Machine Learning (ML), technically realised GRASS GIS scripting software. latter four ML algorithms embedded from Python's Scikit-Learn library. These classifiers have been to detect subtle changes as derived showing different vegetation conditions spring autumn periods central northern

Language: Английский

Citations

0

Land Cover Analysis in the Yangtze River Basin for Detection of Wetland Agriculture and Urban Dynamics in Wuhan Area (China) DOI Open Access
Polina Lemenkova

Transylvanian Review of Systematical and Ecological Research, Journal Year: 2025, Volume and Issue: 27(1), P. 1 - 16

Published: April 1, 2025

Abstract This study presents environmental analysis of the Yangtze River Basin, Wuhan region central China, performed using machine learning (ML) methods Remote Sensing (RS) data classification. The workflow is Geographic Resources Analysis Support System (GRASS) Information (GIS) scripting software for processing Landsat images by two approaches: unsupervised clustering and supervised ML algorithms. Six were taken biennially in autumn from 2013 to 2023 detect wetland changes area. article demonstrates application GIS landscape dynamics riverine lacustrine areas around River.

Language: Английский

Citations

0