Machine learning methods of satellite image analysis for mapping geologic landforms in Niger: A comparison of the Aïr mountains, Niger River basin and Djado Plateau DOI Creative Commons
Polina Lemenkova

Podzemni radovi, Journal Year: 2024, Volume and Issue: 45, P. 27 - 47

Published: Jan. 1, 2024

This study analyses geological landforms and land cover types of Niger using spaceborne data. A landlocked African country rich in structures, is notable for contrasting environmental regions which were examined compared: 1) lowlands (Niger River basin); 2) Aïr Mountains; 3) Djado Plateau. The methodology based on machine learning (ML) models programming applied Earth observation Spatio-temporal analysis was performed Landsat 8-9 OLI-TIRS multispectral images classified by GRASS GIS. Data processed scripts ML algorithms modules r.random, r.learn.train, r.learn.predict, i.cluster, i.maxlik. probabilistic forecasting included support vector (SVM), random forest (RF), decision tree classifier K neighbors classifier. Variations landscapes caused water deficit soil erosion analyzed, parallels between geologic setting drawn. intra-landscape variability patches within revealed from 2014 to 2024. Landscape patterns are affected drought periods central Niger, mountains, distribution crust Karst pits sinkholes Eastern Niger. Western region the basin shown linked hydrological effects erosion. paper shows use methods geological-environmental analysis.

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

5
Landscape Fragmentation and Deforestation in Sierra Leone, West Africa, Analysed Using Satellite Images DOI Open Access
Polina Lemenkova

Transylvanian Review of Systematical and Ecological Research, Journal Year: 2024, Volume and Issue: 26(1), P. 13 - 26

Published: April 1, 2024

Abstract Monitoring rainforests in West Africa is necessary for natural resource management. Remote sensing valuable mapping tropical ecosystems and evaluation of landscape heterogeneity. This study presents analysis Sierra Leone which affects wildlife habitats biodiversity. Methods include modules “r.mapcalc”, “r.li.mps”, “r.li.edgedensity”, “r.forestfrag” GRASS GIS satellite image processing by computation mean patch size, edge density index fragmentation with six levels: exterior, patch, transitional, edge, perforated, interior. The results demonstrate increased deforestation over a 10-year period (2013 to 2023).

Language: Английский

Citations

5
Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data DOI Creative Commons
Polina Lemenkova

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(5), P. 709 - 709

Published: April 25, 2024

This study presents the environmental mapping of Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed machine learning (ML) methods. The largest brackish water lagoon in Asia, Lake, is a wetland international importance included Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds rare species. vulnerable ecosystems Lagoon are subject climate effects (monsoon effects) anthropogenic activities (overexploitation through fishing pollution by microplastics). Such pressure results eutrophication lake, erosion, fluctuations size, changes land cover types surrounding landscapes. monitoring lagoons complex difficult implement with conventional Geographic Information System (GIS) In particular, landscape variability, patch fragmentation, dynamics play crucial role along eastern coasts Bay Bengal, which strongly affected Indian monsoon system, controls precipitation pattern ecosystem structure. To improve methods areas, this employs ML Artificial Neural Networks (ANNs), present powerful tool computer vision, image classification, analysis Earth Observation (EO) data. Multispectral data were several classification methods, including Random Forest (RF), Support Vector Machine (SVM), ANN-based MultiLayer Perceptron (MLP) Classifier. compared discussed. approach outperformed other terms accuracy precision mapping. Ten classes around identified via spatio-temporal variations from 2019 until 2024. provides ML-based maps implemented Resources Analysis (GRASS) GIS software aims support processes over India.

Language: Английский

Citations

3
Approche cartographique par le SIG GRASS pour l'analyse de la structure du paysage au Libéria, Afrique de l'Ouest DOI
Polina Lemenkova

Dynamiques environnementales, Journal Year: 2024, Volume and Issue: 53, P. 1 - 36

Published: Jan. 1, 2024

L'extraction automatique des caractéristiques du paysage est de plus en rendue possible grâce à l'utilisation croissante algorithmes SIG et méthodes avancées d'analyse données géospatiales. L'article aborde le potentiel GRASS pour l'analyse la géométrie unités travers calcul raster. Les ont été obtenues se basant sur images satellites classifiées Libéria, Afrique l'Ouest, entre 2014 2023. La dynamique a analysée dans les changements diachroniques six indices indiquant déforestation au Libéria : indice densité paysagères, forme, numéro paysage, écart type, coefficient variation plage patch. L'analyse numérique effectuée techniquement utilisant script par modules suivants r.li.patchdensity, r.li.shape, r.li.patchnum, r.li.padsd, r.li.padcv r.li.padrange. A l’échelle parcellaire, l’indice forme passé 2,86 4,09 2023, ce qui indique l’augmentation somme longueurs bords donc une fragmentation accrue parcellaire. courbure zone paysagère séparabilité éléments individuels indiquent également processus mais forêts. Cette différentes échelles s’accompagne d’une baisse notable part forêt dense, information prouvée valeurs inférieures patchs 2023 (1,15) qu’en (1,68). En global, superficie forestière réduite 12 % suggère un taux annuel moyen 0,9 Liberia.

Citations

0
Machine learning methods of satellite image analysis for mapping geologic landforms in Niger: A comparison of the Aïr mountains, Niger River basin and Djado Plateau DOI Creative Commons
Polina Lemenkova

Podzemni radovi, Journal Year: 2024, Volume and Issue: 45, P. 27 - 47

Published: Jan. 1, 2024

This study analyses geological landforms and land cover types of Niger using spaceborne data. A landlocked African country rich in structures, is notable for contrasting environmental regions which were examined compared: 1) lowlands (Niger River basin); 2) Aïr Mountains; 3) Djado Plateau. The methodology based on machine learning (ML) models programming applied Earth observation Spatio-temporal analysis was performed Landsat 8-9 OLI-TIRS multispectral images classified by GRASS GIS. Data processed scripts ML algorithms modules r.random, r.learn.train, r.learn.predict, i.cluster, i.maxlik. probabilistic forecasting included support vector (SVM), random forest (RF), decision tree classifier K neighbors classifier. Variations landscapes caused water deficit soil erosion analyzed, parallels between geologic setting drawn. intra-landscape variability patches within revealed from 2014 to 2024. Landscape patterns are affected drought periods central Niger, mountains, distribution crust Karst pits sinkholes Eastern Niger. Western region the basin shown linked hydrological effects erosion. paper shows use methods geological-environmental analysis.

Language: Английский

Citations

0