MAPPING WOODLANDS IN ANGOLA, TROPICAL AFRICA: CALCULATION OF VEGETATION INDICES FROM REMOTE SENSING DATA DOI Creative Commons
Polina Lemenkova

The Journal Agriculture and Forestry, Journal Year: 2024, Volume and Issue: 70(3)

Published: Sept. 30, 2024

This paper presents the application of scripting algorithm GRASS GIS for calculation and visualization vegetation indices using satellite data.The data include images Landsat-8 OLI/TIRS covering tropical rainforests central Angola.The were acquired in July 2013 2023.The methodology is based on module 'i.vi' which automatically calculated 10 indices: DVI, NDVI, ARVI, EVI, GEMI, MSAVI2, NDWI, PVI, GARI IPVI.The algorithms processing are presented scripts.The results extracted information distribution bright green compared with other land cover types: forests coastal areas distinguished from artificial surfaces urban areas, soils shores.The indicated landscape dynamics Angola decline since until machine-based workflow increases computational efficiency through fast use scripts demonstrated that programming method automated extraction effective environmental monitoring African landscapes rainforests.

Language: Английский

Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python DOI
Polina Lemenkova

Examples and Counterexamples, Journal Year: 2025, Volume and Issue: 7, P. 100180 - 100180

Published: Feb. 3, 2025

Language: Английский

Citations

2
Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS DOI Creative Commons
Polina Lemenkova

Geomatics, Journal Year: 2025, Volume and Issue: 5(1), P. 5 - 5

Published: Jan. 20, 2025

This article presents the application of novel cartographic methods vegetation mapping with a case study Rif Mountains, northern Morocco. The area is notable for varied geomorphology and diverse landscapes. methodology includes ML modules GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, ‘r.random’ algorithms supervised classification implemented from Scikit-Learn libraries Python. approach provides platform processing spatiotemporal data satellite image analysis. objective to determine robustness “DecisionTreeClassifier” “ExtraTreesClassifier” algorithms. time series images covering Morocco consists six Landsat scenes 2023 bimonthly interval. Land cover maps are produced based on processed, classified, analyzed images. results demonstrated seasonal changes in land types. validation was performed using dataset Food Agriculture Organization (FAO). contributes environmental monitoring North Africa processing. Using RS combined powerful functionality FAO-derived datasets, topographic variability, moderate-scale habitat heterogeneity, distribution types have been assessed first time.

Language: Английский

Citations

1
Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review DOI Creative Commons

Souad Saidi,

Soufiane Idbraim,

Younes Karmoude

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3852 - 3852

Published: Oct. 17, 2024

Remote sensing images provide a valuable way to observe the Earth’s surface and identify objects from satellite or airborne perspective. Researchers can gain more comprehensive understanding of by using variety heterogeneous data sources, including multispectral, hyperspectral, radar, multitemporal imagery. This abundance different information over specified area offers an opportunity significantly improve change detection tasks merging fusing these sources. review explores application deep learning for in remote imagery, encompassing both homogeneous scenes. It delves into publicly available datasets specifically designed this task, analyzes selected models employed detection, current challenges trends field, concluding with look towards potential future developments.

Language: Английский

Citations

8
Disaster Management Systems: Utilizing YOLOv9 for Precise Monitoring of River Flood Flow Levels Using Video Surveillance DOI

G. Shankar,

M. Kalaiselvi Geetha,

P. Ezhumalai

et al.

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(3)

Published: March 14, 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
MAPPING WOODLANDS IN ANGOLA, TROPICAL AFRICA: CALCULATION OF VEGETATION INDICES FROM REMOTE SENSING DATA DOI Creative Commons
Polina Lemenkova

The Journal Agriculture and Forestry, Journal Year: 2024, Volume and Issue: 70(3)

Published: Sept. 30, 2024

This paper presents the application of scripting algorithm GRASS GIS for calculation and visualization vegetation indices using satellite data.The data include images Landsat-8 OLI/TIRS covering tropical rainforests central Angola.The were acquired in July 2013 2023.The methodology is based on module 'i.vi' which automatically calculated 10 indices: DVI, NDVI, ARVI, EVI, GEMI, MSAVI2, NDWI, PVI, GARI IPVI.The algorithms processing are presented scripts.The results extracted information distribution bright green compared with other land cover types: forests coastal areas distinguished from artificial surfaces urban areas, soils shores.The indicated landscape dynamics Angola decline since until machine-based workflow increases computational efficiency through fast use scripts demonstrated that programming method automated extraction effective environmental monitoring African landscapes rainforests.

Language: Английский

Citations

2