Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(4), P. 871 - 871

Published: April 20, 2023

This paper addresses the issue of satellite image processing using GRASS GIS in mangrove forests Niger River Delta, southern Nigeria. The estuary Delta Gulf Guinea is an essential hotspot biodiversity on western coast Africa. At same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems result rapid decline habitats. motivates monitoring vegetation patterns advanced cartographic methods data analysis. As a response to this need, study aimed calculate map several indices (VI) scripts as programming integrated geospatial studies. include four Landsat 8-9 OLI/TIRS images covering segment Bight Benin for 2013, 2015, 2021, 2022. techniques included ’i.vi’, ’i.landsat.toar’ other modules GIS. Based ’i.vi’ module, ten VI were computed mapped estuary: Atmospherically Resistant Vegetation Index (ARVI), Green (GARI), (GVI), Difference (DVI), Perpendicular (PVI), Global Environmental Monitoring (GEMI), Normalized Water (NDWI), Second Modified Soil Adjusted (MSAVI2), Infrared Percentage (IPVI), Enhanced (EVI). results showed variations habitats situated over last decade well increase urban areas (Onitsha, Sapele, Warri City) settlements State due urbanization. analysis enabled us identify visualize changes patterns. technical excellence was demonstrated used study.

Language: Английский

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

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

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Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

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

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Mastering Geospatial Analysis With Python

Advances in geospatial technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 120 - 149

Published: April 29, 2024

This chapter explores the intricate realm of geospatial analysis leveraging power Python. embarks on a journey through fundamentals data types, formats, and sources, laying robust foundation for navigating complexities spatial analysis. Key Python libraries such as Geopandas, GDAL, Fiona are meticulously dissected, elucidating their pivotal roles in processing, analyzing, visualizing data. Matplotlib's contribution to visualization adds insight, enhancing information's communicative power. Furthermore, delves into integration techniques, showcasing how seamlessly integrates with GIS tools extend, customize, streamline analyses. By unraveling functionalities these essential tools, this equips readers knowledge skills necessary master

Language: Английский

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Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa

Die Bodenkultur Journal of Land Management Food and Environment, Journal Year: 2023, Volume and Issue: 74(1), P. 49 - 64

Published: March 1, 2023

Summary Lake Chad, situated in the semi-arid region of African Sahel, plays a vital role hydrogeological balance regional ecosystems. It presents an essential water source and provides habitat for rare wildlife species including migrating waterbirds. However, lake has shrunk significantly since 1960s continued to reduce size extent during recent decades. Trends drying shrinking Chad are caused by environmental factors changed climate. The desiccation is threatening sustainability. This study focused on identification changes area, wetland extent, associated land cover types. methods include Geographic Resources Analysis Support System (GRASS) Information (GIS) remote sensing data classification. maximum likelihood discriminant analysis classifier was applied multispectral Landsat 8–9 OLI/TIRS images 2013, 2017, 2022. Detected types reflect variations area around over Cartographic scripting tools GRASS GIS provide efficient method digital image processing monitoring endorheic lakes Central Africa. opportunity automatically classify Earth observation with cartographic scripts monitoring.

Language: Английский

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Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Analytics, Journal Year: 2023, Volume and Issue: 2(3), P. 745 - 780

Published: Sept. 21, 2023

This paper presents the object detection algorithms GRASS GIS applied for Landsat 8-9 OLI/TIRS data. The study area includes Sudd wetlands located in South Sudan. describes a programming method automated processing of satellite images environmental analytics, applying scripting GIS. documents how land cover changed and developed over time Sudan with varying climate settings, indicating variations landscape patterns. A set modules was used to process by language. It streamlines geospatial tasks. functionality image is called within scripts as subprocesses which automate operations. cutting-edge tools present cost-effective solution remote sensing data modelling analysis. based on discrimination spectral reflectance pixels raster scenes. Scripting syntax are run from terminal, enabling pass commands module. ensures automation high speed processing. algorithm challenge that patterns differ substantially, there nonlinear dynamics types due factors effects. Time series analysis several multispectral demonstrated changes Sudd, affected degradation landscapes. map generated each 2015 2023 using 481 maximum-likelihood discriminant approaches classification. methodology segmentation ‘i.segment’ module, clustering classification ‘i.cluster’ ‘i.maxlike’ modules, accuracy assessment ‘r.kappa’ computing NDVI cartographic mapping implemented benefits techniques reported effects various threshold levels segmentation. performed 371 times 90% minsize = 5; converged 37 41 iterations. following segments defined images: 4515 2015, 4813 2016, 4114 2017, 5090 2018, 6021 2019, 3187 2020, 2445 2022, 5181 2023. percent convergence 98% processed images. Detecting possible spaceborne datasets advanced applications algorithms. implications approach discussed. wrapper functions

Language: Английский

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Risk Colored Snake (RCS): An Innovative Method for Evaluating Flooding Risk of Linear Hydraulic Infrastructures

Water, Journal Year: 2024, Volume and Issue: 16(3), P. 506 - 506

Published: Feb. 5, 2024

Floods are probably the most hazardous global natural event as well main cause of human losses and economic damage. They often hard to predict, but their consequences may be reduced by taking right precautions. In this sense, hydraulic infrastructures, such dams, generally widely used management elements significantly mitigate risk. However, others, linear ones, mainly ditches canals, can both in themselves potentially active risk-generating factors vectors flooding risk propagation. The aim research is develop an accurate detailed technique for assessing intrinsic these infrastructures due flood events. This performed based on two key factors: proximity urban areas water level reached infrastructures. Consequently, developed through a double geomatic component organized into four steps: topological processing, parameter computation, calculation, development Risk Colored Snake (RCS) technique. was successfully applied network irrigation Almoradí Alicante (Spain), which characterized high exposure hazards. RCS valuable tool easily assess potential each section By means color-coding RCS, it simpler end user quickly detect problematic locations manner.

Language: Английский

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Unsupervised burned areas detection using multitemporal synthetic aperture radar data

Journal of Applied Remote Sensing, Journal Year: 2024, Volume and Issue: 18(01)

Published: Feb. 9, 2024

Climate change is a critical concern that has been greatly affected by human activities, resulting in rise greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as surge the frequency severity fires. This paper presents data-driven framework unifies time series remote sensing images, statistical modeling, unsupervised classification for mapping fire-damaged areas. To validate proposed methodology, multiple images acquired Sentinel-1 satellite between August October 2021 were collected analyzed two case studies comprising Brazilian biomes burns. Our results demonstrate approach outperforms another method evaluated terms precision metrics visual adherence. methodology achieves highest overall accuracy 58.15% F1 score 0.72, which are higher than other method. These findings suggest our more effective detecting burned areas may practical applications environmental issues landslides, flooding, deforestation.

Language: Английский

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The Research of Collaborative System of Remote Sensing Monitoring Based on Bimodal Cloud

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences, Journal Year: 2024, Volume and Issue: XLVIII-1-2024, P. 805 - 812

Published: May 11, 2024

Abstract. Cloud service is based on cloud computing, Offering a On-Demand to every terminal equipment of computing resource pool. This paper designed and developed coordinated operating system bimodal cloud. taken mutual scheduling mechanism into account, which capable storing massive amounts heterogeneous remote sensing data provides fast indexing various characteristics, integrated Satellite transit forecast, DOM Produce, change information extraction results sharing Nginx load balancing, in addition, the two layer security ensure safety results.The "YunYao" geographic rendering engine built dual-state platform significantly outperforms mainstream platforms same testing environment. Its speed surpasses ArcGIS Desktop by more than times, exceeds GeoServer four over seven times faster Server. Remote practitioners can quickly conveniently utilize this system, while providing convenient functionalities that enable scientists independently conduct scientific research development using system. Experimentation practice shows simplified routine work flow, improved efficiency, has important reference meaning monitoring.

Language: Английский

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Enhanced Remote Sensing Monitoring Through a Bimodal Cloud Infrastructure: A Dual-State Cloud Service Approach

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 97405 - 97416

Published: Jan. 1, 2024

This study addresses significant challenges within the field of remote sensing monitoring, including operational inefficiency, data confidentiality concerns, high hardware costs, and issues with management distribution. To tackle these problems, we introduce a synergistic monitoring framework that leverages bimodal cloud infrastructure, facilitated by services to provide on-demand resource allocation efficient management. Our research focuses on designing developing an integrated operating system optimizes processes enhances efficiency through use mutual scheduling mechanism rapid indexing capabilities. The is underpinned dual-state service mechanism, combining Memory Cloud (Flash Cloud) known for its high-speed processing Storage (Persistent long-term retention. approach establishes multi-level caching ensure quick access frequently requested spatial data. Additionally, two-tier security implemented safeguard integrity confidentiality. "YunYao" geographic information rendering engine, this platform, demonstrated remarkable performance advantages over mainstream platforms in identical testing environments. Specifically, it outperformed ArcGIS Desktop two times, exceeded GeoServer more than four was seven times faster Server speeds. Experimental practical applications have shown our streamlines routine workflows work efficiency, making critical reference monitoring. Furthermore, comparative analysis conducted quantitatively demonstrate superior method handling large volumes data(Including satellite imagery UAV imagery). Despite advancements, integration technology requires further development, particularly regarding establishment private clouds internal collaborative computing mechanisms domain. paves way future advancements eventual full models into

Language: Английский

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