Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102488 - 102488

Опубликована: Янв. 20, 2024

Язык: Английский

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Geo-spatial analysis of urbanization and environmental changes with deep neural networks: Insights from a three-decade study in Kerch peninsula

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102513 - 102513

Опубликована: Фев. 7, 2024

This study presents a comprehensive analysis of land use and cover (LULC) changes on the Kerch Peninsula over last thirty years, utilizing advanced satellite data spatial modeling techniques. The research used Landsat 5, 7 8 images to capture intricate dynamics LULC from 1990 2020. A quantitative approach was adopted, involving convolutional neural networks (CNN) for enhanced classification accuracy. methodology allowed detailed precise identification various classes, revealing significant trends transformations in region's landscape. incorporated this exploration both large-scale patterns localized changes, providing insights into drivers consequences dynamics. statistical revealed notable increase urbanized areas, coupled with decline natural ecosystems such as forests wetlands. These reflect impact sustained urban growth agricultural expansion, underscoring need informed management conservation strategies. findings contribute understanding urbanization processes their ecological implications, valuable guidance sustainable regional planning environmental protection.

Язык: Английский

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Identifying soil groups and selecting a high-accuracy classification method based on multi-textural features with optimal window sizes using remote sensing images

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102563 - 102563

Опубликована: Март 18, 2024

Determining the spatial distribution of soil groups accurately is crucial for managing resources. However, limitations persist in mapping using multi-textural features derived from remote sensing images. Identification optimal window size feature extraction and most effective classification method group recognition remains unresolved. In this study, we investigated a representative area Jiaodong Peninsula. We extracted mean entropy texture parameters various sizes (3 × 3 to 25 odd increments) Landsat 8 images determine extraction. The efficacy identifying via textural was analyzed maximum likelihood (MLC), support vector machine (SVM), artificial neural network (ANN), random forest (RF) methods ascertain suitable approach. results indicate that were 19 parameter 23 parameter. SVM outperformed MLC, ANN, RF terms accuracy. Notably, reached peak accuracy 71.61% when combining with windows. This demonstrates feasibility different information These findings have notable implications guiding digital features.

Язык: Английский

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Assessing changes in land cover, NDVI, and LST in the Sundarbans mangrove forest in Bangladesh and India: A GIS and remote sensing approach

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 36, С. 101289 - 101289

Опубликована: Июль 4, 2024

Mangrove ecosystems, although limited in diversity and area compared to tropical forests, provide essential ecological economic services, such as carbon sequestration coastal protection. The Sundarbans mangrove forest, shared by Bangladesh India, is one of the largest ecosystems world crucial for biodiversity, economy, climate regulation. Unfortunately, this ecosystem has been under severe stress over years, with alarming rates deforestation leading habitat loss a decline services. This study analyzes spatiotemporal changes forest coverage from 1973 2023 using supervised image classification on Landsat images. It also assesses relationship between Normalized Difference Vegetation Index Land Surface Temperature MODIS data which were extracted Google Earth Engine. finds that, despite denser areas, an improvement overall vegetation health visible, suggests natural resilience within forest. result shows weak but statistically significant negative correlation Index, indicating that depletion could have impact area's surface temperature. As such, regressed Temperature. results confirm Temperature, Coefficient Determination low. other factors water bodies intersect may play important role influencing paper reveals nuanced picture Sundarbans' state, both declining densities signs recovery. highlights need comprehensive conservation strategies mitigate further degradation.

Язык: Английский

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Examining the impact of land use and land cover changes on land surface temperature in Herat city using machine learning algorithms

GeoJournal, Год журнала: 2024, Номер 89(5)

Опубликована: Сен. 28, 2024

Язык: Английский

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Dynamic Estimation of Mangrove Carbon Storage in Hainan Island Based on the InVEST-PLUS Model

Forests, Год журнала: 2024, Номер 15(5), С. 750 - 750

Опубликована: Апрель 25, 2024

Mangrove ecosystems are pivotal to the global carbon budget. However, there is still a dearth of research addressing impact regional mangrove land use and cover change (LUCC) on sequestration its associated spatial distribution patterns. To investigate different development scenarios storage capacity ecosystems, we focused Hainan Island. We used LUCC data from 2010 2020 mangrove-inhabited regions. The Markov-PLUS model was applied predict spatiotemporal dynamics coverage under natural increase scenario (NIS) protection (MPS) over next 40 years. Carbon estimated using InVEST based field-measured density data. outcomes show following: (1) model, with an overall accuracy 0.88 Kappa coefficient 0.82, suitable for predicting patterns (2) Environmental factors were main drivers historical changes Island, explaining 54% variance, elevation, temperature, precipitation each contributing 13%. (3) From 2025 2065, area Island projected by approximately 12,505.68 ha, mainly through conversions forest (12.73% NIS 12.37% MPS) agricultural (39.72% 34.53% MPS). (4) increment within Island’s mangroves at 2.71 TgC whole island, notable increases expected in eastern, northern, northwestern regions, modest gains other areas. In this study, comprehensively investigated future trends offering invaluable guidance long-term management realization neutrality goals 2060.

Язык: Английский

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Monitoring and modeling vulnerability of land use changes in the current flood hazard conditions using novel hybrid GIS-based approaches and remote sensing data

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 18, 2025

Язык: Английский

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Mapping, Dynamics, and Future Change Analysis of Sundarbans Delta Using Cellular Automata and Artificial Neural Network Modeling

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 5594 - 5603

Опубликована: Янв. 1, 2024

Язык: Английский

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Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India

Journal of Earth System Science, Год журнала: 2024, Номер 133(2)

Опубликована: Июнь 5, 2024

Язык: Английский

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Land Use Change Impacts over the Indus Delta: A Case Study of Sindh Province, Pakistan

Land, Год журнала: 2024, Номер 13(7), С. 1080 - 1080

Опубликована: Июль 17, 2024

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans nature, particularly in diverse evolving landscapes. This study employs remote sensing (RS) data machine learning algorithms (MLAs) to investigate LULCC dynamics within Indus River Delta region of Sindh, Pakistan. The focus is on tracking trajectories mangrove forests associated ecosystem services over twenty years. Our findings reveal a modest improvement forest specific areas, with an increase from 0.28% 0.4%, alongside slight expansion wetland areas 2.95% 3.19%. However, significant increases cropland, increasing 22.76% 28.14%, built-up 0.71% 1.66%, pose risks such as altered sedimentation runoff patterns well habitat degradation. Additionally, decreases barren 57.10% 52.7% reduction rangeland 16.16% 13.92% indicate intensified conversion logging activities. highlights vulnerability ecosystems agricultural expansion, urbanization, resource exploitation, mismanagement. Recommendations include harmonizing developmental ambitions ecological conservation, prioritizing integrated coastal area management, reinforcing protection measures, implementing sustainable planning practices. These actions essential ensuring long-term sustainability region’s human communities.

Язык: Английский

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