Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin

Environmental Science and Pollution Research, Год журнала: 2022, Номер 29(57), С. 86337 - 86348

Опубликована: Фев. 3, 2022

Язык: Английский

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Development of a map for land use and land cover classification of the Northern Border Region using remote sensing and GIS

The Egyptian Journal of Remote Sensing and Space Science, Год журнала: 2023, Номер 26(2), С. 341 - 350

Опубликована: Май 12, 2023

The land use and cover study (LULC) play an essential role in regional socio-economic development natural resource management to develop sustainable vegetation changes, water quantity quality, resources, coastal management. This uses remote sensing data investigate LULC the Northern Border Region (NBR) Kingdom of Saudi Arabia. purpose this is obtain a better understanding patterns drivers changes NBR over past three decades. Remote from Landsat imagery between 1990 2022 were used classify types, time series analysis was performed using detect time. classification finds four main classes: bare land, built-up area, rocks, vegetation. results indicate significant increase urban development. outcomes revealed that most urbanization occurred outskirts cities, where previously there soil lands. population growth economic These findings have important implications for city planning, green spaces, cities. Maximum Likelihood classifier perform classification. accuracy assessment demonstrated satisfactory results, with overall 92.6%. paves way further monitoring geographic location. technique adequate address objectives study.

Язык: Английский

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Assessment of land use land cover change and its effects using artificial neural network-based cellular automation

Journal of Engineering and Applied Science, Год журнала: 2024, Номер 71(1)

Опубликована: Март 14, 2024

Abstract The challenge of urban growth and land use cover (LULC) change is particularly critical in developing countries. remote sensing GIS has helped to generate LULC thematic maps, which have proven immensely valuable resource land-use management, facilitating sustainable development by balancing developmental interests conservation measures. research utilized socio-economic spatial variables such as slope, elevation, distance from streams, roads, built-up areas, the center town determine their impact on 2016 2019. integrates Artificial Neural Network with Cellular Automta forecast establish potential changes for years 2025 2040. Comparison between predicted actual maps 2022 indicates high agreement kappa hat 0.77 a percentage correctness 86.83%. study that area will increase 8.37 km 2 2040, resulting reduction 7.08 1.16 protected agricultural respectively. These findings assist planners lawmakers adopt management strategies balance expansion natural resources leading cities.

Язык: Английский

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Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models

The Science of The Total Environment, Год журнала: 2020, Номер 734, С. 139474 - 139474

Опубликована: Май 15, 2020

Язык: Английский

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Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms

Energy and Buildings, Год журнала: 2022, Номер 275, С. 112452 - 112452

Опубликована: Сен. 10, 2022

Язык: Английский

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Machine learning-based monitoring and modeling for spatio-temporal urban growth of Islamabad

The Egyptian Journal of Remote Sensing and Space Science, Год журнала: 2022, Номер 25(2), С. 541 - 550

Опубликована: Март 31, 2022

LULC maps are important thematic that provide a baseline for monitoring, assessing, and planning activities. This study incorporates spatio-temporal land use/ cover (LULC) monitoring (1991–2021) urban growth modeling (2021–2041) of Islamabad, Pakistan to deduce the changes in various classes past future by incorporating realistic influential layers Artificial Neural Network-Cellular Automata (ANN-CA) machine learning algorithms. Three decades Landsat satellite imagery were used classify using random forest algorithm with high Kappa indexes ranging from 0.93 0.97. Simulations 2011 2021 done well-calibration model (>0.85) spatial similarity (>75%) MOLUSCE plugin QGIS software. Future predictions years 2031 2041 analyze patterns. The satellite-based during 1991–2021 exhibited 142.4 km2 increase net growth. had detrimental effects on other classes: decrease forests 38.4 waterbodies 2.9 km2. projected areas 2021–2041 will be 58.2 Visual sprawl assessment was highlight type sprawls. Overall, it sensed city's urbanization has been unplanned erratic; leading dire consequences environmental systems. Therefore, necessitates better enforcing policies necessary measures.

Язык: Английский

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Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS MOLUSCE plugin

Spatial Information Research, Год журнала: 2023, Номер 31(4), С. 429 - 438

Опубликована: Фев. 27, 2023

Язык: Английский

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Classification of land use/land cover using artificial intelligence (ANN-RF)

Frontiers in Artificial Intelligence, Год журнала: 2023, Номер 5

Опубликована: Янв. 6, 2023

Because deep learning has various downsides, such as complexity, expense, and the need to wait longer for results, this creates a significant incentive impetus invent adopt notion of developing machine because it is simple. This study intended increase accuracy machine-learning approaches land use/land cover classification using Sentinel-2A, Landsat-8 satellites. aimed implement proposed method, neural-based with object-based, produce model addressed by artificial neural networks (limited parameters) random forest (hyperparameter) called ANN_RF. used multispectral satellite images (Sentinel-2A Landsat-8) normalized digital elevation input datasets Sana'a city map 2016. The results showed that (ANN_RF) better than ANN classifier Sentinel-2A satellites individually, which may contribute development through newer researchers specialists; also conventionally developed traditional seven ten layers but access 1,000's millions simulated neurons without resorting techniques (ANN_RF).

Язык: Английский

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Spatiotemporal LULC change detection and future prediction for the Mand catchment using MOLUSCE tool

Environmental Earth Sciences, Год журнала: 2024, Номер 83(2)

Опубликована: Янв. 1, 2024

Язык: Английский

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Modelling of soil erosion susceptibility incorporating sediment connectivity and export at landscape scale using integrated machine learning, InVEST-SDR and Fragstats

Journal of Environmental Management, Год журнала: 2024, Номер 353, С. 120164 - 120164

Опубликована: Янв. 31, 2024

Язык: Английский

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