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Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network

The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 199...

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Main Authors: Bao Pham Q., Ajim Ali S., Parvin F., Van On V., Mohd Sidek L., ?urin B., Cetl V., ?amanovi? S., Nguyet Minh N.
Other Authors: 57208495034
Format: Article
Published: Elsevier Ltd 2025
Subjects:
Forecasting
Forestry
Land use
Multilayer neural networks
Remote sensing
Binh duong province
Land cover maps
Land use/land cover
Land-use land-cover changes
Land-use prediction
Landuse change
Multi-spectral
Random forests
Remote sensing and GIS
Remote-sensing
Geographic information systems
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spelling my.uniten.dspace-365542025-03-03T15:43:03Z Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network Bao Pham Q. Ajim Ali S. Parvin F. Van On V. Mohd Sidek L. ?urin B. Cetl V. ?amanovi? S. Nguyet Minh N. 57208495034 57208693930 57210162430 56803785700 58617132200 55596817500 23471894000 57190570766 58591026000 Forecasting Forestry Land use Multilayer neural networks Remote sensing Binh duong province Land cover maps Land use/land cover Land-use land-cover changes Land-use prediction Landuse change Multi-spectral Random forests Remote sensing and GIS Remote-sensing Geographic information systems The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. ? 2024 Final 2025-03-03T07:43:03Z 2025-03-03T07:43:03Z 2024 Article 10.1016/j.asr.2024.03.027 2-s2.0-85191613022 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191613022&doi=10.1016%2fj.asr.2024.03.027&partnerID=40&md5=a7d02e76db581da686e4571599a03c7d https://irepository.uniten.edu.my/handle/123456789/36554 74 1 17 47 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Forecasting
Forestry
Land use
Multilayer neural networks
Remote sensing
Binh duong province
Land cover maps
Land use/land cover
Land-use land-cover changes
Land-use prediction
Landuse change
Multi-spectral
Random forests
Remote sensing and GIS
Remote-sensing
Geographic information systems
spellingShingle Forecasting
Forestry
Land use
Multilayer neural networks
Remote sensing
Binh duong province
Land cover maps
Land use/land cover
Land-use land-cover changes
Land-use prediction
Landuse change
Multi-spectral
Random forests
Remote sensing and GIS
Remote-sensing
Geographic information systems
Bao Pham Q.
Ajim Ali S.
Parvin F.
Van On V.
Mohd Sidek L.
?urin B.
Cetl V.
?amanovi? S.
Nguyet Minh N.
Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
description The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. ? 2024
author2 57208495034
author_facet 57208495034
Bao Pham Q.
Ajim Ali S.
Parvin F.
Van On V.
Mohd Sidek L.
?urin B.
Cetl V.
?amanovi? S.
Nguyet Minh N.
format Article
author Bao Pham Q.
Ajim Ali S.
Parvin F.
Van On V.
Mohd Sidek L.
?urin B.
Cetl V.
?amanovi? S.
Nguyet Minh N.
author_sort Bao Pham Q.
title Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
title_short Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
title_full Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
title_fullStr Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
title_full_unstemmed Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
title_sort multi-spectral remote sensing and gis-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network
publisher Elsevier Ltd
publishDate 2025
_version_ 1825816110373535744
score 13.244413

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