Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico
This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the peri...
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Copernicus Publications
2024-11-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-3-2024/371/2024/isprs-annals-X-3-2024-371-2024.pdf |
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| author | K. D. Rodríguez González L. E. Arista Cázares F. D. Yépez Rincón |
| author_facet | K. D. Rodríguez González L. E. Arista Cázares F. D. Yépez Rincón |
| author_sort | K. D. Rodríguez González |
| collection | DOAJ |
| description | This study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development. |
| format | Article |
| id | doaj-art-5cb12997ea784b1e80000536a6dfe32c |
| institution | OA Journals |
| issn | 2194-9042 2194-9050 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-5cb12997ea784b1e80000536a6dfe32c2025-08-20T02:18:42ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502024-11-01X-3-202437137510.5194/isprs-annals-X-3-2024-371-2024Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, MexicoK. D. Rodríguez González0L. E. Arista Cázares1F. D. Yépez Rincón2Geomatics Department, Instituto de Ingeniería Civil, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, MexicoGeomatics Department, Instituto de Ingeniería Civil, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, MexicoGeomatics Department, Instituto de Ingeniería Civil, Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza, MexicoThis study evaluates four Machine Learning Algorithms—Random Forest (RF), K-Means Clustering, Support Vector Machine (SVM), and Classification and Regression Trees (CART)—for precise land use and land cover (LULC) classification in the Monterrey Metropolitan Area. During the period 2016-2019, and with alternating wet and dry season classifications, the research addresses challenges in identifying narrow rivers, using geospatial tools and it does notably the Pesqueria River, which is specially the most narrow and shallow river in the area. Five classes—Water, Vegetation, Urban, and Soil—were classified, achieving precision rates above 85%. Remarkably, SVM exhibited an excellent accuracy, particularly for narrow rivers, showcasing its utility in complex urban landscapes. The study utilizes high resolution satellite imagery with a spatial resolution of 4.7m, contributing to the reliability of the results. Emphasizing temporal dynamics, the research links LULC changes to urbanization, infrastructure, and seasonal variations, offering vital insights for sustainable urban development.https://isprs-annals.copernicus.org/articles/X-3-2024/371/2024/isprs-annals-X-3-2024-371-2024.pdf |
| spellingShingle | K. D. Rodríguez González L. E. Arista Cázares F. D. Yépez Rincón Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico |
| title_full | Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico |
| title_fullStr | Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico |
| title_full_unstemmed | Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico |
| title_short | Spatiotemporal land use land cover (LULC) change analysis of urban narrow river using Google Earth Engine and Machine learning algorithms in Monterrey, Mexico |
| title_sort | spatiotemporal land use land cover lulc change analysis of urban narrow river using google earth engine and machine learning algorithms in monterrey mexico |
| url | https://isprs-annals.copernicus.org/articles/X-3-2024/371/2024/isprs-annals-X-3-2024-371-2024.pdf |
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