Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two platforms: Google Earth Engine (GEE) and ENVI. Sentinel-2 satellite imagery from 2017, 2020, and 2023 was utilized to classify four major land cover classes (water, bareland, built-up, and vegetation) using supervised classification techniques. Preprocessing included cloud masking, filtering, and subsetting, while training samples were generated from high-resolution Google Earth Pro and Copernicus Sentinel imagery. Accuracy was assessed using user’s accuracy, producer’s accuracy, overall accuracy, and the Kappa coefficient derived from confusion matrices. Results indicate a steady increase in built-up areas (from 24.75% in 2017 to 37.06% in 2023) and bareland, alongside a marked decline in vegetation. The RF algorithm in GEE achieved the highest performance with an overall accuracy of 98.43% and Kappa coefficient of 0.9773 in 2023, demonstrating strong stability across all years. SVM, while slightly less consistent, achieved 97.6% user accuracy and 98.8% producer accuracy for the water class in 2023, outperforming RF in that category. ENVI-based SVM models attained an overall accuracy of 91.96% and Kappa coefficient of 0.8862, performing well for vegetation but showing slightly lower robustness than RF in GEE. In conclusion, the integration of cloud-based (GEE) and desktop (ENVI) remote sensing platforms with machine learning algorithms proved highly effective for large-scale urban monitoring. The findings highlight rapid urban expansion and vegetation loss in Kathmandu and offer valuable insights for sustainable urban planning and environmental management.
| Published in | American Journal of Environmental Science and Engineering (Volume 9, Issue 4) |
| DOI | 10.11648/j.ajese.20250904.12 |
| Page(s) | 167-182 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Land Cover Change, Google Earth Engine (GEE), ENVI, SVM, RF, Urban Expansion
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APA Style
Bisht, B. S., Subedi, N., Bisht, G., Yadav, A. (2025). A Multi-Temporal Land Cover Analysis of Kathmandu Using Google Earth Engine and ENVI: A Comparative Study of SVM and RF Algorithms. American Journal of Environmental Science and Engineering, 9(4), 167-182. https://doi.org/10.11648/j.ajese.20250904.12
ACS Style
Bisht, B. S.; Subedi, N.; Bisht, G.; Yadav, A. A Multi-Temporal Land Cover Analysis of Kathmandu Using Google Earth Engine and ENVI: A Comparative Study of SVM and RF Algorithms. Am. J. Environ. Sci. Eng. 2025, 9(4), 167-182. doi: 10.11648/j.ajese.20250904.12
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Download@article{10.11648/j.ajese.20250904.12,
author = {Bhuwan Singh Bisht and Nabaraj Subedi and Gaurav Bisht and Amit Yadav},
title = {A Multi-Temporal Land Cover Analysis of Kathmandu Using Google Earth Engine and ENVI: A Comparative Study of SVM and RF Algorithms
},
journal = {American Journal of Environmental Science and Engineering},
volume = {9},
number = {4},
pages = {167-182},
doi = {10.11648/j.ajese.20250904.12},
url = {https://doi.org/10.11648/j.ajese.20250904.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20250904.12},
abstract = {Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two platforms: Google Earth Engine (GEE) and ENVI. Sentinel-2 satellite imagery from 2017, 2020, and 2023 was utilized to classify four major land cover classes (water, bareland, built-up, and vegetation) using supervised classification techniques. Preprocessing included cloud masking, filtering, and subsetting, while training samples were generated from high-resolution Google Earth Pro and Copernicus Sentinel imagery. Accuracy was assessed using user’s accuracy, producer’s accuracy, overall accuracy, and the Kappa coefficient derived from confusion matrices. Results indicate a steady increase in built-up areas (from 24.75% in 2017 to 37.06% in 2023) and bareland, alongside a marked decline in vegetation. The RF algorithm in GEE achieved the highest performance with an overall accuracy of 98.43% and Kappa coefficient of 0.9773 in 2023, demonstrating strong stability across all years. SVM, while slightly less consistent, achieved 97.6% user accuracy and 98.8% producer accuracy for the water class in 2023, outperforming RF in that category. ENVI-based SVM models attained an overall accuracy of 91.96% and Kappa coefficient of 0.8862, performing well for vegetation but showing slightly lower robustness than RF in GEE. In conclusion, the integration of cloud-based (GEE) and desktop (ENVI) remote sensing platforms with machine learning algorithms proved highly effective for large-scale urban monitoring. The findings highlight rapid urban expansion and vegetation loss in Kathmandu and offer valuable insights for sustainable urban planning and environmental management.
},
year = {2025}
}
TY - JOUR T1 - A Multi-Temporal Land Cover Analysis of Kathmandu Using Google Earth Engine and ENVI: A Comparative Study of SVM and RF Algorithms AU - Bhuwan Singh Bisht AU - Nabaraj Subedi AU - Gaurav Bisht AU - Amit Yadav Y1 - 2025/10/28 PY - 2025 N1 - https://doi.org/10.11648/j.ajese.20250904.12 DO - 10.11648/j.ajese.20250904.12 T2 - American Journal of Environmental Science and Engineering JF - American Journal of Environmental Science and Engineering JO - American Journal of Environmental Science and Engineering SP - 167 EP - 182 PB - Science Publishing Group SN - 2578-7993 UR - https://doi.org/10.11648/j.ajese.20250904.12 AB - Rapid urbanization and land cover change have emerged as major environmental concerns in developing regions, particularly within the Kathmandu District of Nepal. This study aims to analyze multi-temporal land cover changes and compare the performance of two machine learning algorithms—Support Vector Machine (SVM) and Random Forest (RF)—across two platforms: Google Earth Engine (GEE) and ENVI. Sentinel-2 satellite imagery from 2017, 2020, and 2023 was utilized to classify four major land cover classes (water, bareland, built-up, and vegetation) using supervised classification techniques. Preprocessing included cloud masking, filtering, and subsetting, while training samples were generated from high-resolution Google Earth Pro and Copernicus Sentinel imagery. Accuracy was assessed using user’s accuracy, producer’s accuracy, overall accuracy, and the Kappa coefficient derived from confusion matrices. Results indicate a steady increase in built-up areas (from 24.75% in 2017 to 37.06% in 2023) and bareland, alongside a marked decline in vegetation. The RF algorithm in GEE achieved the highest performance with an overall accuracy of 98.43% and Kappa coefficient of 0.9773 in 2023, demonstrating strong stability across all years. SVM, while slightly less consistent, achieved 97.6% user accuracy and 98.8% producer accuracy for the water class in 2023, outperforming RF in that category. ENVI-based SVM models attained an overall accuracy of 91.96% and Kappa coefficient of 0.8862, performing well for vegetation but showing slightly lower robustness than RF in GEE. In conclusion, the integration of cloud-based (GEE) and desktop (ENVI) remote sensing platforms with machine learning algorithms proved highly effective for large-scale urban monitoring. The findings highlight rapid urban expansion and vegetation loss in Kathmandu and offer valuable insights for sustainable urban planning and environmental management. VL - 9 IS - 4 ER -
Land Management Training Center, Dhulikhel, Nepal
Land Management Training Center, Dhulikhel, Nepal
Department of Geomatics Engineering, Kathmandu University, Dhulikhel, Nepal
Department of Geomatics Engineering, Kathmandu University, Dhulikhel, Nepal
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