Application of spectral indices and deep learning (convolutional neural network model) on land cover change analysis
DOI:
https://doi.org/10.61511/aes.v3i1.2025.1883Keywords:
land cover change, spectral indices, convolutional neural network (CNN), Semarang City, urbanization, remote sensing, coastal managementAbstract
Background: Understanding land cover change is crucial for sustainable urban development, particularly in rapidly growing coastal cities such as Semarang City, Central Java, Indonesia. Methods: This study investigates spatial and temporal patterns of land cover change from 2000 to 2025 by integrating multi-temporal Landsat satellite imagery, key spectral indices—namely the normalized difference vegetation index, normalized difference water index, and normalized difference built-up index—and a deep learning approach based on convolutional neural networks. Annual Landsat images were preprocessed for atmospheric correction, cloud masking, and spatial subsetting using Google Earth Engine. Adaptive thresholding was then applied to each spectral index to delineate vegetation, water bodies, and built-up areas. Findings: Quantitative analysis revealed a significant decline in vegetation cover, with the normalized difference vegetation index dropping from 53.66% (397.59 km²) in 2000 to 46.83% (346.98 km²) in 2025, driven by urban expansion and landscape conversion, especially in coastal and lowland areas. Normalized difference water index analysis indicated a reduction and fragmentation of water bodies after 2015, linked to reclamation, sedimentation, and urban encroachment. Conversely, built-up areas expanded steadily, confirming accelerated urbanization. Scatter plot and regression analyses showed strong inverse relationships among vegetation, water, and built-up land, emphasizing ecological trade-offs and the loss of green-blue infrastructure. Conclusion: To enhance classification accuracy, a convolutional neural network was trained and validated on image patches, achieving a validation accuracy of 60%—outperforming conventional threshold-based methods by better capturing complex spatial patterns. The integrated remote sensing and deep learning framework offers robust potential for long-term, large-area land cover monitoring. Novelty/Originality of this article: The novelty of this research lies in its combined use of spectral indices and deep learning for multi-decadal land cover change analysis, providing a transferable methodology for other rapidly urbanizing coastal cities.
References
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. OSDI 16: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, 265–283. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience & Remote Sensing, 57(1), 1–20. https://doi.org/10.1080/15481603.2019.1650447
Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., ... & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3008513
Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Apress. https://link.springer.com/book/10.1007/978-1-4842-4470-8
Chen, X., et al. (2023). Urban land use change detection using attention-based convolutional neural networks and multitemporal satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 119, 103326. https://doi.org/10.1016/j.jag.2023.103326
Chollet, F. (2021). Deep Learning with Python (Second Edition). Manning Publications. https://www.manning.com/books/deep-learning-with-python-second-edition
Firman, T. (2009). The continuity and change in mega-urbanization in Indonesia: A survey of Jakarta–Bandung Region (JBR) development. Habitat International, 33(4), 327–339. https://doi.org/10.1016/j.habitatint.2008.08.007
Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Gao, X., et al. (2023). Improved urban expansion mapping from Landsat time series using temporal attention-based deep neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 199, 72–85. https://doi.org/10.1016/j.isprsjprs.2023.05.011
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. Science, 319(5864), 756–760. https://doi.org/10.1126/science.1150195
Kaimaris, D., Patias, P., Stylianidis, E., & Georgoula, O. (2019). Urban land cover mapping using NDBI and NDVI indices. Urban Science, 3(2), 53. https://doi.org/10.3390/urbansci3020053
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. https://arxiv.org/abs/1412.6980
Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509. https://doi.org/10.3390/rs10101509
Li, M., Fang, S., Ma, X., & Zhang, X. (2020). Land use and land cover change in coastal zone: A case study of the Yangtze River Delta, China. Ecological Indicators, 118, 106771. https://doi.org/10.1016/j.ecolind.2020.106771
Li, S., Du, Q., Sun, Y., & Gong, W. (2021). Improved land cover classification with convolutional neural network and Sentinel-2 imagery. Remote Sensing, 13(3), 434. https://doi.org/10.3390/rs13030434
Lin, Y., et al. (2024). A review of deep learning methods for land use/land cover classification and change detection. ISPRS International Journal of Geo-Information, 13(1), 17. https://doi.org/10.3390/ijgi13010017
Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Zhang, Y., & Homayouni, S. (2021). Meta-analysis of deep learning approaches for land cover mapping using remote sensing data. Remote Sensing, 13(19), 3876. https://doi.org/10.3390/rs13193876
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714
Neumann, B., Vafeidis, A. T., Zimmermann, J., & Nicholls, R. J. (2015). Future coastal population growth and exposure to sea-level rise and coastal flooding: A global assessment. PLOS ONE, 10(3), e0118571. https://doi.org/10.1371/journal.pone.0118571
Prechelt, L. (1998). Early stopping—But when? Neural Networks: Tricks of the Trade, 55–69. https://link.springer.com/chapter/10.1007/3-540-49430-8_3
Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multi-temporal Landsat imagery. Remote Sensing, 6(5), 4173–4189. https://doi.org/10.3390/rs6054173
Santoso, H., Nugroho, R. A., & Dewi, K. A. (2022). Urban land cover change and its impact on water bodies in Jakarta Metropolitan Area. Sustainability, 14(4), 2135. https://doi.org/10.3390/su14042135
Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6(8), e23777. https://doi.org/10.1371/journal.pone.0023777
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958. http://www.jmlr.org/papers/v15/srivastava14a.html
Wang, Q., Li, P., Chen, H., & Liu, Y. (2022). Deep learning for land use and land cover change detection: Recent progress and future challenges. Remote Sensing of Environment, 264, 112566. https://doi.org/10.1016/j.rse.2021.112566
White, J. C., Wulder, M. A., Hermosilla, T., Coops, N. C., & Hobart, G. W. (2014). Pixel-based image compositing for large-area dense time series applications and science. Remote Sensing of Environment, 141, 275–292. https://doi.org/10.1016/j.rse.2013.11.021
Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 1353691. https://doi.org/10.1155/2017/1353691
Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Yao, Y., Zhang, J., Liu, X., Liu, P., & Sun, X. (2021). Urban expansion and its driving forces in China’s megacities: A multi-temporal analysis based on remote sensing and census data. Remote Sensing, 13(12), 2304. https://doi.org/10.3390/rs13122304
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987
Zhu, X., & Woodcock, C. E. (2016). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 152, 217–234. https://doi.org/10.1016/j.rse.2014.12.001
Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36. https://doi.org/10.1109/MGRS.2017.2762307
Published
How to Cite
Issue
Section
Citation Check
License
Copyright (c) 2025 Nur ‘Izzatul Hikmah, Parluhutan Manurung
This work is licensed under a Creative Commons Attribution 4.0 International License.
