Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine
Abstract
By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.
Keyword : GEE, remote sensing, classification, Landsat, Sentinel, satellite data
How to Cite
Srivastava, A. (2024). Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine. Geodesy and Cartography, 50(4), 162–178. https://doi.org/10.3846/gac.2024.20106
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Qiao, M., Wong, C., & Zheng, W. (2019). Sustainable urbanisation and community well-being in suburban neighbourhoods in Beijing, China. International Journal of Community Well-Being, 2(1), 15–39. https://doi.org/10.1007/s42413-019-00019-9
Serwa, A. (2012). New method for feature reduction of MSS satellite bands to produce single equivalent band. Al-Azhar University Engineering Journal, 7(1), 519–526.
Serwa, A., El-Nokrashy, M., O Ali, O., & Dief-Allah, M. A. M. (2010). New method to determine the optimum bands of MSS satellite images for unsupervised classification. Al-Azhar University Engineering Journal, 5(1), 727–735.
Serwa, A., & Elbialy, S. (2021). Enhancement of classification accuracy of multi-spectral satellites’ images using Laplacian pyramids. Egyptian Journal of Remote Sensing and Space Science, 24(2), 283–291. https://doi.org/10.1016/j.ejrs.2020.12.006
Srivastava, A., & Ahmad, P. (2016). A probabilistic Gossip-based secure protocol for unstructured P2P networks. Procedia Computer Science, 78, 595–602. https://doi.org/10.1016/j.procs.2016.02.122
Srivastava, A., & Biswas, S. (2023). Analyzing land cover changes over Landsat-7 data using Google Earth Engine. In Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy (pp. 1228–1233), Coimbatore, India. https://doi.org/10.1109/ICAIS56108.2023.10073795
Srivastava, A., & Sharma, H. (2024). AI-driven environmental monitoring using Google Earth Engine. In B. Pradhan & S. Mukhopadhyay (Eds.), Smart sensors, measurement and instrumentation: Vol. 50. IoT sensors, ML, AI and XAI: Empowering a smarter world (pp. 375–385). Springer. https://doi.org/10.1007/978-3-031-68602-3_19
Srivastava, A., Bharadwaj, S., Dubey, R., Sharma, V. B., & Biswas, S. (2022). Mapping vegetation and measuring the performance of machine learning algorithm in Lulc classification in the large area using Sentinel-2 and Landsat-8 datasets of Dehradun as a test case. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 529–535. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-529-2022
Srivastava, A., Dubey, R., & Biswas, S. (2023a). Comparison of Sentinel and Landsat data sets over Lucknow region using gradient tree boost supervised classifier. In A. Noor, K. Saroha, E. Pricop, A. Sen, & G. Trivedi (Eds.), Lecture notes in networks and systems: Vol. 730. Proceedings of third emerging trends and technologies on intelligent systems (pp. 221–232). Springer. https://doi.org/10.1007/978-981-99-3963-3_18
Srivastava, A., Umrao, S., & Biswas, S. (2023b). Exploring forest transformation by analyzing spatial-temporal attributes of vegetation using vegetation indices. International Journal of Advanced Computer Science and Applications, 14(5), 1110–1117. https://doi.org/10.14569/IJACSA.2023.01405114
Srivastava, A., Umrao, S., Biswas, S., Dubey, R., & Zafar, M. I. (2023c). FCCC: Forest cover change calculator user interface for identifying fire incidents in forest region using satellite data. International Journal of Advanced Computer Science and Applications, 14(7), 948–959. https://doi.org/10.14569/IJACSA.2023.01407103
Thenkabail, P. S., Teluguntla, P. G., Xiong, J., Oliphant, A., Congalton, R. G., Ozdogan, M., Gumma, M. K., Tilton, J. C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., & Foley, D. (2021). Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud (Professional Paper No. 1868). US Geological Survey. https://doi.org/10.3133/pp1868
Viana, C. M., Girão, I., & Rocha, J. (2019). Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing, 11(9), Article 1104. https://doi.org/10.3390/rs11091104
Campos-Taberner, M., Moreno-Martínez, Á., García-Haro, F. J., Camps-Valls, G., Robinson, N. P., Kattge, J., & Running, S. W. (2018). Global estimation of biophysical variables from Google Earth Engine platform. Remote Sensing, 10(8), Article 1167. https://doi.org/10.3390/rs10081167
El Morr, C., Jammal, M., Ali-Hassan, H., & El-Hallak, W. (2022). Support vector machine. In International series in operations research and management science: Vol. 334. Machine learning for practical decision making (pp. 385–411). Springer. https://doi.org/10.1007/978-3-031-16990-8_13
Evgeniou, T., & Pontil, M. (2014). Support vector machines: Theory and applications. In G. Paliouras, V. Karkaletsis, & C. D. Spyropoulos (Eds.), Lecture notes in computer science: Vol. 2049. Machine learning and its applications (pp. 249–257). Springer. https://doi.org/10.1007/3-540-44673-7_12
Fernandino, G., Elliff, C. I., & Silva, I. R. (2018). Ecosystem-based management of coastal zones in face of climate change impacts: Challenges and inequalities. Journal of Environmental Management, 215, 32–39. https://doi.org/10.1016/j.jenvman.2018.03.034
Forkuor, G., Dimobe, K., Serme, I., & Tondoh, J. E. (2018). Landsat-8 vs. Sentinel-2: Examining the added value of Sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience and Remote Sensing, 55(3), 331–354. https://doi.org/10.1080/15481603.2017.1370169
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
Ghanem, S., Couturier, R., & Gregori, P. (2021). An accurate and easy to interpret binary classifier based on association rules using implication intensity and majority vote. Mathematics, 9(12), Article 1315. https://doi.org/10.3390/math9121315
Goldblatt, R., Rivera Ballesteros, A., & Burney, J. (2017). High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the Semi-Arid Brazilian Sertão. Remote Sensing, 9(12), Article 1336. https://doi.org/10.3390/rs9121336
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
Gupta, K. (2015). Unprecedented growth of Dehradun urban area: A spatio-temporal analysis. https://www.researchgate.net/publication/282334185_Unprecedented_growth_of_Dehradun_urban_area_a_spatio-temporal_analysis
Jin, S., Liu, X., Yang, J., Lv, J., Gu, Y., Yan, J., Yuan, R., & Shi, Y. (2022). Spatial-temporal changes of land use/cover change and habitat quality in Sanjiang plain from 1985 to 2017. Frontiers in Environmental Science, 10, 1–12. https://doi.org/10.3389/fenvs.2022.1032584
Liu, C., Liu, Y., Lu, Y., Liao, Y., Nie, J., Yuan, X., & Chen, F. (2019). Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity. PeerJ, 6(1), Article e6240. https://doi.org/10.7717/peerj.6240
Liu, Y., Liu, L., & Yan, Y. (2020). Network topology change detection based on statistical process control. In ACM International Conference Proceeding Series (pp. 145–151). ACM. https://doi.org/10.1145/3409501.3409532
Nooni, I. K., Duker, A. A., Van Duren, I., Addae-Wireko, L., & Osei Jnr, E. M. (2014). Support vector machine to map oil palm in a heterogeneous environment. International Journal of Remote Sensing, 35(13), 4778–4794. https://doi.org/10.1080/01431161.2014.930201
Osuna, E. E., Freund, R., & Girosi, F. (1999). Support vector machines: Training and applications. https://www.researchgate.net/publication/2592728_Support_Vector_Machines_Training_and_Applications
Pintelas, P., & Livieris, I. E. (2020). Special issue on ensemble learning and applications. Algorithms, 13(6), Article 140. https://doi.org/10.3390/a13060140
Pontius, R. G., & Millones, M. (2011). Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407–4429. https://doi.org/10.1080/01431161.2011.552923
Qiao, M., Wong, C., & Zheng, W. (2019). Sustainable urbanisation and community well-being in suburban neighbourhoods in Beijing, China. International Journal of Community Well-Being, 2(1), 15–39. https://doi.org/10.1007/s42413-019-00019-9
Serwa, A. (2012). New method for feature reduction of MSS satellite bands to produce single equivalent band. Al-Azhar University Engineering Journal, 7(1), 519–526.
Serwa, A., El-Nokrashy, M., O Ali, O., & Dief-Allah, M. A. M. (2010). New method to determine the optimum bands of MSS satellite images for unsupervised classification. Al-Azhar University Engineering Journal, 5(1), 727–735.
Serwa, A., & Elbialy, S. (2021). Enhancement of classification accuracy of multi-spectral satellites’ images using Laplacian pyramids. Egyptian Journal of Remote Sensing and Space Science, 24(2), 283–291. https://doi.org/10.1016/j.ejrs.2020.12.006
Srivastava, A., & Ahmad, P. (2016). A probabilistic Gossip-based secure protocol for unstructured P2P networks. Procedia Computer Science, 78, 595–602. https://doi.org/10.1016/j.procs.2016.02.122
Srivastava, A., & Biswas, S. (2023). Analyzing land cover changes over Landsat-7 data using Google Earth Engine. In Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy (pp. 1228–1233), Coimbatore, India. https://doi.org/10.1109/ICAIS56108.2023.10073795
Srivastava, A., & Sharma, H. (2024). AI-driven environmental monitoring using Google Earth Engine. In B. Pradhan & S. Mukhopadhyay (Eds.), Smart sensors, measurement and instrumentation: Vol. 50. IoT sensors, ML, AI and XAI: Empowering a smarter world (pp. 375–385). Springer. https://doi.org/10.1007/978-3-031-68602-3_19
Srivastava, A., Bharadwaj, S., Dubey, R., Sharma, V. B., & Biswas, S. (2022). Mapping vegetation and measuring the performance of machine learning algorithm in Lulc classification in the large area using Sentinel-2 and Landsat-8 datasets of Dehradun as a test case. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2022, 529–535. https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-529-2022
Srivastava, A., Dubey, R., & Biswas, S. (2023a). Comparison of Sentinel and Landsat data sets over Lucknow region using gradient tree boost supervised classifier. In A. Noor, K. Saroha, E. Pricop, A. Sen, & G. Trivedi (Eds.), Lecture notes in networks and systems: Vol. 730. Proceedings of third emerging trends and technologies on intelligent systems (pp. 221–232). Springer. https://doi.org/10.1007/978-981-99-3963-3_18
Srivastava, A., Umrao, S., & Biswas, S. (2023b). Exploring forest transformation by analyzing spatial-temporal attributes of vegetation using vegetation indices. International Journal of Advanced Computer Science and Applications, 14(5), 1110–1117. https://doi.org/10.14569/IJACSA.2023.01405114
Srivastava, A., Umrao, S., Biswas, S., Dubey, R., & Zafar, M. I. (2023c). FCCC: Forest cover change calculator user interface for identifying fire incidents in forest region using satellite data. International Journal of Advanced Computer Science and Applications, 14(7), 948–959. https://doi.org/10.14569/IJACSA.2023.01407103
Thenkabail, P. S., Teluguntla, P. G., Xiong, J., Oliphant, A., Congalton, R. G., Ozdogan, M., Gumma, M. K., Tilton, J. C., Giri, C., Milesi, C., Phalke, A., Massey, R., Yadav, K., Sankey, T., Zhong, Y., Aneece, I., & Foley, D. (2021). Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud (Professional Paper No. 1868). US Geological Survey. https://doi.org/10.3133/pp1868
Viana, C. M., Girão, I., & Rocha, J. (2019). Long-term satellite image time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sensing, 11(9), Article 1104. https://doi.org/10.3390/rs11091104