Water is a very important resource, which plays an important role in human life and production. Rapid and accurate water body information acquisition is of great significance for national resource management, planning, development, and rapid disaster assessment. The purpose of this paper is to explore and analyze the temporal and spatial changes of water bodies in the northeastern coastal areas of China in recent 10 years. Taking shandong province and heilongjiang province as the research area, Based on Google Earth engine, we used Landsat5, 7 and 8 images as data sources, comparing various water extraction methods, we finally selected the modified normalized water index model (MNDWI) to extract water, study the dynamic changes of water in ten years, and use supervised classification method to verify the accuracy. According to the results, the total water area in Shandong province decreased from 13067.08km2 in 2010 to 9418.58km2 in 2020, with an area decrease of 27.92%. The total water area of Heilongjiang province increased from 6154.782174km2 in 2008 to 7179.088516 km2 in 2018, an increase of about 14%. Then, according to the change of water area, the influencing factors are analyzed from natural and human perspectives. It is of great theoretical and practical significance for protecting and utilizing water resource by study the temporal and spatial changes of water bodies.

Jinghan Guo. Spatio-Temporal Changes in the Northeastern Coast of China in Recent 10 Years Based on Google Earth Engine and Intelligent Image Processing Technology—A Case Study of Heilongjiang Province and Shandong Province. Academic Journal of Environment & Earth Science (2023) Vol. 5 Issue 4: 65-73. https://doi.org/10.25236/AJEE.2023.050410.

[1] Noel Gorelick, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, Rebecca Moore. Google Earth Engine: Planetary-scale geospatial analysis for everyone [J]. Remote Sensing of Environment, 2017, 202

[2] M. C. Hansen, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, J. R. G. Townshend. High-Resolution Global Maps of 21st-Century Forest Cover Change [J]. Science, 2013, 342 (6160)

[3] Peng Gong, Xuecao Li, Wei Zhang. 40-Year (1978–2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing [J].Science Bulletin, 2019, 64(11):756-763.

[4] Carolina Echavarría-Caballero, José Antonio Domínguez-Gómez, Concepción González-García, María Jesús García-García. Assessment of Landsat 5 Images Atmospherically Corrected with LEDAPS in Water Quality Time Series [J]. Canadian Journal of Remote Sensing, 2019, 45(5)

[5] Feyisa G L, Meilby H, Fensholt R, et al. Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery [J]. Remote Sensing of Environment. 2014, 140(1):23-35.

[6] Chen H F, Wang J L, Chen Z, et al. Comparison of Water Extraction Methods in Mountainous Plateau Region from TM Image [J]. Remote Sensing Technology and Application, 2004, 19(6):479-484.

[7] Rouse J W, Haas R H, Schell J A, et al. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS [A]. Third ERTS Symposium[C]. NASA SP-351, 1:309-317. 

[8] Mcfeeters S K. 1996. The Use of Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features[J]. International Journal of Remote Sensing, 17(7):1425-1432. 

[9] Gao B C. 1996. NDWI-A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space [J]. Remote Sensing of Environment, 58: 257-266.

[10] Yumei Liu, Comparison of Extraction Effects of Different Water Extraction Methods [J]. Shaanxi Water Resources, 2021(11):104-106. DOI:10.16747/j.cnki.cn61-1109/tv. 2021.11.036.