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LONG TIME SERIES EXTRACTION AND CHANGE ANALYSIS OF PERENNIAL AND SEASONAL WATER SURFACE IN HEILONGJIANG BASIN BASED ON GOOGLE EARTH ENGINE
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摘要: 遥感影像空间覆盖广、更新周期短，是快速提取大区域水面信息的可行技术，对水资源开发利用、管理和保护具有重要意义。针对年际内受降水波动性影响，充分应用年份内所有可用的影像数据构建年均水体指数，来减少单期影像难以准确反映年内水面特征的问题，利用Google Earth Engine （GEE）遥感云平台解决了传统影像下载和桌面端处理海量影像数据效率低的问题。以黑龙江流域为研究区，以Landsat影像为数据源，结合地形数据，提取1987—2019年常年水面和季节性水面。研究结果表明：1）相较于单期影像数据，年均水体指数能更全面反映水面的时间信息，水面提取总体精度达到95.32%，其中，常年和季节性水面的总体精度分别为96.59%、94.61%；2）与已有数据产品相比，提取的常年水面更加连续、完整，质量更好；3）近32年来，黑龙江流域常年水面面积波动较大，呈减少趋势，年均减少14.82 km2；季节性水面面积相对平稳，呈增加趋势，年均增加12.81 km2。
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关键词:
- 黑龙江流域 /
- Google Earth Engine /
- Landsat /
- 自适应阈值 /
- 年均水体指数
Abstract: Remote sensing image has wide spatial coverage and short update period, which is a feasible technology to extract water surface information in large area in time, and of great significance to the development, utilization, management and protection of water resources.In view of the influence of precipitation fluctuation within the year, this paper made full use of all available image data to construct the annual average water body index, so as to reduce the problem that was difficult to accurately reflect the water surface characteristics in a single period image. The Google Earth Engine (GEE) remote sensing cloud platform was used to solve the problem of low efficiency of traditional image download and desktop processing of massive image data. Taking Heilongjiang Basin as the research area, taking Landsat Image as data source, combined with topographic data, annual water surface and seasonal water surface from 1987 to 2019 were extracted. The results showed that:1) compared with the single period image data, the annual average water body index could reflect the time information of water surface more comprehensively, and the overall accuracy of water surface extraction was 95.32%, in which the annual and seasonal water surface were 96.59% and 94.61% respectively; 2) compared with the existing data products, the annual water surface extracted in this paper was more continuous, complete and of better quality; 3) in recent 32 years, the annual water surface area of Heilongjiang Basin fluctuated greatly, showing a decreasing trend, with an average annual decrease of 14.82 km2; the seasonal water surface was relatively stable, showing an increasing trend, with an average annual increase of 12.81 km2.-
Key words:
- Heilongjiang Basin /
- Google Earth Engine /
- Landsat /
- adaptive threshold /
- annual average water index
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赵尚飞,杜彦良,王瑜,等. 松花江梧桐河生态修复工程鱼类栖息地模拟及调查[J].水生态学杂志2019,40(5):1-7. 于杰,宁静,董芳辰,等.1950-2013年三江平原东北部耕地分布变化特征分析[J]. 干旱区资源与环境2017,31(12):79-86. 毛德华,王宗明,罗玲,等.1990-2013年中国东北地区湿地生态系统格局演变遥感监测分析[J].自然资源学报2016,31(8):1253-1262. 韩伟孝,黄春林,王昀琛,等. 基于长时序Landsat 5/8多波段遥感影像的青海湖面积变化研究[J].地球科学进展,2019,34(4):346-355. 王大钊,王思梦,黄昌.Sentinel-2和Landsat 8的四种常用水体指数地表水体提取对比研究[J].国土资源遥感,2019,31(3):1-9. CLEMENT K, EDWARD M O J,ADWOA S A. Comparing of Landsat 8 and Sentinel 2A using water extraction indexes over Volta River[J]. Journal of Geography and Geology,2018(1):1-7. 卢善龙,肖高怀,贾立,等.2000-2012年青藏高原湖泊水面时空过程数据集遥感提取[J].国土资源遥感, 2016, 28(3):181-187. 赵哲,况润元,廖启卿.基于Landsat 8影像的赣抚尾闾区水体提取研究[J].测绘地理信息,2019,44(2):97-100. 宋英强,杨联安,许婧婷,等.基于Landsat-8卫星OLI影像和AdaBoost算法的水体信息提取[J].测绘地理信息,2017,42(3):44-47. 贾祎琳,张文,孟令奎.面向GF-1影像的NDWI分割阈值选取方法研究[J].国土资源遥感,2019,31(1):95-100. 龚林松,李士进.基于改进SLIC和OTSU的遥感影像水体提取[J].计算机技术与发展,2019,29(1):145-149. 赵紫薇.基于OTSU算法利用新型水体指数进行Landsat数据自适应阈值水体自动提取研究[J].测绘与空间地理信息,2016,39(9):57-60. ADRAIN F,NEIL F,TIM D. Comparing Landsat water index methods for automated water classification in eastern Australia[J]. Remote Sensing of Environment,2016,175:167-182. 何海清,杜敬,陈婷,等.结合水体指数与卷积神经网络的遥感水体提取[J].遥感信息,2017,32(5):82-85. 万建鹏,官云兰,叶素倩,等.基于综合权重水体指数的水体提取研究:以鄱阳湖为例[J].东华理工大学学报(自然科学版),2018,38(2):206-211. NOEL G, MATT H, MIKE D,et al, Google Earth Engine:planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment,2017,202:18-27. LI H,WAN W,FANG Y,et al.A Google Earth Engine-enabled software for efficiently generating high-quality user-ready Landsat mosaic images[J]. Environmental Modelling & Software,2019,112:16-22. MURAIL K G, PRASAD S T, PARDHASARADHI G T,et al.Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30 m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud[J]. GI Science & Remote Sensing,2020:302-322. 徐晗泽宇,刘冲,王军邦,等.Google Earth Engine平台支持下的赣南柑橘果园遥感提取研究[J].地球信息科学学报,2018,20(3):396-404. 陈炜,黄慧萍,田亦陈,等.基于Google Earth Engine平台的三江源地区生态环境质量动态监测与分析[J],地球信息科学学报2019(9):1382-1391. 王英,龚家国,贾仰文,等. 基于不同分辨率DEM提取坡度值的转换关系研究[J].水利水电技术,2019(8):45-51. -
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