地理国情监测水面数据时空一致性优化方法

地理国情监测利用遥感、地理信息等技术,动态获取地表覆盖等多样化的地理要素,经统计、分析、评价和应用,服务于政治、经济、文化、资源、环境等多个领域,为政府提供全面、准确、基础的地理信息情报。针对地理国情监测在全国范围基于多时相遥感影像采集水面信息存在时空不一致性的现状,提出一种基于精细DEM的水面数据时空一致性优化方法。利用栅格图形区域生长算法,采用8邻域算子,对水面种子点进行迭代生长计算,得出基于精细DEM的区域生长结果;通过与地理国情监测水面数据对比分析,实现空间化结果的修正,从而达到时空一致性优化的目的。分析了典型研究区水面数据特征,利用该方法进行了优化处理,结果显示：研究区水面数据空间范围相对于时点监测修正了7.99%,满足了地理国情监测时点一致性需求。研究表明：该方法的应用,能够使得在全国尺度上统计的水面数据反映同一季节或可接受时段内的状况,避免或缩小了由于影像数据源的差异造成的时间和空间上的不一致性带来的误差,满足了地理国情监测时点一致性需求,能够在地理国情监测等地表覆盖水面信息提取、优化中推广应用,为政府有效决策提供客观、准确和基础的水面信息。

Spatial and Temporal Consistency Optimization Method of Surface Water Data for the National Geographic Conditions Monitoring Project of China

The National Geographic Conditions Monitoring Project of China, based on the high technology of remote sensing and geospatial information, has dynamically acquired various kinds of geographic features. The goal of this project is to assist the government's decision making in multiple domains such as politics, economics, culture, resources and environments by statistical calculation, analyses, evaluation and application of the accurate and basic geospatial information. However, as China is such a huge country, it's particularly difficult to acquire all of the images with the resolution better than 1 meter around the time of 30^th June with high quality. Temporal and spatial inconsistency in the image acquisition is quite normal which has resulted in the temporal and spatial inconsistency of water data. In this study, a method of spatial and temporal consistency optimization of surface water cover data based on detailed DEM is proposed. Region growing algorithm of raster graphics and 8 neighborhood arithmetic operators were used. By iterative calculations started from the water seeds the graphics results could obtained based on detailed DEM. Through the comparison of the region growing results with water data source of the National Geographic Conditions Monitoring, the spatial correction results could be realized, so as to achieve the purpose of spatial and temporal consistency optimization. Typical study region's water data characteristics were analyzed, and optimization processing was carried out by using this method. The results show that the spatial areas relative to the fixed monitoring time point was revised 7.99%, which meets the consistency requirement of National Geographic Conditions Monitoring’s time point. The research shows that this method can make the water statistics at the national scale reflecting the same season or the acceptable time, to avoid or reduce the errors due to the spatial and temporal inconsistency in image data sources. The method could be popularized and applied for water data's optimization, and provides objective, accurate and basic water information for the government to make the decisions.