福建闽江河口水下地形变化的遥感分析

本文利用Landsat-TM影像,建立了闽江河口的遥感水深模型,对河口区的水下地形进行了解译,并分析了其水下地形变化状况及影响因素,认为淤积主要发生在水动力条件较弱以及泥沙含量较高的地区,主要是在梅花水道琅岐岛南侧、鳝鱼沙及其附近区域、乌渚水道靠近主航道的部分以及内拦门沙区等大片区域。冲刷区主要是发生在水流转向或者水动力条件强的区域,主要是闽江从亭江至头深水槽、梅花水道中径流作用弱而潮流作用强的区域等。闽江主航道深水槽处于冲刷状态,而主航道两侧淤积。

基于RS的黄三角经济区海岸带提取方法

在分析黄河三角洲及附近海域潮汐规律的基础上,提取了高潮线、海岸线和低潮线等海岸带的基本要素,海岸线和高潮线的提取采用简便易行的目视解译方法直接获得;对于较难提取的低潮线,引入了遥感水深理论,建立水深模型,结合当地潮汐、潮位资料和DEM数据进行了推算,最终完成黄河三角洲经济区海岸带的提取工作。结果表明:利用水深遥感模型反演得到的低潮线虽然有一定误差,但并不影响进一步研究,与传统方法相比具有快速、简捷的优点。

Study on Remote Sensing of Water Depths Based on BP Artificial Neural Network

A momentum BP neural network model （MBPNNM） was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between the reflectance derived from Landsat 7 satellite data and the water depth information. Results showed that MBPNNM, which exhibited a strong capability of nonlinear mapping, allowed the water depth information in the study area to be retrieved at a relatively high level of accuracy. Affected by the sediment concentration of water in the estuary, MBPNNM enabled the retrieval of water depth of less than 5 meters accurately. However, the accuracy was not ideal for the water depths of more than 10 meters.