中文摘要：

以香港米埔后海湾湿地2009年1月份15个区的湿地依赖性水鸟（鹗、黑翅鸢、普通鵟、东方月瑶、红嘴蓝鹊等13种水鸟）调查数据,及同期高、中分辨率遥感影像数据、气候数据及地形数据为基础,利用GIS和RS技术来获取9种与水鸟密切相关的环境变量因子,选取GARP生态位模型进行水鸟空间分布模拟。由于水鸟数据空间不连续,模拟结果并不理相,因此引入BP神经网络对结果改进：BP神经网络改进GARP模拟水鸟实际分布情况有所改善。训练样本点的AUC均值由0.762提高到了0.905。模拟水鸟空间分布状态与实际水鸟的分布基本吻合。潮间带、大片的基塘区域是其主要栖息场所,红树林,草滩区为其次要栖息场所,而深水域、城市用地及城市绿地区域,由于人类活动的频繁干扰了水鸟的栖息,水鸟分布较少。因此,较好的揭示了水鸟与地理环境之间的空间变化及作用关系。

英文摘要：

Based on waterfowl birds＇ survey data, such as Osprey, Black winged kite, Common buzzard, Oriental monty Yao, Red billed blue magpie and other 13 species of waterbirds, we select the 15 districts of it in Maipo-Deep Bay wetland of Hong Kong, January 2009 as a case. Then, collect the high resolution remote sensing data, climate and terrain data of this area at the same period. Finally, use GIS and RS technology to obtain nine kinds of impact factors, what is closely related to the birds. The Genetic Algorithm for Rule-Set Prediction （GARP） niche model is used to simulate the space distribution of a random sampling points of wetland-dependant birds. The result was not i- deal for the waterfowl data space is not continuous, and we do not consider the waterfowl non-Presence points in the GARP model. So we introduce the BP neural network model to improve the simulation results of waterfowl. The results showed that the actual distribution of GARP model simulation waterfowl improved by BP algorithm has achieved good effect. AUC value from ROC curve was used to check and measure the training sample points, the AUC value of 0. 905, has a great increase compared with the last result of 0. 762. The simulation results demonstrate the character- istics of the distribution of wetland-dependent birds, intertidal zone and large areas of the pond area is its main habitat, mangrove, marsh, followed by habitat, due to human aetivities frequently interfered with the habitat of waterfowl, the waterfowl distribution less or no in deep bay, urban land used and urban green space area. The relationship between waier birds and geographical environment was revealed very good.