State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of CAS, Beijing 100875, China.
Sci Total Environ. 2011 Jun 1;409(13):2567-76. doi: 10.1016/j.scitotenv.2011.03.023. Epub 2011 Apr 14.
Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling.
植被吸收氮去除是非点源污染（NSP）的河岸带缓冲带的最重要功能之一，许多关于在河流范围内的氮吸收的研究已经证明了植物在控制养分污染方面的有效性。然而，在流域尺度上，河岸带形成了树枝状网络，因此，可能是流域和景观中主要的空间结构特征。因此，评估河岸带系统在流域尺度上的功能非常重要。在本研究中，采用了一种将遥感和生态模型相结合的新方法，以大空间尺度评估河岸植被的氮去除功能。研究地点位于中国北京官厅水库周围，由于 1997 年严重的非点源污染，该水库被弃用作为北京的水源系统。使用 SPOT 5 数据来绘制土地覆盖图，使用 Landsat-5 TM 时间序列图像来检索土地表面参数。改进的森林养分循环和生物量模型（ForNBM）用于模拟氮去除，并且由遥感图像时间序列驱动改进的净初级生产力（NPP）模块。除了遥感数据外，必要的数据库还包括气象数据、土壤化学和物理数据以及植物养分数据。盆载和田间试验用于校准和验证模拟。我们的研究证明，通过将遥感数据和参数检索技术与植物生长过程模型相结合，可以通过基于空间像素的建模来提高流域尺度上的氮吸收速率估算。
