寒冷地区滨岸缓冲带植物筛选

为在寒冷地区建立高效的滨岸缓冲带水体工程,选择早熟禾、高羊茅、3种本地耐寒性植物,研究剪股颖构建河流滨岸缓冲带植物的经济性、生长特性、光合作用及其对悬浮物、氮、磷的净化能力。结果表明,3种植物在经济性方面高羊茅与早熟禾差别不大,远低于剪股颖;在生长特性方面,早熟禾与高羊茅的表现相近,高羊茅略好,但比剪股颖表现优秀,早熟禾在净光合作用速率、蒸腾速率以及水分利用效率方面比高羊茅及剪股颖出色;在面源污染物去除率方面,地表径流悬浮物的去除中,早熟禾表现突出;在溶解性磷与总氮的净化中,3种植物表现差异不大,早熟禾略有优势。综合比较各项指标,早熟禾可作为寒冷地区构建滨岸缓冲带较为理想的备选植物。     

Runoff water quality from manured riparian grasslands with contrasting drainage and simulated grazing pressure

Globally, management of grazed riparian areas is critically important to agricultural sustainability and environmental quality. However, the potential impacts of riparian grazing management on water quality are not well-documented, particularly in the southeastern USA. The objective of this work was to determine sediment and nutrient export under simulated rainfall from poorly drained and well-drained riparian soils where heavy or light grazing pressure by cattle was simulated. Plots were established on stands of existing vegetation to create grazing pressure treatments of (a) light-use (full ground cover, uncompacted), and with stands modified to establish (b) heavy-use (bare ground, compacted) treatments. Vegetation on poorly drained soils consisted of several typical wetland species (e.g., Pontederia cordata L., Juncus coriaceus Mackenzie) in the southeastern USA, whereas mixed tall fescue (Festuca arundinacea Schreb.)–dallisgrass (Paspalum dilatatum Poir.) stands were the dominant vegetation on well-drained soils. Runoff volume was generally greater from heavy-use than from light-use for poorly drained soils and for well-drained soils. Greater runoff volume was also observed from poorly drained soils compared to well-drained soils for both light-use and for heavy-use treatments. Light-use plots were remarkably effective at minimizing export of total suspended solids (TSS) on both soils (<30 kg ha⁻¹). Mean total Kjeldahl P (TKP) export was fourfold greater from heavy-use plots than from light-use plots on both soils. While export of nitrate-nitrogen (NO₃-N) was unaffected by grazing pressure and soil drainage, mean ammonium-nitrogen (NH₄-N) and total N (TN) export from poorly drained heavy-use plots was greater than fivefold that from well-drained light-use plots. Results indicate that livestock heavy-use areas in the riparian zone may export substantial TSS and nutrients, especially on poorly drained soils. However, when full ground cover is maintained on well-drained soils, TSS and nutrient losses may be limited.     

Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed

Riparian zones are deemed significant due to their interception capability of non-point source impacts and the maintenance of ecosystem integrity region wide. To improve classification and change detection of riparian buffers, this paper developed an evolutionary computational, supervised classification method--the RIparian Classification Algorithm (RICAL)--to conduct the seasonal change detection of riparian zones in a vast semi-arid watershed, South Texas. RICAL uniquely demonstrates an integrative effort to incorporate both vegetation indices and soil moisture images derived from LANDSAT 5 TM and RADARSAT-1 satellite images, respectively. First, an estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) images was conducted via the first-stage genetic programming (GP) practice. Second, for the statistical analyses and image classification, eight vegetation indices were prepared based on reflectance factors that were calculated as the response of the instrument on LANDSAT. These spectral vegetation indices were then independently used for discriminate analysis along with soil moisture images to classify the riparian zones via the second-stage GP practice. The practical implementation was assessed by a case study in the Choke Canyon Reservoir Watershed (CCRW), South Texas, which is mostly agricultural and range land in a semi-arid coastal environment. To enhance the application potential, a combination of Iterative Self-Organizing Data Analysis Techniques (ISODATA) and maximum likelihood supervised classification was also performed for spectral discrimination and classification of riparian varieties comparatively. Research findings show that the RICAL algorithm may yield around 90% accuracy based on the unseen ground data. But using different vegetation indices would not significantly improve the final quality of the spectral discrimination and classification. Such practices may lead to the formulation of more effective management strategies for the handling of non-point source pollution, bird habitat monitoring, and grazing and live stock management in the future.