以下辽河平原为研究对象,选取1980年、2010年和2018年Landsat TM/ETM+OLI卫星图像进行解译,得到3期土地利用数据,定量分析下辽河平原近40年土地利用时空变化特征.结合地形、交通通达度及限制转化因子采用FLUS(Future Land Use Simulation)模型对流域未来土地利用变化情景及景观格局进行了预测.结果表明:1980-2018年水田、林地、草地、沼泽面积均减少,其中水田面积减少量最大,占比减少了8.59%,旱地、水域和城镇的面积均有所上升,旱地的增长面积最大,占比增加了6.19%;水田、林地、水域转入为旱地面积最大,旱地转出为建设用地面积最大;1980-2018年景观格局发生了较大变化,景观的破碎化程度降低,斑块之间的连通度、聚集程度升高,土地利用的集约化程度增大;2018-2040年,下辽河平原建设用地和水田的变化面积最大,城市化过程更加显著,景观的多样性及空间异质性降低,人类对环境的干扰能力变大. 相似文献
The forest litter is an essential reservoir of nutrients in forests, supplying a large part of absorbable base cations(BC) to topsoil, and facilitating plant growth within litter-soil system. To characterize elevational patterns of base cation concentrations in the forest litter and topsoil, and explore the effects of climate and tree species, we measured microclimate and collected the forest litter and topsoil(0-10 cm) samples across an elevational range of more than 2000 m(1243 ~ 3316 m a.s.l.),and analyzed the concentrations of BC in laboratory. Results showed that: 1) litter Ca concentration displayed a hump-shaped pattern along the elevational gradients, but litter K and Mg showed saddle-shaped patterns. Soil Ca concentration increased with elevation, while soil K and Mg had no significant changes. 2) Ca concentration in the forest litter under aspen(Populus davidiana) was significantly higher than that in all other species, but in topsoil, Ca concentration was higher under coniferous larch and fir(Larix chinensis and Abies fargesii). Litter K and Mg concentrations was higher under coniferous larch and fir, whereas there were nosignificant differences among tree species in the concentrations of K and Mg in topsoil. 3) Climatic factors including mean annual temperature(MAT), growing season precipitation(GSP) and non-growing season precipitation(NGSP) determined BC concentrations in the forest litter and topsoil. Soil C/N and C/P also influenced BC cycling between litter and soil. Observation along elevations within different tree species implies that above-ground tree species can redistribute below-ground cations, and this process is profoundly impacted by climate. Litter and soil Ca, K and Mg with different responses to environmental variables depend on their soluble capacity and mobile ability. 相似文献
To reveal the effect of shale reservoir characteristics on the movability of shale oil and its action mechanism in the lower third member of the Shahejie Formation(Es3l), samples with different features were selected and analyzed using N2 adsorption, high-pressure mercury injection capillary pressure(MICP), nuclear magnetic resonance(NMR), high-speed centrifugation, and displacement image techniques. The results show that shale pore structure characteristics control shale oil movability directly. Movable oil saturation has a positive relationship with pore volume for radius > 2 μm, as larger pores often have higher movable oil saturation, indicating that movable oil is present in relatively larger pores. The main reasons for this are as follows. The relatively smaller pores often have oil-wetting properties because of organic matter, which has an unfavorable effect on the flow of oil, while the relatively larger pores are often wetted by water, which is helpful to shale oil movability. The rich surface provided by the relatively smaller pores is beneficial to the adsorption of immovable oil. Meanwhile, the relatively larger pores create significant pore volume for movable oil. Moreover, the larger pores often have good pore connectivity. Pores and fractures are interconnected to form a complex fracture network, which provides a good permeability channel for shale oil flow. The smaller pores are mostly distributed separately;thus, they are not conducive to the flow of shale oil. The mineral composition and fabric macroscopically affect the movability of shale oil. Calcite plays an active role in shale oil movability by increasing the brittleness of shale and is more likely to form micro-cracks under the same stress background. Clay does not utilize shale oil flow because of its large specific surface area and its block effect. The bedding structure increases the large-scale storage space and improves the connectivity of pores at different scales, which is conducive to the movability of shale oil. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.