中国植物空间分异规律

以省级行政区为分析单元,借助ArcGIS空间分析功能对中国行政分区图、中国森林覆盖率图、中国植物区系图、中国植被分区图进行叠加分析。通过对同一植被分区下分析单元数据进行整合,给出全国不同植物区系、植被分区、森林覆盖率信息。采用二阶聚类综合判别法,提取中国植被分区总体信息,探求植被空间分异规律,发现一类、二类、三类植被的比例为30∶37∶23,反映出植被的区域分布特性较强;四类植被比例大致占10%,反映出大区域内部的局地小生境造成的植被分布的整体差异。另外,一类植被多分布在中高纬度地带,二类植被贯穿中国全纬度,三类植被多分布在低纬度地带。     

A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

含植物河道水动力特性研究进展

水生植物群落是河流生态系统的重要组成部分,利用天然植被护坡固土、净化水质、改善河流生态环境已成为河流生态修复的重要措施。本文从水生植物影响下的河道水流阻力特性及水流结构两方面,总结了国内外对含植物河道水动力特性的研究进展,并对该领域的未来研究方向提出了展望。     

柴达木盆地植被覆盖分布规律及影响因素

植被是陆地生态系统的主体,是自然生态系统中最活跃和敏感的因子,能够反映出气候、水、土壤等的变化。柴达木盆地地处干旱区,生态环境脆弱,采用MODIS遥感数据分析柴达木盆地植被类型、植被覆盖度的时空演化特征,结果表明:柴达木盆地总体植被覆盖较差,植被覆盖度较低,裸地或极稀疏植被占较大面积,2001—2012年裸地或极稀疏植被面积持续减小。植被指数一元线性回归分析结果表明盆地大部分区域植被呈明显好转趋势,这与近年来盆地内降水量增大密不可分。此外,地形地貌也是影响植被空间分布的重要因素。     

丹江口坝区扩建工程水土保持建设与效果分析

南水北调中线丹江口水库大坝加高工程坝区建设项目占地较多,包括永久征地、临时占地、租赁占地等,项目施工将直接破坏地表,造成水土流失。为防止工程建设造成严重的水土流失,项目业主单位成立了以总经理为第一责任人的领导机构和专门负责水土保持工程建设与管理的机构,并制定了专门的管理办法。在各参建单位的共同努力下,通过采取一系列的工程措施、植物措施与临时措施,取得了良好的水土保持效果,工程建设区的水土流失控制在国家标准的轻度流失范围内。     

FACTORS INFLUENCING BENDING RIGIDITY OF SUBMERGED VEGETATION

The bending rigidity of submerged vegetation is closely related with vegetative drag force. This work aims at determining the effects of flow conditions and characteristics of vegetation on the bending rigidity of submerged vegetation. Based on the dimensional analysis method, the factors influencing the bending rigidity of individual submerged vegetation were analyzed. The relationship between the relative bending rigidity and its influencing factors was investigated by experimental observation, and a rela...     

Spectral‐ALS data fusion for different roughness parameterizations of forested floodplains

Natural river floodplains and adjacent wetlands grow typically a diverse and heterogeneous combination of herbs, shrubs and trees, which play an essential role in determining the total flow resistance. Hydrodynamic effects of trees in forested floodplains can provide the majority of flow resistance during flood events. Nevertheless, ground‐based techniques to acquire vegetation parameters are expensive and difficult to apply over long reaches. This paper presents a novel method of automated roughness parameterization of riparian woody vegetation by fusion of Quickbird multi‐spectral image with airborne laser scanning (ALS) data. The data fusion approach includes: individual tree detection and estimation of vegetation metrics from light detection and ranging (LiDAR) data, assessment of predictive models for the vegetation parameters and spatial mapping of the vegetation parameters for the forest plants in the riparian corridor. The proposed method focuses on estimation of plant density (d), crown diameters (D_C), tree height (h), stem diameter (D_S), crown base height (cbh) and leaf area index (LAI). The procedure is tested along a 14‐km reach of the Sieve River (Tuscany, Italy) characterized by high woody plant density. Due to the complex study area, the data fusion approach explains with variable reliability the local vegetation properties (R²(D_C) = 0.14, R²(h) = 0.84, R²(D_S) = 0.25, R²(cbh) = 0.66). The generated structural parameter maps represent spatially explicit data layers that can be used as inputs to hydrodynamic models used to analyse flow resistance effects in different submergence conditions of vegetation. A simple flow resistance model was applied over a test area comparing the results of the proposed method and a traditional ground‐based approach. The modelling results showed that the new method is able to provide accurate output data to describe the interaction between water levels and bio‐mechanical characteristics of vegetation. The proposed methodology provides a fast, repeatable and accurate way of obtaining floodplain roughness, which enables regular updating of vegetation characteristics. Copyright © 2010 John Wiley & Sons, Ltd.     

Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing

Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m^− 1) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted (R² = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models.     

利用植被覆盖度-地表温度特征空间提取厦门市土壤湿度信息

土壤湿度是水文学、气象学以及农业科学研究领域的一个重要参数。利用1989年和1996年两个时相Landsat TM遥感卫星影像来获取厦门的植被覆盖度和地表温度,并由此构建了植被覆盖度和地表温度组成的特征空间,最后利用该特征空间反演了土壤湿度,并分析湿度在两个时相中的变化情况。结果显示,在所研究的时间段内,厦门西南老城区的湿度得到增加。但是在东北部城市建成区扩展的地区,土壤湿度则明显降低。     

基于高光谱激光雷达的滞尘叶片光谱特征分析

城市绿植为城市生态系统提供自净功能, 起到净化空气以及滞尘降尘等多种环境保护作用, 而滞尘等因素也 会对绿植产生影响。为了研究滞尘对城市绿植叶片光谱特征的影响, 采集了四种常绿绿植 (八角金盘、石楠、香樟和 玉兰) 叶片样本, 使用高光谱激光雷达系统获取高光谱点云数据, 分析了滞尘对叶片光谱特征的影响。分析结果表明: 对于不同种类叶片, 滞尘对可见光波段反射率均有较大影响; 对于同种类叶片, 滞尘对近红外波段的反射率差异影响 较大, 可见光波段的反射率差异为 1.21%∼3.41%, 近红外为 1.76%∼8.49%; 线性四点内插法计算和光谱导数分析表明 滞尘对四种叶片的红边位置无显著影响; 四种叶片的叶面水含量指数 (LWI) 对滞尘的响应程度最小 (均小于 3.7%), 而 比值植被指数 (RVI) 对滞尘的响应程度最大 (除香樟外, 均大于 20.0%), 红边指数 (SDr)、简单比值指数 (SR) 和叶面叶 绿素指数 (LCI) 的响应程度稳定性较差。进一步建立了滞尘植被指数和响应程度的线性相关性拟合模型并进行了检 验, 其中以 LCI 为自变量建立的模型为最稳定拟合模型, 可表示为 y = −1.527x + 0.6597, 决定系数约为 0.88。