密云水库流域植被覆盖度变化对输沙量的影响

封闭流域中,泥沙的产生是多种因素相互作用,相互制约的结果,而在这些众多的环境影响因素中,植被是土壤侵蚀中最重要的环境控制因子,目前,植被覆盖度变化对输沙量的影响一直是区域生态环境研究中比较热门的话题。文章使用遥感（Remote Sensing（RS））、地理信息系统（Geographic In formmionSystem（GIS））与统计分析相结合的方法评价了密云水库流域内2个子流域出口处2002年至2005年植被覆盖度变化对输沙量的影响。密云水库流域多为山地,其降雨具有明显的季节性。本文的主要目的在于分析2002年至2005年植被覆盖度变化对流域输沙量的影响,并分析了植被覆盖度变化对气候改变及人类活动的综合影响,结果表明：在密云水库流域内,输沙量是降雨情况和人为因素引起的地表植被覆盖度变化情况的综合反映,人口增长、经济发展和城市化是密云水库流域内引起土地利用变化和地表植被覆盖度变化的主要驱动力。并且,输沙量可以被看作是一个很好的指标来定量分离出生物生理和人为的影响,并从中能够找出在流域生态系统中获得显著结果关键的临界点。研究结果对制定流域的合理开发和管理计划将有所帮助。     

基于地理探测器的区域植被覆盖度的定量影响——以京津风沙源区为例

植被覆盖度（FVC）是衡量生态环境优秀的重要指标.本文基于MODIS NDVI数据,运用像元二分法模型,估算了2000~2018年京津风沙源区FVC,分析了其时空变化特征.在此基础上,基于地理探测器模型,定量分析了自然因素和人类活动因素等12个因子对京津风沙源区FVC空间分布的影响.结果表明,近20a来京津风沙源区FVC整体上呈增加趋势,增加率为8.2%.空间上,研究区73.7%的区域FVC在增加,其中显著增加的区域主要集中在晋北山地丘陵亚区、燕山丘陵山地水源保护亚区、大兴安岭南部亚区和科尔沁沙地亚区的南部.2000~2018年,降水是影响京津风沙源区FVC空间分布的主要自然因子,解释力为63.3%;年末大牲畜头数是影响FVC空间分布的主要人为因子,解释力为34.8%.自然因素和人类活动之间的双因子交互作用以双协同作用和非线性协同作用为主,表明相对于单因子,自然因素和人类活动间的交互作用对FVC的解释力更强.其中,降水与其他11个因子的交互作用对研究区FVC起主导作用,解释力超过60%.     

改性植被混凝土基材力学与植生试验研究

为探明羧甲基纤维素钠(CMC)与麦秸秆纤维对植被混凝土基材力学与植生性能的影响,通过直剪试验、植生试验研究了改性后植被混凝土基材(简称改性基材)的抗剪性能与黑麦草生长性能.结果 表明:①改性基材的剪切强度随CMC掺量的增加呈先增后减的变化趋势,0.5％CMC掺量时基材的剪切强度出现峰值,而随纤维掺量的增加改性基材剪切强...     

Uneven distribution of urban green spaces in a coastal city in northwest Mexico

ABSTRACT

Extent and distribution of urban green spaces (UGS) in Mazatlan (Mexico) are analysed using remote sensing and GIS techniques. Vegetated areas (2,270?ha), a third of the urban area in 2015, were reclassified into green spaces (GS), urban tree (UT) and open spaces (OS), based in the normalised difference vegetation index, relating them with demographic and socioeconomic data. UGS allocation per capita amount 55?m², mainly represented by the UT class, with the largest patches associated with low developed and very high marginalised areas, and also with very low marginalised sectors, while the lowest allocation correspond to medium and low marginalisation, highly populated sector, without significant correlations. Despite the USG allocation, it is required a better urban planning to maintain public UGS and to protect the local flora, threatened by the introduction of exotic, ornamental species (64% of UT), to guarantee the provision of ecosystem services to the population.     

Spectral characteristics of plant communities from salt marshes:A case study from Chongming Dongtan,Yangtzeestuary,China

The spectral reflectance of recently formed salt marshes at the mouth of the Yangtze River, which are undergoing invasion by Spartina alterniflora, were assessed to determine the potential utility of remotely sensed data in assessing future invasion and changes in species composition. Following a review of published research on remote sensing of salt marshes, 53 locations along three transects were sampled for paired data on plant species composition and spectral reflectance using a FieldSpec™ Pro JR Field Portable Spectroradiometer. Spectral data were processed concerning reflectance, and the averaged reflectance values for each sample were reanalysed to correspond to a 12-waveband bandset of the Compact Airborne Spectral Imager. The spectral data were summarised using principal components analysis (PCA) and the relationships between the vegetation composition, and the PCA axes of spectral data were examined. The first PCA axis of the reflectance data showed a strong correlation with variability in near infrared reflectance and ‘brightness’, while the second axis was correlated with visible reflectance and ‘greenness’. Total vegetation cover, vegetation height, and mudflat cover were all significantly related to the first axis. The implications of this in terms of the ability of remote sensing to distinguish the various salt marsh species and in particular the invasive species S. alterniflora were discussed. Major differences in species with various physiognomies could be recognised but problems occurred in separating early colonising S. alterniflora from other species at that stage. Further work using multi-seasonal hyperspectral data might assist in solving these problems.     

林(竹)草不同植被恢复模式下的土壤物理特性

利用建立生态定位观测场,对华西雨屏区洪雅县低山区退耕还林中的桦木+扁穗牛鞭草、苦竹+扁穗牛鞭草、纯扁穗牛鞭草和撑绿杂交竹＋扁穗牛鞭草4种林（竹）草植被类型进行定位观测,研究不同植被恢复类型对土壤物理特性的改良。试验结果表明：林（竹）草不同植被类型植被恢复5年后,土壤0～40 cm土层的砂粒和粗粉粒百分含量均有一定的下降,在一定程度上逆转了原坡耕地土壤继续“粗化”的趋势;4种植被恢复类型土壤0～40 cm土层的物理性粘粒含量与农耕地相比均有一定提高,在一定程度上增强了固持土壤养分的能力,以苦竹＋扁穗牛鞭草改良效果最好;与农耕地相比,4种植被恢复类型土壤0～40 cm土层土壤容重有增有减,以苦竹＋扁穗牛鞭草和撑绿杂交竹＋扁穗牛鞭草植被恢复类型改良效果相对较优;虽然除纯牛鞭草地外其它3种植被恢复类型土壤0～40 cm的总孔隙度与农耕地相比都有一定程度的增加,但土壤总孔隙度都偏小,非毛管孔隙/毛管孔隙值也不合理。可见土壤结构的改良并非退耕后植被恢复短期内就能实现的,还需长时间的植被保护、合理的经营。〖     

Identification of Forest Vegetation Using Vegetation Indices

Abstract

Spectral feature of forest vegetation with remote sensing techniques is the research topic all over the world, because forest plays an important role in human beings' living environment. Research on vegetation classification with vegetation index is still very little recently. This paper proposes a method of identifying forest types based on vegetation indices, because the contrast of absorbing red waveband with reflecting near-infrared waveband strongly for different vegetation types is recognized as the theoretic basis of vegetation analysis with remote sensing. Vegetation index is highly related to leaf area index, absorbed photosynthetically active radiation and vegetation cover. Vegetation index reflects photosynthesis intensity of plants and manifests different forest types. According to reflectance data of forest canopy and soil line equation NIR=1.506R+0.0076 in Jingyuetan, Changchun of China, many vegetation indices are calculated and analyzed. The result shows that the relationships between vegetation indices and forest types are that perpendicular vegetation index (PVI) identifies broadleaf forest and coniferous forest the most easily; the next is transformed soil-adjusted vegetation index (TSVI) and modified soil-adjusted vegetation index (MSVI), but their calculation is complex. Ratio vegetation index (RVI) values of different coniferous forest vary obviously, so RVI can classify conifers. Therefore, the combination of PVI and RVI is evaluated to classify different vegetation types.     

Assessing conservation opportunity on private land: socio-economic, behavioral, and spatial dimensions

This study presents a method for assessing conservation opportunity on private land based on landholders' socio-economic, behavioral, and farm characteristics. These characteristics include age, gender, education, level of off-farm income, farm size, proportion of remnant native vegetation on-farm, and ecological value of native vegetation on-farm. A sample of landholders who own greater than 2 ha of land in the South Australian Murray-Darling Basin region were sent a mail-based survey about their values and preferences for environmental management (N = 659, 52% response). Cross-tabulations and ANOVA statistical analysis techniques were used to compare the socio-economic attributes across three landholder classes: disengaged, moderately engaged, and highly engaged in native vegetation planting. Results indicate that highly engaged landholders were more likely to be female, formally educated, hobby farmers who managed small parcels of land and have high off-farm incomes, whereas disengaged landholders held significantly stronger farming connections (more farming experience, family have lived on the farm for more generations). Spatial analysis revealed area-specific differences in conservation opportunity and conservation priority. In some areas, properties of high ecological value were managed by highly engaged landholders, but nearby properties of high value were managed by moderately engaged or disengaged landholders. Environmental managers therefore cannot assume areas of high conservation priority will be areas of high conservation opportunity. At the regional scale, the potential for revegetation seems most promising within the moderately engaged landholder group considering the vast amount of land managed by this group in areas of high ecological value, particularly within the less represented Mallee and Coorong and Rangelands sub-regions. We suggest that incentive schemes which purchase conservation need to be targeted at disengaged landholders; mentoring schemes led by commercial farmers highly engaged in native vegetation planting should be directed at moderately engaged landholders, and; awards programs which acknowledge conservation successes should be targeted at highly engaged landholders.     

Modelling forest management within a global vegetation model—Part 2: Model validation from a tree to a continental scale

The construction of a new forest management module (FMM) within the ORCHIDEE global vegetation model (GVM) allows a realistic simulation of biomass changes during the life cycle of a forest, which makes many biomass datasets suitable as validation data for the coupled ORCHIDEE-FM GVM. This study uses three datasets to validate ORCHIDEE-FM at different temporal and spatial scales: permanent monitoring plots, yield tables, and the French national inventory data. The last dataset has sufficient geospatial coverage to allow a novel type of validation: inventory plots can be used to produce continuous maps that can be compared to continuous simulations for regional trends in standing volumes and volume increments. ORCHIDEE-FM performs better than simple statistical models for stand-level variables, which include tree density, basal area, standing volume, average circumference and height, when management intensity and initial conditions are known: model efficiency is improved by an average of 0.11, and its average bias does not exceed 25%. The performance of the model is less satisfying for tree-level variables, including extreme circumferences, tree circumference distribution and competition indices, or when management and initial conditions are unknown. At the regional level, when climate forcing is accurate for precipitation, ORCHIDEE-FM is able to reproduce most productivity patterns in France, such as the local lows of needleleaves in the Parisian basin and of broadleaves in south-central France. The simulation of water stress effects on biomass in the Mediterranean region, however, remains problematic, as does the simulation of the wood increment for coniferous trees. These pitfalls pertain to the general ORCHIDEE model rather than to the FMM. Overall, with an average bias seldom exceeding 40%, the performance of ORCHIDEE-FM is deemed reliable to use it as a new modelling tool in the study of the effects of interactions between forest management and climate on biomass stocks of forests across a range of scales from plot to country.