黄河三角洲湿地生物多样性保护工程植被修复效果评估

开展修复效果的跟踪监测和评估是湿地生态修复的重要环节。跟踪监测可以观察湿地恢复状态和过程,找出修复过程潜在问题以便及时调整修复手段和技术的运用,从而提高修复效率,亦可为评价湿地修复效果提供数据支持。通过野外生态监测、遥感反演等手段,对黄河三角洲湿地生物多样性保护工程的植被修复效果进行评估。结果表明：南、北岸修复区土壤含水率分别提升45.86%和25.17%,土壤盐度分别下降70.51%和64.71%;南、北岸修复区的植被盖度分别提升5%和54%;NDVI指数分别提升48.4%和44.1%,修复区植被种类趋于多样,植被群落结构得到改善。不同生态修复措施对植被恢复均有效,效果差异不显著。修复区植被以芦苇为优势种,优势度为0.748,其他植被的优势度较低,植被总体盖度、芦苇密度和株高与未修复区相比差异不大。总体来说,北岸修复区植被尚未恢复至较高水平,南岸修复区植被恢复效果良好。建议在生态修复工程实施时应减轻对原生物种的破坏,实施后应关注植被恢复的滞后性,加强湿地管理以及外来物种入侵防治。该成果将为我国滨海湿地生态修复提供数据和技术参考。     

甘肃省植被与对流层甲醛关系及影响因素分析

植被可以截获和吸收大气中的颗粒物、SO₂和NO_x等,对大气污染物具有一定净化作用,但也释放大量挥发性有机物,对光化学烟雾污染的形成具有促进作用.以甘肃省为例,利用卫星资料反演手段,解译了2008-2016年NDVI（植被覆盖指数）和对流层HCHO（甲醛）柱浓度,并探讨了二者之间的关系及影响因素.结果表明:①甘肃省2008-2016年NDVI空间分布梯度呈东南向西北递减的趋势,其年际动态不显著,季节性动态显著,与对流层HCHO柱浓度时空分布及动态有一定的相似性.②甘肃省对流层HCHO柱浓度和NDVI的年变化范围分别为7×1015~11×1015 molec/cm2和0.22~0.25,并且二者之间呈显著正相关,相关系数为0.63.③甘肃省NDVI和对流层HCHO柱浓度的分布与气象因素（如辐射、气温和降水量）有关,并且甘肃省中部对流层HCHO柱浓度分布还与甘肃省人类足迹分布特征相似.研究显示:甘肃省中部人类足迹指数高,HCHO主要来源于人类活动;而甘肃省西部和南部人类足迹指数低,HCHO主要来源于自然排放.      

中国植被生长的最适温度估算与分析

利用中国区域的GIMMS NDVI数据集和相应时期气象站点实测的温度数据集,分别在时间尺度和空间地域上对比分析植被生长过程与环境温度变化的关系,并由此提取区域植被生长关键阶段对应的温度,最后得到了中国各生态地理区植被生长的最适温度及其变化区间。结果显示:①研究首次给出了不同生态区植被生长的最适温度,其中青藏高原的参考最适温度最低,在10 ℃左右,而长江中下游和华南地区的较高,很多区域的值都超过了25 ℃,说明植被生长的最适温度具有很强的地域分异性;②根据论文结果,通过海拔高度和纬度两个地理因子,可快速拟合得到中国陆地植被生长的近似最适温度。研究结果可以为生态系统模型的参数本地化和空间化提供参考。     

华北平原1981～2001年作物蒸散量的时空分异特征

利用土壤-植被-大气传输机理模型(VIP模型),以GIS背景数据库(土地利用图、土壤质地图和数字高程图)为支撑,在NOAA-AVHRRNDVI数据和气象信息的驱动下,连续模拟了1981～2001年华北平原冬小麦和夏玉米生育期的蒸散过程。结果表明:模拟的作物蒸散量与Lysimeter观测值和其他学者的田间试验研究结果具有较好的一致性。华北平原冬小麦多年平均蒸散量空间上呈现南高北低的趋势,其中黄河以北地区和山东半岛的蒸散量在200～400mm之间,南部地区在400～466mm之间。对玉米而言,北部的海河低地平原以及津、冀、鲁的沿海地区多年平均蒸散量变化在230～380mm,其余大部分地区蒸散量在380～470mm。除本区最南端的极少部分地区外,华北平原大部分地区冬小麦生育期内的自然降水都小于蒸散量,水分亏缺量大于200mm,而夏玉米生育期内大部分地区的降水大于蒸散量。     

西藏当雄县高寒草地NDVI的海拔分异特征及其指示

通过提取西藏当雄县Landsat ETM影像NDVI以及DEM的海拔要素,在坡面的小尺度水平上分析了NDVI的海拔格局,并利用沿海拔梯度的草地地上生物量实测数据进行验证,进一步在全县尺度上分析了当雄NDVI >0.2 的区域内草地NDVI 的海拔分异特征.主要结果如下:①在坡面尺度上,随海拔的增加,不同计算方式的NDVI均表现为先增加后降低的单峰格局,与沿海拔梯度实测的地上生物量格局一致,NDVI最大值与围栏外生物量最大值均出现在4 950 m左右;②在全县尺度上,当雄草地NDVI随海拔的变化略为复杂:在4 300 m以上,NDVI随海拔的增加表现为先升高后降低,最高值出现在4 700 m左右;在4 300 m以下植被可能以湿地草甸为主,形成另一个NDVI高值区,但其分布面积仅为总草场面积的10%左右.上述结果表明,海拔是影响当雄县NDVI 空间分布的重要地形因子,NDVI 的海拔格局体现了较大尺度上草地植被对不同水热组合环境的适应特征,这对于在气候变化背景下实施高寒地区生态保护工程以及制定合理的草场资源配置方案具有重要指导意义.     

Preliminary Evidence of a Barrier Effect of a Railroad on the Migration of Mongolian Gazelles

Abstract:  Much remains to be learned about how anthropogenic structures affect migration in large mammals. We examined the potential influence of the international railroad in Mongolia on migration in Mongolian gazelles (Procapra gutturosa). We examined gazelle movements and the normalized difference vegetation index (NDVI) on both sides of the railroad. The tracked gazelles never crossed the railroad, despite movements that mainly followed the railroad in winter and higher NDVI values on the opposite side. It is likely that the railroad had a barrier effect on gazelle migration because it split the gazelles' habitat. Our results, although based on an extremely small sample, have utility in indicating approaches that might be useful for examining migration in ungulates.     

Sensitivity of modelled gross primary productivity to topographic effects on surface radiation: A case study in the Cotter River Catchment, Australia

Gross primary productivity (GPP) is a critical response variable for many environmental problems, including terrestrial carbon accounting and the calculation of catchment water balances. Various approaches for modelling GPP have been developed and applied at continental and landscapes scales, but little attention has been given to the sensitivity of GPP to the spatial scale of its driving variables. A key driving variable is surface radiation (R_s) which is influenced by both meso-scale factors (latitude, time of year, cloudiness) and the topographic variables of slope, aspect and horizon shading. We compared the sensitivity of modelled GPP to two different sources of surface radiation (R_s): (1) the ANUCLIM method which only captures the meso-scaled factors; and (2) the SRAD method which incorporates the topographic effects GPP was calculated using the radiation use efficiency (RUE) model (Roderick et al., 2001) to discern general patterns of vegetation productivity at a sub-catchment (i.e. sub-water shed) scale. The radiation use efficiency approach uses the normalized difference vegetation index (NDVI) derived from satellite data (MODIS TERRA), along with estimates of solar radiation at the top of the atmosphere (R_o) and canopy (R_s). In this approach, R_o and R_s capture the influence of diffuse irradiance in canopy photosynthesis and vegetation productivity respectively. This research showed that R_s calculated using the SRAD program provides important discrimination of GPP regimes at a sub-catchment scale, as the result of minimum and maximum daily radiation varying between shaded and exposed surfaces. However, mean daily radiation at a whole-of-catchment scale did not differ between the two sources as the differences in the minimum and maximum daily values tend to cancel each other out. Applications of GPP models therefore need to consider whether topographic factors are important and select the appropriate source of R_s values. GPP models should also reflect understanding of radiation use efficiency. However, further research is required especially with respect to the influence of water stress on plant response.     

Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data

Obtaining quantitative information about the recovery of fire-affected ecosystems is of utmost importance from the management and decision-making point of view. Nowadays the concern about natural environment protection and recovery is much greater than in the past. However, the resources and tools available for its management are still not sufficient. Thus, attention and precision is needed when decisions must be taken. Quantitative estimates on how the vegetation is recovering after a fire can be of help for evaluating the necessity of human intervention on the fire-affected ecosystem, and their importance will grow as the problem of forest fires, climate change and desertification increases.This article performs a comparison of methods to extract quantitative estimates of vegetation cover regrowth with Landsat TM and ETM+ data in an area that burned during the summer of 1998 in the Liguria region (Italy). In order to eliminate possible sources of error, a thorough pre-processing was carried out, including a careful geometric correction (reaching RMSE lower than 0.3 pixels), a topographic correction by means of a constrained Minnaert model and a combination of absolute and relative atmospheric correction methods. Pseudo Invariant Features (PIF) were identified by implementing an automated selection method based in temporal Principal Component Analysis (PCA), which has been called multi-Temporal n-Dimensional Principal Component Analysis (mT-nD-PCA).Spectral Mixture Analysis (SMA) was compared against quantitative vegetation indices which are based on well known traditional vegetation indices like Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Accuracy assessment was performed by regressing vegetation cover results obtained with each method against field data gathered during the fieldwork campaign carried out in the study area. Results obtained showed how vegetation cover fractions extracted from the NDVI based quantitative index were the most accurate, being superior to the rest of the techniques applied, including SMA.     

Knowing fire incidence through fuel phenology: A remotely sensed approach

Fire is a basic ecological factor that contributes to determine vegetation diversity and dynamics in time and space. Fuel characteristics play an essential role in fire ignition and propagation; at the landscape scale fuel availability and flammability are closely related to the vegetation phenology that directly affects wildfire pattern in time and space. In this view, the annual normalized difference vegetation index (NDVI) profiles derived from high temporal resolution satellites, like SPOT Vegetation, represent an effective tool for monitoring the coarse-scale vegetation seasonal timing. The objective of this study thus consists in quantifying the explanatory power of multitemporal NDVI profiles on the fire regime characteristics of the potential natural vegetation (PNV) types of Sardinia (Italy) over a 5-year period (2000-2004). The results obtained show a good association between the NDVI temporal dynamics of the PNV of Sardinia and the corresponding fire regime characteristics, emphasizing the role of the bioclimatic timing of the vegetation in controlling the coarse-scale wildfire spatio-temporal distribution of Sardinia. By providing a sound phytogeographical framework for describing different wildfire regimes, PNV maps can thus be considered helpful cartographic documents for fire management strategies at the landscape scale.     

Changes in vegetation persistence across global savanna landscapes, 1982–2010

We present a global analysis of the changing face of vegetation persistence in savanna ecosystems by boreal seasons. We utilized nearly 30 years of monthly normalized difference vegetation index data in an innovative time-series approach and developed associated statistical significance tests, making the application of continuous vegetation metrics both more rigorous and more useful to research. We found that 8,000,000–11,000,000 km² of savanna have experienced significant vegetation decline during each season, while 20,000,000–23,000,000 km² have experienced an increase in vegetation persistence during each season, relative to the baseline period (1982–1985). In addition, with the exception of the March–April–May season, which is mixed, the pattern of significant vegetation persistence in the Northern Hemisphere is almost exclusively positive, while it is negative in the Southern Hemisphere. This finding highlights the increasing vulnerability of the Southern Hemisphere savanna landscapes; either resulting from changing precipitation regimes (e.g., southern Africa) or agricultural pressures and conversions (e.g., South America).