Driving Mechanism of Gross Primary Production Changes and Implications for Grassland Management on the Tibetan Plateau

The contribution of climatic change and anthropogenic activities to vegetation productivity are not fully understood. In this study, we determined potential climate-driven gross primary production (GPPp) using a process-based terrestrial ecosystem model, and actual gross primary production (GPPa) using MODIS Approach in alpine grasslands on the Tibetan Plateau from 2000 to 2015. The GPPa was influenced by both climatic change and anthropogenic activities. Gross primary production caused by anthropogenic activities (GPPh) was calculated as the difference between GPPp and GPPa. Approximately 75.63% and 24.37% of the area percentages of GPPa showed increasing and decreasing trends, respectively. Climatic change and anthropogenic activities were dominant factors responsible for approximately 42.90% and 32.72% of the increasing area percentage of GPPa, respectively. In contrast, climatic change and anthropogenic activities were responsible for approximately 16.88% and 7.49% of the decreasing area percentages of GPPa, respectively. The absolute values of the change trends of GPPp and GPPh of meadows were greater than those of steppes. The GPPp change values were greater than those of GPPh at all elevations, whereas both GPPp and GPPh showed decreasing trends when elevations were greater than or equal to 5000 m, 4600 m and 4800 m in meadows, steppes and all grasslands, respectively. Climatic change had stronger effects on the GPPa changes when elevations were lower than 5000 m, 4600 m and 4800 m in meadows, steppes and all grasslands, respectively. In contrast, anthropogenic activities had stronger effects on the GPPa changes when elevations were greater than or equal to 5000 m, 4600 m and 4800 m in meadows, steppes and all grasslands, respectively. Therefore, the causes of actual gross primary production changes varied with elevations, regions and grassland types, and grassland classification management should be considered on the Tibetan Plateau.     

Mapping shoreline indicators on a sandy beach with supervised edge detection of soil moisture differences

This study describes a method to map shoreline indicators on a sandy beach. The hypothesis is that, on this beach, spectral albedo is predominantly determined by moisture content and water lines can, therefore, be detected as albedo contrasts. A laboratory experiment is performed to relate moisture content to image albedo, and supervised edge detection is subsequently used to map the shoreline indicators with remote sensing imagery. The algorithm is tested with data from visible, near-infrared and shortwave-infrared wavelength regions. These results are compared to shoreline indicators obtained by a field survey and a shoreline indicator derived from a digital elevation model. Both the water line present when the imagery was acquired, as well as the maximum extent of the last flood, can be detected as a single edge. Older high water lines are confused with the last high water line and appear dispersed, as there are multiple debris lines present on the beach. The low water line, usually in saturated sand, also appears dispersed due to the presence of channels and troughs. Shorelines are constant moving boundaries, which is why shoreline indicators are used as a proxy. Unlike a mathematical indicator that is based on an elevation model, our method is more sensitive to the dynamic nature of shorelines. Supervised edge-detection is a technique for generating reproducible measurements of shoreline indicator positions over time, and aids in the monitoring of coastline migration.     

草原矿区长时序植被覆盖度变化趋势对比分析

呼伦贝尔草原区生态脆弱,在人类活动和气候等因素影响下草原生态变化备受关注。本文以宝日希勒矿区及周边为研究区,应用1985-2015年Landsat年度最大合成NDVI数据,采用像元二分模型反演植被覆盖度;分别利用一元线性回归法和Sen+Mann-Kendall法对研究区植被覆盖度趋势和空间差异进行了对比分析。结果表明：两种方法得到的植被变化趋势基本一致,Sen+Mann-Kendall方法相较于一元线性回归法对植被覆盖度改善和退化反应更为敏感。研究结果有助于科学评价长时序煤炭开发活动对地表生态的影响并为长时序植被变化监测提供方法参考。     

基于Google Earth Engine与机器学习的省级尺度零散分布草地生物量估算

大区域草地地上生物量估算对草地资源利用管理及全球碳循环研究具有重要意义。为高效快速地估算大区域零散分布草地地上生物量,本文选取安徽省为研究区,在谷歌地球云引擎（Google Earth Engine）平台的支撑下,通过机器学习方法建立Landsat 8 OLI及其他辅助数据与地面实测草地地上生物量之间的联系,开展了草地零散分布地区省级尺度草地地上生物量高分辨率估算,并与传统的基于归一化植被指数（NDVI）回归模型进行了比较。研究结果表明,综合利用光谱与地形因子的机器学习方法,估算零散化分布草地地上生物量的精度可以达到65%以上,其中分类回归树（CART）模型R²=0.57,预测精度为68.60%,支持向量机（SVM）模型R²=0.59,预测精度为75.74%,而使用NDVI的回归分析产生的误差较大,R²=0.37,预测精度为57.51%,因此机器学习方法相对于传统基于NDVI的回归分析具有明显优势。另外,谷歌地球云引擎平台数据来源广泛、获取方便,可以高效地实现海量影像数据的预处理及计算分析,大大提升了工作效率,与地面调查数据的结合可实现更大区域乃至全国尺度上的零散分布草地地上生物量高分辨率遥感估算。     

放牧草地生态系统中氮素的损失和管理

对于大多数放牧草地,Ｎ素都是限制生产力的最重要因素之一,而生态系统中Ｎ素的损失量又很大,Ｎ素以ＮＯ－３、ＮＨ３、Ｎ２Ｏ等形态进入水圈和大气圈,不仅造成了生产上的资源浪费,而且对全球环境和人类健康都产生了深远的影响。本文将对草地生态系统中Ｎ素损失的主要过程（如氮挥发、反硝化和ＮＯ－３淋溶等）及影响因素研究的进展情况进行简要地综述,然后对ＩＭＧＡＲＳＳ项目研究中关于Ｎ素损失研究部分提出一些建议。草地生态系统中Ｎ素损失的主要途径包括：土壤、植物、动物排泄物和肥料的氨挥发;生物和化学反硝化;淋溶;动物体和动物产品对Ｎ素的固持;动植物残体和动物排泄物的燃烧;动物以排泄物形式将Ｎ素从生产区转移到非生产区;通过土壤侵蚀而损失等。放牧加速了草地生态系统中Ｎ素的损失速率。从全球角度分析ＮＨ３的来源后认为,生物残体燃烧是最大的ＮＨ３源,其次是自然土地。对欧洲ＮＨ３来源的研究表明,与人类活动有关的ＮＨ３挥发总量的大部分来源于牲畜排泄物的氨挥发。由于方法的限制,对田间条件下的反硝化测定较少,动物排泄物对Ｎ２Ｏ的贡献及对全球变化影响的研究数据较少。但研究表明,草地尿、粪斑处Ｎ２Ｏ释放速率很高,动物排泄物可能是最重要的Ｎ２Ｏ源。在半干     

Biogeographical patterns of arbuscular mycorrhizal fungi diversity in China's grasslands

Arbuscular mycorrhizal fungi(AMF) are universally mutualistic symbionts that colonize the fine roots of most vascular plants. However, the biogeographical patterns and driving factors of AMF diversity of plant roots in grasslands are not well investigated. In this study, we used high-throughput sequencing techniques and bioinformatics to evaluate the AMF richness of 333 individual plant roots in 21 natural grassland ecosystems in northern China, including the Loess Plateau(LP), the Mongolian Plateau(MP), and the Tibetan Plateau(TP). The AMF richness showed a significant parabolic trend with increasing longitude. In regional situations, the AMF richness in the grasslands of the MP(60.4 ± 1.47) was significantly higher than those of the LP(46.4 ± 1.43) and TP(44.3 ± 1.64). Plant traits(including plant families, genera, and functional groups) explained the most variation in the AMF richness across China's grasslands, followed by energy and water; soil properties had the least effects. The results showed the biogeographical patterns of the AMF richness and the underlying dominant factors, providing synthetic data compilation and analyses in the AMF diversity in China's grasslands.     

甘肃省肃南县草地过度放牧的社会经济成因分析

我国草地资源位居世界第二位,但目前退化情况非常严重,已经成为牧区生态环境保护和经济发展的主要制约因素。导致草地退化的原因多种多样,其中自然因素是不可调控因素,但在近50年内并非为导致我国草地大面积退化的主要原因,实际上,人为因素才是最为主要的原因。在导致草地退化的诸多人为因素中,过度放牧被认为是造成草地退化的主要因素。通过对肃南县草地退化进行的农户问卷调查,结合有关统计资料,从农户角度阐释过度放牧行为产生的外在机制和内在机制,认为农户追求更高的家庭收入与现实经济状况之间的矛盾是导致其过度放牧行为产生的根本原因。     

基于InVEST模型的宁夏盐池县禁牧草地生态补偿标准空间识别

以农牧交错带宁夏盐池县草地为研究对象,综合集成草地植被与土壤调查、草地信息提取、遥感反演、空间分析等技术方法,基于本地化的InVEST模型,选取碳储存、土壤保持和水源涵养3项关键生态系统服务类型,对禁牧前后（2000年,2015年）草地生态系统服务物质量进行精准评估,在此基础上,采用市场价值法对其价值化,测算禁牧前后草地生态系统服务价值变化量,以此确定生态补偿标准,并借助冷热点、三维趋势分析对生态补偿标准进行不同尺度的空间识别。结果显示：① 禁牧以来,盐池县草地生态系统服务显著增强,其中碳储量增加45.66×10⁴ t、强度增加0.42 t/hm²,水源涵养量增加9 351×10⁴ t、强度增加211.14 t/hm²,土壤保持量增加2 091×10⁴ t,强度增加44.93 t/hm²;② 草地生态补偿标准县域为309.21元/hm²、乡镇尺度介于7.25~1 146.36元/hm²、村级介于3.04~2 074.56元/hm²,村级尺度更能体现草地的生态贡献;③ 补偿标准的三维趋势呈现由西向东提高、南北向呈“U”型分异的特征,补偿热点区集中于盐池县东南部和东北部。     

三江源南部草地退化时空特征分析

本文利用三江源南部1977年MSS、1990年TM和2004年TM三期遥感影像,结合1∶10万地形图、1∶100万草地图、1∶100万植被图和野外考察等资料,分析该区草地退化的时空变化特征和规律。结果表明:该区草地总体呈轻度退化,即为轻度破碎化退化、轻度覆盖度下降、轻度破碎化和轻度覆盖度下降复合型退化,共占研究区退化总面积的84.3%;轻度退化草地主要散布在唐古拉山东北部区域,中度退化草地多数分布于河流、居民点周围,草地轻度沙化/盐化退化主要发生在湖盆周围、河岸附近、滩地等区域。草地退化面积随海拔高度升高近似于正态分布,退化面积集中4 400～5 300m左右。1977～1990年和1990～2004年前后两期草地退化面积占研究区草地面积12.6%和18.9%,指示着草地状况的恶化。同时,对前后两个时段比较发现,草地退化发生以破碎化为先导,随后发生覆盖度持续降低。沼泽和沼泽化草甸的变化反映了区内气候的趋干化。另外,受气候条件等因素的影响,前期草地和沼泽变化有转好迹象。     

深水沉积研究前缘问题

深水沉积研究经历了50年,争论也持续了50年。从浊流及鲍玛序列开始,随着对浊流定义的过分使用,到今天对鲍玛序列作为浊积岩相序及相关的扇模式普遍持否定态度,深水沉积研究经历了一个认识的旋回。主要问题和争论的焦点是：是否所有深水砂岩都是浊流成因,鲍玛序列能否代表浊积岩相序;是否所有的深水扇水道下方都能形成席状的、平行的、加厚的、具有丘状外形的浊积砂岩沉积;是否可以利用地震方法识别深水扇的砂岩储层。对“浊流”概念的过分使用把深水扇模式内几乎所有深水沉积都解释成浊流成因,导致了曾经为之建立模式的学者纷纷撰文抛弃原有扇模式。深水沉积研究面临着对过往认识的否定和如何建立新的理论模式。尽管浊流及相关的深水扇模式研究走向穷途末路,石油工业却从浊流理论和相关模式中获得了许多油气发现,勘探家们仍然希望通过这些模式寻找更多的油气,科学理论和应用出现了分化。对深水沉积过程和流态的认识及沉积模式的建立是当今深水沉积研究的难点,实现深水砂岩储层的有效预测是深水沉积研究的主要目的。我国深海油气勘探在即,深水沉积的科学问题同样不可逾越。