GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine

The loess area in the northern part of Baoji City, Shaanxi Province, China is a region with frequently landslide occurrences. The main aim of this study is to quantitatively predict the extent of landslides using the index of entropy model(IOE), the support vector machine model(SVM) and two hybrid models namely the F-IOE model and the F-SVM model constructed by fractal dimension. First, a total of 179 landslides were identified and landslide inventory map was produced, with 70%(125) of the landslides which was optimized by 10-fold crossvalidation being used for training purpose and the remaining 30%(54) of landslides being used for validation purpose. Subsequently, slope angle, slope aspect, altitude, rainfall, plan curvature, distance to rivers, land use, distance to roads, distance to faults, normalized difference vegetation index(NDVI), lithology, and profile curvature were considered as landslide conditioning factors and all factor layers were resampled to a uniform resolution. Then the information gain ratio of each conditioning factors was evaluated. Next, the fractal dimension for each conditioning factors was calculated and the training dataset was used to build four landslide susceptibility models. In the end, the receiver operating characteristic(ROC) curves and three statistical indexes involving positive predictive rate(PPR), negative predictive rate(NPR) and accuracy(ACC) were applied to validate and compare the performance of these four models. The results showed that the F-SVM model had the highest PPR, NPR, ACC and AUC values for training and validation datasets, respectively, followed by the F-IOE model.Finally, it is concluded that the F-SVM model performed best in all models, the hybrid model built by fractal dimension has advantages than original model, and can provide reference for local landslide prevention and decision making.     

Soil erosion of unpaved loess roads subjected to an extreme rainstorm event: a case study of the Jiuyuangou watershed on the Loess Plateau,China

Rainfall can cause serious soil loss in the Loess Plateau hilly and gully region, but little focus has been placed on the extreme rainstorm effects on unpaved loess road soil erosion. A field survey method was used to investigate the erosional effects of the "7·26" heavy rainfall event on unpaved loess roads in the Jiuyuangou watershed of the Loess Plateau, China. The results showed that the average and maximum widths of the eroded gullies that formed on the unpaved roads were 0.65-1.48 m and 1.00-3.60 m, respectively. The average and maximum depths of theeroded gullies were 0.42-1.13 m and 0.75-4.30 m, respectively. The average width-to-depth ratio was 1.31, indicating that the widening effect was greater than the downcutting effect in the eroded gullies. In addition, the gully density ranged from 0.07 to 0.29 m m~(-2), and the road surface dissection degree ranged from 0.03 to 0.41 km~2 km~(-2). Eroded gullies generally developed at the slope toe of the cut bank side. The average eroded gully width and depth at turns in the road were 1.47-2.64 times and 1.30-3.47 times greater, respectively, than those in other road sections. The road erosion modulus increased from the upper section to the lower section of the roads. The average road erosion modulus of the study catchment was 235,000 t km~(-2). Turns in the road were associated with collapses, sinkholes and other gravitational erosion phenomena. The amount of road erosion under extreme rainfall conditions is mainly related to the interactions among road length, width, slope and soil bulk density. Our results provide a useful reference for developing further measures for preventing road erosion on the Loess Plateau.     

Evaluation of the integrated multi-satellite retrievals for global precipitation measurement over the Tibetan Plateau

The availability of high-resolution satellite precipitation measurement products provides an opportunity to monitor precipitation over large and complex terrain and thus accurately evaluate the climatic, hydrological and ecological conditions in those regions. The Global Precipitation Measurement(GPM) mission is an important new program designed for global satellite precipitation estimation, but little information has been reported on the applicability of the GPM’s products for the Tibetan Plateau(TP). The object of this study is to evaluate the accuracy of the Integrated Multi-Satellite Retrievals for GPM(IMERG) Final Run product under different terrain and climate conditions over the TP by using 78 ground gauges from April 2014 to December 2017. The results showed the following:(1) the 3-year average daily precipitation estimation in the IMERG agrees well with the rain gauge observations(R~2=0.58, P0.01), and IMERG also has a considerable ability to detect precipitation, as indicated by a high probability of detection(78%-98%) and critical success index(65%-85%);(2) IMERG performed better at altitudes from 3000 m to 4000 m with a small relative bias(RB) of 6.4%. Precipitation change was not significantly affected by local relief;(3) the climate system of the TP was divided into four climate groups with a total of 12 climate types based on the K?ppen climate classification system, and IMERG performed well in all climate types with the exception of the arid-desert-cold climate(Bwk) type. Furthermore, although IMERG showed the potential to detect snowfall, it still exhibits deficiencies in identifying light and moderate snow. These results indicate that IMERG could provide more accurate precipitation data if its retrieval algorithm was improved for complex terrain and arid regions.     

Assessing effects of “source-sink” landscape on non-point source pollution based on cell units of a small agricultural catchment

Ascertaining the relationship between "source-sink" landscape and non-point source(NPS) pollution is crucial for reducing NPS pollution, however, it is not easy to realize this target on cell unit scale. To reveal the relationships between "sourcesink" landscape and NPS pollution based on cell units of a small catchment in the Three Gorges Reservoir Region(TGRR), the runoff and nutrient yields were simulated first by rainfall events on a cell unit scale based on the Annualized AGricultural Non-Point Source Pollution Model(AnnAGNPS). Landscape structure and pattern were quantified with "sourcesink" landscape indicators based on cell units including landscape area indices and locationweighted landscape indices. The results showed that:the study case of small Wangjiagou catchment highlighted a good prediction capability of runoff and nutrient export by the AnnAGNPS model. Throughout the catchment, the spatial distribution trends of four location-weighted landscape indices were similar to the trends of simulated total nitrogen(TN) and total phosphorus(TP), which highlighted the importance of spatial arrangement of "source" and "sink" landscape types in a catchment when estimating pollutant loads. Results by Pearson correlation analysis indicated that the location-weighted landscape index provided a more comprehensive account of multiple factors, and can better reflect NPS-related nutrient loss than other landscape indices applied in single-factor analysis. This study provides new findings for applying the "source-sink" landscape indices based on cell units in small catchments to explain the effect of "source-sink" landscape on nutrient export based on cell unit, and helps improve the understanding of the correlation between "source-sink" landscape and NPS pollution.     

祁连山中段青海云杉林土壤肥力质量评价研究

以祁连山排露沟流域青海云杉林为研究对象,研究了海拔梯度上土壤肥力因子的分布特征及变化规律,并运用主成分分析法对青海云杉林土壤肥力状况进行了评价。结果表明：（1）研究区土壤呈碱性,pH值均大于8.0;高海拔地区（3 300 m）含水量达到过饱和状态,各土层含水量均大于100%;随海拔升高,全氮含量呈增大趋势,全钾含量呈减小趋势,而全磷含量呈先减小后增大趋势;不同海拔梯度速效磷含量差异不显著（P>0.05）,海拔3 300 m处速效钾含量显著高于其他海拔段（P<0.05）。（2） 不同海拔梯度下土壤有机质、全氮、全磷、全钾、速效磷和速效钾含量都有明显的“表聚效应”,其中3 300 m处0~10 cm土层有机质含量高达325.93 g·kg^-1,是本海拔段其他土层的1.6~1.8倍,是同土层其他海拔段的1.3~2.0倍。（3） 土壤肥力因子间关系密切,土壤含水量与有机质、全氮呈极显著正相关关系,与土壤容重、pH和全钾呈极显著负相关关系,土壤养分含量之间存在不同程度的显著正相关关系。（4） 不同海拔梯度土壤肥力质量为：3 300 m>3 200 m>3 100 m>3 000 m>2 900 m。     

关于确保甘肃省祁连山生态保护红线落地并严守的科技支撑建议

祁连山是中国西部重要生态安全屏障,是国家重点生态功能区,其生态保护与治理一直受到国家、地方政府和当地居民高度关注。目前,在祁连山的生态保护工作中,缺乏对现状基础数据的全面掌握,区域经济发展与生态环境保护矛盾突出,生态环境综合监测系统不完备,生态补偿机制推进实施缺乏创新。在全面梳理祁连山目前存在的重大生态问题的基础上,提出了祁连山生态保护红线落地并严守的建议,包括加快开展祁连山综合科学考察、完善并落实开展生态补偿机制、探索以正面清单为导向的协调发展模式、促进生态安全与地方经济社会可持续发展相协调、进一步实施重大配套政策、完善保障支撑体系等。     

基于时间序列遥感数据的嵯岗国家沙化土地封禁保护成效评估

2013年中国启动实施了内蒙古自治区新巴尔虎左旗嵯岗国家沙化土地封禁保护区试点。本研究利用2001-2017年生长季NDVI数据分析了嵯岗封禁保护区及周边区域植被长势时空演变特征,分析了多年的生长季降水量和历年旱情,之后利用植被降水利用效率和NDVI残差趋势分析对保护成效进行了评估。结果表明:(1)封禁项目实施之前,封禁区内外植被长势变化趋势基本一致,而在项目实施后的2016、2017年,封禁区内NDVI距平明显高于封禁区外;(2)降水为影响该区域植被长势的主导气候因素,在封禁实施后的2015-2017年该区域连续3 a干旱,极大地限制了区域植被生长;(3)封禁区内植被降水利用效率和NDVI残差均呈现明显的增加趋势,而封禁区外变化不明显,说明封禁提升了植被的自我修复能力;(4)嵯岗封禁保护区由3个地块组成,其中嵯岗林场封禁效果比牧场八队和牧场十一队明显。国家沙化土地封禁保护措施有效促进了植被自我修复,提高了沙漠生态系统应对气候变化的能力。     

国外第二居所研究特征、阶段与热点——基于CiteSpace的可视化分析

基于CiteSpace的可视化分析,并结合高被引文献的阅读,总结了国外第二居所研究的特征、阶段与热点;国外第二居所研究呈现出总量上波动上升、学科上呈交叉融合、区域上由发达国家向发展中国家扩散的趋势,欧洲、北美与南非是第二居所研究的核心区域,该领域的研究已形成两个核心学术共同体;第二居所的研究可以划分为3个阶段:初始复兴期(1996年~2003年),研究成果较少;快速复兴期(2004年~2012年),研究成果迅速增加,研究视角趋于多元化;全面复兴期(2013年~2018年),研究区域不断向欠发达地区扩张,新的热点不断出现。主要研究热点包括第二居所的概念体系与理论辨析,第二居所旅游影响,第二居所业主行为特征与情感依恋。     

大亚湾西部海域沉积物中生物硅的含量及其分布特征

采用2. 0 mol/dm~3Na_2CO_3溶液5h单点化学提取-硅钼蓝连续分光光度分析法分别测定了大亚湾西部海域13个表层沉积物和1个沉积物岩芯中生物硅的含量.表层沉积物和沉积物岩芯中生物硅含量占比分别为0. 69%～2. 02%和1. 24%～2. 05%,平均值分别为1. 42%和1. 60%.结果证实西大亚湾沉积物中生物硅含量水平与我国南海近岸海域基本一致.在210Pbex测年的基础上,通过分析沉积物岩芯中生物硅、有机物、无机碳等多指标,揭示近百年来大亚湾周边环境变化对海洋环境的影响,证实了上世纪80年代以来西大亚湾周边农业、海水养殖业和工业发展等人类活动加剧了该海域生态环境变化,尤其90年代核电站运行对海洋生态变化造成一定的影响.     

Comparative study of otolith and sulcus morphology for stock discrimination of yellow drum along the Chinese coast

Otolith morphology is widely used for fish stock identification. The sulcus, a structure on the medial side of the otolith, is an important feature in morphological analysis. This study was conducted to evaluate the feasibility of using sulcus morphology for stock identification and to compare its performance with commonly used otolith morphology analysis. Otoliths were collected and analyzed from three geographical groups (the Huanghe (Yellow) River estuary, HHE;the Jiaozhou Bay, JZB;and the Changjiang (Yangtze) River estuary, CJE) of yellow drum Nibea albiflora. The results show that the analysis of sulcus morphology based on shape indices (SIs), elliptic Fourier coefficients (EFc), and a combination of the two parameters identified stocks at overall classification rates of 51.0%, 72.5%, and 73.2%, respectively. These classification rates are similar to those obtained using otolith morphology analysis (57.0%, 73.8%, and 76.5% by SIs, EFc, and their combination, respectively). The findings suggest that sulcus morphology is comparable to the commonly used otolith morphology for identifying stocks of sciaenids, such as the yellow drum. For both otolith and sulcus morphology, EFc could identify the stocks more efficiently than SIs, while the combination of SIs and EFc was even better.