Subjects: Geosciences >> Geography submitted time 2023-04-07 Cooperative journals: 《干旱区地理》
Abstract： Aerodynamic roughness is defined as the height at which the wind speed becomes zero under neutral and stable conditions. It is an important parameter for measuring the momentum and energy exchange between the underlying surface and atmosphere, and it is critical for investigating various surface processes and climate change. However, it has always been difficult to estimate aerodynamic roughness accurately at the regional scale, and there is no unified estimation model presently. Therefore, the parameterization of aerodynamic roughness is a topic worthy of further study. As a long-range monitoring method, remote sensing technology has the advantages of macroscopic and rapid acquisition of ground feature information, and its ability to achieve dynamic monitoring at the regional scale or a larger scale in estimating the aerodynamic roughness of vegetation-covered surfaces. Therefore, using remote sensing technology to estimate aerodynamic roughness has become a hot issue at home and abroad in recent years. In this study, the progress of research on aerodynamic roughness at home and abroad in recent years is systematically described. The estimation methods are divided into two categories: one is based on measured data, and the other is the remote sensing method, which is rapidly advancing. This study primarily introduces the method of estimating the aerodynamic roughness of the underlying surface of vegetation by remote sensing technology. Methods based on measured data include the canopy height fixed ratio method, field experiment method, and wind tunnel method; remote sensing methods include vegetation index, LIDAR, and multisource remote sensing synergistic methods. In addition, the advantages and disadvantages of the different methods are summarized at the end of each section. Finally, this study analyses the influence of meteorological factors and morphological characteristics of surface roughness elements on aerodynamic roughness and discusses the development trends and problems of remote sensing techniques in estimating aerodynamic roughness, aiming to provide ideas for subsequent research on remote sensing monitoring of aerodynamic roughness.
Subjects: Geosciences >> Geography submitted time 2023-02-27 Cooperative journals: 《干旱区地理》
Abstract： The landscape diversity index (LDI) is not only an important indicator in landscape ecology research but also an important component in biodiversity conservation. Based on the land use/cover raster data (30-m resolution), the spatiotemporal variation and scale- dependence characteristics of the LDI of an oasis along the Yellow River in Ningxia, China, have been studied using Neighborhood and Focal tools in ArcMap from 1975 to 2000. The results are as follows: (1) The LDI, which is measured by a square with a side length from 90 m to 6000 m, had obvious spatial scale-dependent characteristics based on five times repeat, and its turning point was 3000 m. (2) The change trend of the LDI had been cyclical in the past decades, with a turning point of 2000. During the study period, the LDI exhibited a decreasing trend from 1975 to 2000, and the analysis of LDI zoning indicated that the main characteristics were as follows: the class area (CA) of the degraded area was the largest, and the CA of the improved area was the smallest, which were 6840 km2 and 1332 km2 , respectively. In contrast, there was an increasing trend for the LDI from 2000 to 2020, mainly characterized by the maximum CA in the impervious area and the minimum CA in the degraded area, which were 7848 km2 and 792 km2 , respectively. Because the initial LDI in 2000 was the lowest in the entire period, its improvement status in the later period did not reach that of the early period. (3) The conversions of the LDI-grading area were mainly characterized by the transfer from the early improved area to the late impervious area (796 km2 , 60.5% of the improved area) and the transfer from the degraded area to the impervious area (3519 km2 , 51.5% of the degraded area) and the improved area (3036 km2 , 44.4% of the degraded area), respectively. (4) The change in the landscape diversity pattern was characterized by a negative correlation between CA and relative splitting index, and this relationship mechanism was universal in different periods and change types. Thus, it is to best understand the landscape diversity change with credible spatiotemporal scales in a regional landscape study. It is necessary to ensure that the research results are not only used for reference and sharing but also used to visualize and analyze regional landscape diversity
Subjects: Environmental Sciences, Resource Sciences >> Basic Disciplines of Environmental Science and Technology submitted time 2020-04-26 Cooperative journals: 《干旱区研究》
Abstract： 降雨波动是荒漠植被生长发育最主要制约因子，直接影响到植被组成、结构和功能的变化。梭梭作为干旱区植被的优势种之一，在防风固沙、涵养水源方面扮演重要角色，本文的目的是理解不同林龄人工梭梭林植被生产力对降雨事件的响应过程。本文以黑河中游荒漠-绿洲过渡带不同林龄人工梭梭为研究对象，以2017—2018年5月1日至9月31日时序MODIS-NDVI（近似于年生产力）日数据和对应日降雨数据为基础，采用阈值模型和统计学方法分析了其NDVI对不同降雨等级的滞后响应过程。结果表明：① 降雨小于2 mm时，不同林龄梭梭NDVI增长率均在5%以上,大于40 a生的反应最迅速，增长率为10%～56%；2～5 mm时，10～20 a生的变幅最大，增长率为11%～83%；5～10 mm时，20～40 a的最为敏感，增长幅度最高达到170%；大于20 mm时，大于40 a的NDVI增长率大于其他3个林龄，增幅最高达76%。② 降雨后，不同林龄人工梭梭NDVI响应时间不同，0～10 a的为7.4(±2.8)d，10～20 a的为8.3(±3.1)d，20～40 a的为8.1(±2.7)d，大于40 a的为8.2(±3.2)d。③ 降雨脉动是促成荒漠-绿洲过渡带植被生产力迅速变化的关键因素。不同林龄梭梭对不同级别降雨响应模式不同，2～10 mm降雨条件下，人工梭梭林NDVI增加幅度最大，高于其他2个降雨量量级NDVI响应变化幅度。依据上述分析可以推断，在荒漠-绿洲过渡带以10 mm以下降雨为主导的条件下，10～20 a生及20～40 a生人工梭梭林在防护体系的作用是最重要的。
Subjects: Geosciences >> Atmospheric Sciences submitted time 2018-10-23 Cooperative journals: 《干旱区地理》
Abstract： 利用北方风蚀区155个气象站点1971—2015年平均风速数据，采用气候趋势分析、空间插值和小波分析等方法分析北方风蚀区平均风速的时空变化趋势。结果表明：近45a 来，北方风蚀区年平均风速为2.70 m•s-1，呈明显减小趋势，其递减速率为0.017 m•(s•a) -1 (α=0.001)，1980s风速减小最快，1990s减小最缓慢,2010s风速出现增大趋势；我国北方风蚀区四季的平均风速均呈现下降趋势，下降速度春季>夏季>秋季>冬季（α=0.001），不同年代不同季节风速变化存在较大差异，2010s除春季外其他季节风速均呈现增大趋势；空间分布上显示，风速变化幅度空间分布差异明显，北方风蚀区内的新疆西北部和东南部、青海、内蒙古中部和东北部、黑龙江以及吉林为风速降低较快的区域，甘肃东南部、宁夏、陕西和山西北部以及新疆的东北部和西部等地区是风速降低不明显的区域。春季和夏季风速降低较快的区域面积扩大，冬季和秋季风速降低较缓的区域扩大；平均风速存在多时间尺度的周期性结构特征，28 a时间尺度左右为风速变化的主周期，平均变化周期为18 a。
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