福建地壳运动观测网络观测成果初步分析

利用最新GAMIT软件,对福建省地壳运动观测网络的厦门、福州、泉州、龙岩、南平5个GPS基准站2004年3月1日至2005年3月6日的观测资料在ITRF2000全球坐标系统框架下,使用IGS精密星历数据及其核心观测站的坐标和运动速率为控制参数进行了统一解算,并利用GLOBK软件进行联合平差,得到了5个测站的观测精度。结果表明,福建地区形变南强北弱、东强西弱,且该地区随中国大陆较均匀地向SE（SEE）方向运动。     

风廓线雷达大气风场观测误差分析

依据风廓线雷达工作原理和风的计算公式,分析影响大气风场观测误差的主要因素,重点分析了雷达回波SNR对风的观测精度影响和GPS探空对比试验。结果表明:①风速观测精度主要取决于波束倾角、雷达技术参数和大气折射率结构常数C2n的垂直分布;风速及风速观测精度越大,风向观测精度越大。②在同种观测模式下,波束倾角与C2n越大,风场观测精度越高。③同一观测模式的SNR越大,风速观测误差越小;不同模式间的大气风场观测精度相差较大。④对比试验的风速风向相关性较好,但相对偏差较大,尤其低空更为明显。     

基于旋翼无人机观测的雾天和霾天VOCs垂直分布特征研究

为研究雾和霾天气下VOCs时空变化特征,于2020年11月19日—2021年1月15日在江苏省东海国家气象观测站进行为期58 d的外场观测试验。利用自主研发的多旋翼无人机捕获2次辐射雾和2次霾天气过程,获得气温、气压、相对湿度、风向、风速、VOCs、O₃等7种要素100多条垂直廓线。结果表明：时间上,霾过程夜间VOCs体积浓度(0.225～0.253 ppm(parts per million, 1 ppm=10^-6))明显高于白天(0.191～0.205 ppm),雾形成前体积浓度(0.121～0.239 ppm)显著高于雾过程(0.056～0.209 ppm)。雾过程中VOCs体积浓度与雾强度变化相反,雾层高度与VOCs体积浓度剧烈变化高度一致,雾层(<200 m)中VOCs体积浓度(0.172～0.178 ppm)明显减小,显著低于雾形成前(0.195～0.240 ppm),雾层以上浓度变化大,雾结束后1 h内保持雾过程中分布特点。雾对逆温层中的水溶性污染物有清除作用,VOCs体积浓度和O₃质量浓度均下降。     

Hilbert变换在海浪波高与周期联合分布函数中的应用

Longuet-Higgins(1983)[1]导出了波高与周期的联合分布函数,此分布函数虽然与实际数据符合良好,但存在很大的缺陷,如:由此分布函数得出的波高分布为形式较为复杂的非Rayleigh分布,很难应用于工程计算中。孙孚(1988a)[2]应用射线理论导出了一种波高与周期联合分布,虽然弥补了Longuet-Higgins的一些缺陷,但推导过程过于复杂。本文在窄谱假定下通过应用Hilbert变换方法得出新的分布函数并与前两者比较,表明Hilbert变换的方法不但简便,而且完全克服Longuet-Higgins的不足,可以方便的应用于工程计算中。本文也为Hilbert变换的方法在工程中的应用提供了理论依据。     

南海开放共享航次关键科学问题的思考——从多尺度海洋动力学角度出发

南海是西太平洋最大的边缘海, 通过一系列的海峡与西太平洋和印度洋相联通, 其不同时空尺度的海洋环流动力过程及其生态环境效应是南海区域海洋学研究的重要内容。自20世纪50年代末全国第一次海洋普查开始, 我国对海洋调查的支持力度不断加大, 以科学考察船为代表的海洋科学观测平台建设不断加强; 进入新千年以来, 国内海洋科考船依托的各主要研究所和院校本着开放的理念, 先后组织多单位联合进行海上观测。尤其是最近10年, 国家自然科学基金委员会支持实施了船时共享航次计划, 进一步促进了国内海洋界的交流和合作, 南海区域海洋学的相关研究取得了很多重要的成果。从多尺度环流动力学的角度出发, 本文简要回顾了南海海洋观测的发展历程, 并初步总结了近些年来南海关键科学问题的研究进展, 包括南海和西太平洋的水体交换过程、南海中小尺度过程、多尺度相互作用及其生态环境效应等; 并且在现有的研究基础上, 对未来南海的观测和科学问题提出若干思考与展望。     

野外科学观测研究台站（网络）和科学数据中心建设发展

中国科学院地理科学与资源研究所成立80年来,十分重视野外台站（网络）和科学数据中心的建设,取得了辉煌成就。研究所建立了4个野外观测研究网络,引领了中国生态系统研究网络的建设与发展;成立了2个国家级科学数据中心,1个中国科学院数据中心,1个数据出版系统并于2016年加入了世界数据系统;拥有2个国家级野外观测研究站,1个中国科学院野外研究站,形成了独具特色的野外观测研究平台和数据共享服务平台。本文回顾了中国生态系统研究网络、国家生态系统观测研究网络、中国通量观测研究网络、中国物候观测网和禹城站、拉萨站、千烟洲站以及地球系统科学数据中心、生态科学数据中心、资源环境科学数据中心和全球变化科学研究数据出版系统的发展历程。地理资源所台站（网络）从无到有,不断发展壮大,引领了中国野外观测研究事业的发展,支撑了地理学、生态学等重要科学成果产出,科技支撑能力和示范能力大幅提升,有力支撑了华北平原、青藏高原以及南方山地丘陵区的生态文明建设;成为中国地球系统科学、野外台站、资源环境等学科和领域最大的科学数据汇聚中心,数据共享服务成效显著,在国内外具有广泛影响力。在未来发展中,地理资源所将充分发挥野外台站（网络）综合中心作用,强化生态系统、碳水通量、物候等观测研究网络的能力建设,稳步提升野外观测研究站条件保障能力和科学数据中心的数据汇聚能力、分析挖掘能力以及共享服务能力,持续推动和引领中国科学数据的共享,在科学研究和支撑国家需求等方面做出更大贡献。     

植被覆盖、放牧和季节对牧草种类组成和生物量的影响:以埃塞俄比亚南部Yabello牧场为例

Yabello牧场是埃塞俄比亚博拉纳的一个半干旱地区的牧场,目前面临着草场退化的严重挑战。植被覆盖的变化、过度放牧和季节性变化极大地影响了Yabello牧场的牧草组成和生物量。本文评估了植被覆盖、放牧和季节对Yabello牧场的牧草组成和生物量的影响。首先采用1 m×1 m的随机样方进行实验,根据植被覆盖类型和放牧变化选择样点,并对季节影响进行评估。使用SAS统计软件和MicrosoftExcel分析牧草组成、牧草高度和质量数据。本研究总共记录了26种草种,其中Chloris roxburghiana, Chrysopogon aucheri和Chrysopogon aucheri草种均表现出最高的平均单种覆盖高度和生物量产量。因此,建议将这些草种用于研究区域退化草地的恢复。研究结果还表明,植被覆盖类型、放牧和季节变化是决定牧草种类组成、牧草高度和生物量产量的关键因素。最后,研究结论还认为控制灌木丛植被并平衡放牧水平的可持续管理对于该地区的可持续牧草生产和生物多样性保护至关重要。     

Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS)

Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide.     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.