Accurate estimation of desert vegetation biomass is crucial for monitoring changes in carbon stocks and productivity status. Unmanned aerial vehicle (UAV) remote sensing allows large-scale biomass surveys at the individual or patch scale. However, since desert shrubs are short and sparse, the UAV-based techniques do not always accurately capture biomass-related indicators at any flight height. This study investigated the effects of flight height on above-ground biomass (AGB) estimation using UAV images of typical shrub communities (Reaumuria soongarica) captured at different heights (i.e., 30 m, 50 m, 70 m, 90 m, 110 m, 130 m, and 150 m) in desert-grassland ecosystems. Several structural indicators associated with shrub allometric growth were extracted for AGB modeling, including canopy area (horizontal properties), canopy height (vertical properties), and canopy volume. Results revealed that the values of canopy height and volume decreased with increasing flight height, which made the poor performance of AGB models based on these indicators worse. For example, the variance explained (VE) of the models based on the mean canopy height decreased from about 62% to -137%, while the root mean square error (RMSE) increased from about 39 g to 92 g. In contrast, the canopy area was less affected by flight height, maintaining stable AGB models with VE around 72% and RMSE at 33 g. Adjusting the coefficients of linear models based on canopy height and volume with flight height significantly improved their predictive performance, with VE between 54% and 77% and RMSE between 30 g and 43 g for the optimized models based on mean canopy height. Furthermore, a higher flight height (e.g., 90-110 m) could be chosen to enhance operational efficiency while ensuring the accuracy of biomass observation. Our study offers valuable insights and guidance for vegetation surveys and research in desert-grassland ecosystems.

Dynamics in long-term evapotranspiration (ET) and its controlling variables are essential for understanding how a high-altitude wetlands ecosystem responds to climate change. The rising temperature is expected to agitate the regional hydrological cycle and water balance, particularly in the subalpine wetland valley of Jiuzhaigou, located in the transition zone between the northeast Qinghai-Tibet Plateau and the Sichuan Basin, Southwest China. Here, we used growing season multi-year (2013-2021) Bowen ratio data to assess the variability in ET and its key controlling parameters at different timescales in Jiuzhaigou valley. This study also explored the ratio of ET to precipitation (P). The wetland daily mean ET varied from 0.06 to 6.77 mm d-1, with a mean value of 2.64 mm d-1 for the nine years. Fluctuations in daily ET were primarily driven by available energy (net radiation, Rn), explaining 86 % of the variation. Seasonal patterns in ET were largely similar to environmental parameters, i.e., Rn, air temperature (Ta), and vapor pressure deficit (VPD), peaking in August with an interannual monthly mean value of 3.48 mm d-1. Interannual monthly mean ET had a strong positive linear relationship with Rn, Ta, and VPD, while there was no significant correlation with P on a growing season basis. Furthermore, monthly ET was shown to be regulated by Ta largely in high-temperature months and minimally in low-temperature months. The growing season ET varied interannually, and the ET to P ratio (i.e., ET/P) ranged between 0.52 and 1.16. Interannual variation in annual ET was controlled by Ta and P, which individually explained 73 and 61 % of the variation, respectively. The multiple regression model indicated that Ta and P together elucidated 92 % of the variation in annual ET. The increased sensitivity (e.g., regression slopes) of ET to P over 2014-2021 indicates that ET consumed most of P, which leads to decreasing runoff and streams drying up. This study clarifies the temporal dynamics in ET for wetlands and its environmental controls at multiple timescales. It is demonstrated that the proportion of ET could increase in response to increasing temperature without an associated increase in P, affecting local water balance. These results could potentially contribute to sustainable water management in high-altitude wetlands and environmental planning under future climate change.

Evapotranspiration (ET) cooling of urban spaces is an effective and economical way to improve the urban thermal environment. However, the distribution of urban ET rate is typically unknown owing to the high heterogeneity of urban land covers, which limits the application of many conventional techniques for measuring ET, such as ground-based observations and satellite remote sensing. In this study, an improved approach called "UAV + IRs + 3T", combining unmanned aerial vehicle (UAV), thermal infrared remote sensing, and a threetemperature model (3T), was developed for estimating urban ET and validated by Bowen ratio method. Results showed that the proposed method could accurately measure urban LE with R2 = 0.95, MAE = 21.98 W m- 2, RMSE = 30.33 W m- 2, RRMSE =19.65%. The proposed method could obtain urban ET with an ultra-high spatial resolution (approximately 15.5 cm) and temporal resolution (once per hour). Furthermore, 9 plant species distributed across the 18 sample plots showed significant differences in mean intra-day ET rates. Even for the same plant species at different sites, such as Ficus concinna and Zoysia matrella, their average intra-day ET rates differed by 50% and 400%, respectively. These large differences could be attributed to artificial pavement and infrastructure, different artificial irrigation methods, and difference in artificial and natural shade. In conclusion, there is spatio-temporal variability in urban ET rates, which can be precisely revealed by the proposed method. Therefore, the "UAV + IRs + 3T" method has the potential for a wide range of applications in urban environmental planning.

With drastic changes to the environment arising from global warming, there has been an increase in both the frequency and intensity of typhoons in recent years. Super typhoons have caused large-scale damage to the natural ecological environment in coastal cities. The accurate assessment and monitoring of urban vegetation damage after typhoons is important, as they contribute to post-disaster recovery and resilience efforts. Hence, this study examined the application of the easy-to-use and cost-effective Unmanned Aerial Vehicle (UAV) oblique photography technology and proposed an improved detection and diagnostic measure for the assessment of street-level damage to urban vegetation caused by the super typhoon Mangkhut in Shenzhen, China. The results showed that: (1) roadside trees and artificially landscaped forests were severely damaged; however, the naturally occurring urban forest was less affected by the typhoon. (2) The vegetation height of roadside trees decreased by 20–30 m in most areas, and that of artificially landscaped forests decreased by 5–15 m; however, vegetation height in natural forest areas did not change significantly. (3) The real damage to vegetation caused by the typhoon is better reflected by measuring the change in vegetation height. Our study validates the use of UAV remote sensing to accurately measure and assess the damage caused by typhoons to roadside trees and urban forests. These findings will help city planners to design more robust urban landscapes that have greater disaster coping capabilities.

Cephanolides A-D are cephalotane-type diterpenoids featuring a novel 6/6/6/5 tetracyclic core embedded with a bridged δ-lactone. The asymmetric and divergent total syntheses of cephanolides A-D have been accomplished, proceeding in 11-14 steps from a known alcohol. The salient features of the present work include (i) a substrate-controlled diastereoselective intermolecular Diels-Alder reaction to form the 6-6 cis-fused rings, (ii) a palladium-catalyzed formal bimolecular [2 + 2 + 2] cycloaddition reaction via a partially intermolecular cascade reaction sequence involving multiple carbometalations to rapidly install the key tetracyclic skeleton, and (iii) lactonization and late-stage oxidative diversification to complete total syntheses of the four benzenoid cephanolides.

在全球CO2持续升高背景下,游客的CO2排放导致热门景点周围的大气CO2升高速度远远大于全球平均CO2升高速度.针对这一问题,基于涡度相关系统,对九寨沟树正寨犀牛湖区域进行长期CO2浓度监测,结合年均径流量和基于CO2浓度得到的钙华(CaCO3)损失速率,估算九寨沟世界自然遗产地的钙华年损失量,探究钙华退化的机理.结果表明,游客呼吸可进一步升高CO2,升幅可达250～300 μL/L,导致水体的CaCO3损失率增加18％～21％.全球和局地CO2升高的叠加效应是导致以钙华景观为主的九寨沟钙华消失和环境退化的关键要因,游客的 CO2排放的确在毁坏九寨沟世界自然遗产景观.

Accurate estimation of the shrub above-ground biomass (AGB) is an essential basis for determining carbon storage and monitoring desertification risk in arid ecosystems. However, significant uncertainties and biases exist in large-scale monitoring of desert AGB due to the scale mismatch and spatio-temporal topological errors be-tween the coarse resolution of satellite images and the limited area that can be surveyed via ground measurements. The rapid development of unmanned aerial vehicle (UAV) technology has the prospective advantage of multi-scale vegetation observation. However, its potential to bridge the gap between satellites and the ground in desert AGB estimation has not yet been explored. This study developed a procedure to fill the gap between satellite and ground measurements based on low-cost and easy-to-use UAV visible-light technology in typical desert shrub communities in Inner Mongolia, China. First, canopy area (CA), canopy maximum height (CH), and canopy volume (CV) metrics derived from UAV-RGB (Red, Green, Blue) images, coupled with structure-from -motion photogrammetry, were used to invert the UAV-based AGB. Then, the UAV-based AGB data were aggregated to different scales to align with satellite data. Here, we focused on examining the performance of generalized additive models between the upscaled UAV-based AGB and vegetation indices (VIs) generated from PlanetScope (resolution: 3 m), Sentinel-2A MSI (resolution: 10 m, 20 m), and Landsat 8 OLI (resolution: 30 m). Finally, we investigated the effects of scale and spectra in the upscaling and modeling process. Results showed that the UAV-based AGB linear prediction models developed by the CV metric performed best for Reaumuria soongarica (R-2 = 0.749, RMSE = 65.5 g) and Salsola passerina (R-2 = 0.919, RMSE = 56.7 g), which allowed for accurately mapping the desert AGB at the individual plant level with ultra-high resolution (2 cm). The performance of satellite biomass models was excellent based on the upscaled UAV-based AGB and VIs (best-performing models for different satellites: adjusted R-2 = 0.625-0.934, RMSE = 35.1-119.1 g/m(2)). As the resolution of satellite data increases, the increase in variance is a big scale-related challenge in AGB model performance evaluation. It is worth noting that the UAV-based AGB data are unmodifiable due to their area-independent nature, which can avoid the scale-related effect. Moreover, the satellite VIs most closely related to the desert AGB were associated with Red, NIR, and Red-Edge bands. Specifically, we recommend the Red-Edge band when using Sentinel-2A data. This study proves that UAV technology can not only provide exhaustive information on vegetation biomass but also can train and validate satellite data beyond individual UAV observations, which can significantly improve large-scale biomass monitoring if widely applied in the future.

ABSTRACT Quadrat sampling is one of the most widely accepted methods for conducting vegetation surveys over the world for centuries. However, it is difficult to determine an optimal size to adequately represent the community compositions in quadrat samplings. Traditional labour-intensive census-based quadrat sampling is also very time consuming and insufficient to represent spatial community characters outside of the quadrat extent. In order to improve the above deficiencies, an unmanned aerial vehicle (UAV)/red-green-blue (RGB) photography based vegetation survey methodology was proposed in this study. The essential steps in the proposed method include: 1) obtaining high spatial resolution optical images from the UAV; 2) extracting species based on the orthographic image after mosaic; 3) performing statistics on a given species within the image through different quadrat sizes; 4) determining an optimal quadrat size according to the changing trend of the statistical data. In addition, a case study applied this proposed methodology was conducted in a desert area in Northwest China, where Ammopiptanthus mongolicus and Zygophyllum xanthoxylon dominated. The results show that the remote sensing UAV method could obtain RGB images and orthoimage with flexible control. The statistical data of species density decreased with the increase of quadrat size, but the values changed slightly after 20 m × 20 m, which was larger than the typical quadrat size (10 m × 10 m) used to investigate shrubs. An analysis based on the relationship among species density, plants distribution, and quadrat size further indicated the reasonability of determining the optimal size. Based on the results, it is concluded that a minimum quadrat size of 20 m × 20 m should be adopted for investigating the density and spatial pattern characteristics of A. mongolicus and Z. xanthoxylon, and the proposed UAV-based method provides an alternative for vegetation survey with high efficiency and accuracy.

Above-ground biomass (AGB) is an essential indicator for assessing ecosystem health and carbon storage in desert shrub-related research. Above-ground volume (AGV) of vegetation is a crucial parameter to estimate the AGB. In unmanned aerial vehicle (UAV) remote sensing, the AGV and AGB are mainly estimated by vegetation feature metrics (for example, spectral indices, textural, and structural metrics). However, there is limited study on the AGV and AGB estimation in desert shrub communities by using UAV, and it is difficult to determine the contribution of these metrics to AGV models under eliminating the influence of background factors. Taking a typical desert shrub area in Inner Mongolia, China as an example, this study develops an improved approach to extracted three types of feature metrics simultaneously using UAV RGB (Red, Green, Blue) images. First, digital orthophoto map (DOM) and digital surface model (DSM) were created through the photogrammetric procedure based on UAV RGB images. Second, the digital terrain model (DTM) for canopy height calculation was generated based on DOM and DSM by object-oriented image binary classification and ground elevation interpolation. Here, we recommended the ENVI Landsat Gap-fill tool to interpolate the ground elevation of vegetation areas. Meanwhile, 21 spectral indices, eight textural metrics, and five structural metrics were extracted. Finally, single-variable and multi-variable commonly used regression models were established based on these metrics and measured AGV with a leave-one-out cross-validation. Results showed that: (1) in the proposed model, the contribution of structural, textural, and spectral metric to shrub AGV models was 86.68, 7.08, and 6.24%, respectively. (2) The horizontal and vertical structural metrics, textural metrics, or spectral indices reflected the one-dimensional change of AGV, which had a saturation effect. (3) The canopy volume, combining the horizontal and vertical characteristics of vegetation canopy, could describe the overall change of AGV and played the most essential role in AGV modelling (R2 = 0.928, relative RMSE = 26.8%). The study findings provide a direct reference in determining suitable vegetation feature metrics for monitoring shrub AGV. The proposed approach for DTM generation and AGV estimation is more efficient, accurate and low-cost than before, and it can be a useful bridge between ground-based investigation and satellite remote sensing.

地震及其伴生地貌过程破坏山体植被和土壤,产生的松散物源进入湖泊可能会带来泥沙淤积与沼泽化的风险.为探明九寨沟"8.8"大地震后湖泊淤积与沼泽化现状,以及松散物源的影响,本研究选取九寨沟景区内4个淤积与沼泽化程度不同的湖泊,进行了实地勘查,采用植被指标为主、促淤效应为辅的沼泽化综合评价方法进行现状评价;同时,结合无人机获取的湖泊周边环境和受灾程度信息,探讨了地震后伴生地貌过程及产物对九寨沟湖泊淤积与沼泽化的影响.结果表明:(1)四个湖泊的沼泽化发展模式均是由岸边向湖心推进.(2)各湖淤积与沼泽化程度空间分布特征存在差异,按发展程度从高到低依次表现为:五花海沼泽化程度最高,西北侧处于沼泽化后期(沼泽化指标值为[3,4]);镜海次之,局部处于沼泽化盛期(沼泽化指标值为[1.5,3]);犀牛海再次,仅出入水口处于沼泽化前期(沼泽化指标值为[1.5,2.5]);箭竹海最低,整体处于沼泽化前期(沼泽化指标值为[0,1.5]).(3)松散物源输入造成湖岸淤积程度增加、促进水生植被扩张,是造成九寨沟湖泊淤积和沼泽化发展的重要原因.管理者需重点关注小微湖泊、出入水口、受灾点的沼泽化发展情况.

叶绿素a浓度是表征水体富营养化程度的重要指标,通过遥感手段反演叶绿素a浓度是实现水体富营养化监测的一个有效途径,已衍生出了一系列叶绿素a浓度反演算法.这些算法各有所长,适用范围也各自有别.由于水体光学特征差异,盲目套用这些算法难以取得预期效果.为了推动水质遥感的进一步发展,从遥感反演的原理和数据源出发,对国内外利用遥感技术反演水体叶绿素a浓度的算法进行综述.根据算法结构设计的不同,将反演算法分为6大类,分别为荧光峰和反射峰算法、波段算法、指数算法、智能算法、基于水体分类的算法体系以及分析类算法,系统地梳理各类算法并分析算法特征.从算法适用的叶绿素a浓度区间和水体类型等角度出发,总结各类算法的适用范围,评述各类算法的优缺点,以期为环境和遥感工作者提供参考.主要结论如下:①Ⅱ类水体算法外推适应性较弱,应建立并补充实测数据集,研究各类水体光学特性异同点,构建基于水体分类的通用算法体系;②无人机技术与高光谱传感器的结合可为内陆水体水质监测提供新思路;③应结合机器学习算法与机理模型,发展物理原理约束的高精度反演模型.

植被调查常用的地面样方法,需要大量人力和时间.同时,由于标准样方的尺度通常只有100 m2,许多植被信息可能无法被揭示出来.为此,本研究开发了一套基于无人机技术的荒漠植被样方调查方法,在此基础上进一步开发出一套植被空间分布格局分析方法,并以内蒙古巴彦淖尔荒漠区典型灌木群落为例,进行了实证研究.具体研究步骤是:首先,通过无人机低空航拍,获取研究区1.44×104 m2的可见光影像.其次,经图像处理,进行植被种类识别与分类.最后,利用得到的不同植被类型分布矢量图进行多尺度随机样方布设和植株位置提取,实现基于植被信息完全统计的样方分析和点格局分析.结果表明:分类过程精度高,样方尺度和数量比传统的地面调查扩大数十至百倍,植被信息数据也成倍增加,实现了流程自动化.据我们所知,这是第一个提出基于无人机进行荒漠植被分布格局方法的研究.本研究结果可应用于多种植被和生态学调查,由于可操作性强,该方法也可以推广应用于大范围的荒漠植被调查.