The complexity and diversity of scenarios, along with the presence of environmental noise in factory settings, pose significant challenges to the implementation of deep learning-based vision-guided robots for smart manufacturing. In response to these challenges, we introduce a novel Semi-Supervised Knowledge Distillation (SSKD) framework that has been extensively validated and deployed across numerous real-world production lines. The proposed SSKD framework combines the advantages of semi-supervised learning and knowledge distillation to offer optimization for the majority of deep learning models. Experiments conducted in real-world factory settings demonstrate that the SSKD framework significantly enhances the performance of deep learning models, reducing inference time from 185 ms to 45 ms and improving generalizability across different working environments, achieving recall and precision values that exceed 99.5% and 92.6%, respectively, achieved a remarkable 200% improvement in labor efficiency. Our innovative SSKD framework provides a reliable and scalable solution for enhancing manufacturing productivity and product quality. The success of this approach in transforming vision-guided robotic systems for smart manufacturing highlights its potential for broader industry adoption. The SSKD framework offers a reliable and scalable solution for enhancing manufacturing productivity and product quality. Our results underscore the potential of this innovative approach to transform vision-guided robot systems in smart manufacturing, making it an attractive candidate for widespread adoption in the industry. We are proud to report that, as of the end of 2022, the SSKD framework has been successfully implemented in 50 robots – a more than ten-fold increase from the initial 4 in 2020 – resulting in an annual yarn production capacity exceeding 100,000 kg. This accomplishment underscores the practical impact and effectiveness of the SSKD framework in real-world production lines.

With rapid urbanization and climate change, water consumption and land-use pattern has dramatically changed, resulting in altered eco-hydrological processes and high ecological water requirements in megacities. However, the water uptake strategies may differ in urban and natural environment, and which remains largely unknown. Therefore, this study investigated the water use patterns of two greening plants species (Ficus concinna and Ligustrum vicaryi) and their responses to rainfall events in a megacity of subtropical China using the stable isotope methods. The results indicated that the two greening plants species showed different water use strategies. F. concinna mainly absorbed water from the shallower soil layer (0-20 cm, 56.29 %) in the wet season and deeper soil water (30-50 cm, 41.13 %) in the dry season, whereas L. vicaryi mainly relied on the shallower soil water (0-20 cm, 48.28 %) throughout the whole year. L. vicaryi absorbed water from the shallower soil layer (0-20 cm) before rainfall events and changed into deeper soil water (30-50 cm) after rainfall events in both dry and wet season; on the contrary, F. concinna did not show these dynamics throughout the year. These results suggested that the water use pattern of F. concinna showed more ecological plasticity, facilitating the adaptation of the plant to seasonal drought and other environment fluctuations in subtropical China urban areas.

The urban green infrastructure such as the low impact development (LID) facility and traditional garden that are relatively small and characterized by decentralized distributions has been proposed as the most effective way to mitigate urban heat through its evaporative cooling effect. Recently, there have been increasing studies on its temperature reduction and evapotranspiration (ET) rate, but few of them correlate ET with external surface temperature reductions. Therefore, this study investigated the evaporative cooling effects, ET rates, and their relationships by the three-temperature (3T) model and ground-based thermal infrared remote sensing. Results show that the cooling effect of both vegetated LID facilities and traditional gardens is significantly stronger than that of non-vegetated LID facilities. Due to a thinner soil layer and lower water connectivity of LID facilities, their ET rates are significantly reduced in the dry period while the evaporative cooling effect of traditional gardens covered by the same vegetation can maintain high. The dependency of their cooling effect can be largely explained by the ET rates. When ET < 0.6 mm h(-1), an increase in ET of 0.1 mm h(-1) can enhance the cooling effect by 3.66 degrees C. When ET exceeds 0.6 mm h(-1), the evaporative cooling effect saturates. Vegetation types and soil water conditions are two main factors that govern evaporative cooling effect. Specifically, shrubs with higher ET rates are more efficient in urban heat mitigation than herbs. The responses of the evaporative cooling effect to soil water availability vary among species, which may require species-specific irrigation regime. These results may have implications on the best management practices for urban heat mitigation by the small widely- distributed green spaces.

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.

Heavy metal contamination in soils can pose severe challenges to the safety of geotechnical engineering projects. Loess, which is widely distributed in Northwest China, is a preferred engineering construction material for anti-fouling barriers. Therefore, research on the influence of heavy metal ions on its seepage performance is urgently required. To obtain new insights into the seepage behavior of heavy metal-contaminated loess and its underlying geochemical mechanism, laboratory investigations were performed on the saturated hydraulic conductivity (Ksat), leaching, and microstructural characteristics of loess contaminated with Cu2+ and Zn2+. The results indicate that the hydrolysis of Zn2+ creates an acidic environment, which promotes the dissolution of carbonate minerals in loess, enhances the leaching capacity, and leads to the quantitative transformation of small pores (2–8 μm) to mesopores (8–32 μm). Meanwhile, the alternating adsorption of Zn2+ and its diffuse double-layer effect compresses the diffusion layer, increasing the abundance of free water channels. Thus, the Ksat of Zn-contaminated loess increases by 81.2% during the seepage period. As for Cu-contaminated loess, its seepage behavior is the opposite of that of Zn-contaminated loess, with a Ksat decrease of nearly 50%. The primary factor controlling this phenomenon is the formation and enrichment of Cu2O in the lower part of the soil, which inhibits the enlargement of pores and reduces the effective connectivity of pores. The findings of this work provide insight into the seepage behavior of saturated loess under erosion by heavy metals and the underlying geochemical mechanism thereof.

Wetland evapotranspiration (ET), which involves the land-atmosphere exchange of energy and water, is dynamic and affects the spatiotemporal distribution of water resources. However, due to the variability and complexity of wetlands, accurate estimation of patch-scale ET and its spatial variability remain insufficiently characterized. To overcome this challenge, an advanced unmanned aerial vehicle (UAV) technology was developed by combining the three-temperature (3T) model, which is robust to estimate transpiration and its spatial variability with UAVbased thermal infrared remote sensing, and Penman equation, which is commonly used to estimate open water evaporation. The combined approach was verified using the Bowen ratio system over a subalpine wetland. The results show that the proposed method is simple and applicable for estimating wetland ET and its spatial variability, with a determination coefficient (R2) of 0.93, mean absolute percentage error (MAPE) of 7.90%, root mean squared error (RMSE) of 0.05 mm h-1, and Nash-Sutcliffe efficiency (NSE) of 0.93. It depicts a large spatial variability in wetland ET with respect to surface vegetation characteristics, water regimes, meteorological factors, and larger transpiration rates than open water evaporation. With its limited inputs and no calibration requirements, the proposed method is concluded to be simple and to easily reveal the high temporal and spatial resolution characteristics of patch-scale ET and its components.

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 ongoing climate change and rapid urbanization, the influence of extreme weather conditions on long-term nocturnal sap flow (Qn) dynamics in subtropical urban tree species is poorly understood despite the importance of Qn for the water budgets and development plantation. We continuously measured nighttime sap flow in Ficus concinna over multiple years (2014–2020) in a subtropical megacity, Shenzhen, to explore the environmental controls on Qn and dynamics in plant water consumption at different timescales. Nocturnally, Qn was shown to be positively driven by the air temperature (Ta), vapor pressure deficit (VPD), and canopy conductance (expressed as a ratio of transpiration to VPD), yet negatively regulated by relative humidity (RH). Seasonally, variations in Qn were determined by VPD in fast growth, Ta, T/VPD, and meteoric water input to soils in middle growth, and RH in the terminal growth stages of the trees. Annual mean Qn varied from 2.87 to 6.30 kg d−1 with an interannual mean of 4.39 ± 1.43 kg d−1 (± standard deviation). Interannually, the key regulatory parameters of Qn were found to be Ta, T/VPD, and precipitation (P)-induced-soil moisture content (SMC), which individually explained 69, 63, 83, and 76% of the variation, respectively. The proportion of the nocturnal to the total 24-h sap flow (i.e., Qn/Q24-h × 100) ranged from 0.18 to 17.39%, with an interannual mean of 8.87%. It is suggested that high temperatures could increase transpirational demand and, hence, water losses during the night. Our findings can potentially assist in sustainable water management in subtropical areas and urban planning under increasing urban heat islands expected with future climate change.

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.

基于蒸腾扩散系数,对几种亚热带城市典型植物的蒸腾降温潜力进行评估,并对小叶榕进行持续观测,定量地研究其蒸腾降温特征和节能减碳效益,得到如下结论.1)在研究区域内的几种典型植被中,小叶榕作为乡土树种,在相同的环境条件下表现出最强的蒸腾降温潜力.2)单株小叶榕的日均蒸腾水量为32.48 kg,总体上呈现夏秋高、春冬低的特点;同时,小叶榕能有效地缓解热岛效应,尤其是在热岛效应严重的夏季夜间,其蒸腾降温作用使得观测区域86％的时间为无热岛状态.3)单株小叶榕每年通过蒸腾降温作用间接减少的二氧化碳排放量达到1442.10 kg.

Accurate global terrestrial evapotranspiration (ET) estimation is essential to better understand Earth's energy and water cycles. Although several global ET products exist, recent studies indicate that ET estimates exhibit high uncertainty. With the increasing trend of extreme climate hazards (e.g., droughts and heat waves), accurate ET estimation under extreme conditions remains challenging. To overcome these challenges, we used 3 h and 0.25∘ Global Land Data Assimilation System (GLDAS) datasets (net radiation, land surface temperature (LST), and air temperature) and a three-temperature (3T) model, without resistance and parameter calibration, in global terrestrial ET product development. The results demonstrated that the 3T model-based ET product agreed well with both global eddy covariance (EC) observations at daily (root mean square error (RMSE) = 1.1 mm d−1, N=294 058) and monthly (RMSE = 24.9 mm month−1, N=9632) scales and basin-scale water balance observations (RMSE = 116.0 mm yr−1, N=34). The 3T model-based global terrestrial ET product was comparable to other common ET products, i.e., MOD16, P-LSH, PML, GLEAM, GLDAS, and Fluxcom, retrieved from various models, but the 3T model performed better under extreme weather conditions in croplands than did the GLDAS, attaining 9.0 %–20 % RMSE reduction. The proposed daily and 0.25∘ ET product covering the period of 2001–2020 could provide periodic and large-scale information to support water-cycle-related studies. The dataset is freely available at the Science Data Bank (https://doi.org/10.57760/sciencedb.o00014.00001, Xiong et al., 2022).

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.

Climate models predict rising temperatures and more frequent and prolonged urban heat islands (UHI) in southern China. The urban vegetations have become a focal point to mitigate the detrimental effects of UHI and to provide cooling through transpiration. However, transpiration in trees such as Ficus concinna in relation to extreme long-term conditions of UHI is scarcely documented. Here, we investigated the transpiration dynamics and its cooling effects in F. concinna induced by changes in site environmental variables in a subtropical megacity, Shenzhen over five consecutive years (2015-2019) based on continuous sap flow measurements. Seasonally, the transpiration (T-r) and its cooling effect (i.e., heat energy absorbed (Q) and temperature reduction (Delta T) by T-r) were highest in summer, peaking in the month of July with the mean values of 1.98 mm d(-1), 4.91 MJ m(-2) d(-1) and 3.93 degrees C m(-2) d(-1), respectively. The highest cooling effect was shown during warmer and wet years. Daily Tr had a positive linear relationship with shortwave radiation (R-s) in May-June, air temperature (T-a) and volumetric soil water content (SWC30) in July-August and vapor pressure deficit (VPD) in September, respectively. Furthermore, the main regulatory variables of T-r within each season were found to be the spring R-s, summer T-a and SWC30, and autumn Rs and VPD, which explain 49, 82 and 74% of the variation, respectively. The influence of these variables (i.e., T-a, R-s and VPD) on T-r was modified by the effect of SWC30. Interannually, T-a, SWC30 and precipitation (PPT) were responsible for most of the observed variation in T-r, individually explaining 72, 81, and 66% of the variation. Furthermore, multiple regression model indicated that together T-a, SWC30 and PPT explained 89% of the variation in T-r. The diminished sensitivity of T-r to environmental variables and enhanced sensitivity to SWC30 during dry years point to species acclimatization to soil dryness. Our findings clearly indicate the temporal dynamics in T-r and its cooling effectiveness for F. concinna over longer timescales. It is suggested that F. concinna could be better suited in response to increasing temperature in subtropical urban areas, as the species may be capable to provide efficient cooling based on its high T-r rate and has the potential to mitigate the UHI effect.

基于2012年黑河绿洲HiWATER高密度通量观测数据,对比研究模型结构差异(单源Penman-Monteith/PM公式与双源PM公式、双源PM公式与双源三温模型)以及PM公式中阻抗参数化差异对蒸散发估算的影响.结果表明:1)与模型结构相对复杂的双源PM公式相比,单源PM公式计算的蒸散发平均相对误差(MAPE)为34％,略优于双源PM公式的40％;2)对于两种模型结构差异显著的双源模型,模型中不含阻抗参数的三温模型比模型中含阻抗参数的PM公式具有更高的估算精度,前者的MAPE为18％(R2=0.85),后者为40％(R2=0.34);3)两种单源和一种双源阻抗参数化方法导致PM公式计算的蒸散发出现不同程度的差异,MAPE可相差6％;4)使用先验知识/数据事前率定阻抗参数化方法,可显著地提高单源PM公式的计算精度(MAPE可降低22％),但随着模型结构与参数化复杂度增加,事前率定双源PM公式的阻抗参数化方法难以提高计算精度(MAPE仅减小0.8％).

绿地是城市生态资源的重要组成部分,定量评估其时空分布、构建多尺度监测方法体系,是自然资源管理、生态文明城市建设等领域的迫切需求.以城市植被和城市水体为例,从资源的数量、质量与生态价值3个层次,梳理对比主流的监测评价方法,探讨这些方法的优势与存在的问题,为我国新时期城市自然资源评估与监测提供方法参考.结果表明:①尽管基于样方的传统抽样方法可获得城市绿地的数量信息,但城市绿地高度斑块化特征限制了样方结果尺度推绎;②卫星遥感是监测城市绿地的有效手段,可准确获取绿地空间分布、面积、种类、质量变化等信息,但生物量(或蓄积量)、体积等信息需要米级(<5 m)遥感数据和其他新技术支持精细化研究;③无人机可获得亚米级(如<5 cm)数据,满足精细化监测需求,但受飞行管制、电池续航能力等限制,数据覆盖范围有限,且数据拼接等后处理复杂、传统的数据处理或反演算法可能不适用于亚米级空间分辨率数据;④城市绿地对城市热环境调节功能研究较多,但当前100 m(及更粗空间分辨率)的热红外地表温度数据难以支持绿地蒸腾降温机理等精细化研究.可见,城市绿地的精细化资源评估与监测仍面临诸多挑战.

The urban heat island (UHI) effect is a widespread phenomenon because of increased urbanization, making the urban thermal environment less comfortable. The UHI effect may worsen during heat waves (HWs), with projected increases in extreme climatic events in the future due to global warming. Researchers have revealed interactions between the UHI effect and HWs using weather station data and proposed mitigation schemes at a city scale. However, the UHI effect in urban areas with different land use/land cover (LULC) types should respond differently to HWs, which has drawn little attention. Hence, this study conducted a mobile transect experiment in the subtropical megacity of Shenzhen and obtained high spatial resolution data. The results showed that the UHI effect was significantly amplified during HWs. The UHI intensity (UHII) of the transect increased from 0.56 +/- 0.50 degrees C under non-heat wave (NHW) conditions to 0.68 +/- 0.65 degrees C during HWs. LULC types had a significant influence on this interaction. The UHII in more urbanized areas increased during HWs, whereas less urbanized areas had improved cooling effects. These interactions were more evident at nighttime. Increasing the natural underlying surface coverage mitigated the intensity and warming potential of the UHI effect. With a 10% increase in the natural underlying surface coverage, the nighttime UHII decreased by 0.38 degrees C and 0.39 degrees C during NHWs and HWs, respectively. These cooling effects were attributed to the increased latent heat consumption during HWs by vegetation. Therefore, different measures should be taken in other areas to mitigate UHII amplification during HWs.

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.

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.