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.

为解明粤港澳大湾区城市红树林生态系统健康状况,基于PSR(压力-状态-响应)模型和层次分析法,构建了城市红树林生态系统健康评价指标体系,对大湾区的香港米埔、深圳福田、广州南沙和珠海淇澳岛4个典型城市红树林进行生态系统健康评价,识别健康问题并提出管理对策.结果表明:红树林生态系统健康指数(EHI)为淇澳岛(3.05,健康)>米埔(3.03,健康)>南沙(2.54,亚健康)>福田(2.13,亚健康).就压力指标而言,米埔和福田红树林的自然压力源为病虫害和生物入侵,人为压力源为人口、经济相关指标及城镇生活污水排放,福田还受到工业废水排放的压力.就状态指标而言,红树林受海水营养盐污染严重,南沙和淇澳岛红树林存在严重的有机污染和重金属污染;红树植物多样性(除南沙红树林外)和大型底栖动物生物多样性偏低,但鸟类生物多样性处于较高水平.就响应指标而言,福田和南沙红树林由于面积小而生态服务功能偏低,南沙红树林的管理水平不足.粤港澳大湾区城市红树林存在的主要健康问题包括生态失衡导致的病虫害与生物入侵、受纳外源污染导致的环境污染、栖息地破坏导致的生物多样性下降的共性问题及自身特征与管护水平差异导致的其它个性问题.针对上述健康问题,建议:以缓解生态失衡为目标高效监测并推广基于自然法则的生态恢复,以源头控制为根本整体改善环境质量,以保护生物多样性为重点提高红树林生态系统稳定性,因地制宜充分发挥城市红树林经济-社会-生态效益.

基于盐度和潮汐淹水对红树植物筛选和定植的重要作用,选择5种中国造林工程中常用的红树植物,采用文献检索和荟萃分析方法,探究其耐盐-耐淹性.结果表明:白骨壤(Avicennia marina)的耐盐-耐淹性最强,能够生长于高盐度(40‰)和长时间淹水(16 h/d)环境中;秋茄(Kandelia obovata)和桐花树(Aegiceras corniculatum)具有较强的耐盐-耐淹性,能够生长于中盐度(30‰)和较长时间淹水(12 h/d)环境中;木榄(Bruguiera gymnorrhiza)和无瓣海桑(Sonneratia apetala)的耐盐性较低,能够生长于低盐度(20％0)环境中,前者可种植在较长时间淹水(12 h/d)区域;超过单一盐度或淹水胁迫耐受限度时,复合胁迫会降低红树植物的耐受性;在实际造林应用中,白骨壤、秋茄和桐花树具有较强的耐盐-耐淹性,适合中国南部沿海的红树林宜林地.最后,提出相应的工程应用对策,可为中国红树林的精准修复提供科学依据.

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.

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.