In many arid ecosystems, vegetation frequently occurs in high-cover patches interspersed in a matrix of low plant cover. However, theoretical explanations for shrub patch pattern dynamics along climate gradients remain unclear on a large scale. This context aimed to assess the variance of the Reaumuria soongorica patch structure along the precipitation gradient and the factors that affect patch structure formation in the middle and lower Heihe River Basin (HRB). Field investigations on vegetation patterns and heterogeneity in soil properties were conducted during 2014 and 2015. The results showed that patch height, size and plant-to-patch distance were smaller in high precipitation habitats than in low precipitation sites. Climate, soil and vegetation explained 82.5% of the variance in patch structure. Spatially, R. soongorica shifted from a clumped to a random pattern on the landscape towards the MAP gradient, and heterogeneity in the surface soil properties (the ratio of biological soil crust (BSC) to bare gravels (BG)) determined the R. soongorica population distribution pattern in the middle and lower HRB. A conceptual model, which integrated water availability and plant facilitation and competition effects, was revealed that R. soongorica changed from a flexible water use strategy in high precipitation regions to a consistent water use strategy in low precipitation areas. Our study provides a comprehensive quantification of the variance in shrub patch structure along a precipitation gradient and may improve our understanding of vegetation pattern dynamics in the Gobi Desert under future climate change.
We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital map products will be used for vegetation management (e.g. monitoring the health individual species or a broader profile of the community). Where individual plants are to be monitored over time, a feature-based approach that represents plants as vector points is appropriate. The CNN approach emerged as a promising technique in this regard as it leveraged spatial information from the UAV images within the architecture of the learning framework by enforcing a local connectivity pattern between neurons of adjacent layers to incorporate the spatial relationships between features that comprised the shape of the Lomandra tussocks detected. 相似文献
滨海湿地作为人类活动和全球变化反应最为敏感的区域，其沉积记录可以反映出周边地区环境变化及人类活动信息。珠江口淇澳岛滨海湿地钻孔分析结果表明，在中全新世期间淇澳岛附近海域为河口湾环境，在风化层以上开始出现淤积，但在4 200 a BP前后受极冷气候的影响，沉积物粗化；自2 500 a BP以来，沉积环境相对稳定，在小冰期期间略有变化。沉积速率计算结果显示:淇澳岛附近海域自中全新世高海面以来的平均沉积速率为0.29 cm/a，4 160~2 500 a BP、2 500 a BP-1488年、1488-1893年、1893-1986年、1990-2007年期间的平均沉积速率分别为:0.17 cm/a、0.23 cm/a、0.35 cm/a、1.37 cm/a和5.94 cm/a，沉积速率逐渐增大，反映了珠江三角洲演化过程中沉积相与沉积环境的变化；1986-1990年期间的海堤建造极大地扰动了该钻孔上部的沉积过程，在工程施工期间共沉积了厚度约112 cm的沉积层，而在海堤建成后，沉积速率也显著增大。沉积物总有机碳、总氮和C/N值的垂向分布表明，在4 160~2 500 a BP期间受海洋环境影响较大，沉积物中有机碳以海源为主，2 500 a BP以来沉积物中碳、氮含量明显增大，C/N也相应变大，有机碳主要来源于陆源输入，但在小冰期期间海源有机碳贡献略有所增大；近百年来由于受人类活动影响显著，陆源有机碳的贡献快速增加。 相似文献
