基于无人机遥感表型监测的苎麻优质种质资源筛选方法

苎麻是重要的纤维作物之一,由于土地资源紧缺及优良品种的推广应用等原因,苎麻遗传变异和遗传多样性减少,对苎麻种质资源多样性调查和保护的需求日趋加大。基于无人机遥感的作物表型测量方法可以对不同基因型作物的生长特性进行频繁、快速、无损、精准的监测,实现作物种质资源调查,筛选特异优质品种。为了实现苎麻种质资源表型的高效综合评价,辅助筛选优势苎麻品种,本研究提出了一种基于无人机遥感影像的苎麻种质资源表型监测及筛选方法。首先,基于无人机遥感影像,利用Pix4dmapper软件生成试验区的数字地表模型（Digital Surface Model,DSM）和正射影像;然后,对苎麻种质资源关键表型参数（株高、株数、叶面积指数、叶片叶绿素含量、含水量）进行估测。基于DSM采用“差分法”提取苎麻株高,基于正射图像采用目标检测算法提取苎麻株数,采用机器学习方法估测苎麻叶面积指数（Leaf Area Index,LAI）、叶片叶绿素含量（SPAD值）、含水量;最后,根据提取的各项遥感表型参数,采用变异性分析和主成分分析方法对苎麻种质资源进行遗传多样性分析。结果表明,（1）基于无人机遥感的苎麻表型估测效果较好,株高的拟合精度为0.93,均方根误差为5.65 cm;SPAD值、含水量、LAI的拟合指标分别达到0.66、0.79、0.74,RMSE分别为2.03、2.21、0.63;（2）苎麻种质资源的遥感表型存在较大差异,LAI、株高和株数的估测值变异系数分别达到20.82%、24.61%和35.48%;（3）利用主成分分析法将苎麻种质资源的遥感表型聚类为因子1（株高、LAI）和因子2（LAI、SPAD值）,因子1可用于苎麻种质资源结构特征评价,因子2可以作为高光效苎麻资源的筛选指标。本研究将为作物种质资源表型监测和育种相关分析提供参考。     

A tale of urban forest patch governance in four eastern US cities

Urban forests are important components of societal interactions with nature. We focused on urban forest patches, a distinct and underexplored subset of the urban forest that spans land uses and ownerships, and requires silvicultural practices to address their unique biophysical characteristics and management regimes. Our goal was to elucidate multi-scalar urban forest patch governance arrangements as they translated to on-the-ground management in four urban areas (Chicago, New York City, Philadelphia, and Baltimore) within the eastern United States. A transdisciplinary knowledge co-production framework was used to guide identification of the prominent management challenge or dilemma motivating change to forest patch management in each location, and to describe the dynamic interplay of decision-making and governance processes across locations as they advanced toward desired forest conditions. A common management goal existed across all four locations: multi-age, structurally complex forests dominated by regionally native species. Ecological and social concerns affected by local context and city capacity served as starting points prompting management action and new collaborations. Disparate governance arrangements including top-down municipal resources, regional conservation facilitated by landowners, and grass-roots community-driven stewardship led to diverse support-building processes and innovative strategies that served as forces initiating and shaping new management actions. Science and iterative learning and adaptation influenced change in all locations, reinforcing new management arrangements and practices. Among the four study areas, the earliest management of urban forest patches started in the 1980 s, historically lacking embeddedness in urban forest management more broadly, and experiencing challenges with integration into existing governance infrastructure. Ultimately, new management and governance approaches to urban forest patches in all four study areas have evolved uniquely and organically, driven by place-based historical legacies and ongoing socio-ecological feedbacks. The generalization of findings for broader urban forest management guidelines, such as for trees and park, would lead to misguided outcomes.     

侧柏风景林美景度的林内色彩斑块非线性模型研究

利用景观生态学的景观空间格局分析原理,以照片的色彩斑块结构为基础,开展风景林林内景观质量评价研究。分析美景度等级和14个斑块指数的关系,利用简相关法筛选出对美景度影响大的SPLIT、SHDI、DIV、SIEI、ACOV和LPI 6个指数,研究表明:随着美景度等级的降低,DIV、SIEI、ACOV和LPI呈曲线变化趋势;SPLIT、SHDI呈线性增大的变化趋势。依据6个指标,利用聚类方法将侧柏人工林分为4种类型。以指数为自变量分别构建了线性和非线性美景度模型。对比结果表明:非线性模型拟合精度高于线性模型,所以选用二次多项式逐步回归模型从总体上较好地反映出景观指数与美景度间的关系。      

红枫湖苔藓植物的物种多样性

为苔藓植物的保护提供本底资料,并为红枫湖地区水源涵养、水土保持和环境污染检测提供理论依据,采用相似性系数、重要值和多样性指数等对红枫湖4个岛屿的苔藓植物物种多样性进行调查研究。结果表明：红枫湖苔藓植物共有18科34属107种,优势科为丛藓科、青藓科和羽藓科,美灰藓、东亚小羽藓和短肋羽藓为优势种。4个岛屿苔藓植物的 Dahl 指数、Shannon 指数和 Simpson 指数均表现为鸳鸯岛1＞兴隆半岛＞野鸡坡＞鸳鸯岛2,鸳鸯岛1的多样性指数最高,为0．9137;鸳鸯岛2的多样性指数最低,为0．7044。苔藓植物群落相似性以鸳鸯岛1和鸳鸯岛2的最高,为0．3279;兴隆半岛和鸳鸯岛2的相似性最低,仅为0．1111。不同生境的 Dahl 指数、Shannon 指数和 Simpson 指数大小均为土生＞石生＞石土混生＞树生≥腐生,土生和石生2种基质类型为该区苔藓植物最丰富的生境,腐木生生境的苔藓植物分布最少。苔藓植物多样性与岛屿面积、水分、土壤、基质及小生境多样化等有关。     

Attention embedded lightweight network for maize disease recognition

Crop disease has a negative impact on food security. If diverse crop diseases are not identified in time, they can spread and influence the quality, quantity, and production of grain. Severe crop diseases can even result in complete failure of the harvest. Recent developments in deep learning, particularly convolutional neural networks (CNNs), have exhibited impressive performance in both image recognition and classification. In this study, we propose a novel network architecture, namely Mobile-DANet, to identify maize crop diseases. Based on DenseNet, we retained the structure of the transition layers and used the depthwise separable convolution in dense blocks instead of the traditional convolution layers, and then embedded the attention module to learn the importance of interchannel relationship and spatial points for input features. In addition, transfer learning was used in model training. By this means, we improved the accuracy of the model while saving more computational power than deep CNNs. This model achieved an average accuracy of 98.50% on the open maize data set, and even with complicated backdrop conditions, Mobile-DANet realized an average accuracy of 95.86% for identifying maize crop diseases on a local data set. The experimental findings show the effectiveness and feasibility of the Mobile-DANet. Our data set is available at https://github.com/xtu502/maize-disease-identification .     

社会学习、环境认知对农户亲环境行为影响研究

基于山东、陕西、甘肃省710份调研数据,通过结构方程模型分析社会学习、环境认知对农户亲环境行为的影响。结果表明:(1)环境认知能够直接正向影响农户亲环境行为,标准化路径系数为0.090;(2)社会学习对农户亲环境行为有直接负向影响,并通过环境认知也会间接负向影响农户亲环境行为,标准化路径系数分别为-0.400、-0.010。据此提出:政府应建立亲环境行为补贴制度,降低农户实施亲环境行为时所承担的短期成本和风险,在宣传过程中注重知识的全面性,提升农户环境认知水平,设立实施亲环境行为示范户,规范社会学习途径,促进农户向亲环境行为转变。     

基于样点个体代表性的大尺度土壤属性制图方法

大空间尺度范围的土壤属性分布信息是陆地表层过程模拟的基础信息.基于野外样点进行空间插值是获得土壤属性空间分布信息的重要手段.现有的空间插值方法通常要求所用样点对研究区土壤属性空间分布规律具有良好的全局代表性.然而,受采样经费和野外采样条件的限制,所采集的样点往往难以全面地反映研究区土壤属性的空间分布规律.基于这样的样点用现有空间插值方法得到的土壤属性分布图通常精度较低,并且由样点全局代表性差带来的推测不确定性也无法得到度量.为了合理利用这些已采集的但全局代表性不好的样点,本文提出了基于样点“个体代表性”推测土壤属性空间分布并度量推测不确定性的方法.该方法在两点环境条件越相似、土壤属性就越相似的假设下,认为每一样点可以代表与其环境条件相似的地区,并且代表程度可以由两点的环境相似度度量;通过分析环境相似度计算推测不确定性,并以环境相似度为权重计算样点可代表地区的土壤属性值.将该方法应用于推测新疆伊犁地区土壤表层有机质含量,经验证,本文方法能够有效地利用全局代表性差的样点推测样点能够代表地区的土壤属性空间分布,并且所得的推测不确定性与预测残差呈现正向关系,能够有效地指示推测结果的可靠程度.     

基于改进轻量级卷积神经网络MobileNetV3的番茄叶片病害识别

番茄病害的及时检测可有效提升番茄的质量和产量.为实现番茄病害的实时无损伤检测,本研究提出了一种基于改进MobileNetV3的番茄叶片病害分类识别方法.首先选择轻量级卷积神经网络Mobile?NetV3,在Image Net数据集上进行预训练,将预训练得到的共享参数迁移到对番茄叶片病害识别的模型上并做微调处理.采用相同...     

土层深度和季节变化对保护地西瓜连作土壤中丛枝菌根真菌群落结构的影响

The characteristics of arbuscular mycorrhizal fungal (AMF) community structure in various soil depths and growing seasons of watermelon (Citrullus vulgaris) grown in commercial greenhouses in Daxing of Beijing and Weifang and Laiyang of Shandong, China were investigated using both morphological identification and denaturing gradient gel electrophoresis. The sampled soils had been used for continuous greenhouse production of watermelon for 0, 5, 10, 15, or 20 years. Glomus claroideum was the dominant species in the greenhouse soils planted for 5, 10, and 15 years in Laiyang, while Glomus mosseae and Glomus etunicatum were dominant in the nearby open farmland soil. Sorenson’s similarity index of AMF community composition ranged from 0.67 to 0.84 in the soils planted for 5 years, and from 0.29 to 0.33 for 20 years among the three locations. Spore abundance, species richness, and the Shannon index were highest near the soil surface (0-10 cm) and decreased with soil depth, and higher in June and October than in August and December. Canonical correspondence analysis showed that available P and the number of years that soil had been used for greenhouse production were the main factors contributing to the variance of AMF community composition. It was concluded that the community structure of AMF was mainly influenced by soil available P and planting time of watermelon as well as by soil depth and seasonal variation in the commercial greenhouse.     

土地利用数据综合中图斑多边形决策推理

针对土地利用数据综合中图斑的化简处理,将智能体技术引入到图斑的综合处理过程中,建立了顾及多种条件的图斑重要性判断的决策机制,将可操作的几何、语义控制指标融入到推理中,实现了从图斑重要性判断到尺度变换算子选择的自动化过程,最后通过实际生产进行检验,结果显示该过程能够大幅度地提高生产效率,并能够很好地维护土地利用数据的面积平衡性以及全覆盖、无重叠、无缝隙的特征.该研究可为多比例尺国土资源数据库提供技术支持.