Transformational adaptation on the farm: Processes of change and persistence in transitions to ‘climate-smart’ regenerative agriculture

Regenerative agriculture, an alternative form of food and fiber production, concerns itself with enhancing and restoring resilient systems supported by functional ecosystem processes and healthy, organic soils capable of producing a full suite of ecosystem services, among them soil carbon sequestration and improved soil water retention. As such, climate change mitigation and adaptation are incidental to a larger enterprise that employs a systems approach to managing landscapes and communities. The transformative potential of regenerative agriculture has seen growing attention in the popular press, but few empirical studies have explored the processes by which farmers enter into, navigate, and, importantly, sustain the required paradigm shift in their approach to managing their properties, farm businesses, and personal lives. We draw on theories and insights associated with relational thinking to analyze the experiences of farmers in Australia who have undertaken and sustained transitions from conventional to regenerative agriculture. We present a conceptual framework of “zones of friction and traction” occurring in personal, practical, and political spheres of transformation that both challenge and facilitate the transition process. Our findings illustrate the ways in which deeply held values and emotions influence and interact with mental models, worldviews, and cultural norms as a result of regular monitoring; and how behavioral change is sustained through the establishment of self-amplifying positive feedbacks involving biophilic emotions, a sense of well-being, and an ever-expanding worldview. We conclude that transitioning to regenerative agriculture involves more than a suite of ‘climate-smart’ mitigation and adaptation practices supported by technical innovation, policy, education, and outreach. Rather, it involves subjective, nonmaterial factors associated with culture, values, ethics, identity, and emotion that operate at individual, household, and community scales and interact with regional, national and global processes. Findings have implications for strategies aimed at facilitating a large-scale transition to climate-smart regenerative agriculture.     

Climate-governance entrepreneurship,higher-order learning,and sustainable consumption: the case of the state of Oregon,United States

The ongoing devolution of climate policy-making to sub-national levels has prompted growing interest in policy entrepreneurship by individuals who are politically and technically creative and institutionally resourceful. This paper investigates the case of the materials-management programme in the Oregon Department of Environmental Quality which has emerged as a national and international leader by focusing on the role of household consumption in greenhouse gas (GHG) emissions. Two noteworthy innovations involve the development of a consumption-based GHG emissions inventory and introduction of policies aimed at facilitating construction of small homes (so-called Accessory Dwelling Units, ADU). The case traces over several decades the higher order learning processes within the group and their entrepreneurship toward affecting broader changes in emission accounting and climate policies in Oregon. The paper identifies the enabling factors for these innovations, and considers: how to create the conditions for learning, experimentation, and policy entrepreneurship; how to reproduce these conditions in different locales; and how to recognize and foster innovations that arise outside the established mainstream ‘climate community’. It also stresses the benefits of breaking down the barriers between science-based analysis and policy. The two questions frequently raised in the climate policy debate – how to bring researchers and practitioners together to develop efficacious policies; and how to replicate successful programmes and policies across different communities, jurisdictions, and locations – should be re-examined. It may be more appropriate to ask instead: How to create conditions for learning, experimentation, and policy entrepreneurship; and how to reproduce these conditions in different locales.

Key policy insights

• Using a consumption-based greenhouse gas emission inventory instead of a sector-based inventory radically changes climate policy priorities, shifting the emphasis from technological fixes to curbing household consumption.

• Policy innovations thrive in teams that combine technical and scientific competencies with: a commitment to addressing societal problems; interest in inquiry, experimentation, and learning; entrepreneurship; and strategic and political savvy.

• These qualities require breaking down artificial barriers between science and policy.

• Transformative policy ideas can originate within institutional nodes that operate outside of an established community of expertise and authority; and these should be identified and fostered.

     

基于深度学习的FY3D/MERSI和EOS/MODIS云检测模型研究

针对FY3D/MERSI和EOS/MODIS的云检测问题,提出了一种基于深度学习技术的全自动云检测算法,首次将深度学习引入到卫星影像云检测领域。本算法使用深度全卷积神经网络(Deep Convolutional Neural Networks)作为核心结构,基于EOS/MODIS基本云检测原理选择合适的通道作为特性向量参数,针对不同的场景进行分类和网络模型的训练,最终得到基于深度学习的云检测模型。经过EOS/MODIS数据和FY3D/MERSI数据的测试,云检测的精度达到98%以上,可以看出基于深度学习的云检测算法能够用于云检测,该算法具有效率高、精度高等特点,云检测效果理想。     

Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions

In some studies on landslide susceptibility mapping (LSM), landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form. Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes (LSIs); moreover, the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the LSM. To address this issue by accurately drawing polygonal boundaries based on LSM, the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes, such as landslide points and circles, are compared. Within the research area of Ruijin City in China, a total of 370 landslides with accurate boundary information are obtained, and 10 environmental factors, such as slope and lithology, are selected. Then, correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio (FR) method. Next, a support vector machine (SVM) and random forest (RF) based on landslide points, circles and accurate landslide polygons are constructed as point-, circle- and polygon-based SVM and RF models, respectively, to address LSM. Finally, the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis, and the uncertainties of the predicted LSIs under the above models are discussed. The results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy, compared to those based on the points and circles. Moreover, a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input variables. Additionally, the expression of the landslide boundary as circles introduces errors in measurement and is not as accurate as the polygonal boundary in most LSM modelling cases. In addition, the results under different conditions show that the polygon-based models have a higher LSM accuracy, with lower mean values and larger standard deviations compared with the point- and circle-based models. Finally, the overall LSM accuracy of the RF is superior to that of the SVM, and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models.     

基于小样本卷积神经网络的主动源P 波初至拾取方法

地震走时成像结果的准确性取决于初至到时的拾取精度,人工挑选初至效率低、成本高。前人研究表明深度学习可以应用于初至的自动拾取,然而传统的深度学习方法往往需要大量人工挑选的初至作为神经网络的训练集。文章利用U型卷积神经网络拾取单炮多道P波初至,研究表明P波初至拾取的均方根误差会随着训练集数量的增加而减少。训练集分别采用35炮和597炮数据时,对应的均方根误差分别为11.4和6.5 ms。参考半监督学习中数据增强方法,选取适合主动源数据的增强方法（随机剪裁、随机擦除等）用于拓展训练集。结果显示,以人工拾取总数据量的5%（35炮）作为小样本并进行随机擦除数据增强后,实现了均方根误差在5.5 ms（约3个采样点）以内,比未经增强的误差减少51%。与前人的深度学习方法相比,本文应用的数据增强方法可以在小样本的情况下实现主动源地震初至的高精度拾取。     

基于YOLOv5算法的飞机类型光学遥感识别

飞机类型检测是遥感影像分析领域的研究热点,在机场监控和情报分析等应用中起着重要的作用。其中,深度学习方法作为遥感影像分析领域广泛应用的手段之一,在飞机类型检测任务中仍面临许多问题,如使用未公开的自制数据集、实验复现困难、无法验证泛化能力等。同时,光学遥感影像易受光照条件、云雨变化等因素影响,使检测任务更加困难。为了解决这些问题,本文首先利用MTARSI数据集对样本进行筛选,再结合Google Earth等开源方法收集飞机影像,采用随机旋转、改变亮度等方法构建新的飞机类型检测数据集。其次,采用YOLOv5作为基础网络框架,针对其多层卷积和池化操作可能会削弱或完全丢失飞机特征的问题,进行多尺度优化训练,有效检测飞机类型特征。最后,利用跨数据集验证模型的泛化能力。实验结果表明,本文方法能准确、有效地检测出光学遥感影像中的飞机的具体类型,具有较强的鲁棒性和泛化能力,跨数据集进行飞机类型检测正确率达到82.12%,可为智能化的飞机目标语义分析、星上应用等研究提供技术支撑。     

增强型遥感影像SRGAN算法及其在三维重建精度提升中的应用

遥感影像是地形测绘、三维重建等任务的主要数据源之一,分辨率影响着被测目标在影像上的表示能力,对后期三维模型的定位精度及重建效果起着重要作用。针对遥感影像像幅较大且目标特征表现复杂、细节丰富的特点,结合实景三维模型重建的需求,提出了一种增强型遥感影像SRGAN算法。克服了传统方法进行超分重建时易出现边缘效应、产生模糊重建的情况,改进了简单卷积网络仅能提取影像中较为浅层的特征信息,无法在提高分辨率的同时保留影像丰富细节的局限。本文所提算法在生成模型中使用密集剩余残差块进行深层特征提取,在判别模型中引入多尺度判别思想,从而保证遥感影像重建时特征纹理、细节信息、高频目标的完整与精确。实验构建不同时间、不同类型区域的遥感影像数据集,在此基础上将本文算法与Bicubic、SRGAN、ESRGAN算法进行对比分析,在超分重建中PSNR较对比算法提升约3个单位,渗透指数PI更趋向且稳定于1,SSIM与清晰度指标Q同样得到较好改善;在三维重建中影像密集匹配点数量得到提升,同时误差减少,模型精细程度和定位精度得到提高。结果表明,本文算法适用于遥感影像超分辨率重建问题,并在实景三维模型重建中对精度的提升表现较好。     

Learning to combine multiple string similarity metrics for effective toponym matching

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics.     

机器学习在岩矿地球化学研究中的应用——综述与思考

岩石与矿物的地球化学成分数据具有高维度特征。传统的岩矿地球化学成分研究主要采用二元/三元图解判别法,准确率不高,在数理统计方法上有欠缺。机器学习方法非常适用于对大样本高维度的岩矿成分数据进行数理统计处理。笔者等在介绍机器学习常见算法基本原理的基础上,总结近5年来国内外学者将机器学习方法应用于岩石矿物成分数据研究的实例,包括：(1)根据矿物成分溯源其母岩(源岩)、判别矿床类型,(2)新生代火山岩溯源,(3)判别变质岩原岩,(4)依据岩浆岩成分判别大地构造环境等。已有的研究实例显示,机器学习方法的准确度明显优于传统的低维度判别法。机器学习本质是分析大样本数据的高维度变量之间的相关、归类等多元统计问题。推广机器学习的应用需要建设开放获取(Open Access)的矿物、岩石成分数据库,同时全面实施开放研究(Open Research)的发表策略。