APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.     

A novel index to evaluate discretization methods:A case study of flood susceptibility assessment based on random forest

The selection of a suitable discretization method(DM)to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment.However,few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV.These issues were well addressed in this study.The information loss rate(ILR),an index based on the informa-tion entropy,seems can be used to select optimal DM for each SCV.However,the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV.Facing this issue,we propose an index,infor-mation change rate(ICR),that focuses on the changed amount of information due to the discretization based on each cell,enabling the identification of the optimal DM.We develop a case study with Random Forest(training/testing ratio of 7:3)to assess flood susceptibility in Wanan County,China.The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR.The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases.Moreover,we observed the ILR values are unnaturally small(＜1％),whereas the ICR values are obviously more in line with general recognition(usually 10％-30％).The above results all demonstrate the superiority of the ICR.We consider this study fills up the existing research gaps,improving the ML-based natural hazard susceptibility assessments.     

面向自然资源信息提取的多源异构数据融合技术——以汉江流域NDVI数据为例

高时空分辨率的自然资源指标数据对大尺度自然资源动态观测与趋势评估至关重要。大数据时代下的海量多源数据为数据高效融合利用提供了可能。以重构汉江流域归一化植被指数(Normalized Difference Vegetation Index,NDVI)数据为例,搭建了PostgreSQL自然资源时空大数据处理底层架构,集成了数据级融合法、特征级融合法和决策级融合法,基于机器学习算法构建了一套面向自然资源信息提取的多源异构数据智能融合技术,实现了多源数据的高效利用与特征空间优选。同时,重构了2000—2019年汉江流域NDVI 1 km逐年数据集,全面反映了汉江流域植被动态变化。研究结果可为地球科学时空大数据的高效提取与模拟分析提供科学参考,为定量核算林草资源禀赋规模、探究生态系统时空演变规律提供一种更精准、更便捷的技术手段。     

人工智能在冰雹识别及临近预报中的初步应用

基于广东10部S波段多普勒天气雷达的三维拼图资料,利用机器学习技术开发了一种冰雹识别和临近预报的人工智能算法。算法设计时以雷达回波反射率的垂直和水平扫描数据为基础训练集,将冰雹云的雷达反射率扫描数据作为正样本,将其他雷达反射率扫描数据作为负样本,通过贝叶斯分类法对正、负样本数据集进行机器学习,训练人工智能识别冰雹云内在规律的能力。训练时以广东省2008-2013和2015-2016年的数据作为训练集,使用了2014年广东省12次冰雹过程的数据做检验。对比检验的结果表明,人工智能法比传统的概念模型法击中率高9个百分点。研究结果表明了人工智能对冰雹这类非线性强天气过程具有较强的识别能力。      

Integrating farmers’ adaptive knowledge into flood management and adaptation policies in the Vietnamese Mekong Delta: A social learning perspective

Flood management and adaptation are important elements in sustaining farming production in the Vietnamese Mekong Delta (VMD). While over the past decades hydraulic development introduced by the central government has substantially benefited the rural economy, it has simultaneously caused multiple barriers to rural adaptation. We investigate the relational practices (i.e., learning interactions) taking place within and across the flood management and adaptation boundaries from the perspective of social learning. We explore whether and how adaptive knowledge (i.e., experimental and experiential knowledge) derived from farmers’ everyday adaptation practices contributes to local flood management and adaptation policies in the selected areas. We collected data through nine focus groups with farmers and thirty-three interviews with government officials, environmental scientists, and farmers. Qualitative analysis suggests that such processes are largely shaped by the institutional context where the boundary is embedded. This study found that while the highly bureaucratic operation of flood management creates constraints for feedback, the more informal arrangements set in place at the local level provide flexible platforms conducive to open communication, collaborative learning, and exchange of knowledge among the different actors. This study highlights the pivotal role of shadow systems that provide space for establishing and maintaining informal interactions and relationships between social actors (e.g., interactions between farmers and extension officials) in stimulating and influencing, from the bottom-up, the emergence of adaptive knowledge about flood management and adaptation in a local context.     

遥感影像分析在新增建设用地督察中的应用

以卫星影像大数据和时空数据挖掘技术为基础,采用深度学习框架支撑,构建了面向新增建设用地遥感监测应用需求的深度学习训练平台,可智能发现自然资源督察线索,全面提高自然资源督察问题的发现能力.该技术可广泛服务于大范围、高频次的督察工作,为督察工作提供更及时客观全面的信息基础和保障.     

Rethinking small-scale fisheries compliance

Fisheries compliance has been identified as key to sustainable fisheries management. However, it has been widely acknowledged that the approach by many governments worldwide to achieve compliance is largely through law enforcement efforts. Although theories of normative action have expanded compliance thinking from that of rationalist approaches, this paper takes the argument further. By drawing on criminology and security discourse, and on empirical research on small-scale fisheries in South Africa, this paper argues for a new approach to our understanding of compliance. Furthermore, it argues that rather than taking law as given, the formation of law and the power dynamics influencing such processes are critical aspects that need to be acknowledged and understood in fisheries compliance theory.     

中国碳强度关键影响因子的机器学习识别及其演进

碳强度影响因子数量众多,通过在众多因子中评估其重要性以识别出关键影响因子进而解析碳强度关键因子的变化规律,是中国2030年碳强度能否实现比2005年下降60%~65%目标的科学基础。传统的回归分析方法对于评估众多因子的重要性存在多重共线性等问题,而机器学习处理海量数据则具有较好的稳健性等优点。本文从能源结构、产业结构、技术进步和居民消费等方面选取了56个中国碳强度影响因子指标,采用随机森林算法基于信息熵评估了1980-2014年逐年各项因子的重要性,通过指标数量与信息熵的对应关系统一筛选出每年重要性最大的前22个指标作为相应年度关键影响因子,最终依据关键影响因子的变化趋势划分了3个阶段作了演进分析。结果发现：1980-1991年,碳强度的关键因子主要以高耗能产业规模及占比、化石能源占比和技术进步为主;1992-2007年,中国经济进入快车道增长时期,服务业占比和化石能源价格对碳强度的影响作用开始显现,居民传统消费的影响作用在增大;2008年全球金融危机后,中国进入经济结构深化调整时期,节能减排力度大大增强,新能源占比和居民新兴消费的影响作用迅速显现。为实现2030年碳强度下降60%~65%目标,优化能源结构和产业结构,促进技术进步,提倡绿色消费,强化政策调控是未来需要采取的主要措施。     

It’s Time To Learn About Learning: Where Should the Environmental and Natural Resource Governance Field Go Next?

This Commentary reflects on the state of the scholarship on learning for environmental and natural resource policy and governance. How have we been learning about learning? We highlight theoretical and empirical advancements related to learning, as well as areas of divergence between learning theories and frameworks, and underdeveloped knowledge around processes and outcomes. To address these limitations and improve progress in both theory and practice, we offer recommendations for learning scholarship by focusing on how to collectively engage in ‘learning about learning’.     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。