Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions, which may cause severe coastal water problems without adequate environmental management. Effective mapping of mariculture areas is essential for the protection of coastal environments. However, due to the limited spatial coverage and complex structures, it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution (MSR) images. To solve this problem, we propose to use the full resolution cascade convolutional neural network (FRCNet), which maintains effective features over the whole training process, to identify mariculture areas from MSR images. Specifically, the FRCNet uses a sequential full resolution neural network as the first-level subnetwork, and gradually aggregates higher-level subnetworks in a cascade way. Meanwhile, we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously, leading to rich and representative features. As a result, FRCNet can effectively recognize different kinds of mariculture areas from MSR images. Results show that FRCNet obtained better performance than other classical and recently proposed methods. Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning.

Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km² account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km² has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km² account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

In order to fundamentally support work for achieve "climate action, life below water, and life on land" 3 Sustainable Development Goals of the United Nations, and to realize unified monitoring and refined management of natural resources, this paper collects, organizes, analyzes, and summarizes national policy, management department documents, relevant papers, and the latest industry development trends through the literature induction method. It systematically studies current monitoring networks from the perspectives of land resources monitoring, mineral resources monitoring, forest resources monitoring, grassland resources monitoring, water resources monitoring, wetland resources monitoring, marine resources monitoring. It indicates such problems as inconsistent classification standards and monitoring technical standards for natural resources, lack of smooth connection among monitoring networks, and insufficient high-tech applications. It proposes suggestions of building 1+N monitoring system, coordinating multi-level and multi-category monitoring mechanisms, improving monitoring intelligence level, strengthening the ability to obtain multi-source spatiotemporal data, and constructing an information sharing platform, to better build a unified natural resource monitoring system, and serve the refined management of natural resources.

健全国家自然资源资产管理体制和加强自然资源资产管理是生态文明建设的内在要求。明确自然资源资产的概念、特征与分类，对新时代自然资源资产综合管理体制和资源科学学科的建设与发展具有重要的科学价值与实践意义。针对当前自然资源资产概念界定不清、分类体系繁多且边界交叉重叠等问题，系统梳理了“自然资源资产”的概念源流，在辨析其与“自然资源”“自然资本”和“生态资产”等概念逻辑关系的基础上，明确了自然资源资产的概念内涵。同时，总结了自然资源资产的基本特征和外延属性，并分别从学理、法理与管理维度整理了自然资源分类的研究进展，指出当前分类体系存在的系统性不足、多维分类之间衔接不足等问题。以此为基础，研究提出了适应新时代自然资源资产综合管理的分类原则，形成“学理-法理-管理”三元融合的分类思路，构建涵盖8个一级类别、31个二级类别及162个三级类别的分层分类体系。基于辨析概念—归纳特征—梳理问题—明确原则的流程构建的自然资源资产分类体系科学、合理、可靠，可为提升自然资源资产管理效能、推动资源科学学科发展提供理论支撑。