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Aquaculture is one of China's fastest-growing animal food production sectors. It accounts for the largest share in the world, mainly distributed in coastal areas. Due to the depletion of offshore resources and increasing domestic demand for aquatic products, more and more land, including newly reclaimed land, is gradually being used to build aquaculture ponds. Understanding the location, spatial pattern, scale, and other properties is critical for China's food and protein security. However, until recently, how to detect, monitor and map the aquaculture ponds with remote sensing is still a problem, which hinders the understanding of its magnitude and value, and interferes sustainable management of coastal ecosystems. Here we proposed a framework for extracting aquaculture ponds by integrating existing multi-source remote sensing data on the Google Earth Engine platform. Taking Shanghai as a study area, the Multi-threshold Connected Component Segmentation and Random Forest algorithm method were used to extract aquaculture ponds automatically. The results show that this method can effectively generate the maps of Shanghai's aquaculture ponds from 2016 to 2019, and the overall accuracy of the classification results in 2018 can reach 91.8%. This method can greatly improve the efficiency of extracting aquaculture ponds, and has a good performance in detecting non-intensive aquaculture pond areas. It can also be easily used and has high spatio-temporal transferability with the help of the Google Earth Engine platform.
Zilong Xia; Xiaona Guo; Ruishan Chen. Automatic extraction of aquaculture ponds based on Google Earth Engine. Ocean & Coastal Management 2020, 198, 105348 .
AMA StyleZilong Xia, Xiaona Guo, Ruishan Chen. Automatic extraction of aquaculture ponds based on Google Earth Engine. Ocean & Coastal Management. 2020; 198 ():105348.
Chicago/Turabian StyleZilong Xia; Xiaona Guo; Ruishan Chen. 2020. "Automatic extraction of aquaculture ponds based on Google Earth Engine." Ocean & Coastal Management 198, no. : 105348.
Globally, the loss of forest is of great concern as forest plays many key roles in the earth system, for example, it contributes to biogeochemical cycles and rural livelihoods. Forest could provide ecosystem services such as soil retention and flood regulation and is especially critical in mountain environments. Deforestation in such regions further results in carbon emission and biodiversity loss and may reduce agricultural productivity and increase the poverty rate. In China, recognition of these problems has prompted a series of ecological construction programs, including “Returning Farmland to Forest” (RFF), which advocates stopping farming on sloping land that is prone to soil erosion and promotes afforestation and recovery of forest vegetation and was initially implemented in 1999. The program has been widely applied in Guizhou Province, a typical fragile karst mountain area of southwest China. There is, however, a lack of knowledge of the effectiveness of the RFF policy, and the relative roles played by possible factors that lead to forest change. Here we analyze the pattern and process of forest change in the karst mountain regions of Guizhou province between 1980 and 2018 and evaluate how RFF and other driving forces contribute to these changes. Based on a temporal sequence of satellite images, we develop a Markov model to examine the forest change, and found that most of the forests grow on the slopes of 15-25°, the forest cover has increased by 1,410 km2 between 1980 and 2019, and 36% of cropland in Guizhou province has been converted to forest since 1980. Out of nine municipalities in the province, the most significant increases in forest cover occurs in Qiandongnan, which accounts for 20% (583 km2) of the increased area. we also found that the RFF program has had a marked positive impact on forest cover and has also improved hydrothermal conditions in the region. However, population, GDP, and traffic accessibility have a negative impact on forest cover. Climate factors appear to have the least impact on forest change during the period of 1980 to 2018. The findings offer useful information for resource managers to engage in forest protection, deforestation prevention, and ecological restoration in regions with similar conditions.
KEYWORDS: forest; restoration; RFF; GDP; karst areas
Xiaona Guo; Ruishan Chen; Qiang Li; Michael E. Meadows; Zhenzhen Pan. Forest recovery and its driving forces in karst areas of southwest China. 2020, 1 .
AMA StyleXiaona Guo, Ruishan Chen, Qiang Li, Michael E. Meadows, Zhenzhen Pan. Forest recovery and its driving forces in karst areas of southwest China. . 2020; ():1.
Chicago/Turabian StyleXiaona Guo; Ruishan Chen; Qiang Li; Michael E. Meadows; Zhenzhen Pan. 2020. "Forest recovery and its driving forces in karst areas of southwest China." , no. : 1.
In contemporary studies in areas such as the field of classical theory, there has been an increasing emphasis on E-Business. In this study, we examine central place theory principles using China’s consumer-to-consumer online game marketing as a case of E-business. The results indicate that virtual goods in China’s C2C online game marketing are distributed in areas with high economic development and where residents have high purchasing power. Further, we found that the transmission capacity of virtual goods is affected by the level of telecommunication services because they are transmitted through information flow. Such effects illustrate that virtual goods are no longer affected by spatial distance and traffic conditions because virtual goods do not need to be touched; however, they are still affected by market forces. We conclude that there are key differences between the marketing and transport principles of CPT based on the hierarchical model describing new internal and external relations and the level of complexity in E-Business processes.
Qiang Li; Qing Liu; Xiaona Guo; Shuo Xu; Jingyu Liu; Heli Lu. Evolution and Transformation of the Central Place Theory in E-Business: China’s C2C Online Game Marketing. Sustainability 2019, 11, 2274 .
AMA StyleQiang Li, Qing Liu, Xiaona Guo, Shuo Xu, Jingyu Liu, Heli Lu. Evolution and Transformation of the Central Place Theory in E-Business: China’s C2C Online Game Marketing. Sustainability. 2019; 11 (8):2274.
Chicago/Turabian StyleQiang Li; Qing Liu; Xiaona Guo; Shuo Xu; Jingyu Liu; Heli Lu. 2019. "Evolution and Transformation of the Central Place Theory in E-Business: China’s C2C Online Game Marketing." Sustainability 11, no. 8: 2274.