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Extensive urbanization around the world has caused a great loss of farmland, which significantly impacts the ecosystem services provided by farmland. This study investigated the farmland loss due to urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China from 1980 to 2018 based on multiperiod datasets from the Land Use and Land Cover of China databases. Then, we calculated ecosystem service values (ESVs) of farmland using valuation methods to estimate the ecosystem service variations caused by urbanization in the study area. The results showed that 3711.3 km2 of farmland disappeared because of urbanization, and paddy fields suffered much higher losses than dry farmland. Most of the farmland was converted to urban residential land from 1980 to 2018. In the past 38 years, the ESV of farmland decreased by 5036.7 million yuan due to urbanization, with the highest loss of 2177.5 million yuan from 2000–2010. The hydrological regulation, food production and gas regulation of farmland decreased the most due to urbanization. The top five cities that had the largest total ESV loss of farmland caused by urbanization were Guangzhou, Dongguan, Foshan, Shenzhen and Huizhou. This study revealed that urbanization has increasingly become the dominant reason for farmland loss in the GBA. Our study suggests that governments should increase the construction of ecological cities and attractive countryside to protect farmland and improve the regional ESV.
Xuege Wang; Fengqin Yan; Yinwei Zeng; Ming Chen; Bin He; Lu Kang; Fenzhen Su. Ecosystem Services Changes on Farmland in Response to Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area of China. Land 2021, 10, 501 .
AMA StyleXuege Wang, Fengqin Yan, Yinwei Zeng, Ming Chen, Bin He, Lu Kang, Fenzhen Su. Ecosystem Services Changes on Farmland in Response to Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area of China. Land. 2021; 10 (5):501.
Chicago/Turabian StyleXuege Wang; Fengqin Yan; Yinwei Zeng; Ming Chen; Bin He; Lu Kang; Fenzhen Su. 2021. "Ecosystem Services Changes on Farmland in Response to Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area of China." Land 10, no. 5: 501.
Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.
Xiaojun Li; Haowen Yan; Weiying Xie; Lu Kang; Yi Tian. An Improved Pulse-Coupled Neural Network Model for Pansharpening. Sensors 2020, 20, 2764 .
AMA StyleXiaojun Li, Haowen Yan, Weiying Xie, Lu Kang, Yi Tian. An Improved Pulse-Coupled Neural Network Model for Pansharpening. Sensors. 2020; 20 (10):2764.
Chicago/Turabian StyleXiaojun Li; Haowen Yan; Weiying Xie; Lu Kang; Yi Tian. 2020. "An Improved Pulse-Coupled Neural Network Model for Pansharpening." Sensors 20, no. 10: 2764.