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Dawei Wen
College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China

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Journal article
Published: 23 June 2021 in Sustainability
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Assessment of ecosystem services supply, demand, and budgets can help to achieve sustainable urban development. The Guangdong-Hong Kong-Macao Greater Bay Area, as one of the most developed megacities in China, sets up a goal of high-quality development while fostering ecosystem services. Therefore, assessing the ecosystem services in this study area is very important to guide further development. However, the spatial pattern of ecosystem services, especially at local scales, is not well understood. Using the available 2017 land cover product, Sentinel-1 SAR and Sentinel-2 optical images, a deep learning land cover mapping framework integrating deep change vector analysis and the ResUnet model was proposed. Based on the produced 10 m land cover map for the year 2020, recent spatial patterns of the ecosystem services at different scales (i.e., the GBA, 11 cities, urban–rural gradient, and pixel) were analyzed. The results showed that: (1) Forest was the primary land cover in Guangzhou, Huizhou, Shenzhen, Zhuhai, Jiangmen, Zhaoqing, and Hong Kong, and an impervious surface was the main land cover in the other four cities. (2) Although ecosystem services in the GBA were sufficient to meet their demand, there was undersupply for all the three general services in Macao and for the provision services in Zhongshan, Dongguan, Shenzhen, and Foshan. (3) Along the urban–rural gradient in the GBA, supply and demand capacity showed an increasing and decreasing trend, respectively. As for the city-level analysis, Huizhou and Zhuhai showed a fluctuation pattern while Jiangmen, Zhaoqing, and Hong Kong presented a decreasing pattern along the gradient. (4) Inclusion of neighborhood landscape led to increased demand scores in a small proportion of impervious areas and oversupply for a very large percent of bare land.

ACS Style

Dawei Wen; Song Ma; Anlu Zhang; Xinli Ke. Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method. Sustainability 2021, 13, 7044 .

AMA Style

Dawei Wen, Song Ma, Anlu Zhang, Xinli Ke. Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method. Sustainability. 2021; 13 (13):7044.

Chicago/Turabian Style

Dawei Wen; Song Ma; Anlu Zhang; Xinli Ke. 2021. "Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method." Sustainability 13, no. 13: 7044.

Journal article
Published: 05 November 2020 in International Journal of Applied Earth Observation and Geoinformation
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Timely and accurate global urban maps are fundamental in monitoring urbanization process and understanding environmental degradation. Therefore, this paper proposed a locally adaptive and fully automated global mapping method and produced an updated 250 m MODIS global urban area product (MGUP) from 2001 to 2018. The proposed approach mainly consists of 1) automated samples extraction from existing global products, 2) locally adaptive samples selection and trained classification in each 5° × 5° grid, and 3) post-processing in terms of the spatio-temporal context. To validate the product, 9 groups of samples for every two years from 2001 to 2018, amounting to over 150,000 sample points, were collected manually from Landsat imagery as global validation dataset. Accuracy assessment indicates that MGUP has a F-score of 0.88, achieving better results than the contemporary global products, i.e., MCD12Q1.v5 (0.82), MCD12Q1.v6 (0.86), and CCI-LC (0.86). Analysis of urban expansion based on MGUP shows that the world’s urban area increased to 802233 km2 and accounted for 0.54% of the Earth’s land surface in 2018. The total global urban area expanded by 1.68 times from 2001 to 2018. At continent level, urban density varies considerably, and the highest and lowest one is in Europe (1.78%) and Oceania (0.15%), respectively. At national level, large increment of urban area mainly occurs in North America, Asia, and South America; and countries having high growth rates are mainly developing countries in Africa and Asia. MGUP can be downloaded at https://www.researchgate.net/publication/339873537_MGUP_annual_global_2001_2018.

ACS Style

Xin Huang; Jiongyi Huang; Dawei Wen; Jiayi Li. An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach. International Journal of Applied Earth Observation and Geoinformation 2020, 95, 102255 .

AMA Style

Xin Huang, Jiongyi Huang, Dawei Wen, Jiayi Li. An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach. International Journal of Applied Earth Observation and Geoinformation. 2020; 95 ():102255.

Chicago/Turabian Style

Xin Huang; Jiongyi Huang; Dawei Wen; Jiayi Li. 2020. "An updated MODIS global urban extent product (MGUP) from 2001 to 2018 based on an automated mapping approach." International Journal of Applied Earth Observation and Geoinformation 95, no. : 102255.

Journal article
Published: 29 March 2019 in Remote Sensing
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Inner-city redevelopment is regarded as an effective way to promote land-use efficiency and optimize land-use structure, especially with the establishment of urban growth boundaries in Chinese cities. However, inner-city redevelopment as compared to urban sprawl has been rarely monitored in 2D space, let alone in 3D space. Therefore, in this paper, a novel approach to generate time-series 3D building maps (i.e., building footprint and height) based on high-resolution (2 m) multi-view ZY-3 satellite imagery was proposed. In the proposed method, the building footprint was updated by an object-based image-to-map change detection method, which employed spectral (i.e., HSV and NDVI) and structural features (i.e., morphological building index) to extract non-building and building objects, respectively; building height was estimated automatically through semi-global matching of multi-view images. We applied the proposed method to four representative Chinese megacities, i.e., Beijing, Xi’an, Shanghai, and Wuhan, for the period 2012–2017, and detected building footprints with overall accuracies ranging from 84.84% to 97.60%. The building height estimation was also relatively accurate, with the bias, slope, and root-mean-square error being −0.49–2.30 m, 0.93–1.10 m, and 4.94–7.31 m, respectively. Our results show that the total building coverage decreased over the study period, accompanied by an increase in both area-weighted building height and floor area ratio. In addition, compact low-rise buildings have been replaced by open high-rise buildings in the urban redevelopment process. Moreover, due to the scattered spatial distribution of the redevelopment sites, the local spatial aggregation patterns of building density are unlikely to shift between hotspots (i.e., spatial aggregation of high values) and coldspots (i.e., spatial aggregation of low values).

ACS Style

Dawei Wen; Xin Huang; Anlu Zhang; Xinli Ke. Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery. Remote Sensing 2019, 11, 763 .

AMA Style

Dawei Wen, Xin Huang, Anlu Zhang, Xinli Ke. Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery. Remote Sensing. 2019; 11 (7):763.

Chicago/Turabian Style

Dawei Wen; Xin Huang; Anlu Zhang; Xinli Ke. 2019. "Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery." Remote Sensing 11, no. 7: 763.