Land cover information is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results. Informing these issues, here we developed a robust framework to improve the mapping results of 10-m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multi-source remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), and the Random Forest (RF) algorithm with a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that RF models achieved better performance than classification and regression trees (CART), minimum distance (MD), and support vector machine (SVM) models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps, but also provide certain guidelines for future land cover mapping from regional to global scales.

Rural communities have long been overlooked since globalization and urbanization struck traditional rural values. Building a sustainable rural community has become a contested issue in academia, especially after implementing the Rural Revival Strategy. This research attempted to establish a participatory planning approach, which considers rural planners as the external impetus and local villagers as an internal driving force, to promote the sustainability of rural communities. Carrying on the theoretical framework of public participation, the researchers carried out a pilot practice in Hongtang Village, Yunan Province, China, where planners tried to reestablish the social relationship in a village by designing and building a rural yard. We do not solely finish the design, but also advocate, communicate, and work with villagers to activate the vitality of the village. The research argues that the focus of rural planning and construction in China is to develop a participatory planning approach, which is human-oriented and place-based. Planners, acting as an external impetus, advocate public participation in the whole planning process, including mapping, designing, constructing, and managing. We try to empower villagers themselves rather than impose. During this participatory planning process, villagers improve the quality of their settlements through continuous dialogue and cooperation, and the social relationships among them become enhanced. Thus, rural communities are restructuring in both environmental and social dimensions to promote rural sustainability. Additionally, participatory planning in rural areas is constantly correcting itself to be more local, scientific, and rational and become a more powerful protector and promoter of public interests in this process.

Night-time lights (NTLs) collected from the Defense Meteorological Satellite Program‘s Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar Partnership satellite have been widely used in multiple disciplines. However, the defects of DMSP and VIIRS data itself, and the inconsistency between them, hinder their applications in long-term finer studies. Despite some effective efforts, existing relevant researches are still limited by the shortcomings of data inaccessibility, data deficiency neglection, and spatial resolution degradation. To resolve these issues, a novel cross-sensor calibration method was developed in this article by considering three Chinese metropolises (Beijing, Shanghai, and Guangzhou) as the study area. First, the original DMSP NTL images for 2000–2013 were calibrated through stepwise calibration, background noise removal and vegetation adjustment. Second, stable VIIRS annual composites for 2012–2019 were produced after seasonal noise removal, yearly aggregation, background noise removal, vegetation adjustment, and outliers correction. Third, a power regression model was applied to align pixel values of the processed DMSP and the processed VIIRS data for the overlapped years, and consistent NTLs for 2000–2019 were further generated using the regression results. The evaluations based on statistical coefficients, spatial patterns, profile curves, dynamic changes, and correlations with socioeconomic statistics, indicated the robustness and effectiveness of the proposed approach in filling the gaps between DMSP and VIIRS data. The consistent, continuous, and stable NTL time series could serve as input data for further applications, such as urban dynamics capture, economic growth estimation, and population distribution mapping.

Resolving the severe housing problem of low-income households is a stumbling block for many local governments globally. Policy transfer by drawing on the successful experiences of other cities is of increasing importance. Singapore’s public housing policy has made profound achievements in establishing an extensive public housing system. Since 2000, to formulate public housing policies, China’s central government has officially asked local authorities to learn from Singapore’s public housing experiences; however, is Singapore’s housing policy transferable to China, a marketized socialist city, to what extent, and why? Using Guangzhou as a case, this paper examines the transferability of Singapore’s public housing policy to Chinese cities in terms of the policy environment including government structure, funding mechanism, and land acquisition. This paper argues that Singapore’s public housing policy can only be transferred partially to Guangzhou because of rapidly urbanizing contexts and complicated relationships between the central and local governments embedded in Chinese political culture. This paper suggests implications for other Chinese cities facing similar challenges in resolving issues pertaining to housing unaffordability for low-income residents.