Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

Regular visits to park green space offer remarkable benefits for the physical and mental health of urban residents. Achieving a comprehensive understanding of the utilization across the entire city is a prerequisite for improving the overall utilization rate of park green spaces. Traditional social survey methods are limited by their sample size and time-consuming nature, while methods based on geographic location big data are gaining momentum. This study focuses on Xuchang, a medium-sized city in China, and systematically analyzes the current state and influencing factors of park green space utilization by mining GPS trajectory big data from April 3 to 12, 2022. Results indicate that residents' choices of park green spaces are highly diverse. Approximately 20% of visitors on holidays and weekends, and about 25% of visitors on weekdays, prefer the park green space closest to their homes. Notably, the distance threshold for park green space visits on weekdays, weekends, and holidays is 3633, 3824, and 4127 m, respectively. These distances are significantly higher than the several hundred meters specified in planning documents or commonly used in accessibility analyses. For individuals who frequently visit park green spaces, distance is the most critical influencing factor. Conversely, for those who occasionally visit, distance is not the primary consideration. For individuals who rarely or never visit park green spaces, personal attitudes play an essential role. In comparison to weekdays, the number of visitors on holidays and weekends is larger, the travel distance is longer, and they are more inclined to choose larger parks. Visits are concentrated in the afternoon and evening, and weather changes remarkably affect park green space utilization. Importantly, no compensatory effect is observed between the frequency and duration of park green space visits. These findings hold important implications for urban planning, management, and the promotion of park green space utilization.

Park green space is an important ecological factor of the urban built-up environment, and it plays an increasingly important role in improving human welfare and the quality of urban life. Accessibility analysis of urban park green space is an issue of social equity and environmental justice that has received widespread attention. The accessibility of a city’s park green space must be evaluated under the correct scale and resolution before it can be applied to urban green space planning. To measure the impact of different research scales on accessibility, Weidu District of Xuchang City, Henan Province, China, was taken as the experimental area. The Gaussian-based two-step floating catchment area method was used to compare and analyze the accessibility differences under three scales: subdistrict, community, and residential quarter. The influence of the source and destination point modes on accessibility was analyzed at the residential quarter scale. Results show that the accessibility of park green space at the subdistrict scale is different from that at the community and residential quarter scales in terms of spatial distribution characteristics and quantitative relationship. The accessibility of the geometric center mode and the entrance and exit mode at the residential quarter scale is similar in overall distribution and different in local quantity. Overall, the accessibility of the park green space at the residential quarter scale under the entrance and exit mode and the spatial fairness of visiting the park green space are better than that under the geometric mode. Therefore, accessibility analysis of park green space should be performed cautiously when taking the aggregation unit as the basic research unit. The accessibility of the aggregation unit is not the statistical summary of its subunits, especially when the area of the aggregation unit is far larger than the walking range of residents, and is thus likely to lead to wrong conclusions. Accessibility analysis should be conducted on the finest scale possible rather than the aggregation scale and use true distance rather than the centroid-to-centroid surrogate to obtain reliable results for further guiding urban green space planning.