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.

In urban and rural areas under rapid urbanization, a proper understanding of the relationship between population and construction land use is important for regional sustainable development. The decoupling method is a particularly useful way to examine this relationship. However, the decoupling method only considers the current relationship between population and construction land, ignoring the initial status in the base year. Based on an improved decoupling model, vector land-use data (1:10,000 scale) from two National Land Investigations, and demographic data from the China Statistics Bureau, this paper analyzes population and construction land use change patterns as well as the types of coupling in urban and rural Hubei. The results better reflect the relationship between population and construction land use. The outcomes show that from 2009 to 2014, both the urban population and urban construction land use increased while the rural population decreased as rural settlement land use expanded. In 61 counties, the relationship between the urban population and construction land use showed expansive negative decoupling; that is, the growth rate of urban population was less than the growth rate of the urban construction land use. In 72 counties, the relationship between the rural population and rural settlement land showed a strong negative decoupling; that is, the rural population decreased while the rural settlement land use increased. The relationship between the urban–rural population and construction land use can be divided into eight types. Only nine counties are somewhat coordinated, while the other counties were all uncoordinated. Suggestions are provided by comparing per capita urban construction land and per capita rural settlement land in the base and current years with the national standard and considering the coupling types for population and construction land use change in urban and rural settings. Finally, we discuss approaches to optimize urban–rural space organization and promote coordinated urban–rural development.