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An efficient recognition and extraction of the estuarine turbidity maximum zone (TMZ) is important for studying terrestrial hydrological processes. Although many studies relevant to the TMZ have been conducted worldwide, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. To improve the applicability of the methods adopted in previous studies and to develop a novel model to accurately extract the TMZ in multiple estuaries and different seasons from remote-sensing imageries, this study estimated the total suspended solid (TSS) and chlorophyll a (Chl a) concentrations in three estuaries. These were the Pearl River estuary (PRE), the Hanjiang River estuary (HRE), and the Moyangjiang River estuary (MRE) of Guangdong Province, China. The spatial distribution characteristics of the TSS and Chl a concentrations were analyzed. A nearly opposite association was found between the TSS and Chl a concentrations in the three estuaries, particularly in the PRE. The regions with high (low) TSS concentrations had relatively low (high) Chl a concentrations and, therefore, a turbidity maximum zone index (TMZI), defined as the ratio of the difference and sum of the logarithmic transformation of the TSS and Chl a concentrations, was firstly proposed. By calculating the TMZI values in the PRE on 20 November 2004 (low-flow season), it was found that the criterion TMZI>0.2 could be used to identify the TMZs of the PRE effectively. The TMZ extraction results were generally consistent with the visual-interpretation results. The area-based accuracy measures showed that the quality (Q) of the extraction reached 0.8429. The same criterion was applied in the PRE on 18 October 2015 (high-flow season), and high accuracy and consistency across seasons were observed (Q=0.8171). The western shoal of the PRE was the main distribution area of TMZs. Extracting TMZs by the newly proposed index performed well in different estuaries and on different dates (HRE on 13 August 2008 in the high-flow season and MRE on 6 December 2013 in the low-flow season). Compared to the previous fixed threshold of TSS or turbidity methods, extracting the TMZ using the TMZI had higher accuracy and better applicability (Q : 0.1046–0.4770 vs. 0.8171–0.8429). Evidently, this unified TMZI is potentially an optimized method for the global monitoring and extraction of TMZs of estuaries from different satellite remote-sensing imageries. It can be used to help the understanding of the spatial and temporal variation in TMZs and estuarine processes at regional and global scales as well as improve the management and sustainable development of regional society and the natural environment. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) of Guangdong, Hong Kong, and Macao (GHKM), south china, has undergone significant changes in the last few decades. This study analyze the spatio-temporal LULC changes and urban expansion during 2010–2017 using Landsat TM, ETM+, and OLI. The Landsat images were classified using support vector machine (SVM) into seven classes as forest, grassland, water, fishponds, built-up, bareland, and farmland. Several socioeconomic factors were also obtained to determine their impact on LULC. The result shows that during the studied period, massive economic development and urbanization has increased the built-up area from 8.26% (16,209.61 km2) to 10.31% (20241.77 km2) and substantial reduction in both farmland from 37.64% (73,897.77 km2) to 33.05% (64932.19 km2) and fishponds from 1.25% (2451.12 km2) 0.85% (1674.71 km2). The most dominant conversion were from farmland to built-up and to forest. Furthermore, forest cover increased to 45.02 % (88384.97 km2) in 2017 from 42.38% (83215.59 km2) in 2010 as a result of different afforestation scheme and policies in order to make Greener study area. The analysis of socioeconomic factors shows that increase in gross domestic product (GDP), total investment in fixed assets, and industrialization has led to urbanization growth on a large scale and reduction of farmland. Therefore, there is pressing need for sustainable development and protection of farmlands. [ABSTRACT FROM AUTHOR]

The ways cities grow and evolve spatially are crucial factors which affect urban aerosol pollution. Understanding the spatial distribution of air pollutants and their correlations with urban form is of great significance to the improvement of urban atmospheric environment and regional sustainable development of urbanization. In this study, we firstly examined the spatial variations of satellite-derived PM2.5 and NO2 and urban form metrics in Guangdong, and also explored their relationships. The results indicated that the highest and lowest values of PM2.5 and NO2 mainly occur over the Pearl River Delta (PRD) region, and over the eastern Guangdong, respectively. For the size and shape of urban patches, urban form had significant effects on air pollutants in Guangdong. PM2.5 was positively correlated with AREA_AM, CA and SHAPE_AM, and NO2 was positively correlated with LPI, PLAND and AREA_AM, while both of them were negatively related to PARA _AM and ENN_AM. It is inferred that polycentric urban form was associated with low PM2.5 and NO2 concentration, and reasonable urban planning would help mitigate the fine particle pollution. [ABSTRACT FROM AUTHOR]