Search

The rapid expansion of aquaculture in coastal areas is typically associated with ecological negligence and low water quality owing to the economic exploitation of these areas. However, evaluation of these water bodies tends to be laborious, time-consuming, and costly. Therefore, to overcome the limitations of field surveys, in this study, we evaluated the water quality of the cultured water in the Beibu Gulf of Guangxi, obtained spectral reflectance by unmanned aerial vehicle with multispectral sensors, and constructed inverse models of 11 water quality parameters, namely, ammonia nitrogen (NH3-N), chemical oxygen demand (COD), active phosphate (PO4−), dissolved oxygen, nitrate nitrogen (NO3-N), nitrite nitrogen (NO2-N), inorganic nitrogen, total nitrogen, total phosphorus, suspended solids (SS), and chlorophyll a (chl-a), based on the partial least squares method to invert the water quality distribution of regional aquaculture. Furthermore, we compared the retrieval accuracy of different water quality parameters. The following results were obtained: 1) the constructed model’s results showed that the retrieval models for COD, NO3-N, SS, and chl-a had better accuracy compared with those of other parameters; 2) application of the model to the validation set data yielded a correlation coefficient of 0.93 between the measured and predicted SS values, with a mean absolute error of prediction of 4.65 mg L−1; this parameter constructed the best prediction model. According to the validation set results, the correlation coefficients of chl-a, COD and NO3-N are all greater than 0.8, which had better performance effects compared with the remaining models, which are 0.87, 0.86, and 0.81 respectively. This study provides a reference for remote sensing monitoring of water quality in mariculture in cloudy and rainy areas.

Coronavirus disease 2019 (COVID-19) has become a major public health concern worldwide. In this study, we aimed to analyze spatial clusters of the COVID-19 epidemic and explore the effects of population emigration and socioeconomic factors on the epidemic at the county level in Guangdong, China. Data on confirmed cases, population migration, and socioeconomic factors for 121 counties were collected from 1 December 2019 to 17 February 2020, during which there were a total of 1,328 confirmed cases. County-level infected migrants of Guangdong moving from Hubei were calculated by integrating the incidence rate, population migration data of Baidu Qianxi, and the resident population. Using the spatial autocorrelation method, we identified high-cluster areas of the epidemic. We also used a geographical detector to explore infected migrants and socioeconomic factors associated with transmission of COVID-19 in Guangdong. Our results showed that: 1) the epidemic exhibited significant positive global spatial autocorrelation; high–high spatial clusters were mainly distributed in the Pearl River Estuary region; 2) city-level population migration data corroborated with the incidence rate of each city in Hubei showed significant association with confirmed cases; 3) in terms of potential factors, infected migrants greatly contributed to the spread of COVID-19, which has strong ability to explain the COVID-19 epidemic; besides, the companies, transport services, residential communities, restaurants, and community facilities were also the dominant factors in the spread of the epidemic; 4) the combined effect produced by the intersecting factors can increase the explanatory power. The infected migrant factor interacted strongly with the community facility factor with the q value of 0.895. This indicates that the interaction between infected migrants and community facilities played an important role in transmitting COVID-19 at the county level.

Knowledge of the precise water depth in shallow areas of the ocean is of great significance to the safe navigation of ships and hydrographic surveying. Compared with traditional bathymetry, satellite remote sensing for water depth determination makes it possible to cover large areas by dynamic observation. In this paper, we conducted an optically shallow water bathymetric inversion study using a Stumpf empirical model, random forest model, neural network model, and support vector machine model based on Sentinel-2 satellite images and Ganquan Dao measured bathymetry data. We compared and analyzed the inversion results based on the empirical model and different machine learning models. The results show that the Stumpf empirical and machine learning models are capable of inverting optically shallow water depth. Moreover, the machine learning models had better fitting ability than the Stumpf empirical model with a sufficient number of samples, especially when the water depth was greater than 15 m. In addition, the random forest model had the highest overall accuracy among these models, with a root mean square error (RMSE) of 1.41 m and a regression coefficient (R2) of 0.96 for the test data.

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.

Sustainability is one of the major challenges in the 21st century for humanity. Spatial zoning of ecosystem services is proposed in this study as a solution to meet the demands for the sustainable use of ecosystem services. This study presented a workflow and performed an exploratory analysis using self-organizing maps (SOM) for visualizing the spatial patterns of the ecosystem service value (ESV) of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The zoning was performed based on 11 types of ecosystem services, resulting in 11 ecosystem service zones. Each of the zones derived has its unique characteristics in terms of the dominating ecosystem service types, ESV, land use/land cover patterns, and associated human activity levels. It is recommended that reasonable and effective utilization of the ecosystem services in the GBA should be based on its zonal characteristics rather than haphazard exploitations, which can contribute to the sustainable economy and environment of the region. The applicability of SOM for the GBA ecosystem service zoning has been demonstrated in this study. However, it should be stressed that the method and workflow presented in this study should mainly be used for supporting decision-making rather than used for deriving gold-standard zoning maps.

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns.

Context: Cepharanthine (CPA) has been reported to possess a wide range of pharmacological activities. Objective: This study investigates the pharmacokinetic characteristics after oral or intravenous administration of CPA by using a sensitive and rapid LC–MS/MS method. Materials and methods: A sensitive and rapid LC–MS/MS method was developed for the determination of CPA in Sprague–Dawley rat plasma. Twelve rats were equally randomized into two groups, including the intravenous group (1 mg/kg) and the oral group (10 mg/kg). Blood samples (250 μL) were collected at designated time points and determined using this method. The pharmacokinetic parameters were calculated. Results: The calibration curve was linear within the range of 0.1–200 ng/mL (r = 0.999) with the lower limit of quantification at 0.1 ng/mL. After 1 mg/kg intravenous injection, the concentration of CPA reached a maximum of 153.17 ± 16.18 ng/mL and the t1/2 was 6.76 ± 1.21 h. After oral administration of 10 mg/kg of CPA, CPA was not readily absorbed and reached Cmax 46.89 ± 5.25 ng/mL at approximately 2.67 h. The t1/2 was 11.02 ± 1.32 h. The absolute bioavailability of CPA by oral route was 5.65 ± 0.35%, and the bioavailability was poor. Discussion and conclusions: The results indicate that the bioavailability of CPA was poor in rats, and further research should be conducted to investigate the reason for its poor bioavailability and address this problem.

Impervious surfaces (IS), the most common land cover in urban areas, not only provide convenience to the city, but also exert significant negative environmental impacts, thereby affecting the ecological environment carrying capacity of urban agglomerations. Most of the current research considers IS as a single land-cover type, yet this does not fully reflect the complex physical characteristics of various IS types. Therefore, limited information for urban micro-ecology and urban fine management can be provided through one IS land-cover type. This study proposed a finer IS classification scheme and mapped the detailed IS fraction in Guangzhou City, China using Landsat imagery. The IS type was divided into seven finer classes, including blue steel, cement, asphalt, other impervious surface, and other metal, brick, and plastic. Classification results demonstrate that finer IS can be well extracted from the Landsat imagery as all root mean square errors (RMSE) are less than 15%. Specially, the accuracies of asphalt, plastic, and cement are better than other finer IS types with the RMSEs of 7.99%, 8.48%, and 9.92%, respectively. Quantitative analyses illustrate that asphalt, other impervious surface, and brick are the dominant IS types in the study area with the percentages of 9.68%, 6.27%, and 4.45%, respectively, and they are mainly located in Yuexiu, Liwan, Haizhu, and Panyu districts. These results are valuable for research into urban fine management and can support the detailed analysis of urban micro-ecology.

Blue steel roof is advantageous for its low cost, durability, and ease of installation. It is generally used by industrial areas. The accurate and rapid mapping of blue steel roof is important for the preliminary assessment of inefficient industrial areas and is one of the key elements for quantifying environmental issues like urban heat islands. Here, the DeeplabV3+ semantic segmentation neural network based on GaoFen-2 images was used to analyze the quantity and spatial distribution of blue steel roofs in the Nanhai district, Foshan (including the towns of Shishan, Guicheng, Dali, and Lishui), which is the important manufacturing industry base of China. We found that: (1) the DeeplabV3+ performs well with an overall accuracy of 92%, higher than the maximum likelihood classification; (2) the distribution of blue steel roofs was not even across the whole study area, but they were evenly distributed within the town scale; and (3) strong positive correlation was observed between blue steel roofs area and industrial gross output. These results not only can be used to detect the inefficient industrial areas for regional planning but also provide fundamental data for studies of urban environmental issues.

Walking is one of the most commonly promoted traveling methods and is garnering increasing attention. Many indices/scores have been developed by scholars to measure the walkability in a local community. However, most existing walking indices/scores involve urban planning-oriented, local service-oriented, regional accessibility-oriented, and physical activity-oriented walkability assessments. Since shopping and dining are two major leisure activities in our daily lives, more attention should be given to the shopping or dining-oriented walking environment. Therefore, we developed two additional walking indices that focus on shopping or dining. The point of interest (POI), vegetation coverage, water coverage, distance to bus/subway station, and land surface temperature were employed to construct walking indices based on 50-m street segments. Then, walking index values were categorized into seven recommendation levels. The field verification illustrates that the proposed walking indices can accurately represent the walking environment for shopping and dining. The results in this study could provide references for citizens seeking to engage in activities of shopping and dining with a good walking environment.

Mixed pixels in medium spatial resolution imagery create major challenges in acquiring accurate pixel-based land use and land cover information. Deep belief network (DBN), which can provide joint probabilities in land use and land cover classification, may serve as an alternative tool to address this mixed pixel issue. Since DBN performs well in pixel-based classification and object-based identification, examining its performance in subpixel unmixing with medium spatial resolution multispectral image in urban environments would be of value. In this study, (1) we examined DBN’s ability in subpixel unmixing with Landsat imagery, (2) explored the best-fit parameter setting for the DBN model and (3) evaluated its performance by comparing DBN with random forest (RF), support vector machine (SVM) and multiple endmember spectral mixture analysis (MESMA). The results illustrated that (1) DBN performs well in subpixel unmixing with a mean absolute error (MAE) of 0.06 and a root mean square error (RMSE) of 0.0077. (2) A larger sample size (e.g., greater than 3000) can provide stable and high accuracy while two-RBM-layer and 50 batch sizes are the best parameters for DBN in this study. Epoch size and learning rate should be decided by specific applications since there is not a consistent pattern in our experiments. Finally, (3) DBN can provide comparable results compared to RF, SVM and MESMA. We concluded that DBN can be viewed as an alternative method for subpixel unmixing with Landsat imagery and this study provides references for other scholars to use DBN in subpixel unmixing in urban environments.

The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN) is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remote sensing variables, including vegetation, impervious surface, and soil (V-I-S). The specific objectives of this paper are: (1) To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2) To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3) To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average) and 81mm (occurred once every ten years). Our experiment shows that the proposed method is easy to use and can derive composite CN effectively.

Impervious surface area (ISA) is considered as an indicator of environment change and is regarded as an important input parameter for hydrological cycle simulation, water management and area pollution assessment. The Pearl River Delta (PRD), the 3rd most important economic district of China, is chosen in this paper to extract the ISA information based on Landsat images of 1998, 2003 and 2008 by using a linear spectral un-mixing method and to monitor impervious surface change by analyzing the multi-temporal Landsat-derived fractional impervious surface. Results of this study were as follows: (1) the area of ISA in the PRD increased 79.09% from 1998 to 2003 and 26.88% from 2003 to 2008 separately; (2) the spatial distribution of ISA was described according to the 1998/2003 percentage respectively. Most of middle and high percentage ISA was located in northwestern and southeastern of the whole delta, and middle percentage ISA was mainly located in the city interior, high percentage ISA was mainly located in the suburban around the city accordingly; (3) the expanding direction and trend of high percentage ISA was discussed in order to understand the change of urban in this delta; High percentage ISA moved from inner city to edge of urban area during 1998–2003 and moved to the suburban area that far from the urban area mixed with jumpily and gradually during 2003–2008. According to the discussion of high percentage ISA spatial expanded direction, it could be found out that high percentage ISA moved outward from the centre line of Pearl River of the whole delta while a high ISA percentage in both shores of the Pearl River Estuary moved toward the Pearl River; (4) combining the change of ISA with social conditions, the driving relationship was analyzed in detail. It was evident that ISA percentage change had a deep relationship with the economic development of this region in the past ten years. Contemporaneous major sport events (16th Asia Games of Guangzhou, 26th Summer Universidad of Shenzhen) and the government policies also promoted the development of the ISA. Meanwhile, topographical features like the National Nature Reserve of China restricted and affected the expansion of the ISA. Above all, this paper attempted to extract ISA in a major region of the PRD; the temporal and spatial analyses to PRD ISA demonstrated the drastic changes in developed areas of China. These results were important and valuable for land use management, ecological protection and policy establishment.

Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA.

The rainfall and runoff relationship becomes an intriguing issue as urbanization continues to evolve worldwide. In this paper, we developed a simulation model based on the soil conservation service curve number (SCS-CN) method to analyze the rainfall-runoff relationship in Guangzhou, a rapid growing metropolitan area in southern China. The SCS-CN method was initially developed by the Natural Resources Conservation Service (NRCS) of the United States Department of Agriculture (USDA), and is one of the most enduring methods for estimating direct runoff volume in ungauged catchments. In this model, the curve number (CN) is a key variable which is usually obtained by the look-up table of TR-55. Due to the limitations of TR-55 in characterizing complex urban environments and in classifying land use/cover types, the SCS-CN model cannot provide more detailed runoff information. Thus, this paper develops a method to calculate CN by using remote sensing variables, including vegetation, impervious surface, and soil (V-I-S). The specific objectives of this paper are: (1) To extract the V-I-S fraction images using Linear Spectral Mixture Analysis; (2) To obtain composite CN by incorporating vegetation types, soil types, and V-I-S fraction images; and (3) To simulate direct runoff under the scenarios with precipitation of 57mm (occurred once every five years by average) and 81mm (occurred once every ten years). Our experiment shows that the proposed method is easy to use and can derive composite CN effectively. [ABSTRACT FROM AUTHOR]

The article discusses a study on the extraction of impervious surface area (ISA) in a major region of the Pearl River Delta (PRD) in China. The study considered ISA as an indicator of environmental change and a parameter for hydrological cycle simulation, water management and area pollution assessment. It showed the drastic changes in developed areas of the country. The importance of the study's results to land use management, ecological protection and policy establishment is noted.