Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series.

Surface water plays a crucial role in the ecological environment and societal development. Remote sensing detection serves as a significant approach to understand the temporal and spatial change in surface water series (SWS) and to directly construct long-term SWS. Limited by various factors such as cloud, cloud shadow, and problematic satellite sensor monitoring, the existent surface water mapping datasets might be short and incomplete due to losing raw information on certain dates. Improved algorithms are desired to increase the completeness and quality of SWS datasets. The present study proposes an automated framework to detect SWS, based on the Google Earth Engine and Landsat satellite imagery. This framework incorporates implementing a raw image filtering algorithm to increase available images, thereby expanding the completeness. It improves OTSU thresholding by replacing anomaly thresholds with the median value, thus enhancing the accuracy of SWS datasets. Gaps caused by Landsat7 ETM + SLC-off are respired with the random forest algorithm and morphological operations. The results show that this novel framework effectively expands the long-term series of SWS for three surface water bodies with distinct geomorphological patterns. The evaluation of confusion matrices suggests the good performance of extracting surface water, with the overall accuracy ranging from 0.96 to 0.97, and user's accuracy between 0.96 and 0.98, producer's accuracy ranging from 0.83 to 0.89, and Matthews correlation coefficient ranging from 0.87 to 0.9 for several spectral water indices (NDWI, MNDWI, ANNDWI, and AWEI). Compared with the Global Reservoirs Surface Area Dynamics (GRSAD) dataset, our constructed datasets promote greater completeness of SWS datasets by 27.01%-91.89% for the selected water bodies. The proposed framework for detecting SWS shows good potential in enlarging and completing long-term global-scale SWS datasets, capable of supporting assessments of surface-water-related environmental management and disaster prevention.

Assessment of sediment transport in Luxiapuqu watershed using RUSLE-TLSD and InSAR techniques: Yarlung Tsangpo River, China.

The Yarlung Tsangpo River Basin is characterized by its intricate topography and a significant presence of erosive materials. These often coincide with heavy localized precipitation, resulting in pronounced hydraulic erosion and geological hazards in mountainous regions. To tackle this challenge, we integrated the RUSLE-TLSD (Revised Universal Soil Loss Equation-Transportation-limited sediment delivery) model with InSAR (Interferometric Synthetic Aperture Radar) data, aiming to explore the sediment transport process and pinpoint hazard-prone sites within mountainous small watershed. The RUSLE-TLSD model aids in evaluating multi-year sediment transport dynamics in mountainous zones. And, the InSAR data precisely delineates changes in sediment scouring and siltation at sites vulnerable to hazards. Our research estimates that the potential average soil erosion within the watershed stands at 52.33 t/(hm2 a), with a net soil erosion of 0.69 t/(hm2 a), the sediment transport pathways manifest within the watershed's gullies and channels. Around 4.32% of the watershed area undergoes sedimentation, predominantly at the base of slopes and within channels. Notably, areas (d) and (e) emerge as the most susceptible to disasters within the watershed. Further analysis of the InSAR data highlighted four regions in the typical area (e) from 2017 that are either sedimentation- or erosion-prone, referred to as "hotspots." Among them, R1 exhibits a strong interplay between water and sediment, rendering it highly sensitive to environmental factors. In contrast, R4, characterized by a sharp bend in siltation, remains relatively impervious to external elements. The NDVI (normalized difference vegetation index) stands out as the pivotal determinant influencing sediment transport within the watershed, exerting a pronounced impact on the outlet section, especially in spring. By employing this approach, we gained a deeper understanding of sediment transport mechanisms and potential hazards in small watershed in uninformative mountainous areas. This study furnishes a robust scientific framework beneficial for erosion mitigation and disaster surveillance in mountainous watersheds.

Morphological environment survey and hydrodynamic modeling of a large bifurcation-confluence complex in Yangtze River, China.

Since the establishment of the world-class Three Gorges Dam (TGD) across the Yangtze River, China, the downstream reach has experienced a long-term adjustment with regard to the river morphology and hydrodynamics, imposing a profound impact on the environmental conditions of human living and aquatic ecosystem. This study presents an investigation on the river channel morphological characteristics and hydrodynamic environment of a large bifurcation-confluence complex downstream of the TGD through detailed field survey and numerical modeling. Results show that the main stem, before being bifurcated into two sub-channels (the North Channel and the South Channel), experiences a meander, leading to the severe bed scouring near the outer bank (pools) resulted from a high flow mass flux and bed shear stress. Because of being bifurcated, the river width with largely growing may result in the reduction of flow velocity and sediment deposition (riffles), and thereby two plugbars are formed near the entrance of two sub-channels. In the meantime, the velocity-reversal phenomenon (flow velocity and friction velocity) is identified when low flows are transited into high flows. The flow mass flux, however, is always larger in pool regions, which is highly related to water depth. As a result, the topographic steering of flows by riffles, bars and floodplains may have more impact on flow path under low flow conditions, while the bankline shape would become more important under high flows. Furthermore, the topographic steering could play a key role in the pattern of flow separations near the confluence. More interestingly, the confluence flow separation only occurs under low flow conditions and its occurring location shifts upwards the tributary (the North Channel), which differs from observations in previous studies. The visualized numerical results of friction velocity distribution indicate that sediment is more likely to deposit in the North Channel (entrance) with lower friction velocity, implying the potential closure of the sub-channel.

Investigation of respirable suspended particulate trend and relevant environmental factors in Hong Kong downtown areas.

As the addressing of high demand of good air quality in urban area, a study on air pollutant dispersion and distribution resulting from vehicular exhaust emission is strongly required. In particular, vehicular exhaust emission has become a major air pollution source in metropolitan city like Hong Kong, which is characterized with the heavy, dense traffic flow and the limited land resources. Respirable suspended particulate (RSP) is one of main pollutants resulted from vehicular exhaust emission in urban area. Hence, in this study, we focus on analyzing the variation of RSP levels including diurnal, monthly and annual patterns at selected roadsides in Hong Kong during the period of 1998--2005. Furthermore, the relationships between RSP level and the relevant meteorological factors such as temperature, rainfall and wind conditions in Hong Kong territory have been discussed as well.

Evolving trend and self-similarity of ozone pollution in central Hong Kong ambient during 1984-2002.

The evolving pattern of ozone level in Hong Kong urban air has undergone various changes and corresponds to the regional urban and economic development. We assess such changes by reviewing and analyzing the original ozone pollutant database monitored in central Hong Kong downtown area during the period of 1984-2002. Both fractal analysis and traditional statistical methods are adopted to estimate the ozone evolving characteristics during the studied period. It is found that the ozone evolving pattern has strong self-similarity and the ozone pollution presents increasing trend in Hong Kong region in recent years based on the analysis. The typical fractal dimensions for total time series are D = 0.894 for available data set (N = 5760) and D = 0.859 for complete data set with interpolation (N = 6935), respectively. The fractal analysis can be used to assess the pollution trend in urban environment and may provide an alternative method for environmental study.

Seasonal variation of air pollution index: Hong Kong case study.

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO2, nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999-2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box-Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.