Distribution Of Water Bodies Research Articles

An innovative approach to assessing and optimizing floating solar panels

Floating photovoltaic energy is an emerging solution to the need for decarbonization of the current society. It is currently in the early stages of implementation, so there are not many previous experiences to standardize decision-making and the most relevant operating parameters such as, the tilt angle of a fast as in conventional photovoltaics. In addition, the lack of specific design tools and production calculations for floating solar is a barrier to the correct understanding of the real advantages of floating solar versus conventional solar. From the point of view of the investment, the stakeholders do not have a complete analysis of the profitability of their investment. From a technical, environmental and legislative point of view, there is not enough information available to establish standards and criteria for the design and selection of the most suitable water bodies at local, regional or state level. This research aims to fill this gap by proposing a specific framework based on geographic information systems (GIS), multi-criteria analysis (MCDA) and intelligent optimization (Genetic Algorithm). The objective is to select within a set of water bodies those where the investment is most beneficial from a holistic perspective considering technical, economic, social, environmental, and legislative criteria. Once the optimal location is obtained, the framework obtains the tilt angles that minimize the Levelized Cost of Energy (LCOE) by means of intelligent optimization techniques that evaluate the characteristics of each water body, such as location or available surface. The tilt angle obtained in this research achieves LCOE improvements of between 2.1% and 8.4% with respect to the tilt angle obtained by conventional methods applied to ground photovoltaics. Spain has been chosen as a case study as it is a region with a high solar potential and an even distribution of water bodies in which there is still no legislative framework in force.

Spatial evolution and influencing factors of water-town settlements in Southern China: evidence from 942 villages in Zhongshan City

ABSTRACT Zhongshan, a city characterized by its profound historical heritage and distinctive rural civilization, has been consistently influenced by traditional agricultural practices throughout its history. This research conducts an in-depth exploration of Zhongshan’s unique rural spaces, systematically examining the evolutionary trends and determinants of Zhongshan’s water-town settlements across different historical epochs through the application of multidimensional spatial analysis techniques. The results indicate that these settlements exhibited cohesive spatial distribution characteristics across five historical periods. Notably, there was a significant clustering effect during the Ming and Qing dynasties, while Before the Qing dynasty, the spatial distribution of the settlements presented a pattern with multiple core areas coexisting. After the Qing dynasty, the spatial layout of these principal core areas stabilized and gradually diffused to surrounding areas, forming a balanced and continuous distribution pattern. Throughout the study period, the spatial pattern of the water-town settlements exhibited characteristics of five primary core areas and several secondary core areas. Concurrently, the centroid of the water-town settlements consistently shifted southeastward. When examining the intensity of influence on spatial evolution, the study found that socio-economic (q = 0.345) and accessibility factors (q = 0.356) exert a greater impact on the spatial distribution of rural settlements than do natural environmental factors (q = 0.136). However, the interaction between natural environmental factors and socio-economic and accessibility factors provides greater explanatory power than the interactions within natural factors alone. This suggests that in a specific socio-economic context, the impact of the natural environment on water-town settlements is enhanced. Furthermore, multivariate linear regression analysis demonstrates that the rural population, regional economic status, proximity to urban centers, and the distribution of water bodies positively influence the spatial evolution of water-town settlements.

Water-level changes impact angler effort in a large lake: Implications for climate change

Climate change is expected to influence aquatic habitats and associated fish populations, yet we know little about the impact on recreational anglers. Our goal was to explore whether interannual fluctuations in waterbody surface area and other explanatory variables could be used as indicators of changes in angler fishing effort. Our approach leveraged a combination of remotely sensed waterbody surface area, environmental and fish population data, and onsite angler survey monitoring data for Devils Lake, North Dakota, USA during the open-water fishing period (May 1st to August 31st) for 9 years (1992–2021). The information was used to develop a dynamic waterbody size-angler effort model. Changes in waterbody surface area reliably predicted changes in angler effort (r2 = 0.60). Increases in waterbody surface area led to increases in angler effort, and decreases in waterbody surface area led to decreases in angler effort. Our findings show promise that remotely sensed fluctuations in waterbody surface area could be used as an indicator of interannual angler effort dynamics. Dynamic waterbody size-angler effort models could provide managers the ability to predict changes in angler effort via climate-related hydrological cycles that affect the size and distribution of waterbodies on the landscape.

Unlocking the potential of CYGNSS for pan-tropical inland water mapping through multi-source data and transformer

Cyclone Global Navigation Satellite System (CyGNSS) data are widely recognized for their sensitivity to inland water bodies. However, the detection of water bodies using single CyGNSS data is subject to uncertainties, presenting challenges for large-scale and accurate water system detection. In this study, we employ CyGNSS data for regression estimation to map inland water bodies. In comparison to previous studies, we incorporate additional constraints, including topographic factors, vegetation information, soil moisture, and latitude and longitude data. Leveraging the U-shaped structure, Swin Transformer, and ContextModule, we effectively extract water body distribution information, referred to as CFRT. Through rigorous performance comparison with prevalent deep learning models, our method demonstrates remarkable accuracy. The generated water percent exhibits notable consistency with the reference data, achieving a root mean square error (RMSE) of 7.15% and a mean intersection over union of 0.778 within the reachable area of the CyGNSS data. Our approach emphasizes the significance of utilizing multi-source data to substantially enhance the accuracy of CyGNSS water system detection.

Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

How do landscape patterns in urban parks affect multiple cultural ecosystem services perceived by residents?

Cultural ecosystem services (CES) provided by urban parks are crucial for encouraging residents to engage with nature and enhance their physical and mental well-being. Measuring these services from the residents' perception perspective is essential. Previous studies often focus on a specific type of CES, lacking explicit links between the landscape composition and configuration of urban parks and residents' perceptions of various CES. The main objective of this study, therefore, was to explore the effects of urban park landscape patterns on residents' CES perceptions. We took 12 urban parks in Chengdu, China, and assessed residents' CES perceptions through content analysis of social media texts. Spatial patterns of the parks were analyzed using remote sensing interpretation and field surveys. Correlation analysis examined the relationship between landscape patterns and residents' perceptions, with further verification through questionnaires and face-to-face interviews. Findings revealed that at the landscape level, landscape aggregation of parks was negatively correlated with aesthetic perceptions but positively correlated with recreational perceptions. Landscape diversity negatively impacted perceptions of sports and health (S&H). At the class level, natural elements significantly influenced residents' perceptions of aesthetic and S&H. Specifically, aesthetic perceptions were minimized when the landscape shape index of water bodies reached 6.36 or when the proportion of green space was 56.5 %. Road edge density negatively affected perceptions of S&H and influenced the distribution of water bodies and green spaces. These findings are crucial for optimizing park structures to deliver efficient CES and provide strategies for integrating ecosystem services into environmental management from a public perception perspective.

Multi-Scale Habitat Selection by the Wintering Whooper Swan (Cygnus cygnus) in Manas National Wetland Park, Northwestern China

Habitat selection has been a central focus of animal ecology, with research primarily concentrating on habitat choice, utilization, and evaluation. However, studies confined to a single scale often fail to reveal the habitat selection needs of animals fully and accurately. This paper investigates the wintering whooper swan (Cygnus cygnus) in Manas National Wetland Park, Xinjiang, using satellite tracking to determine their locations. The Maximum Entropy model (MaxEnt) was applied to explore the multi-scales habitat selection needs of Manas National Wetland Park’s wintering whooper swans across nighttime, daytime, and landscape scales. This study showed that the habitat selection of the wintering whooper swans varied in different scales. At the landscape scale, wintering whooper swans prefer habitats with average winter precipitations of 6.9 mm and average temperatures of −6 °C, including water bodies and wetlands, indicating that climate (precipitation and temperature) and land type (wetlands and water bodies) influence their winter habitat selection. During daytime, whooper swans prefer areas close to wetlands, water bodies, and bare land, with a more dispersed distribution of water bodies. For nighttime, they tend to choose areas within the wetland park where human disturbance is minimal and safety is higher. This study can provide scientific basis and data support for habitat conservation and management of wintering waterbirds like whooper swans, recommending targeted conservation measures to effectively manage and protect the wintering grounds of whooper swans.

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Constructed from a dataset capturing the seasonal and annual water body distribution of the lower Qarqan River in the Taitma Lake area from 2017 to 2022, and combined with the meteorological and hydraulic engineering data, the spatial and temporal change patterns of the Taitma Lake watershed area were determined. Analyses were conducted using Planetscope (PS) satellite images and a deep learning model. The results revealed the following: ① Deep learning-based water body extraction provides significantly greater accuracy than the conventional water body index approach. With an impressive accuracy of up to 96.0%, UPerNet was found to provide the most effective extraction results among the three convolutional neural networks (U-Net, DeeplabV3+, and UPerNet) used for semantic segmentation; ② Between 2017 and 2022, Taitma Lake’s water area experienced a rapid decrease, with the distribution of water predominantly shifting towards the east–west direction more than the north–south. The shifts between 2017 and 2020 and between 2020 and 2022 were clearly discernible, with the latter stage (2020–2022) being more significant than the former (2017–2020); ③ According to observations, Taitma Lake’s changing water area has been primarily influenced by human activity over the last six years. Based on the research findings of this paper, it was observed that this study provides a valuable scientific basis for water resource allocation aiming to balance the development of water resources in the middle and upper reaches of the Tarim and Qarqan Rivers, as well as for the ecological protection of the downstream Taitma Lake.

Urban Growth Modeling in Kurunegala, Sri Lanka using Cellular Automata/Markov Chain Multi-criteria Analysis and Markov-Markovian Transition Estimator

With accelerated global urbanization, the expansion of cities has become a pressing concern for urban planners and researchers. The growth and dynamics of urban encroachment are closely tied to changes in land use, especially in urbanized areas. This study seeks to evaluate the accuracy of the CA-Markov (Cellular Automata) model in predicting land use/land cover changes (LULCC) in the Kurunegala district, Sri Lanka as a result of urban expansion. The investigation employs secondary data, including 2007, 2012, 2017, and 2022 Landsat 05, 07, 08, and 09 images respectively. Using software applications such as ArcGIS 10.8, IDRISI 17.0, and MS-Excel 2019, diverse techniques including supervised classification, Markovian transition estimation, and CAMarkov chain analysis, this study attempts to analyze the Urban Growth Modeling for 2027 and 2037 using CA-Markov Chain Multi-criteria Analysis (MCA) under Markov-Markovian Transition Estimator. The study used 32 spatial variables for determining the LULCC. As per the derived results, from 2022 to 2027, urban areas have increased quite markedly. The vegetation cover area has reduced and the areas of water bodies have increased spatially. From 2027 to 2037, the urban area increment is 72.552%. Also, vegetation cover and water body distribution have reduced by 8.051% and 39.91% respectively.

Advancing Flood Risk Assessment through Integrated Hazard Mapping: A Google Earth Engine-Based Approach for Comprehensive Scientific Analysis and Decision Support

This study utilises a comprehensive, multi-layered approach to assess flooding susceptibility in a specific area, integrating diverse environmental datasets such as JRC Global Surface Water, Landsat 8 images, and SRTM elevation data. Employing the GEE FMA, a powerful tool leveraging Google Earth Engine capabilities, the analysis covers water occurrence, permanent water, elevation, distance to water, topographic hazard score, and vegetation indices (NDVI and NDWI). The Water Occurrence layer establishes a foundational understanding of water-body distribution’s correlation with flood vulnerability, while Permanent Water refines this understanding. Distance to Water measures proximity for targeted risk evaluation, and Elevation identifies vulnerable regions based on topography. The GEE FMA synthesises these layers into a Flood Hazard Susceptibility map, categorising vulnerability into Very Low, Low, Medium, High, and Very High. This nuanced understanding is crucial for prioritising interventions. The GEE FMA’s rapid processing speed makes it an invaluable tool for short-term decision support in flood hazard disaster management, offering insights for informed decision-making and resilient infrastructure development. The Topographic Hazard Score provides information on how topography influences flood risk, while the Wetness Hazard Score categorises moisture conditions for identifying flood-prone locations. Decision-makers rely on these values for quick and precise flood susceptibility assessments. In an era of climate uncertainties and urbanisation, the GEE FMA emerges as a reliable tool for decision-making, mitigating flood impacts, and developing effective flood risk management strategies.

Managing Landscape Urbanization and Assessing Biodiversity of Wildlife Habitats: A Study of Bobcats in San Jose, California

In the rapid process of urbanization, crucial habitats for mid-sized felids such as bobcats are increasingly compromised. This study employs Geographic Information System (GIS) tools and Machine Learning to investigate the subtle impacts of urbanization on bobcat habitats. Focused on the San Jose area, our extensive geospatial analysis has developed a complex ecological model for bobcat habitats. Our findings emphasize the significant influence of factors like vegetation cover, water body distribution, road traffic volume, and intersection density on the suitability of habitats for bobcats. Specifically, we discovered that while vegetation cover typically supports habitat suitability, its proximity to busy roads significantly undermines this advantage, indicating a need for strategic urban planning that incorporates wildlife mobility. By synthesizing natural and urban elements, we offer fresh insights into urban ecosystem management and propose specific conservation tactics: identifying optimal wildlife crossings, integrating corridors with urban infrastructure, and placing fencing and signage strategically to facilitate wildlife movement safely. These measures aim to reduce road-related threats and enhance the integrity of natural habitats, strengthening bobcat conservation efforts. More than its direct implications for bobcat conservation, this study offers actionable insights for urban wildlife conservation and introduces innovative methods for assessing and mitigating the broader ecological impacts of urbanization.

Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods

Abstract. Remote sensing extraction of glacial lakes is an effective way of monitoring water body distribution and outburst events. At present, the lack of glacial lake datasets and the edge recognition problem of semantic segmentation networks lead to poor accuracy and inaccurate outlines of glacial lakes. Therefore, this study constructed a high-resolution dataset containing seven types of glacial lakes and proposed a refined glacial lake extraction method, which combines the LinkNet50 network for rough extraction and simple linear iterative clustering (SLIC) dense conditional random field (DenseCRF) for optimization. The results show that (1) with Google Earth images of 0.52 m resolution in the study area, the recall, precision, F1 score, and intersection over union (IoU) of glacial lake extraction based on the proposed method are 96.52 %, 92.49 %, 94.46 %, and 90.69 %, respectively, and (2) with the Google Earth images of 2.11 m resolution in the Qomolangma National Nature Reserve, 2300 glacial lakes with a total area of 65.17 km2 were detected by the proposed method. The area of the minimum glacial lake that can be extracted is 160 m2 (less than 6×6 pixels). This method has advantages in small glacial lake extraction and refined outline detection, which can be applied to extracting glacial lakes in the high-Asia region with high-resolution images.

Особенности разработки нормативов допустимого воздействия на малые реки Севастопольского региона

The aim of the study is the identification of uncertainties to improve information support for the standards of permissible impact (SPI) elaboration for small rivers of the Sevastopol region. Materials and methods. The initial data were the results of surface water bodies monitoring in the Sevastopol region, other materials on the Chernaya River basin, characterizing the conditions for the formation of runoff and the quality of waters of small rivers. Monitoring data has been provided by the territorial bodies of Roshydromet, Rospotrebnadzor, Rosprirodnadzor and State Unitary Enterprise of Sevastopol for Water Use (SUES "Vodokanal"). The shortcomings of information support for the elaboration of SPI are revealed. Pollutants were ranked according to their importance for the ecosystem of the Chernaya River tributaries and distribution in water bodies, the water quality standards are substantiated. The list of pollutants in descending order of importance for the ecosystem includes: phosphates, nitrites, ammonium ion, BOD5, suspended solids, iron, manganese, copper, magnesium, calcium, nickel, zinc, phenols, sulfates, surfactants, oil products. The most common pollutants (indicators of pollution) in the tributaries of the Chernaya River are BOD5, nitrites, phenols, manganese, iron and copper (excesses of standards are recorded for almost all objects). MPC for objects of fishery importance were adopted as standards for SPI elaboration. The exception was BOD5, for this indicator the hygienic standard was used. Assumptions are proposed that allow solving the problem of elaboration SPI under conditions of incomplete information. The calculations of the runoff volume for the years of 50 % and 95 % security, SPIchem for priority pollutants on the main hydrological seasons were performed. Analysis of the results showed that SPI differ significantly by hydrological seasons (up to 48 times) and by each indicator of pollution for different tributaries, which is especially pronounced (more than 10 times) for the Uzundzha and Kalenda Rivers in terms of BOD5, iron, phenols and phosphates. The results obtained confirms the need for differentiated regulation of the permissible impact on individual tributaries of the Chernaya River. When developing the SPI for the small rivers of the Sevastopol region, normative-methodological, informational, large-scale and target uncertainties were identified. The assumptions proposed in the work make it possible to solve practical problems of management in the field of use and protection of water bodies, but do not reduce the relevance of measures aimed at improving the information support for the SPI elaboration. Measures aimed at improving the information support for the SPI elaboration includes: organizing access to the monitoring data of the territorial body of Roshydromet («Crimean UGMS»), increasing the frequency of observations on tributaries of the Chernaya River, organization of systematic observations in the background sections and in the zones of influence of diffuse sources of pollution.

Mapping 10 m monthly surface water dynamics in the Yangtze River basin from 2017 to 2020 using a robust ATMC algorithm

Surface water bodies are irreplaceable in the global climate and ecological system. However, quantifying the extent and change of these water bodies has been challenging due to environmental complexity, climatic diversity, and their highly dynamic nature in seasons and interannual periods. Most established remote-sensing water extraction methods involve a practical trade-off between spatial and temporal resolution. To remedy these deficiencies, we proposed a simple and robust Automatic Threshold–based Multi-source data Combination algorithm to improve the efficiency and accuracy of water mapping, achieving monthly stable periodic monitoring. The algorithm fully considered the principles and advantages of Sentinel-1/2 images for water detection and performed adaptive optimal threshold calculation based on the Edge Otsu algorithm. A new product of Surface Water Dynamics in the Yangtze River Basin was generated on a monthly temporal scale and 10-m spatial scale from 2017 to 2020 using the algorithm based on the Google Earth Engine platform. The confusion matrix-based metrics based on sample points were 95.85 % (producer's accuracy), 99.16 % (user's accuracy), 97.41 % (overall accuracy), 0.95 (Matthews correlation coefficient), and 0.95 (Kappa coefficient). Furthermore, the overall correct extraction rate of water bodies can reach 95.85 % and 91.87 % for small water bodies, with commission and omission errors of 0.04 % and 0.03 % at the sub-pixel scale, respectively. The distribution of water bodies within the basin is highly uneven, and their changes exhibit a distinct seasonal trend. The maximum inundation extent occurred in August 2020 (summer, 53,372.88 km2), while the minimum inundation extent was observed in November 2019 (winter, 42,491.74 km2). The successful application of the algorithm in the Yangtze River Basin established a good benchmark for surface water monitoring of multi-source satellite observations and showed great potential for global-scale applications. It will assist the monitoring and evaluation services of the UN Sustainable Development Goals and provide solutions to more global and regional issues.

Relationship Between Urban Spatial Pattern and Thermal Environment Response in Summer: A Case Study of Hefei City

With the continuous expansion of cities, the land cover type of the region is transformed, a large number of natural landscapes are replaced by man-made landscapes, and the environmental temperature rises. The study of the response relationship between urban spatial pattern and thermal environment provides some guidance for improving the ecological environment and optimizing the urban spatial layout. Based on the Landsat 8 series remote sensing image data of Hefei City in 2020 and analysis platforms such as ENVI and ARCGIS, Pearson correlation and profile lines were used to reflect the correlation between the two. Then, the three spatial pattern components with the greatest correlation were selected to construct multiple regression functions to investigate the influence of urban spatial pattern on urban thermal environment and its mechanism of action. The results showed that:① the high temperature area of Hefei City increased significantly with the advance of time during 2013-2020. For different seasons, the urban heat island effect showed that summer>autumn>spring>winter. ② In the central urban area, the building occupancy, building height, imperviousness occupancy, and population density were significantly higher than those in the suburbs, whereas fractional vegetation coverage presented a higher suburban than urban area and mainly showed a point distribution in the urban area and an irregular distribution of water bodies. ③ The urban high-temperature zone was mainly distributed in various development zones in urban areas, whereas other places in urban areas were dominated by medium-high temperature and above-temperature zoning, and suburban areas were dominated by medium-low temperature. ④ The Pearson coefficients between the spatial pattern of each element and the thermal environment were positively correlated with the building occupancy (0.395), impervious surface occupancy (0.333), population density (0.481), and building height (0.188) and negatively correlated with fractional vegetation coverage (-0.577) and water occupancy (-0.384). The coefficients of the constructed multiple regression functions, including building occupancy, population density, and fractional vegetation coverage, were 8.372, 0.295, and -5.639 respectively, with a constant of 38.555. The results of this study can provide a reference basis for optimizing urban spatial layouts and improving urban living quality.

A dataset of annual surface water distribution in the growing season on the Mongolia Plateau from 2013 to 2022

Mongolia Plateau is located in arid and semi-arid areas, and hydrology and water resources are important constraints for the development of its resources and environment. Grasping the temporal and spatial distribution of water bodies on the Mongolian Plateau is of great significance for indicating the temporal and spatial characteristics of water resources and the water environment and their impacts on and responses to regional climate change as well as disaster prevention and reduction. However, as the vast Plateau spans both China and Mongolia, it is a great challenge to accurately and automatically obtain large-scale and long time series water bodies at the basin scale. In this research, we adopted the method of combining local deep learning training and Google Earth Engine (GEE) distributed computing to endow GEE with deep learning computing capabilities so that GEE could rapidly and automatically deploy deep learning models. Based on this, we obtained the distribution of surface water in the growing season of the Mongolia Plateau from 2013 to 2022 with a spatial resolution of 30 meters. 5,000 verification points were manually selected, and the overall verification rate was 88.0%. The dataset is in the form of TIFF grid, containing 28 tile images of with 5°×5°×10 years, with a data volume of 339 MB (88.1 MB compressed, 189 GB in RAW). The data volume in the raw format is 189 GB. With the method used in this dataset, users can automatically and efficiently map water bodies in the cloud platform, which makes it possible to automatically and efficiently process large-scale and long-time series water bodies in arid and semi-arid regions. This is a valuable dataset for application and promotion.

Identification of Wetland Conservation Gaps in Rapidly Urbanizing Areas: A Case Study in Zhengzhou, China

Exploring protected area (PA) siting from a biodiversity perspective is critical in mitigating human impacts on ecosystems. This paper used the MaxEnt model to predict the geographic distribution patterns of wetland species in Zhengzhou and the environmental factors affecting species’ habitat selection. Environmental variables were screened by correlation analysis to avoid affecting the prediction results due to overfitting of the model. The AUC value of the training set of the model ROC curve was above 0.8, and the prediction accuracy was high. The prediction results showed that the only nature reserve in Zhengzhou, Yellow River Wetland Nature Reserve, currently covers only 10.25% of the total area of the high suitability areas for plants and 17.54% of the high suitability habitat areas for waterfowl in the whole area of Zhengzhou. The potential suitability areas of wetland species outside the reserve can provide a basis for site selection for wetland conservation planning in Zhengzhou. It was found that the geographic distribution of wetland species in Zhengzhou is constrained by the distribution of water bodies, bioclimatic variables, land cover, and population density.

Water extraction from optical high-resolution remote sensing imagery: a multi-scale feature extraction network with contrastive learning

ABSTRACT Accurately spatiotemporal distribution of water bodies is of great importance in the fields of ecology and environment. Recently, convolutional neural networks (CNN) have been widely used for this purpose due to their powerful features extraction ability. However, the CNN methods have two limitations in extracting water bodies. First, the large variations in both the spatial and spectral characteristics of water bodies require that the CNN-based methods have the ability of extracting multi-scale features and using multi-layer features. Second, collecting enough samples is a difficult problem in the training phase of CNN. Therefore, this paper proposed a multi-scale features extraction network (MSFENet) for water extraction, and its advantages are contributed to two distinct features: (1) scale features extractor (MSFE) is designed to extract multi-layer multi-scale features of water bodies; (2) contrastive learning (CL) is adopted to reduce the sample size requirement. Experimental results show that MSFE can effectively improve the small water body extraction performance, and the CL can significantly improve the extraction accuracy when the training sample size is small. Compared with other methods, MSFENet achieves the highest F1-score and kappa coefficient in two datasets. Furthermore, spectral variability analysis shows that MSFENet is more robust than other neural networks in a spectrum variation scenario.

Mosquito Diversity in an Experimental Township in Tamil Nadu, India.

To glean more information on mosquito diversity and distribution in Auroville, a cross-sectional study was carried out by mapping the distribution of water bodies and habitats supporting immature stages on the one hand and the distribution of water bodies/habitats supporting mosquito immature stages on the other. A satellite image covering an area of 8.08 km2 was overlaid with a grid of 500 × 500 m. Fifteen modules were selected and the area of each module served as the sampling site for the entomological survey. Adult and larval stages were sampled. Diversity indices were analyzed to compare mosquito diversity. Rarefaction estimations were used to compare abundance and richness of the mosquito species between different zones. In total, 750 mosquito larvae and 84 resting adults were sampled. Eighteen species of mosquitoes belonging to 11 subgenera and 7 genera were documented. Genera included Aedes (Johann Wilhelm Meigen 1818, Diptera, Culicidae), Anopheles (Johann Wilhelm Meigen 1818, Diptera, Culicidae), Armigeres (Theobald 1901, Diptera, Culicidae), Culex (Carl Linnaeus 1758, Diptera, Culicidae), Lutzia (Theobald 1903, Diptera, Culicidae), and Mimomyia (Theobald 1903, Diptera, Culicidae). Of the 18 mosquito species identified, 8 species are new records for Auroville. The Alpha (α) biodiversity indices show that the mosquito fauna is diverse (S = 18; DMg = 2.732 [95% CI: 2.732–2.732]). The Shannon-Weiner (Hʹ = 2.199 [95% CI: 2.133–2.276]) and Simpson indices (λ = 0.8619 [95% CI: 0.8496–0.8723]) measured species richness, evenness, and dominance. The values of these indices suggest high species richness, evenness, and dominance. Prevailing conditions can provide suitable environment for establishment of different mosquito species in this ecosystem. Given the sociodemographic characteristics of this area, research on mosquito diversity and risk of vector-borne diseases will be of great use.

A New Method for Long-Term River Discharge Estimation of Small- and Medium-Scale Rivers by Using Multisource Remote Sensing and RSHS: Application and Validation

River discharge is an important hydrological parameter of river water resources. Especially in small- and medium-scale rivers, data deficiency is the biggest problem for studies of river discharge. In recent years, remote sensing has become a rapid and convenient method to estimate river discharge. However, remote sensing images still have some difficulty generating continuous long-term river discharge. To address this problem, we developed a new method coupling the remote sensing hydrology station method (RSHS) with statistical regression downscaling, using data from optical satellites (Landsat-8, Sentinel-2), radar satellites (Sentinel-1), and un-manned aerial vehicles (UAVs). We applied this method to monitor monthly river discharge for small- and medium-scale rivers from 2016 to 2020 on Yunnan-Guizhou Plateau and evaluated the accuracy of the results. The results show that (1) by applying the newly constructed method, the water body continuity index obtained by Landsat-8 increased by 7% and the average river length percentage in the channel reached 90.7%, a 40% increase; (2) there were only 10 river flow data points, on average, in the 5-year period obtained before this method was applied; after this method was applied, more than 50 river flow data points could be obtained, on average, extending the quantity of data fivefold; in addition, improper extreme values could also be avoided; (3) with better continuity of water body distribution, the images provided steadier river widths. The relative error of daily flow estimation from Landsat-8 images was reduced by 60% and the mean percentage error was reduced by one-fourth. The relative error of the multisource remote sensing composited flow was reduced by 37% with a reduction in the mean percentage error of over a half; (4) in addition, we found that when the threshold difference between water bodies and land in remote sensing images is more than 0.2, the impact of water body recognition error on flow accuracy can be ignored. This method helps to overcome the absence of remote sensing methods for the long-term estimation of flow series in small- and medium-scale rivers, improves the accuracy of remote sensing methods for calculating flow, and provides ideas for regional water resource management and utilization.