Search

The boundary of Yellow Sea Cold Water Mass (YSCWM) is a key ocean frontal structure influencing the regional ecosystem. Complex oceanic processes such as tidal currents, upwelling, and internal waves influence fine-scale hydrological structures, comprehensively resulting in a significantly highly productive area for plankton and fisheries. However, detailed research requires inaccessible high-resolution data. To investigate the fine-scale and high-frequency effects of oceanic processes on the local hydrological and ecological environment, we conducted comprehensive cruise acoustic observations and intensive station surveys of the hydrological environment around the YSCWM boundary in summer 2021 and 2022, and found that: (1) fine-scale hydrological structures across the YSCWM boundary were directly captured through this specific intensive station observation design; (2) clear zooplankton diel vertical migration (DVM) phenomena match well with the thermocline variation, showing that acoustics are effective indicators that reflect the water mass layering structure in summer in the YS; and (3) the shear excited by internal waves during propagation and flood tides enhances the upward and downward mixing of the water mass near the thermocline, thus thickening and weakening the layer, an effect that will be more pronounced when both are present at the same time, with ebb tide having the opposite effect. Topographically influenced tidal upwelling also causes significant vertical fluctuations in isotherms. This represents a new way of studying the fine-scale hydrodynamic–hydrologic–ecological aspects of key regions through acoustic remote sensing. [ABSTRACT FROM AUTHOR]

The boundary of Yellow Sea Cold Water Mass (YSCWM) is a key ocean frontal structure influencing the regional ecosystem. Complex oceanic processes such as tidal currents, upwelling, and internal waves influence fine-scale hydrological structures, comprehensively resulting in a significantly highly productive area for plankton and fisheries. However, detailed research requires inaccessible high-resolution data. To investigate the fine-scale and high-frequency effects of oceanic processes on the local hydrological and ecological environment, we conducted comprehensive cruise acoustic observations and intensive station surveys of the hydrological environment around the YSCWM boundary in summer 2021 and 2022, and found that: (1) fine-scale hydrological structures across the YSCWM boundary were directly captured through this specific intensive station observation design; (2) clear zooplankton diel vertical migration (DVM) phenomena match well with the thermocline variation, showing that acoustics are effective indicators that reflect the water mass layering structure in summer in the YS; and (3) the shear excited by internal waves during propagation and flood tides enhances the upward and downward mixing of the water mass near the thermocline, thus thickening and weakening the layer, an effect that will be more pronounced when both are present at the same time, with ebb tide having the opposite effect. Topographically influenced tidal upwelling also causes significant vertical fluctuations in isotherms. This represents a new way of studying the fine-scale hydrodynamic–hydrologic–ecological aspects of key regions through acoustic remote sensing.

Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale.

Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines. [ABSTRACT FROM AUTHOR]

Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale. [ABSTRACT FROM AUTHOR]

Sea-ice biophysical properties are characterized by high spatio-temporal variability ranging from the meso- to the millimeter scale. Ice coring is a common yet coarse point sampling technique that struggles to capture such variability in a non-invasive manner. This hinders quantification and understanding of ice algae biomass patchiness and its complex interaction with some of its sea ice physical drivers. In response to these limitations, a novel under-ice sled system was designed to capture proxies of biomass together with 3D models of bottom topography of land-fast sea-ice. This system couples a pushbroom hyperspectral imaging (HI) sensor with a standard digital RGB camera and was trialed at Cape Evans, Antarctica. HI aims to quantify per-pixel chlorophyll-a content and other ice algae biological properties at the ice-water interface based on light transmitted through the ice. RGB imagery processed with digital photogrammetry aims to capture under-ice structure and topography. Results from a 20 m transect capturing a 0.61 m wide swath at sub-mm spatial resolution are presented. We outline the technical and logistical approach taken and provide recommendations for future deployments and developments of similar systems. A preliminary transect subsample was processed using both established and novel under-ice bio-optical indices (e.g., normalized difference indexes and the area normalized by the maximal band depth) and explorative analyses (e.g., principal component analyses) to establish proxies of algal biomass. This first deployment of HI and digital photogrammetry under-ice provides a proof-of-concept of a novel methodology capable of delivering non-invasive and highly resolved estimates of ice algal biomass in-situ, together with some of its environmental drivers. Nonetheless, various challenges and limitations remain before our method can be adopted across a range of sea-ice conditions. Our work concludes with suggested solutions to these challenges and proposes further method and system developments for future research. [ABSTRACT FROM AUTHOR]