Absorptions by non-phytoplankton particles and phytoplankton, and chromophoric dissolved organic matter (CDOM) were measured at 50 sites in large, shallow, Lake Taihu in winter and summer 2006 to study their seasonal and spatial variations, and their relative contributions to total absorption. The CDOM absorption was significantly higher in winter than in summer, due to degradation and release of fixed carbon in phytoplankton and submerged aquatic vegetation (SAV). The hyperbolic model was used to model the spectral absorption of CDOM, and the mean spectral slope of 6.38 nm -1 was obtained. At most sites, the spectral absorption of non-phytoplankton particles was similar to that of the total particles, demonstrating that the absorption of the total particles is dominated by the absorption of non-phytoplankton particles. In summer, phytoplankton absorption increased markedly, due to frequent algal blooms especially in Meiliang Bay. In winter, the significant increase in non-phytoplankton particle absorption resulted from the increase of inorganic particulate matter caused by sediment resuspension. Strong linear relationships were found between a d (440) and total suspended matter (TSM), organic suspended matter (OSM), and inorganic suspended matter (ISM). Strong linear relationships were also found between a ph (440), a ph (675) and chlorophyll a (Chl-a) concentration. The total relative contributions of non-phytoplankton particles over the range of photosynthetically active radiation (PAR) (400-700 nm) were 48.4 and 79.9% in summer and winter respectively. Non-phytoplankton particle absorption dominated the total absorption, especially in winter, in Lake Taihu, due to frequent sediment resuspension in the large shallow lake as a result of strong windy conditions. The results indicate that strong
As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.
Nledia space applications that promote informrd aw~eness in an organization confront an inevitable paradox: the shared video connections between offices and rooms that promote informal awareness rdso cm rob individurds of privacy. A important open problem in this area is how to foster awareness of colleagues while minimizing the accompanying 10SSof privacy. One proposrd put forward is to filter the communicated video streams rather than broadcasting clear video. Such a scheme may facilitate awareness while helping to tieviate some aspects of the privacy loss. In tis ardcle, we describe several image filtering twhniques that provide awareness in informrd group communication applications while blurring the details of an individurd's activities, thus potentially preserving more privacy. We describe studies to quantitatively and qurditatively assess tie degrees of awareness and accuracy that these filtering tmtilques provide.
Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.
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