Suspended sediment concentration (SSC) is one of the most critical parameters in ocean ecological environment evaluation and it can be determined using ocean color remote sensing (RS). The purpose of this study is to develop a model that provides a reliable and sensitive evaluation of SSC retrieval using RS data. Data were acquired for and gathered from the Gulf of Bohai where SSC levels are relatively low with an average value below 30 mg·L −1. The study indicates that the most sensitive band to SSC levels in the study area is the NIR band of Landsat5 TM images. A quadratic polynomial semi-analytical model appears to be the best retrieval model based on the relationship between the inherent optical properties (IOPs) and apparent optical properties (AOPs) of water as described by the quasi-analytical algorithm (QAA). The model has a higher precision and effectiveness for SSC retrieval than data-driven statistical models, especially when SSC level is relatively high. The average relative error and the root mean square error (RMSE) are 12.32% and , respectively, while the correlation coefficient between observed and estimated SSC by the model is 0.95. Using the proposed retrieval model and TM data, SSC levels of the entire study region in the Gulf of Bohai were estimated. These estimates can serve as the baseline for efficient monitoring of the ocean environment in the future.

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Owing to the influence of global change, land cover and land use have changed significantly over the last decade in the cold and arid regions of China, such as Madoi County which is located in the source area of the Yellow River. In this paper, land-use/cover change and landscape dynamics are investigated using satellite remote sensing (RS) and a geographical information system (GIS). The objectives of this paper are to determine land-use/cover transition rates between different cover types in the Madoi County over 10 years e.g., from 1990 to 2000. Second, the changes of landscape metrics using various indices and models are quantified. The impact factors of LUCC (Land-Use land cover Change) are systematically identified by integrating remote sensing as well as statistical data, including climate, frozen soil, hydrological data and the socio-economic data. Using 30 m630 m spatial resolution Landsat (Enhanced) Thematic Mapper (TM/ETM + ) data in our study area, nine land cover classes can be discriminated. Our results show that Grassland, Marshes and Water Bodies decrease notably, while oppositely, Sands -Gobi and Barren land increase significantly. The number of lakes with an acreage larger than six hectares decreased from 405 in 1990 to 261 in 2000. Numerous small lakes dried out. The area of grassland with a high cover fraction decreased as well, while the surface area of grassland with a medium level of cover fraction increased. The medium cover fraction grassland mainly originates from high cover fraction grassland. The desertification of land is a serious issue. (ii) The inter-transformations between Grasslands, Barren Land, Sands, Gobi, Water Bodies and Marshes are remarkable. The Shannon-Weaver Diversity Index (SWDI), the Evenness Index (EI) and the extent of Landscape Heterogeneity (LH) has improved. Marshes have become more fragmented hence, with less connected patches. (iii) In the recent 30 years, average annual temperature, the power of evaporation and the index of dryness did increase significantly. Moreover, soil moisture content (SMC) decreased and the drought trend accelerated. The degradation of frozen soil has impacted on the decrease of surface water area and induced a drop in groundwater levels. Monitoring LUCC in sensitive regions would not only benefit from a study of vulnerable ecosystems in cold and high altitude regions, but would provide scientifically based decision-making tools for local governments as well.

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Based on the field survey, this paper analyzes the relationship between groundwater and ecological environment in arid and semiarid areas in northwest China. Groundwater in the arid and semiarid areas is a natural resource with economic and ecological values. The concept of the ecological value of groundwater is firstly presented. This ecological value can be reflected in four aspects: (1) the maintenance of base flow in rivers and areas of lakes and wetland; (2) the supply of physiological water demanded by vegetations; (3) the regulation of soil moisture and salt content; and (4) the stability of the geological environment (such as land subsidence etc.). In addition, the threshold system between the ecological environment and multi-dimensional indices as variations in levels of the groundwater table, groundwater quality, water and salt content in the vadose zone as well as groundwater exploitation, are proposed in the arid and semiarid areas. It is expected that the system could provide a scientific basis and technological support for the regulation of the groundwater status and the maintenance of sustainable development of the ecological environment and utilization of water resources.

Background With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft’s Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.