Based on GOCI data and the built-in CO2 algorithm, this paper investigated the spatial-temporal distribution characteristics of chlorophyll-a in offshore waters of Yantai and Weihai from 2014 to 2016. Results showed: The chlorophyll-a concentration in the study area has a significant spatial-temporal characteristics, showed a decreased tendency from estuary to offshore area in general. While the lowest value major in the north open seas, the highest value appeared in Sishili Bay and the coastal zone along Weihai, even extended to the western coastal area of Shandong Peninsula. The spatial difference of the concentration of chlorophyll-a in summer was significantly higher than that in winter, and the enrichment effect increased with the increase of temperature. From the perspective of temporal distribution, the chlorophyll-a level was highest in August and lowest in February, and there are small but obvious double peaks in the spring and autumn of May and October. Our work indicated that chlorophyll a concentration level in the study area showed a gradual upward trend in recent 3 years.
Unmanned aerial vehicle (UAV) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAV relatively quickly and repeatedly at low altitudes. In this paper, the wetland vegetation information is extracted from UAV remote sensing images by object-oriented approach. Firstly, the images are segmented and images object are build. Secondly, VDVI, VDWI, spectral information and object geometry information of images objects are comprehensively applied to build wetland vegetation extraction knowledge base. Thirdly, the results of wetland vegetation extraction are improved and completed. The results show that better accuracy of wetland vegetation extraction can be obtained by the proposed method, in contrast to the pixel-oriented method. In this study, the overall accuracy of classified image is 0.968 and Kappa accuracy is 0.934.
As an important complement to satellite observation, the technique of Unmanned Aerial Vehicle (UAV) shows great advantages because of its high spatiotemporal resolutions, low cost and risk. With the development of technology related to UAV, its research was increasingly enhanced and has been applied to many fields such as environmental monitoring. Taking a coastal zone of Yantai as test area, this paper studied how to utilize the UAV system to monitor contaminated water in coastal zones. The results show that the contaminated water information can be extracted from the UAV remote sensing image. The multi-time monitoring conducted in this study can monitor the change of polluted water. This will provide technical support for the monitoring and treatment of polluted water bodies.
Since 2008, the Green Tide has been continuously erupted for 10 years in Yellow Sea. Relevant studies have proved that the source of the green tide burst is the laver rafts in the radiated sand area. In this study, UAV (Unmanned Aerial Vehicle ) and S2A (Sentinel Satellite) data were used to monitor and estimate the biomass of Green tide algae on the rafts of seaweed. Using UAV imagery combined with high-resolution satellite data and field survey data, Accurately monitoring and assessing the biomass of green tide algae in the radiation sandy area can provide a scientific basis for the prevention and early warning of the Southern Yellow Sea green tide disasters.
Unmanned aerial vehicle(UAV) have been increasingly used for natural resource applications in
recent years as a result of their greater availability, the miniaturization of sensors, and the ability to
deploy UAV relatively quickly and repeatedly at low altitudes. UAV remote sensing offer rich
contextual information, including spatial, spectral and contextual information. In order to extract the
information from these UAV remote sensing images, we need to utilize the spatial and contextual
information of an object and its surroundings. If pixel based approaches are applied to extract
information from such remotely sensed data, only spectral information is used. Thereby, in Pixel based
approaches, information extraction is based exclusively on the gray level thresholding methods. To
extract the certain features only from UAV remote sensing images, this situation becomes worse. To
overcome this situation an object-oriented approach is implemented. By object-oriented thought, the
coastal windbreak forest information are extracted by the use of UAV remote sensing images. Firstly,
the images are segmented. And then the spectral information and object geometry information of
images objects are comprehensively applied to build the coastal windbreak forest extraction knowledge
base. Thirdly, the results of coastal windbreak forest extraction are improved and completed. The
results show that better accuracy of coastal windbreak forest extraction can be obtained by the
proposed method, in contrast to the pixel-oriented method. In this study, the overall accuracy of
classified image is 0.94 and Kappa accuracy is 0.92.
In recent years, satellite remote sensing have been widely used in dynamic monitoring of Green Tide. However, the images captured by unmanned aerial vehicles (UAV) are rarely used in floating green tide monitoring. In this paper, a quad-rotor unmanned aerial vehicle was used to mapping the coverage of green tide on the seabeach in Haiyang with three algorithms based on RGB image.The conclusions are as follows: there is discrepancy in both maximum value band among RGB and the difference in the green band for a true color aerial photograph taken from a UAV; the best index for floating green tide mapping on seabeach is GLI. It is possible to have a comprehensive, objective and scientific understanding of the floating green tide mapping with aid of UAV based on RGB image in the seabeach.
Previous studies have shown that Terra moderate resolution imaging spectroradiometer (MODIS) has low detection and characterization efficiency when mapping a green tide (Ulva prolifera) in the Yellow Sea. To quantify the uncertainty in mapping of the green tide using MODIS data, comparisons were conducted between quasi synchronous MODIS images and in situ observation data, as well as an unmanned aerial vehicle (UAV) image. The results show that MODIS images could detect the location of large (>100 m) floating green algae patches with good positional accuracy but tended to ignore the existence of small patches less than 10 m in width. The floating macroalgae area extracted using MODIS was several times larger than the area mapped using the UAV image. The Sentinel-2 multispectral instrument, the Chinese high-resolution GF-1 wide field camera, and the Chinese HJ-1 charge-coupled device are recommended for early green tide detection, whereas MODIS is suitable for green tide monitoring. The UAV could also play an important role in regional green tide monitoring with the advantages of flexibility, smaller dimensions, high spatial resolution, and low cost.
Marine environment protection is an important support for sustainable development of marine ranching. Based on the geographic information system(GIS) and remote sensing(RS), this study developed a 3S system, which integrate Sea surface temperature, chlorophyll concentration, turbidity of sea water and other factors into system. And these factors are important components of marine environment. The system provided data service including loading, browsing, information inquiry, cartography, and also supported the analysis of remote sensing image. In the implementation of the system, the key points of the related technologies have been paid much attention. The practical application shows that it can provide assistance for the environmental protection of marine ranching.
This paper builds a green tide disaster monitoring system based on remote sensing monitoring platform, UAV (Unmanned aerial vehicle) monitoring platform and ship monitoring platform. The system aims at multi-faceted monitoring green tide disasters with remote sensing data, UAV data and ship monitoring data. With this system, the author has continuously monitored the green tide outbreak of Chinese Yellow Sea in 2016. Research conclusions were achieved as follows. The system can quickly get spatial distribution information of green tide disaster, obtain high-resolution remote sensing data and field verification data of key monitoring areas; It can cover the shortage of a single data source by green tide monitoring, significantly improve time resolution and spatial resolution of green tide monitoring data, thus providing data support for dynamic monitoring of green tide; The system can provide data support for the prevention and control of green tide in three different scales.