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.
This paper monitored the outbreak of green tide in the Yellow Sea, China, in 2014 based on GOCI remote sensing image and NDVI extraction method, combined with GIS (Geographical Information System) and visual interpretation technologies. The results show: the green tide is firstly found in the open waters near Yancheng, Jiangsu Province in mid May, and drifted from the southwest to the northeast direction. When reached the neighboring waters between Jiangsu and Shandong in early June, the green tide entered an outbreak stage and reached the maximum coverage area of 2206.54 km2 in 18, June. In early July, the green tide began into a recession stage until all died in early August while its frontline preserved in Yantai – Weihai – Qingdao. Our work shows GOCI image with high temporal resolution is available for the study of migration path and drift speed of green tide.
This paper conducted dynamic monitoring over the green tide (large green alga—Ulva prolifera)
occurred in the Yellow Sea in 2014 to 2016 by the use of multi-source remote sensing data, including GF-1
WFV, HJ-1A/1B CCD, CBERS-04 WFI, Landsat-7 ETM+ and Landsta-8 OLI, and by the combination of
VB-FAH (index of Virtual-Baseline Floating macroAlgae Height) with manual assisted interpretation
based on remote sensing and geographic information system technologies. The result shows that unmanned
aerial vehicle (UAV) and shipborne platform could accurately monitor the distribution of Ulva prolifera in
small spaces, and therefore provide validation data for the result of remote sensing monitoring over Ulva
prolifera. The result of this research can provide effective information support for the prevention and
control of Ulva prolifera.
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.
In recent years, MODIS data were widely used in dynamic monitoring of Green Tide. However, the images may contain lots of mixed pixels because of coarse resolution ,which will cause the error of the monitor result1,2. In this paper, the monitoring error was quantitatively analyzed with the help of GF-1 WFV data, which has a high resolution of 16 merers and the monitoring result of which were considered to be accurate. The conclusions are as follows: there are errors in both dense and sparse Enteromorpha monitoring using MODIS data, and the error in sparse Enteromorpha is larger. Most of the error is concentrated on the edge of the floating Enteromorpha patch. MODIS has a good ability in observing the location of Enteromorpha , and it can play an important role in the dynamic monitoring of multi source data.
In this paper, the green tide (Large green algae-Ulva prolifera) in the Yellow Sea in 2015 is monitored which is based on remote sensing and geographic information system technology, using GF-1 WFV data, combined with the virtual baseline floating algae height index (VB-FAH) and manual assisted interpretation method. The results show that GF-1 data with high spatial resolution can accurately monitoring the Yellow Sea Ulva prolifera disaster, the Ulva prolifera was first discovered in the eastern waters of Yancheng in May 12th, afterwards drifted from the south to the north and affected the neighboring waters of Shandong Peninsula. In early July, the Ulva prolifera began to enter into a recession, the coverage area began to decrease, by the end of August 6th, the Ulva prolifera all died.
Based on SPOT VEGETATION NDVI time-series data, multi-phase China’s land use / land cover (LULC) data were extracted in this study, where land use degree method and land dynamic degree method were used to analyze the spatial and temporal change characteristics of China’s LULC in the latest decade. Moreover, bookkeeping model was applied to analyze the response of China's carbon sink to LUCC. Research conclusions were achieved as follows. China's annual vegetation carbon sink was 0.22- 0.32PgC/year, equivalent to 26% -28% of China's industrial CO2 emissions over the same period. Dynamic changes in woodland and grassland led to carbon sink changed in 11.4-15.7TgC, and the increased carbon sink due to LUCC offset 1.3-1.4% of China’s industrial CO2 emissions.
In this paper, with four remote sensing images from the 1980 to 2010 periods and the coastal survey data as data sources, then integrated use remote sensing and GIS technology, the Efficient Ecological Economic Zone of the Yellow River Delta's coastline and sea reclamation changes were extracted by the means of visual interpretation and the artificial vector method. The conclusions are as follows: The coastline of this study area showed a rising trend during 1980 to 2010, the silty coastline showed a reduction trend while the artificial coastline showed an increasing trend, natural and social factors together determined the evolution of coastline. The reclamation area was the largest during 1980 to 1990 and the area was the smallest during 1990 to 2000, demographic factors and economic factors are the most prominent driving reasons of the reclamation. This paper can provide data support and services for the study area to implement management and sustainable development more efficiently.
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