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.
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.
Coastal wetland is a net carbon sink with a high carbon density. However, coastal reclamation
directly changes the structure of coastal wetland ecosystem and consequent carbon sink function.
The aim of this work was to estimate the reclamation-induced carbon loss in coastal wetlands
using time series GF-1 WVF data. For this purpose, GF-1 WVF imageries of 2013 (before
reclamation) and 2017 (after reclamation) in the Yangtze Estuary were collected and analyzed
combined with field monitoring. Results showed that the converted coastal wetland area occupied
up to 61.60% between 2013 and 2017. Carbon estimation indicated that the coastal wetland before
reclamation had greater potential contribution to the global warming mitigation than the wetland
reclamation to other land cover types. Finally the vulnerability of carbon stores and uncertain
analysis with remote sensing technology in coastal wetlands environment were discussed. We
emphasized that long-term monitoring of coastal wetlands and its carbon dynamic are urgently
needed, because so many uncertain factors exist in short-term monitoring.
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.
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.
This study investigated the associations between land cover changes and evapotranspiration (ET) in the Yellow River Delta during the last 30 years using Landsat imagery. The result showed that the Delta region experienced a distinct increase in area due to sea–land interaction and sediment deposition, accompanied by substantial change in land cover fractions. From 1986 to 2015, 35.48% of land cover changed, mainly due to a transformation into salterns and culture ponds from other land cover types. In general, land cover was converted from less developed into highly developed types. The monowindow algorithm for retrieval of land surface temperature (LST) and the SEBAL model were used to explore the effects of land cover changes on regional ET. The results indicated that the average relative error of daily ET was 9.46%, and there was a significant linear correlation (R2≥0.959, p<0.001) between ET and LST. Relationships existed between LST, ET, fractional vegetation cover, and other relevant vegetation indices, and there were positive and negative correlations between different threshold ranges. During the study period, the transformation of large areas of different land cover types into salterns and culture ponds led to an average increase of 1.43 mm in daily ET.
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.
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.
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