| In recent years,large-scale algal blooms have become more common worldwide,especially in some warm waters,where they are often referred to as“algae blooms”.Algal blooms can cause damage to the aquatic ecosystem and have adverse effects on the stability of the aquatic ecosystem.Ulva pertusa(U.pertusa)is a widely distributed large green algae and one of the important algae that cause green algae disasters.In a small bay in Laizhou Heigangkou Bay,north of the Shandong Peninsula,human activities such as sea enclosure projects and aquaculture have changed the local aquatic environment,causing U.pertusa to grow explosively in some local areas.In order to prevent the occurrence of U.pertusa green tide disaster,scientific monitoring of the growth and distribution of U.pertusa,this paper is based on field data,unmanned aerial vehicle(UAV)and satellite remote sensing data,to analyze the water environment,U.pertusa portion of macroalgae(POM),classification extraction method and U.pertusa spatiotemporal variation in the study area,the main research contents and conclusions are as follows:(1)In the absence of in situ water depth data in the study area,the spectral angle mapper(SAM)was introduced to quantify the spectral shape differences of different water depths.Based on the bio-optical model,the endmember hyperspectral data were simulated for different water depths.The analysis results show that SAM can reflect the relative distribution of water depth in the study area more accurately.(2)The relationship model between U.pertusa POM and vegetation index NDVI(Normalized Difference Vegetation Index),VB-FAH(Virtual-Baseline Floating macro Algae Height)and surface reflectance of different bands was established using Pleiades High-Resolution(HR)high-resolution images obtained from Google Earth,UAV high-resolution images and Sentinel-2 and Landsat-8 satellite images.The results show that there is a strong linear relationship between the green band surface reflectance and U.pertusa POM,with R2values of 0.94 and 0.89 on Sentinel-2 and Landsat-8 satellite images,respectively,and the root mean square errors of the validation results are 0.13 and 0.11,respectively.According to the POM distribution map obtained from the model,the biomass of U.pertusa in summer and autumn of2020 was estimated,with the maximum fresh weight of 8.15×10~5 kg on October 8 and the minimum value of 4.28×10~5 kg on September 18.The spatiotemporal variation of POM distribution reflects the spatial movement of U.pertusa,but unlike the floating Ulva prolifera,its movement is slow and difficult to be detected by low-resolution images such as satellites.(3)The method of drawing the waterline using the MNDWI(Modified Normalized Difference Water Index)index was used to divide the study area into exposed tidal flat area and underwater area,and extract them separately.And the green band,NDVI,MLC(Maximum Likelihood Classification),MD(Minimum Distance),CIE(Commission on Illumination)color system,RF(Random Forest),SAM,SVM(Support Vector Machine)were compared and evaluated for accuracy.The results show that using SAM and RF methods for U.pertusa in the exposed and underwater states of the tidal flat in the study area can effectively improve the extraction accuracy,with an overall accuracy of 88.9%,Kappa coefficient of 77.5,and mapping accuracy of 90.77%.(4)The analysis of the spatiotemporal distribution changes of U.pertusa from2011 to 2022 found that U.pertusa mainly concentrated in the growth and reproduction from April to November,and rarely appeared in winter,showing a strong seasonality.The change of coastline,the formation of artificial bays that reduce the seawater exchange capacity and the wastewater discharge from the surrounding aquaculture ponds,may be one of the important factors for the explosive growth and reproduction of U.pertusa.In addition,the spatial distribution analysis of U.pertusa found that U.pertusa is more likely to aggregate in shallow water areas near buildings or composed of buildings,which have a higher occurrence frequency.This paper uses the combination of UAV and satellite data to explore the accurate classification and extraction method of underwater U.pertusa in the study area.By spatiotemporal analysis of the monitoring data from 2011 to 2022,the relationship between the local outbreak of U.pertusa in Laizhou Heigangkou Bay and human activities was revealed. |
