FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3194667797> ?p ?o ?g. }

• W3194667797 endingPage "3240" @default.
• W3194667797 startingPage "3240" @default.
• W3194667797 abstract "An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018." @default.
• W3194667797 created "2021-08-30" @default.
• W3194667797 creator A5004446933 @default.
• W3194667797 creator A5013850826 @default.
• W3194667797 creator A5020139744 @default.
• W3194667797 creator A5033880099 @default.
• W3194667797 creator A5035425823 @default.
• W3194667797 creator A5076548549 @default.
• W3194667797 creator A5091649724 @default.
• W3194667797 date "2021-08-15" @default.
• W3194667797 modified "2023-10-17" @default.
• W3194667797 title "Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China" @default.
• W3194667797 cites W1562382761 @default.
• W3194667797 cites W1737366265 @default.
• W3194667797 cites W1835246481 @default.
• W3194667797 cites W1966197059 @default.
• W3194667797 cites W1974047452 @default.
• W3194667797 cites W1981721600 @default.
• W3194667797 cites W1982141954 @default.
• W3194667797 cites W1995269330 @default.
• W3194667797 cites W2005413824 @default.
• W3194667797 cites W2008051662 @default.
• W3194667797 cites W2011977862 @default.
• W3194667797 cites W2015354090 @default.
• W3194667797 cites W2023001185 @default.
• W3194667797 cites W2033161614 @default.
• W3194667797 cites W2042923072 @default.
• W3194667797 cites W2050617106 @default.
• W3194667797 cites W2053154970 @default.
• W3194667797 cites W2057792405 @default.
• W3194667797 cites W2060679115 @default.
• W3194667797 cites W2111133050 @default.
• W3194667797 cites W2115176270 @default.
• W3194667797 cites W2133059825 @default.
• W3194667797 cites W2136758825 @default.
• W3194667797 cites W2143095230 @default.
• W3194667797 cites W2145023731 @default.
• W3194667797 cites W2158698691 @default.
• W3194667797 cites W2171995497 @default.
• W3194667797 cites W2180637043 @default.
• W3194667797 cites W2216742482 @default.
• W3194667797 cites W2301042755 @default.
• W3194667797 cites W2346888824 @default.
• W3194667797 cites W2413193320 @default.
• W3194667797 cites W2466208728 @default.
• W3194667797 cites W2467306443 @default.
• W3194667797 cites W2517463731 @default.
• W3194667797 cites W2587159702 @default.
• W3194667797 cites W2592912586 @default.
• W3194667797 cites W2725897987 @default.
• W3194667797 cites W2756735712 @default.
• W3194667797 cites W2796308131 @default.
• W3194667797 cites W2804786453 @default.
• W3194667797 cites W2805785887 @default.
• W3194667797 cites W2888443560 @default.
• W3194667797 cites W2895359416 @default.
• W3194667797 cites W2911408852 @default.
• W3194667797 cites W2925238517 @default.
• W3194667797 cites W2956013814 @default.
• W3194667797 cites W2964982401 @default.
• W3194667797 cites W2970364834 @default.
• W3194667797 cites W2978015257 @default.
• W3194667797 cites W2982275932 @default.
• W3194667797 cites W2997939227 @default.
• W3194667797 cites W3008228787 @default.
• W3194667797 cites W3015021986 @default.
• W3194667797 cites W3037290417 @default.
• W3194667797 cites W3039541382 @default.
• W3194667797 cites W3111715672 @default.
• W3194667797 cites W3111888453 @default.
• W3194667797 cites W3122022089 @default.
• W3194667797 cites W3125987965 @default.
• W3194667797 cites W3135096328 @default.
• W3194667797 doi "https://doi.org/10.3390/rs13163240" @default.
• W3194667797 hasPublicationYear "2021" @default.
• W3194667797 type Work @default.
• W3194667797 sameAs 3194667797 @default.
• W3194667797 citedByCount "13" @default.
• W3194667797 countsByYear W31946677972022 @default.
• W3194667797 countsByYear W31946677972023 @default.
• W3194667797 crossrefType "journal-article" @default.
• W3194667797 hasAuthorship W3194667797A5004446933 @default.
• W3194667797 hasAuthorship W3194667797A5013850826 @default.
• W3194667797 hasAuthorship W3194667797A5020139744 @default.
• W3194667797 hasAuthorship W3194667797A5033880099 @default.
• W3194667797 hasAuthorship W3194667797A5035425823 @default.
• W3194667797 hasAuthorship W3194667797A5076548549 @default.
• W3194667797 hasAuthorship W3194667797A5091649724 @default.
• W3194667797 hasBestOaLocation W31946677971 @default.
• W3194667797 hasConcept C111919701 @default.
• W3194667797 hasConcept C11413529 @default.
• W3194667797 hasConcept C115961682 @default.
• W3194667797 hasConcept C1549246 @default.
• W3194667797 hasConcept C154945302 @default.
• W3194667797 hasConcept C18903297 @default.
• W3194667797 hasConcept C191178318 @default.
• W3194667797 hasConcept C205649164 @default.
• W3194667797 hasConcept C206887242 @default.

• next