Analysis of the interannual variation characteristics of the northernmost drift position of the green tide in the Yellow Sea

Disturbed boundaries extraction in coal-grain overlap areas with high groundwater levels using UAV-based visible and multispectral imagery

With high groundwater levels, coal-grain overlap areas (CGOAs) are vulnerable to subsidence and water logging during mining activities, thereby impacting crop yields adversely. Such damage requires full reports of disturbed boundaries for agricultural reimbursement and ongoing reclamation, but because direct measurements are difficult in such cases because of vast unreachable areas, it is necessary to be able to identify out-of-production boundaries (OBs) and reduced-production boundaries (RBs) in the corresponding region. In this study, an OB was extracted by setting a threshold via the characteristics of the cultivated-land elevation based on a digital surface model and a digital orthophoto map generated using an unmanned aerial vehicle (UAV). Meanwhile, the above-ground biomass (AGB), the soil plant analysis development (SPAD) value of chlorophyll contents, and leaf area index (LAI) were used to select the appropriate vegetation indices (VIs) to produce a reduced-production map (RM) based on power regression (PR), exponential regression (ER), multiple linear regression (MR), and random forest (RF) algorithms. Finally, an improved Otsu segmentation algorithm was used to extract mild and severe RBs. The results showed the following. (1) Crop growth heights in a typical ponding basin of the CGOA rendered a fast and efficient approach to distinguishing the OB. (2) In subsequent sample modeling, the red-edge microwave VI (MVIredge), the normalized difference VI (NDVI), and the red-edge modified simple ratio index (MSRredge) combined with RF were shown to be optimal estimators for AGB (R2 = 0.83, RMSE = 0.114 kg·m-2); the red-edge NDVI (NDVIredge), the green NDVI (GNDVI), and the red-edge chlorophyll index (CIredge) acted as strong tools in SPAD prediction using RF (R2 = 0.83, RMSE = 0.152 SPAD); the red-edge modified simple ratio index (MSRredge), the GNDVI, and the green chlorophyll index (CIgreen) via MR were more accurate when conducting the inversion of LAI (R2 = 0.88, RMSE = 1.070). (3) With the improved Otsu algorithm, multiple degrees of RB extraction can be achieved in RM. This study provides reference methods and theoretical support for determining disturbed boundaries in CGOAs with high groundwater levels for further agricultural compensation and reclamation processes.

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

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.