The decision-tree method does not assume strict data distribution parameters, optimization of the application of multispectral imagery and elevation data becomes possible, and combing with the DRRI index, then results in higher classification accuracies of marine floating rafts. The producer accuracy for marine floating rafts through the DT method was 98.17%, 0.81 percent lower than that of RF, and 1.03 percent lower than that of SVM. The user accuracy for marine floating rafts for DT (98.25%) was also markedly higher than that of RF and SVM methods (93.97% and 86.50%, respectively). The overall accuracy for DT was 98.20% and 1.28 and 4.99 percentage points higher than RF and SVM, respectively. Finally, the classification results were merged into aquaculture rafts and non-aquaculture rafts. The results demonstrate that these three methods can obtain raft information with high accuracy. Additionally, a comparison was made between the decision tree classification method (DT) and the random forest (RF) and support vector machine (SVM) methods. These included the Differential Ratio Floating Raft Index (DRRI), newly proposed in the paper, the Normalized Difference Vegetation Index (NDVI), and visible reflectance. Three indices and spectral features were used in this algorithm to differentiate marine floating rafts from other land-cover and land-use types in Fangchenggang City, China. This research reports how a decision-tree-based procedure was developed to map marine floating raft aquaculture using Sentinel-2A MSI imagery and DEM (Digital Elevation Model) data. The Sentinel-2 Multispectral Instrument (MSI) is used to acquire optical imagery at a high spatial and temporal resolution, sampling 13 spectral bands in the visible, near-infrared, and short-wave infrared parts of the spectrum. It is essential to accurately obtain the spatial distribution of marine floating raft aquaculture to gain the fullest understanding of the development of marine fishery production, optimization of the spatial layout of aquaculture, and protection of the marine environment. Marine floating raft aquaculture forms an integral component of the monitoring of coastal marine environments. These are essential in the spirit of sustainable development and management, particularly in developing countries, which are often more vulnerable to environmental degradation.
The need for accurate prediction of water quality parameters within the context of sustainable shrimp culture demands the application of advanced methods like Artificial Neural Network (ANN) combined with remote sensing and GIS. This paper addresses the potential capabilities of evolving satellite remote sensing technology and GIS for the sustainable management of shrimp culture through the analysis of various dataset depicting the criteria of sustainability. It highlights a selected number of remote sensing case studies on applications of remote sensing and GIS for sustainable management of shrimp culture. This paper briefly describes the status of shrimp culture development in India, discusses its ecological and socio-economic impacts and recommends measures to achieve long term sustainability using advanced tools like remote sensing and Geographic Information System (GIS).
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