A Systematic Review of Desertification Identification with Multispectral LANDSAT Image and Deep Learning Models
Keywords:
LANDSAT, Deep Learning, Desertification, GeoinformaticsAbstract
The use of multispectral Landsat images and deep learning models for desertification detection has been reviewed in this research. The role of deep learning models is found to significantly increase the identification accuracy of the researchers, complemented by the inclusion of Landsat imagery to capture key desertification indicators. The research reviews difficulties including geographical resolution, data variability, uncertainty, and validation, alongside different desertification identification methods, techniques, advancement, and limitations. The research also highlighted the necessity of historical data, data continuity, and data fusion, among other issues on data availability and quality. The research advocates for the combination of high-resolution photography, climate and weather data, and socioeconomic data for better desertification detection while the research has identified more complex deep learning architectures, better uncertainty estimation, explainability and interpretability improvement, and the integration of process-based models as potential areas of research. The research concludes by highlighting the importance of precise desertification identification in effective land administration and ecological preservation.
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