Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq

Degradation and desertification represent serious threats, as they present severe environmental and socio-economic consequences, demanding immediate action. Although a recognized methodology for assessing degradation and desertification is missing, remote sensing has been recognized as a powerful support for its accessibility and efficacy. The aim of this study is to examine the application of remote sensing for assessing land and soil degradation and desertification. A total of 278 research papers retrieved from Scopus/Web of Science database and published over the past decade have been analyzed. From the analysis of scientific publications, a rising interest for these topics and a dominance of research from China has been registered. Established satellite data, Landsat, and MODIS, despite limitations in accuracy and resolution, remain popular due to easy access. This restricts research to broader scales and limits practical applications like land management. The prevalent use of vegetation indexes, while convenient, can be misleading due to their indirect connection to soil health. Consequently, vegetation-based models may not fully capture the complexities involved. To improve understanding, the study suggests a shift towards multi-indexes models and a move away from relying solely on readily available data products. Moreover, the application of data fusion methods could provide a more holistic view.

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Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq

Cited by 2 publications

References 32 publications

Cnns in land cover mapping with remote sensing imagery: a review and meta-analysis

Cnns in land cover mapping with remote sensing imagery: a review and meta-analysis

Rural Land Degradation Assessment through Remote Sensing: Current Technologies, Models, and Applications

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