The titled article Mapping Maize Cropland and Land Cover in Semi-Arid Region in Northern Nigeria Using Machine Learning and Google Earth Engine discusses how machine learning and Google Earth Engine were used to identify maize cropland and land cover in Northern Nigeria’s semi-arid region. This method was found to be more efficient than the traditional manual land cover mapping method and can be replicated in other areas with similar land cover patterns. The article is published by MDPI, which is a scholarly open access publisher of academic journals in science, technology, engineering, and medicine. The company is committed to open access and provides free access to all articles published on their platform.
The author of the article is not specifically mentioned, but it was likely written by a team of researchers who conducted the study. The article provides valuable insights into how technology can be leveraged to improve land cover mapping, which can ultimately aid in better agricultural planning and management in the region. By using machine learning algorithms and Google Earth Engine, the researchers were able to analyze satellite images and accurately identify the areas under maize cultivation and land cover types. This information can be used to support policymaking for sustainable agricultural practices in the area.
In conclusion, the use of technology in land cover mapping can significantly improve the accuracy and efficiency of the process. The study conducted in Northern Nigeria demonstrates the benefits of using machine learning and Google Earth Engine in identifying maize cropland and land cover in semi-arid regions. The article by MDPI provides valuable insights into the methodology used and can serve as a useful reference for future research in this area.
