Machine learning algorithms for mapping Prosopis glandulosa and land cover change using multi-temporal Landsat products: a case study of Prieska in the Northern Cape Province, South Africa
Colette de Villiers(Agricultural Research Council of South Africa), Zinhle Mashaba-Munghemezulu(Agricultural Research Council of South Africa), Philemon Tsele(University of Pretoria), Cilence Munghemezulu(Agricultural Research Council of South Africa), George Chirima(Agricultural Research Council of South Africa)
Cited by 8
Related Papers
Global Research Trends for Unmanned Aerial Vehicle Remote Sensing Application in Wheat Crop Monitoring
|Geomatics|2023|47
Potential of Polycyclic Aromatic Hydrocarbon-Degrading Bacterial Isolates to Contribute to Soil Fertility
|BioMed Research International|2016|46
Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands: <i>Harrisia pomanensis</i> as a case study
|International Journal of Remote Sensing|2018|38