Leaf spectroscopy reveals post-fire phenolic chrono sequence in the Fynbos biome - Bongokuhle S’phesihle Sibiya; Moses Azong Cho; Onisimo Mutanga; John Odindi; Cecilia Masemola; Johannes Van Staden; McMaster Vambe (International Journal of Remote Sensing, 2026)

The utility of radiative transfer models (RTM) on remotely sensed data in retrieving biophysical and biochemical properties of terrestrial biomes: A systematic review - Bongokuhle S’phesihle Sibiya; John Odindi; Onisimo Mutanga; Moses Azong Cho; Cecilia Masemola (Advances in Space Research, 2025)

Determining the value of different wavelength ranges of non-imaging hyperspectral reflectance to estimate carotenoid content using the PROSPECT-5 model - Bongokuhle Sibiya; Moses Cho; Onisimo Mutanga; John Odindi; Cecilia Masemola; Wessel Bonnet (International Journal of Remote Sensing, 2025)

Mapping weed infestation in maize fields using Sentinel-2 data - Yoliswa Mkhize; Sabelo Madonsela; Moses Cho; Basanda Nondlazi; Russell Main; Abel Ramoelo (Physics and Chemistry of the Earth, Parts A/B/C, 2024)

Design and Stope Stability Analysis of Multiple Concurrent Excavated Veins in Underground Mine; Case Study of Hermyingyi Tin-Tungsten (W-Sn) Mine - Cho Thae Oo; Dyson Moses; Takashi Sasaoka; Hideki Shimada; Akihiro Hamanaka; Joan A. Onyango; Ulaankhuu Batsaikhan; Seelae Phaisopha; Ian K. Tsuma (Geotechnical and Geological Engineering, 2023)

Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa - Mcebisi Qabaqaba; Laven Naidoo; Philemon Tsele; Abel Ramoelo; Moses Azong Cho (Applied Geomatics, 2023)
Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, R 2 (RMSE) = 0.39 (36.04%), and in 2015, R 2 (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy R 2 (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.

Machine learning modelling of crop structure within the Maize Triangle of South Africa - Laven Naidoo; Russell Main; Moses A. Cho; Sabelo Madonsela; Nobuhle Majozi (International Journal of Remote Sensing, 2022)

Determining the Wetland-Dryland Boundary of Depressions Using Littoral Gradient Analysis of Soil Edaphic Factors - Basanda Xhantilomzi Nondlazi; Moses Azong Cho; Heidi van Deventer; Erwin Jacobus Sieben (Wetlands, 2021)

Geospatial analysis of meteorological drought impact on Southern Africa biomes - Farai Maxwell Marumbwa; Moses Azong Cho; Paxie W Chirwa (International Journal of Remote Sensing, 2021)

An assessment of remote sensing-based drought index over different land cover types in southern Africa - Farai Maxwell Marumbwa; Moses Azong Cho; Paxie W Chirwa (International Journal of Remote Sensing, 2020)

Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework - Cecilia Masemola; Moses Azong Cho (International Journal of Remote Sensing, 2019)

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga, South Africa - Cecilia Masemola; Moses Azong Cho; Abel Ramoelo (International Journal of Remote Sensing, 2016)

Mapping the occurrence of Chromolaena odorata (L.) in subtropical forest gaps using environmental and remote sensing data - Oupa E. Malahlela; Moses A. Cho; Onisimo Mutanga (Biological Invasions, 2015)

Molecular modeling of plant metabolites with anti-Onchocerca activity - Jonathan Alunge Metuge; Fidele Ntie-Kang; Valery Fuh Ngwa; Smith B. Babiaka; Moses Samje; Fidelis Cho-Ngwa (Medicinal Chemistry Research, 2015)

Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression - Clement Adjorlolo; Onisimo Mutanga; Moses Azong Cho (International Journal of Remote Sensing, 2015)

Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data - Oupa Malahlela; Moses Azong Cho; Onisimo Mutanga (International Journal of Remote Sensing, 2014)

Hyperspectral reflectance features of water hyacinth growing under feeding stresses of Neochetina spp. and different heavy metal pollutants - Solomon W. Newete; Barend F.N. Erasmus; Isabel M. Weiersbye; Moses A. Cho; Marcus J. Byrne (International Journal of Remote Sensing, 2014)

Evaluating variations of physiology-based hyperspectral features along a soil water gradient in a Eucalyptus grandis plantation - Moses Azong Cho; Jan van Aardt; Russell Main; Bongani Majeke (International Journal of Remote Sensing, 2010)