Mr Mahlatse Kganyago

Unit: Earth Observation Position: Remote Sensing Scientist Qualifications: MSc

Mahlatse Kganyago holds MSc degree in Applied Remote Sensing and GIS (Cum Laude). He has extensive experience in image analysis (multispectral and hyperspectral), spectral analysis, land-cover mapping, statistical and spatial modeling of landscapes, and both research and teaching of GIS/RS for natural resources management practices. His work in recent times focuses on enhancing the use, understanding, development, and implementation of innovative remote-sensing techniques to aid agro-ecological systems research and application development. He has a long-standing interest in agriculture and natural vegetation resources assessment and monitoring and participates in international working groups such as GEO GLAM RAPP and LDN as well as CEOS WG CapD.

His current work involves the coordination, research, and product development, based on the users’ needs, co-design, and co-development principles for the implementation of the National Space Infrastructure Hub and other internationally funded projects.


Research Interests

  • Applications of state-of-the-art machine learning algorithms for classification of vegetation and estimation of biophysical and biochemical parameters
  • Time series analysis of seasonal and long-term vegetation dynamics and climate-related disturbances such as wildfires and droughts
  • Multi-sensor and multi-source data fusion for enhanced spatial and temporal properties based on new data architectures such as the Data Cube
  • Data dimensionality reduction and model-agnostic interpretation methods

Publications

Citation DOI
M. Kganyago, P.Mhangara, T. Alexandridis, G. Laneve, G. Ovakoglou and N. Mashiyi (2020).
Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape.
Remote Sensing Letters, 11:10, 883-892.
10.1080/2150704X.2020.1767823
M. Kganyago, L. Shikwambana (2020).
Assessment of the Characteristics of Recent Major Wildfires in the USA, Australia and Brazil in 2018-2019 Using Multi-Source Satellite Products.
Remote Sens. 2020, 12(11), 1803
10.3390/rs12111803
M. Kganyago, L. Shikwambana (2019).
Assessing Spatio-temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality using Multisource Remotely Sensed Data.
Sustainability 2019, 11(23), 6811.
10.3390/su11236811
M. Kganyago, J. Odindi, C. Adjorlolo, P. Mhangara (2017).
Evaluating the capability of Landsat 8 OLI and SPOT 6 for discriminating invasive alien species in the African Savanna landscape.
International Journal of Applied Earth Observation and Geoinformation, 67:10-19.
10.1016/j.jag.2017.12.008
M. Kganyago, J. Odindi, C. Adjorlolo, P. Mhangara (2017).
Selecting a subset of spectral bands for mapping invasive alien plants: a case of discriminating Parthenium hysterophorus using field spectroscopy data.
International Journal of Remote Sensing, 38:20, 5608-5625.
10.1080/01431161.2017.1343510
M. Kganyago, P. Mhangara (2019).
The Role of African Emerging Space Agencies in Earth Observation Capacity Building for Facilitating the Implementation and Monitoring of the African Development Agenda: The Case of African Earth Observation Program.
ISPRS Int. J. Geo-Inf. 2019, 8(7), 292.
10.3390/ijgi8070292