Dr Romano Lottering Pr.Sci.Nat.

Position:

Senior Lecturer


 

 

Qualifications:

BSc (Environmental Science)
BSc Hons (Environmental Science)
MSc with Distinction (Applied Environmental Science)
Ph.D. (Environmental Science)

 

 

Office Block:

Room 9, 3rd Floor

Science Building

PMB Campus

 

 

Telephone:

Fax:

+27 (0) 33 260 5877

+27 (0) 33 260 5344

 

Email:

 

lottering@ukzn.ac.za

 

 

Website Links


Research Gate: https://www.researchgate.net/profile/Romano_Lottering/publications

LinkedIn: https://za.linkedin.com/in/dr-romano-lottering-92a691102

Academia.edu: http://ukzn.academia.edu/RomanoLottering

Biographical sketch

Currently a senior lecturer at the School of Agricultural, Earth and Environmental Sciences, Romano Lottering has been employed at the University of KwaZulu-Natal’s Pietermaritzburg campus since 2012.

Romano Lottering holds a Ph.D. in environmental science from the University of KwaZulu-Natal, South Africa. His research interest involves developing earth observation tools that assist in decision-making for improving commercial forest productivity under changing climatic conditions. He has published a number of peer-reviewed research articles and successfully supervised several postgraduate students.

Romano Lottering is an NRF rated scientist, registered professional natural scientist (Geospatial Science), a member of the Geospatial Science Professional Advisory Committee of SACNASP and a reviewer for several national and international DHET accredited journals.

Current Teaching/Courses

ENVS211: Geographic Information Systems (GIS) (Module coordinator)
 Aim: To introduce students to the concepts, techniques and interdisciplinary application of GIS and remote sensing as environmental decision-making tools.

ENVS316: GIS & Remote Sensing (Module coordinator)
Aim: To provide further insight into GIS as a management tool for spatial data.

Research Interests

Remote sensing of forest health

Land use land cover classification

Object-orientated Remote Sensing

Mapping invasive alien plant species

Detecting drought impact using GIS and Remote sensing


   



Supervision: Complete and continuing

PhD

1. Sizwe Hlatshwayo, continuing: Detecting Impacts of droughts on Commercial Forest Plantations using Remote Sensing Technology.

2. Samuel Khumbula, continuing: Impacts of the Cossid Moth, Coryphodema tristis on Eucalyptus nitens in Mpumalanga, South Africa.

3. Rodney Tatenda, continuing: Climate change and variability effects on inland fisheries in Zimbabwe.

Masters

• Bongumusa Fakude, continuing: Effectiveness of government water distribution measures and people's access to water in South African rural areas during the outbreak of CODIV-19. A case study of the Mbombela Municipality in South Africa.

• Mthokozisi Buthelezi, continuing: The use of machine learning algorithms to analyse the impacts of droughts on commercial forests in KwaZulu-Natal.

• Khayelihle Ndlazi, continuing: Detecting agrometeorological droughts using a combination of weather stations and satellite remote sensing data in South Africa.

• Andile Lubanyana, continuing: Remote sensing of forest drought: Mapping drought occurrence and critical resource risk.

• Thabo Hlongwane, continuing: The application of Remote Sensing and Geographic Information Systems to Monitor Deforestation Trends (2001 and 2016) in Mopani District Municipality, Limpopo Province of South Africa.

• Confidence Mthanti, continuing: Detecting and mapping commercial forest structural attributes using image texture computed from multispectral and hyperspectral imagery.

• Nwabisa Mkize, continuing: Detecting and mapping solanum mauritianum in commercial forest plantations using image texture ratios.

• Philasande Shange, Submitted: Mapping temporal changes in land-use in the eThekwini region and its impacts on land degradation.

• Samantha Chetty, 2020: Using spectral and textural information to detect and map parthenium hysterophorus l. in Mtubatuba, South Africa (awarded Summa Cum Laude).

• Samuel Khumbula, 2018: Detecting and mapping the habitat suitability of the Cossid Moth, Coryphodema tristis on Eucalyptus nitens in Mpumalanga, South Africa (awarded Cum Laude).

• Trylee Matongera, 2016: Detection and mapping of bracken fern weeds using remotely sensed data (awarded Cum Laude).

• Sizwe Hlatshwayo, 2016: Informing Reforestation Practices: Quantifying Live Forest Above Ground Biomass of a Randomly Mixed Natural Forest Plantation using GIS and Remote Sensing Models (awarded Cum Laude).

Honours

• Sanelisiwe Ngcobo, continuing: Documenting the impacts of land-use change on rural communities: The case of Elandskop.

• Thembelani Nxumalo, continuing: Using Worldview-2 Imagery to Map and Detect Chlorophyll Content to Determine Forest Health.

• Mohamed Vawda, continuing: Detecting and mapping drought-affected commercial forests using unmanned aerial vehicles in KZN, South Africa.

• Austin Sharkey, continuing: Detecting the impacts of snow on commercial forest plantations.

• Kiara Brewer, 2019: Detecting and mapping alien invasive wattle using image texture combinations computed from SPOT-6 imagery.

• Duduzile Diza, 2019: Detecting drought in commercial forests using remote sensing-based indices.

• Sarisha Ramjeawon, 2019: Using spot imagery to study bush encroachment patterns over a period of 7 years: A case study of the Bisley valley nature reserve, KwaZulu-Natal, South Africa.

• Bevan Louis, 2018: Detecting and Mapping invasive alien species in commercial forests using Synthetic-aperture radar.

• Micaela Keddie, 2018: Detecting and Mapping Plantation Damage from Chacma Baboons by Bark Stripping using Remote Sensing.

• Sanvir Maharaj, 2018: Spatial prediction of soil organic carbon in the commercial forestry areas of KwaZulu-Natal using MaxEnt modeling.

• Kimara Moodley, 2018: Land-use and land-cover change: a case-study of the city of eThekwini.

• Confidence Mthanti, 2017: Mapping land degradation hotspots in eThekwini region using GIS and Remote Sensing techniques.

• Mackyla Govender, 2017: Detecting and mapping solanum mauritianum (bugweed) in commercial plantations using image texture.

• Stuart Patterson, 2017: Mapping potential soil erosion utilising GIS in the Linakeng community council.

• Nwabisa Mkize, 2016: Species classification using image texture in KwaZulu-Natal, South Africa.

• Revash Singh, 2016: Detecting and mapping the impacts of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations in KwaZulu-Natal, South Africa.

• Dasria Naicker, 2016: Investigating the socio-economic impacts that the informal trading sector has on the livelihoods of informal traders: A case study of the Durban beachfront area, KwaZulu-Natal.

• Sherece Chetty, 2015: Does the ISO 14001 standard improve environmental performance and corporate image? A case study of companies within industry.

Professional Activities

• Undergraduate and post-graduate lecturing in Geographic Information Systems and Remote Sensing;
• Supervision of post-graduate research at an Honours, Masters and Ph.D. level in Earth Observations and GIS;
• Internal and external examination of post-graduate research dissertations/thesis at an Honours, Masters and Ph.D. level;
• Review of academic manuscripts submitted for publication in peer reviewed journals. These journals include:
o Remote Sensing of Environment
o ISPRS Journal of Photogrammetry and Remote Sensing
o Geocarto International
o International Journal of Applied Earth Observation and Geoinformation
o South African Journal of Geomatics
o Remote Sensing
o Journal of Spatial Science
o Crop protection
o South African Geographical Journal

Professional Membership

• National Research Foundation rated scientist
• Professional Natural Scientist (Geospatial Science) – Reg. No.: 120612
• Member of the Geospatial Science Professional Advisory Committee of SACNASP
• Member of the Golden Key Society – UKZN top 15%
• Member of the African Association of Remote Sensing of the Environment
• Member of Society of South African Geographers
• Supplemental Instruction Supervisor

Publications

• Lottering, S, Mafongoya, P. & Lottering R. 2020: Testing the utility of the social vulnerability index to assess small-scale farmer’s vulnerability to drought in uMsinga, KwaZulu-Natal. Journal of Rural Studies. [Impact factor: 3.301]

• Lottering, R., Mutanga, O, Peerbhay, K. & Lottering, S. 2020: Spatially optimising vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations. International Journal of Remote Sensing. [Impact factor: 2.493]

• Lottering, S, Mafongoya, P. & Lottering R. 2020: The impacts and adaptive strategies of small-scale farmers to drought in uMsinga, KwaZulu- Natal, South Africa. Journal of Asian and African Studies. [Impact factor: 0.433]

• Lottering, R., Govender, M., Peerbhay, K. & Lottering, S. 2020: Comparing partial least squares (PLS) discriminant analysis and sparse PLS discriminant analysis in detecting and mapping Solanum mauritianum in commercial forest plantations using image texture. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 271-280. [Impact factor: 6.942]

• Sewell, S. J., Desai, S. A., Mutsaa, E. & Lottering, R. T. 2019. A comparative study of community perceptions regarding the role of roads as a poverty alleviation strategy in rural areas. Journal of Rural Studies. 71, 73-84. [Impact factor: 3.301]

• Peerbhay, K., Mutanga, O., Lottering, R., Agjee, N., & Ismail, R. 2019: Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR. Geocarto International, 1-14. [Impact factor: 2.365]

• Samuel Takudzwa Kumbula, Paramu Mafongoya, Kabir Yunus Peerbhay, Romano Trent Lottering and Riyad Ismail. 2019: Using Multispectral Remote Sensing to Map the Habitat Suitability of the Cossid Moth in Mpumalanga, South Africa. Remote Sensing, 3, 278. [Impact factor: 4.118]

• Lottering, R., Mutanga, O., Peerbhay, K. & Ismail R. 2019: Detecting and mapping Gonipterus scutellatus induced vegetation defoliation using WorldView-2 pan-sharpened image texture combinations and an artificial neural network. Journal of applied remote sensing, 13, 1. [Impact factor: 1.107]

• Hlatshwayo, S., Mutanga, O., Lottering, R., Ismail, R., & Kiala, S., 2019: Mapping Forest Aboveground Biomass in the Reforested Buffelsdraai Landfill Site using Texture Combinations Computed from SPOT-6 Pan-sharpened Imagery. International Journal of Applied Earth Observations and Geoinformation, 74, 65-77. [Impact factor: 4.846]

• Matongera, T., Mutanga, O., Lottering, R., & Dube, T., 2018: Detection and mapping of bracken fern weeds using remotely sensed data: A review of progress and challenges. Geocarto International, 3, 209-224. [Impact factor: 2.365]

• Lottering, R., Mutanga, O. & Peerbhay, K. 2018: Detecting and mapping levels of Gonipterus scutellatus induced vegetation defoliation and leaf area index using spatially optimised vegetation indices. Geocarto International, 3, 277-292. [Impact factor: 2.365]

• Matongera, T., Sewell, S., Lottering, R., and Marambanyika, T., 2017: The Relief Food Aid and its Implications on Food Production and Consumption Patterns: A case studyof Communal Farmers in Chigodora Community, Zimbabwe. Review of Social Sciences, 3, 24-38. [Impact factor: N/A]

• Peerbhay, K., Mutanga, O., Lottering, R., Bangamwabo, V., & Ismail R., 2016: Detecting bugweed (Solanum mauritianum) abundance in plantation forestry using multisource remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 167-176. [Impact factor: 6.942]

• Peerbhay, K., Mutanga, O., Lottering, R. & Ismail, R. 2016: Mapping Solanum mauritianum plant invasions using WorldView-2 imagery and unsupervised random forests. Remote Sensing of Environment, 182, 39-48. [Impact factor: 8.218]

• Lottering, R., & Mutanga, O. 2016: Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 112, 13-22. [Impact factor: 6.942]

• Lottering, R., & Mutanga, O. 2016: Optimising the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at a subspecies level in KwaZulu-Natal, South Africa. Geocarto International, 31, 870-880. [Impact factor: 2.365]

• Lottering, R., & Mutanga, O. 2012: Estimating the road edge effect on adjacent Eucalyptus grandis forests in KwaZulu-Natal, South Africa using texture measures and an artificial neural network. Journal of Spatial Science, 57, 153 – 173. [Impact factor: 1.711]

Conference proceeding

• Peerbhay K, Mutanga O, Lottering R, Ismail, R. 2016. Unsupervised anomaly weed detection in riparian forest areas using hyperspectral data and Lidar. 8th IEEE Workshop on Hyperspectral Image and Signals Processing: Evolution in Remote Sensing (WHISPERS). Los Angeles, California.


Contact Webmaster | View the Promotion of Access to Information Act | View our Privacy Policy
© University of KwaZulu-Natal: All Rights Reserved