Enoch Gyamfi-Ampadu
This researcher has focused their work on advancing remote sensing techniques for forest management, particularly in agricultural contexts, utilizing satellite data to improve predictions of tree species diversity and understand forest cover dynamics both spatially and temporally. They have also reviewed the two-decade progress on remote sensing applications in monitoring tropical and subtropical forests, highlighting advancements in using LiDAR technology. Their research delves into understanding tree diversity patterns and changes over time, as demonstrated by their studies of forest cover changes at Nkandla reserve.
Faculty Biography
Enoch Gyamfi-Ampadu is a Lecturer at the Department of Forest Resources Technology, KNUST. He is a trained forester with over ten years of experience in forest ecosystems management, forest governance and conservation-related projects and research. His PhD was in Environmental Sciences, specialising in Remote Sensing and GIS from the University of KwaZulu-Natal (UKZN), Durban, South Africa. He focused on mapping forest cover, spatiotemporal forest cover change detection and forecasting future cover distribution, tree species diversity prediction, and biomass/carbon stock estimation of a subtropical Afromontane natural forest reserve in South Africa using remote sensing and machine learning modelling. His Master's was in Tropical Forestry from Bangor University, United Kingdom. Gyamfi-Ampadu's research focused on participatory GIS, where he collaborated with forest fringe communities to map sites in forest reserves that community members obtain Non-Timber Forest Products (NTFPs). His Bachelor's degree and Diploma were in Natural Resource Management from the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. The research for his Bachelor degree research focused on employing Remote Sensing and GIS for degradation and forest cover change assessment of a natural forest reserve in Ghana.
Forest ecology and management
Remote Sensing and LiDAR Applications
Remote Sensing in Agriculture
RETRACTED: Gyamfi-Ampadu et al. Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction. Remote Sens. 2021, 13, 1033
Open AccessTree Species Diversity Data
Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review
Open AccessRETRACTED: Evaluating Multi-Sensors Spectral and Spatial Resolutions for Tree Species Diversity Prediction
Open AccessMulti-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa
Multi-Decadal Spatial and Temporal Forest Cover Change Analysis of Nkandla Natural Reserve, South Africa
Open AccessMapping natural forest cover using satellite imagery of Nkandla forest reserve, KwaZulu-Natal, South Africa
Department of Forest Resources Technology
Kwame Nkrumah University of Science and Technology