Multispectral drone image analysis for shade tree functional traits and climate response in cocoa agroforestry systems

Abdulai, I., Grunther, N.P.K., Asare, R., Rahman, M.H., Rotter, R. & Hofmann, M. (2025). Multispectral drone image analysis for shade tree functional traits and climate response in cocoa agroforestry systems. In: “Reconciling land system changes with planetary health”, (1 p.), 10-12 September, Tropentag, Germany.

Cocoa production plays a key role in sustaining rural livelihoods and offers a pathway for climate change adaptation and mitigation in tropical agroecosystems. Whilst the functioning of cocoa agroforestry systems for biodiversity conservation is well established, their functioning for climate change adaptation remains critical due to negative outcomes of competition for water. Shade trees play a key role in regulating above and below ground resource use dynamics in agroforestry systems. Advanced research approach of using remote sensing techniques in agroforestry systems research has been limited. This study will utilize the ‘Green Normalized Difference Vegetation Index’ (GNDVI) to assess variations in shade tree canopy reflectance variations and relations to physiological traits (transpiration and stomatal conductance) over wet and dry seasons. Thirteen (13) shade trees species belonging to different functional groups based on leaf phenology were selected across 10 smallholder cocoa plantations of similar age in the northern cocoa belt of Ghana. Tree morphological traits (DBH, height, canopy area) and leaf phenology were recorded for 8 randomly selected individual shade trees of each species. Physiological traits were measured on 4 replicate per shade tree species. Analysis will be conducted for two distinct time points: wet season (July 2022) and peak-dry season (February 2023) from high resolution multispectral images from a DJI Multispectral camera drone and leaf transpiration and stomatal conductance data measured with Licor Li 600 porometer. The spectral data (GNDVI) will be correlated with the in-situ measurements of leaf transpiration rate and stomatal conductance to understand how spectral reflectance changes with water status between the seasons. This will help to understand how spectral indices correlate with tree water status and soil moisture content, allowing the detection of water stress earlier than through traditional methods of in situ measurements. The study will identify the interactions between seasonal climatic variations and shade tree leaf phenological characteristics and establish a pathway for the usage GNDVI as useful tool for monitoring drought stress in coco agroforestry systems.