Project 2: Cocoa Agroforestry Canopy Gap Predictor
- Estimated canopy cover distribution using in-situ digital hemispherical photographs (DHPs) sampling and estimates of canopy gap fraction.
- Built neural network and random forest regression models of cocoa agroforestry canopy gap fraction and Sentinel-1A SAR backscatter intensity and features.
- Developed a combination of different backscatter variables for predicting the canopy gap variability in agroforestry cocoa production landscapes.
- Utilise a semi-variogram analysis of canopy gap distribution and spatial clustering distances in different cocoa production landscapes.
- Provided new insights into the scale of spatial variability of canopy gaps in relation to farm and landscape management through cocoa agroforestry land use.
- Built a proof-of-concept to support development of management tools or strategies on tree inventorying and decisions regarding incentives for shade tree retention and planting in cocoa landscapes.
Inventory of canopy cover distribution (ground truth)
