Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in Mangrove forest

As unmanned aerial vehicles (UAVs) become more popular, many studies investigate vegetation based on commercial UAV data. Although compared to satellite data, commercial UAV data can have flexible revisit frequencies, the possibility of using an even cheaper data source, consumer UAVs (red, green, and blue (RGB) only), to study vegetation remains unknown. The purpose of most frequent uses of consumer UAVs is recreation. This paper tests the feasibility of using consumer UAVs for mangrove research and proposed a method for mapping leaf area index (LAI) of mangrove. A commercial UAV image is also used for comparison. RGB-based vegetation indices like Excess Green Vegetation Index (ExG), Negative Excess Red Vegetation Index (NegExR), Green Leaf Index (GLI) and Normalized Green-red Difference Index (NGRDI) were used to build regression models against field measured LAI. The results showed that it was feasible to use consumer UAV data for mapping mangrove forest LAI, and the NegExR achieved the highest coefficient of determination (R²) in predicting LAI among all the indices. This paper showed that researchers who are neither familiar with aerial photogrammetry nor have access to commercial UAV data could perform high spatial resolution vegetation studies at a low cost.