Inversion of Mangrove Forest Leaf Area Index Using Consumer Unmanned Aerial Vehicles

Authors: Xinghe Liu*, University At Buffalo, Le Wang, University at Buffalo
Topics: Remote Sensing
Keywords: Mangrove, Consumer UAV, Leaf area index, Vegetation index
Session Type: Illustrated Paper
Day: 4/13/2018
Start / End Time: 10:00 AM / 11:40 AM
Room: Canal St. Corridor, Sheraton, 3rd Floor
Presentation File: No File Uploaded

As unmanned aerial vehicles (UAVs) becoming more popular, many studies investigate vegetation based on commercial UAV data, since their higher spatial/spectrum resolution and not restricted by cloud. Commercial UAV data were used and usually acquired by UAV companies. Although compared to satellite data, commercial UAV data can have higher flexible revisit frequencies, the possibility of using an even cheaper data source, consumer UAVs (RGB-only), to study vegetation remains unknown. By now, 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 that is using DJI Phantom 3 advanced data for mapping leaf area index (LAI) of mangrove in Dandou Sea, Guangxi, China. In the meantime, commercial UAV image is 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 is feasible to use consumer UAV data for mapping mangrove forest LAI, and the NGRDI achieved the highest accuracy in predicting LAI among all the indices in the plot level (5-meter-radius circle). This paper shows that researchers who do not know aerial photogrammetry and the access to commercial UAV data now can perform high spatial resolution studies in vegetation on their own at a low cost.

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