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Land cover monitoring is a major task for remote sensing. Compared to traditional methods of forests monitoring which mostly use orthophotography from satellites or aircraft, there is very little research on the use of 3D canopy structure to monitor forest growth. Unmanned aerial vehicle (UAV) aerial could be a novel and feasible platform to generate more timely and high resolution forest 3D canopy images. In spring, the forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2-day intervals. Each time, 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Excess Green indexes (ExG) was used to extract the green canopy points. The segmented point cloud was triangulated using the greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at three levels: forest level, selected group level and individual tree level. A logistic curve was used to fit the time series canopy growth. Strong correlation was found R2 = 0.8517 at forest level, R2 = 0.9652 at selected group level and R2 = 0.9606 at individual tree level. Moreover, high correlation was found between canopies. By observing these results, we can conclude that the ground 3D model can act as a useful data type to monitor forest growth. Moreover UAV aerial remote sensing has advantages when monitoring forests in periods when the ground vegetation is growing and changing fast.

Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry