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

Abstract

Land cover monitoring is a major task for remote sensing. Comparing to traditional methods for forests monitoring which mostly use orthoimages from satellite or aircraft, there are very few researches use forest 3D canopy structure to monitor the forest growth. UAV aerial can be a novel and feasible platform to provide high resolution and more timely images that can be used to generate high resolution forest 3D canopy. In spring, the small forest is supposed to experience rapid growth. In this research, we used a small UAV to monitor campus forest growth in spring at 2days interval. Each time 140 images were acquired and the ground surface dense point cloud was reconstructed at high precision. Color indexes ExG (Excess Green) was used to extract the green canopy point. The segmented point cloud was triangulated using greedy projection triangulation method into a mesh and its area was calculated. Forest canopy growth was analyzed at 3 level: forest level, selected group level and individual tree level. 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 canopy by observing these results, we can conclude that the ground 3D model can act as a useful data type as orthography to monitor the forest growth. Moreover the UAV aerial remote sensing has advantages at monitoring forest in periods when the ground vegetation is growing and changing fast.

Effects of Differences in Structure from Motion Software on Image Processing of Unmanned Aerial Vehicle Photography and Estimation of Crown Area and Tree Height in Forests

“…In addition, over-exposure of the generated images may have been a consequence of sunlight on the aerial images. Previous studies have pointed out that meteorological conditions (especially wind and light intensities) can cause image artifacts and errors in processing, which can affect the quality of the generated images [18,23,62,63]. Therefore, experiments in strong light should be avoided and, if possible, images should be acquired on a cloudy day when the wind speed is low, or when the influence of wind is at a minimum [23,59,64].…”

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

“…The SOR filter estimates statistical parameters in the surroundings of each point in the point cloud and filters those that fall outside the standard range (i.e., mean ± standard deviation) of the set of points. Based on Zhang et al [35], we set the SOR-filter's neighbours to 50 adjacent points and used one standard deviation. The CSF algorithm inverts the point cloud along the z-axis, fits a surface to the highest points of the inverted point cloud, and classifies the points as ground or not ground according to a distance threshold.…”

MODEL PENDUGAAN DIAMETER POHON BERBASIS CITRA UNMANNED AERIAL VEHICLE (UAV) PADA HUTAN HUJAN TROPIS PAPUA: STUDI DI PULAU MANSINAM PAPUA BARAT (Model of Tree Diameter Estimation Based on Unmanned Aerial Vehicle (UAV) Image in Papua Tropical Rain Forest: a Study in Mansinam Island Papua Barat)

“…Penggunaan foto udara juga telah digunakan untuk mendukung rencana pengelolaan sumber daya alam yang berkelanjutan karena dianggap mampu menghemat biaya dan waktu (Gülci, 2019). Penggunaan UAV untuk penginderaan jauh memiliki banyak manfaat seperti mengurangi biaya, waktu, memiliki resolusi tinggi, dan mudah digunakan dalam waktu dan tempat yang dinamis (Zhang et al, 2019). Penggunaan UAV tidak lepas dari pengolahan foto udara untuk dapat menghasilkan nilai lebih dari foto yang telah didapatkan.…”