Next Article in Journal
Improvements of ADAM3 by Incorporating New Dust Emission Reduction Formulations Based on Real-Time MODIS NDVI
Next Article in Special Issue
3DRIED: A High-Resolution 3-D Millimeter-Wave Radar Dataset Dedicated to Imaging and Evaluation
Previous Article in Journal
Investigation on the Relationship between Satellite Air Quality Measurements and Industrial Production by Generalized Additive Modeling
Previous Article in Special Issue
Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery
Article

Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation

by 1,2,†, 3,†, 1,2,*, 1,2, 3 and 3
1
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
2
Engineering Research Center for Forestry-Oriented Intelligent Information Processing, National Forestry and Grassland Administration, Beijing 100083, China
3
Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work and should be considered co-first authors.
Academic Editor: Lars T. Waser
Remote Sens. 2021, 13(16), 3138; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163138
Received: 27 June 2021 / Revised: 30 July 2021 / Accepted: 4 August 2021 / Published: 8 August 2021
Computer vision technology has promoted the rapid development of forest observation equipment, and video photography (videogrammetry) has provided new ideas and means for forestry investigation. According to the characteristics of videogrammetry, a spiral observation method is proposed. Meanwhile, a new point cloud data processing method is proposed, which extracts a point cloud at the diameter at breast height (DBH) section and determines the DBH of trees through cylinder fitting and circle fitting, according to the characteristics of the point cloud model and the real situation of occlusion in the sampled area, and then calculates the biomass. Through a large number of experiments, a more effective and relatively high-precision method for DBH extraction is obtained. Compared with the field survey data, the bias% of DBH extracted by videogrammetry was −3.19~2.87%, and the RMSE% was 5.52~7.76%. Compared with the TLS data, the bias% of −4.78~2.38%, and the RMSE% was 5.63~9.87%. The above-ground biomass (AGB) estimates from the videogrammetry showed strong agreement with the reference values with concordance correlation coefficient (CCC) and the RMSE values of 0.97 and 19.8 kg. Meanwhile, the AGB estimate from TLS agrees with the CCC values and the RMSE of 0.97 and 17.23 kg. Videogrammetry is not only cheap, low cost, and fast, but also can be observed in a relatively complex forest environment, with strong anti-interference ability. The experimental results prove that its accuracy is comparable to TLS and photogrammetry. Thus this work is quite valuable in a forest resources survey. We believe that the calculation accuracy of our new method can fully meet the needs of the forest survey. View Full-Text
Keywords: videogrammetry; point cloud; DBH; above-ground biomass videogrammetry; point cloud; DBH; above-ground biomass
Show Figures

Figure 1

MDPI and ACS Style

Lian, Y.; Feng, Z.; Huai, Y.; Lu, H.; Chen, S.; Li, N. Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation. Remote Sens. 2021, 13, 3138. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163138

AMA Style

Lian Y, Feng Z, Huai Y, Lu H, Chen S, Li N. Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation. Remote Sensing. 2021; 13(16):3138. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163138

Chicago/Turabian Style

Lian, Yining, Zhongke Feng, Yongjian Huai, Hao Lu, Shilin Chen, and Niwen Li. 2021. "Terrestrial Videogrammetry for Deriving Key Forest Inventory Data: A Case Study in Plantation" Remote Sensing 13, no. 16: 3138. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163138

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop