Use of UAV Photogrammetric Data for Estimation of Biophysical Properties in Forest Stands Under Regeneration

被引:55
作者
Puliti, Stefano [1 ]
Solberg, Svein [1 ]
Granhus, Aksel [1 ]
机构
[1] Norwegian Inst Bioecon Res NIBIO, Div Forest & Forest Resources, Natl Forest Inventory Hogskoleveien 8, N-1433 As, Norway
关键词
unmanned aerial vehicle; forest inventory; stands under regeneration; tree density; airborne laser scanning; TROPICAL FOREST; SYSTEM; MODEL;
D O I
10.3390/rs11030233
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The objective of this study was to assess the use of unmanned aerial vehicle (UAV) data for modelling tree density and canopy height in young boreal forests stands. The use of UAV data for such tasks can be beneficial thanks to the high resolution and reduction of the time spent in the field. This study included 29 forest stands, within which 580 clustered plots were measured in the field. An area-based approach was adopted to which random forest models were fitted using the plot data and the corresponding UAV data and then applied and validated at plot and stand level. The results were compared to those of models based on airborne laser scanning (ALS) data and those from a traditional field-assessment. The models based on UAV data showed the smallest stand-level RMSE values for mean height (0.56 m) and tree density (1175 trees ha(-1)). The RMSE of the tree density using UAV data was 50% smaller than what was obtained using ALS data (2355 trees ha(-1)). Overall, this study highlighted that the use of UAVs for the inventory of forest stands under regeneration can be beneficial both because of the high accuracy of the derived data analytics and the time saving compared to traditional field assessments.
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页数:15
相关论文
共 37 条
[1]  
Agisoft, 2018, AG PHOT US MAN PROF
[2]   Estimating forest canopy fuel parameters using LIDAR data [J].
Andersen, HE ;
McGaughey, RJ ;
Reutebuch, SE .
REMOTE SENSING OF ENVIRONMENT, 2005, 94 (04) :441-449
[3]  
[Anonymous], MACH LEARN MACH LEARN
[4]   Empirical harvest models and their use in regional business-as-usual scenarios of timber supply and carbon stock development [J].
Anton-Fernandez, Clara ;
Astrup, Rasmus .
SCANDINAVIAN JOURNAL OF FOREST RESEARCH, 2012, 27 (04) :379-392
[5]   Processing of laser scanner data - algorithms and applications [J].
Axelsson, PE .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 1999, 54 (2-3) :138-147
[6]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]   High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision [J].
Dandois, Jonathan P. ;
Ellis, Erle C. .
REMOTE SENSING OF ENVIRONMENT, 2013, 136 :259-276
[8]  
DJI, 2018, PHANT 4 PRO PRO US M
[9]   Detection of Coniferous Seedlings in UAV Imagery [J].
Feduck, Corey ;
McDermid, Gregory J. ;
Castilla, Guillermo .
FORESTS, 2018, 9 (07)
[10]   Identifying terrestrial carbon sinks: Classification of successional stages in regenerating tropical forest from Landsat TM data [J].
Foody, GM ;
Palubinskas, G ;
Lucas, RM ;
Curran, PJ ;
Honzak, M .
REMOTE SENSING OF ENVIRONMENT, 1996, 55 (03) :205-216