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

Abstract

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.

Low cost prediction of time consumption for pre-commercial thinning in Finland

“…Therefore, a measure of removal density should be included as a variable in forest resources data, and thus as a predictor in the FRD-model. In the future, such updated and accurate information on young forest stands could be achieved by the use of remotely sensed data such as space borne imagery, aerial imagery or airborne laser scanning data (Korhonen et al 2013;Wennerlund 2018;Puliti et al 2019).…”

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…Feduck et al [157] analysed the ability of UAV-based RGB imagery to detect coniferous seedlings in replanted forest-harvest areas, in leaf-off conditions, obtaining a detection rate of 75.8% (n = 149). In Puliti et al [158], UAV data was used for modelling tree density and canopy height, in young boreal forests stands under regeneration. Considering an ABA, fitted random forest models using ground-truth data and the corresponding UAV data were used.…”

“…Apart from forest applications with more incidence towards tree development and its status, other applications in forestry contexts were explored using UAVs for: forest canopy assessment (canopy cover [125], canopy gaps [152][153][154], LAI [7,155], foliage clumping [7] and leaf angle distribution [156]), regeneration of forests [126,127,157,158], assessment of soil disturbances in post-harvest areas [159][160][161], monitoring of logging operations [162] and tree-stump detection [163]. Most of the studies rely in the use of RGB sensors mounted on rotary-wing UAVs (apart from the multispectral sensor used in [155]), except for canopy gaps [152][153][154] in which a fixed-wing UAVs were used.…”

Automated Detection of Conifer Seedlings in Drone Imagery Using Convolutional Neural Networks

“…However, no count of individual seedlings was attempted. Puliti et al [7] estimated stem density in 580 small (50 m 2 ) circular plots in young conifer stands (mean height 2.5 m, mean density 5479 stems/ha) in Stange, Norway, using random forest with predictors derived from 3 cm GSD drone imagery and obtained a RMSE for stand density of 21.8%. While far more accurate than visual estimates from foresters or even estimates from airborne laser scanning (ALS) data, their method cannot provide information on the location of individual seedlings.…”