Inventory of Small Forest Areas Using an Unmanned Aerial System

By Stefano Puliti, Hans Ole Ørka, Terje Gobakken, Erik Næsset. Remote Sensing, 2015

350 citations recorded by the source corpus.

Acquiring high spatial and temporal resolution imagery from small unmanned aerial systems (sUAS) provides new opportunities for inventorying forests at small scales. Only a few studies have investigated the use of UASs in forest inventories, and the results are inconsistent and incomplete. The present study used three-dimensional (3D) variables derived from UAS imagery in combination with ground reference data to fit linear models for Lorey’s mean height (hL), dominant height (hdom), stem number (N), basal area (G), and stem volume (V). Plot-level cross validation revealed adjusted R2 values of 0.71, 0.97, 0.60, 0.60, and 0.85 for hL, hdom, N, G, and V, respectively, with corresponding RMSE values of 1.4 m, 0.7 m, 538.2 ha−1, 4.5 m2∙ha−1, and 38.3 m3∙ha−1. The respective relative RMSE values were 13.3%, 3.5%, 39.2%, 15.4%, and 14.5% of the mean ground reference values. The mean predicted values did not differ significantly from the reference values. The results revealed that the use of UAS imagery can provide relatively accurate and timely forest inventory information at a local scale. In addition, the present study highlights the practical advantages of UAS-assisted forest inventories, including adaptive planning, high project customization, and rapid implementation, even under challenging weather conditions.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/5a8b54eaa832f10af195bd63b9a49a99a50bd56f

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Citations in the corpus

Short Communication. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal (2016, background): “UAV imagery emerged as a feasible alternative for the monitoring of the three-dimensional (3D) structure of forests (Puliti et al., 2015).”
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging (2017, background): “The use of UAVs in aerial imaging has enabled measurements with higher spatial resolution, improving the resolution of photogrammetric point clouds and the acquisition of three-dimensional (3D) structural data from the forest [36,37].”
Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest (2017, background): “Even though flying at a higher altitude can be viewed as a remedy, it is detrimental towards the overall accuracy with respect to the resolution (ground sample distance) and it is restricted due to shorter signal range; it might create legal conflicts and privacy issues as well [40,100].”
Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests (2019, methodology): “Puliti et al. (2015) [37] used a fixed-wing UAV to acquire high-resolution DAP imagery from southeast Norway.”
Unmanned Aerial Vehicles (UAVs) in environmental biology: a review (2019, methodology): “Puliti et al. (2015) used UAV-derived data to develop models estimating mean height, dominant height, stem number, basal area and stem volume for forest inventory information at a local scale, whereas Zhang et al. (2016) used it for long-term forest monitoring.”
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion (2018, methodology): “Further, compared to lidar, point-cloud construction using SfM is more sensitive to lighting conditions [22], limited to visible crown surfaces, and subject to degradation in vegetation canopies even with a light breeze.”
Forestry applications of UAVs in Europe: a review (2017, methodology): “This reflected into a basic difference between UAV imagery and photogrammetric aerial imagery, with the first often having large perspective distortion, poor camera geometry, and lack of spectral consistency (Puliti et al. 2015).”
On the Use of Unmanned Aerial Systems for Environmental Monitoring (2018, neutral): “…[109,110]; plant species invasion [111]; restoration ecology [112]; disturbances [113]; phenology [114]; pest infestation in forests [115,116]; and land cover Many studies have focused on the retrieval of vegetation structural information to support forest assessment and management [118,119].”
Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests (2017, methodology): “[17] showed that adding spectral information into a biophysical property estimation using a UAV photogrammetric point cloud resulted in a limited improvement.”
Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure (2015, background): “Personal remote sensing systems have enabled accurate mapping of canopy height and biomass density as well as the discrimination of individual tree structural, spectral, and phenological traits [14,21–23].”