Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle

Abstract

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.

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

“…UAVs showed the potential for the inventory of forest stands under regeneration, due to the high accuracy of data and the time saving compared to traditional field techniques. Imangholiloo et al [127] investigated the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions by estimating tree density and height, in young seedling stands in the southern boreal forests of Finland. A CHM using an ALS DTM was created, then, watershed segmentation was used to delineate the tree canopy boundary at individual tree level, obtaining its height and spectral information.…”

“…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.…”

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

“…The evaluation of density and canopy cover of western juniper in a treated (juniper removed) and an untreated watershed and an assesment of the effectiveness of using low altitude UAV-based imagery to measure juniper-sapling population density and canopy cover were also studied [6]. The investigation of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions were researched in [7]. The study of multispectral UAV images that can be used to classify burn severity, including the burned surface class, were considered in [8].…”

“…Various data processing methods were used in this Special Issue. Image analysis workflow, where a three-step, object-based process consisting of image segmentation, automated classification using a classification and regression tree (CART) machine- Seven papers in this special issue reported results from UAV platform using RGB cameras [1][2][3][4][5][6][7], three paper used multispectral cameras [6,8,9], while one paper used a hyperspectral camera [6].…”

Introduction to geoinformation science

“…Airborne laser scanning (ALS) started the three dimensional revolution in remote sensing in environmental viewpoint, followed by re-invention of the image-matching derived photogrammetric point clouds that can both be used to characterize terrain, vegetation but also urban areas with unprecedented level of detail [9,10] . Miniaturization of sensors and GNSS technology has enabled use of drones for small-scale environmental mapping and monitoring [11][12][13] . Geospatial data collected by these varying sensor technologies easily become geospatial big data and one of the current challenges is how to efficiently analyse geospatial big data and how to handle issues related to data quality when data from different sources are fused in analyses.…”