Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach

Abstract

This study is concerned with the assessment of application possibilities for remote sensing data within a forest inventory in close-to-nature forests. A combination of discrete airborne laser scanning data and multispectral aerial images separately evaluated main tree and forest stand characteristics (i.e., the number of trees, mean height and diameter, tree species, tree height, tree diameter, and tree volume). We used eCognition software (Trimble GeoSpatial, Munich, Germany) for tree species classification and reFLex software (National Forest Centre, Zvolen, Slovakia) for individual tree detection as well as for forest inventory attribute estimations. The accuracy assessment was conducted at the ProSilva demo site Smolnícka Osada (Eastern Slovakia, Central Europe), which has been under selective management for more than 60 years. The remote sensing data were taken using a scanner (Leica ALS70-CM) and camera (Leica RCD30) from an average height of 1034 m, and the ground reference data contained the measured positions and dimensions of 1151 trees in 45 plots distributed across the region. This approach identified 73% of overstory and 28% of understory trees. Tree species classification within overstory trees resulted in an overall accuracy slightly greater than 65%. We also found that the mean difference between the remote-based results and ground data was −0.3% for tree height, 1.1% for tree diameter, and 1.9% for stem volume. At the stand level, the mean difference reached values of 0.4%, 17.9%, and −21.4% for mean height, mean diameter, and growing stock, respectively.

Analysing the potential of UAV point cloud as input in quantitative structure modelling for assessment of woody biomass of single trees

“…Thus, the applications of ALS in forest studies usually focus on the z dimension -the tree height. Sačkov et al (2017) detected and corrected tree heights using ALS data, and the result showed there was no statistically significant difference compared with the field measured data. Furthermore, in the study of Bouvier et al (2015), it was proved that the heterogeneous canopy structure could be extracted successfully from the ALS data.…”

An integrated framework for Web-based visualisation of forest resources estimated from remote sensing data

“…This technology concept has previously been verified in different types of central European forests (e.g. Sačkov et al 2016Sačkov et al , 2017aSačkov et al , 2019. The web-map application was developed as a single page application primarily using Arcgis API libraries for Javascript.…”

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

“…Hartling et al [31] demonstrated that deep learning techniques could improve broadleaf species classification by at least 30% compared to RF and SVM. Adding other variables such as LiDAR metrics or topological measures could also improve the classification [8,14,16,22,39,131]. Finally, an expert procedure could be implemented to select a maximum number of each categorical variable to limit over-representation [136].…”

“…The first technique generally gives good results, but it is time-consuming, complex and requires advanced LiDAR sensors [17]. The second technique has been studied much more, both at the stand level [18,19] and at the tree level [20][21][22], as there are a variety of algorithms that provide rapid ITC segmentation, which gives satisfactory results [14,16,23].…”