Forest Road Detection Using LiDAR Data and Hybrid Classification

Abstract

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2.

Detection and Modeling of Unstructured Roads in Forest Areas Based on Visual-2D Lidar Data Fusion

“…For the UGVs to navigate in a forest environment, they need the ability to perform real-time road detection and direction estimation simultaneously [33,34]. Therefore, the UGV systems need to have the ability to comprehensively process the lidar point cloud data and visual images.…”

3D Point Clouds in Forest Remote Sensing

“…Among these, only one paper exclusively uses passive RS data [21], while 29 papers use at least one LiDAR dataset in the analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][22][23][24][25][26][27][28][29][30]. Ten papers exclusively use airborne laser scanning (ALS) data [4,6,7,10,11,13,18,23,26,27], nine papers exclusively use terrestrial laser scanning (TLS) data in the analysis [3,9,15,16,20,22,24,25,30], two papers exclusively use mobile laser scanning (MLS) data …”

“…Finally, five papers use combined active and passive remote sensing data sets [2,14,17,19,28]. Regarding the scale of the analysis, 18 of the studies perform individual tree level (ITL) analysis [1][2][3][8][9][10][11][12][14][15][16]19,20,[23][24][25][26]30], eight papers report stand level (SL) analysis [6,7,17,18,21,22,27,29] and four report a combination of ITL and SL [4,5,13,28]. Tree position, diameter at breast height (DBH) and individual tree height (h) are the most common variables of interest, analyzed in nine, six and six papers, respectively, while the most commonly used methods are 3D reconstruction, point filtering and statistical modelling, which are used in eight, five and five papers, respectively (see Table 1).…”

“…The SM include 30 published manuscripts (see Table 1): 28 research papers [1,2,[4][5][6][7][8][9][10][11][12][13][14][15][17][18][19][20][21][22][23][24][25][26][27][28][29][30], one letter [16] and one technical note [3]. Among these, only one paper exclusively uses passive RS data [21], while 29 papers use at least one LiDAR dataset in the analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]…”

“…The SM were edited by two Guest Editors (GEs): Dr. Ramón Alberto Díaz Varela (University of Santiago de Compostela, Spain) and Dr. Eduardo Manuel González Ferreiro (University of León, Spain). Both GEs contributed by co-authoring three scientific papers in the SM [6,22,27] and handling a total of 44 manuscripts over 20 months, between May 2019 (when the call for papers was opened and disseminated) and the end of December 2020 (the deadline for submissions). In addition to the GEs, five Associate Editors intervened in the SM, handling a total of five manuscripts [6,19,22,27,30] to prevent conflicts of interest in the evaluation of the manuscripts submitted by the GEs and colleagues.…”