scholarly journals AUTOMATIC DETECTION OF FOREST-ROAD DISTANCES TO IMPROVE CLEARING OPERATIONS IN ROAD MANAGEMENT

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1083-1088 ◽  
Author(s):  
A. Novo ◽  
H. González-Jorge ◽  
J. Martínez-Sánchez ◽  
L. M. González-de Santos ◽  
H. Lorenzo
Keyword(s):  
Forest Management ◽  
Forest Fires ◽  
Sustainable Forest Management ◽  
Lidar Data ◽  
Escape Route ◽  
Forest Road ◽  
The Road ◽  
Complex Relation ◽  
Road Management ◽  
Management Optimization

<p><strong>Abstract.</strong> There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation.</p><p>The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30&amp;thinsp;m. The minimum distance calculation is 2&amp;thinsp;m, for the vegetation of 5&amp;thinsp;m height and a maximum of 60&amp;thinsp;m for vegetation 30&amp;thinsp;m height. The height of vegetation has a directly relation with the distance separation with the road.</p>

scholarly journals Forest Road Detection Using LiDAR Data and Hybrid Classification

2021 ◽  
Vol 13 (3) ◽  
pp. 393
Author(s):  
Sandra Buján ◽  
Juan Guerra-Hernández ◽  
Eduardo González-Ferreiro ◽  
David Miranda
Keyword(s):  
Forest Management ◽  
Sustainable Forest Management ◽  
Decision Rules ◽  
Ground Truth ◽  
Rural Landscape ◽  
Lidar Data ◽  
Ground Truth Data ◽  
Forest Road ◽  
Point Density ◽  
Hybrid Classification

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.

Optimizing forest road planning in a sustainable forest management area in the Brazilian Amazon

2021 ◽  
Vol 288 ◽  
pp. 112332
Author(s):  
Marcelo Otone Aguiar ◽  
Gilson Fernandes da Silva ◽  
Geraldo Regis Mauri ◽  
Adriano Ribeiro de Mendonça ◽  
Cesar Junio de Oliveira Santana ◽  
...  
Keyword(s):  
Forest Management ◽  
Sustainable Forest Management ◽  
Brazilian Amazon ◽  
Forest Road ◽  
Management Area ◽  
Road Planning

Global review on forest road optimization planning: Support for sustainable forest management in Amazonia

2021 ◽  
Vol 492 ◽  
pp. 119159
Author(s):  
Verônica Satomi Kazama ◽  
Ana Paula Dalla Corte ◽  
Renato Cesar Gonçalves Robert ◽  
Carlos Roberto Sanquetta ◽  
Julio Eduardo Arce ◽  
...  
Keyword(s):  
Forest Management ◽  
Sustainable Forest Management ◽  
Forest Road ◽  
Planning Support

scholarly journals A COMPARISON OF RANDOM FOREST AND LIGHT GRADIENT BOOSTING MACHINE FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF LANDSAT, ALOS PALSAR, AND AIRBORNE LIDAR DATA

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 163-168
Author(s):  
H. Tamiminia ◽  
B. Salehi ◽  
M. Mahdianpari ◽  
C. M. Beier ◽  
L. Johnson ◽  
...  
Keyword(s):  
Forest Management ◽  
Random Forest ◽  
Sustainable Forest Management ◽  
Airborne Lidar ◽  
Synthetic Aperture ◽  
Gradient Boosting ◽  
Lidar Data ◽  
Alos Palsar ◽  
Light Gradient ◽  
Gradient Boosting Machine

Abstract. Sustainable forest management is a critical topic which contributes to ecological, economical, and socio-cultural aspect of the environment. Providing accurate AGB maps is of paramount importance for sustainable forest management, carbon accounting, and climate change monitoring. The main goal of this study was to leverage the potential of two machine learning algorithms for predicting AGB using optical and synthetic aperture radar (SAR) datasets. To achieve this goal random forest (RF) and light gradient boosting machine (LightGBM) models were deployed to predict AGB values in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. Both models were trained and evaluated based on airborne light detection and ranging (LiDAR) data, Landsat imagery, advanced land observing satellite (ALOS) phased array type L-band Synthetic Aperture Radar (PALSAR), and their combination. The integration of airborne LiDAR, optic, and SAR datasets provided the best results in terms of root mean square error (RMSE) and mean bias error (MBE). The RF model outperformed the LightGBM in all scenarios (LiDAR, Landsat 5, ALOS PALSAR, and their combination). The RF model was able to predict AGB values with the RMSE of 51.90 Mg/ha and MBE of −0.189 Mg/ha for the combination of LiDAR, optic, and SAR data, while LightGBM estimated the AGB values with the RMSE of 52.78 Mg/ha and MBE of −0.253 Mg/ha. LightGBM is more sensitive to noise and there are lots of hyperparameters that need to be tuned which highly affect its performance.

scholarly journals Solution of the Forest Road Network Optimization in the Transformation to a Selective Silviculture Method

2019 ◽  
Vol 22 (2) ◽  
pp. 88-92
Author(s):  
Jaromír Skoupil ◽  
Petr Pelikán ◽  
Jiří Kadlec
Keyword(s):  
Forest Management ◽  
Network Optimization ◽  
Road Network ◽  
Specific Method ◽  
Network Efficiency ◽  
Forest Road ◽  
The Road ◽  
Surrounding Environment ◽  
Dense Forest ◽  
Negative Impacts

Abstract The paper is focused on the forest road access in the area of supposed specific method of forest management. The studied forest area of 81 hectares (ha) is intended for transformation by selective silviculture method demanding dense forest road network. The parameters of the current road network were analysed by Beneš method based on quantifying the general geometric and configuration criteria of the road network. The new road distribution was designed with respect to the results of the terrain slope and runoff concentration analyses to reduce the negative impacts of the roads on the surrounding environment. The new road layout resulted to the decrement of all types of skidding distances. The real skidding distance Ds decreased by 51% to the value of 72 m. In addition, the road network efficiency was increased by 14%.

scholarly journals MAPPING THE RISK OF FOREST WIND DAMAGE USING AIRBORNE SCANNING LiDAR

2015 ◽  
Vol XL-3/W2 ◽  
pp. 189-196 ◽  
Author(s):  
N. Saarinen ◽  
M. Vastaranta ◽  
E. Honkavaara ◽  
M. A. Wulder ◽  
J. C. White ◽  
...  
Keyword(s):  
Forest Management ◽  
Open Access ◽  
Boreal Forests ◽  
Sustainable Forest Management ◽  
Risk Model ◽  
Wind Damage ◽  
Forest Damage ◽  
Multivariate Logistic Regression Model ◽  
Lidar Data ◽  
Damage Risk

Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts. The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage. To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. The probability of wind-induced forest damage (PDAM) in southern Finland (61°N, 23°E) was modelled for a 173 km<sup>2</sup> study area of mainly managed boreal forests (dominated by Norway spruce and Scots pine) and agricultural fields. Wind damage occurred in the study area in December 2011. LiDAR data were acquired prior to the damage in 2008. High spatial resolution aerial imagery, acquired after the damage event (January, 2012) provided a source of model calibration via expert interpretation. A systematic grid (16 m x 16 m) was established and 430 sample grid cells were identified systematically and classified as damaged or undamaged based on visual interpretation using the aerial images. Potential drivers associated with PDAM were examined using a multivariate logistic regression model. Risk model predictors were extracted from the LiDAR-derived surface models. Geographic information systems (GIS) supported spatial mapping and identification of areas of high PDAM across the study area. The risk model based on LiDAR data provided good agreement with detected risk areas (73 % with kappa-value 0,47). The strongest predictors in the risk model were mean canopy height and mean elevation. Our results indicate that open-access LiDAR data sets can be used to map the probability of wind damage risk without field data, providing valuable information for forest management planning.

scholarly journals FOREST ROADIDENTIFICATION AND EXTRACTIONOF THROUGH ADVANCED LOG MATCHING TECHNIQUES

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 201-207
Author(s):  
W. Zhang ◽  
B. Hu ◽  
L. Quist
Keyword(s):  
Road Network ◽  
Test Area ◽  
Lidar Data ◽  
Tensor Voting ◽  
Multispectral Imagery ◽  
Visual Examination ◽  
Forest Road ◽  
The Road ◽  
Matching Techniques ◽  
Road Segments

A novel algorithm for forest road identification and extraction was developed. The algorithm utilized Laplacian of Gaussian (LoG) filter and slope calculation on high resolution multispectral imagery and LiDAR data respectively to extract both primary road and secondary road segments in the forest area. The proposed method used road shape feature to extract the road segments, which have been further processed as objects with orientation preserved. The road network was generated after post processing with tensor voting. The proposed method was tested on Hearst forest, located in central Ontario, Canada. Based on visual examination against manually digitized roads, the majority of roads from the test area have been identified and extracted from the process.

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