scholarly journals A Topographic Wetness Index for Forest Road Quality Assessment: An Application in the Lakeland Region of Finland

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1165
Author(s):  
Katalin Waga ◽  
Jukka Malinen ◽  
Timo Tokola
Keyword(s):  
Boreal Forests ◽  
Road Network ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Topographic Wetness Index ◽  
Forest Roads ◽  
Wetness Index ◽  
Forest Road ◽  
Airborne Lidar Data ◽  
Quality Assessments

Research Highlights: A Topographic Wetness Index calculated using LiDAR-derived elevation models can help in identifying unpaved forest roads that need maintenance. Materials and Methods: Low-pulse LiDAR data were used to calculate a Topographic Wetness Index to predict unpaved forest roads’ quality. Results: The results of this analysis and comparison of road-quality features derived from LiDAR data at resolutions of 1, 10 and 25 m for assessing road quality in the boreal forests of Finnish Lakeland show that the wetness index can predict road quality correctly in up to 70% of cases and up to 86% when combined with other auxiliary GIS-based variables. Conclusions: Road-quality assessments, using airborne LiDAR data, can greatly help forest managers to decide which sections of the ageing road network will benefit the most from maintenance, while reducing the need of field visits.

scholarly journals Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data

2017 ◽  
Vol 194 ◽  
pp. 437-446 ◽  
Author(s):  
Rubén Valbuena ◽  
Matti Maltamo ◽  
Lauri Mehtätalo ◽  
Petteri Packalen
Keyword(s):  
Boreal Forests ◽  
Structural Features ◽  
Airborne Lidar ◽  
Lidar Data ◽  
L Moments ◽  
Airborne Lidar Data

scholarly journals ROAD NETWORK MAPPING USING AIRBORNE LiDAR DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 707-711 ◽  
Author(s):  
S. Upadhayay ◽  
M. Yadav ◽  
D. P. Singh
Keyword(s):  
Road Network ◽  
Spatial Databases ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Classification Approach ◽  
Modified Maximum Likelihood ◽  
Road Network Extraction ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> The accurate, detailed and up-to-date road information is highly essential geo-spatial databases for transportation, smart city and other related applications. Thus, the main objective of this research is to develop an efficient algorithm for road network extraction from airborne LiDAR data using supervised classification approach. The proposed algorithm first classifies the input data into the road and non-road features using modified maximum likelihood classification approach. Then Digital Terrain Model (DTM) mask is generated by removing non-ground features from Digital Surface Model using hierarchical morphology and road candidate image if obtained. The parking lots are removed and road network is extracted successfully.</p>

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data
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