Retrieving Suppressed Trees from Model-Based Height Distribution by Combining High- and Low-Density Airborne Laser Scanning Data

2014 ◽  
Vol 40 (3) ◽  
pp. 233-242 ◽  
Author(s):  
Qing Xu ◽  
Zhengyang Hou ◽  
Matti Maltamo ◽  
Timo Tokola
2019 ◽  
Vol 66 (4) ◽  
pp. 501-508 ◽  
Author(s):  
Katalin Waga ◽  
Piotr Tompalski ◽  
Nicholas C Coops ◽  
Joanne C White ◽  
Michael A Wulder ◽  
...  

Abstract Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.


Forests ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 158 ◽  
Author(s):  
Darío Domingo ◽  
María Lamelas ◽  
Antonio Montealegre ◽  
Alberto García-Martín ◽  
Juan de la Riva

2011 ◽  
Vol 41 (1) ◽  
pp. 96-107 ◽  
Author(s):  
Göran Ståhl ◽  
Sören Holm ◽  
Timothy G. Gregoire ◽  
Terje Gobakken ◽  
Erik Næsset ◽  
...  

In forest inventories, regression models are often applied to predict quantities such as biomass at the level of sampling units. In this paper, we propose a model-based inference framework for combining sampling and model errors in the variance estimation. It was applied to airborne laser (LiDAR) data sets from Hedmark County, Norway, where the model error proportion of the total variance was found to be large for both scanning (airborne laser scanning) and profiling LiDAR when biomass was estimated. With profiling LiDAR, the model error variance component for the entire county was as large as 71% whereas for airborne laser scanning, it was 43% of the total variance. Partly, this reflects the better accuracy of the pixel-based regression models estimated from scanner data as compared with the models estimated from profiler data. The framework proposed in our study can be applied in all types of sample surveys where model-based predictions are made at the level of individual sampling units. Especially, it should be useful in cases where model-assisted inference cannot be applied due to the lack of a probability sample from the target population or due to problems of correctly matching observations of auxiliary and target variables.


2017 ◽  
Vol 54 (5) ◽  
pp. 721-740 ◽  
Author(s):  
Antonio Luis Montealegre-Gracia ◽  
María Teresa Lamelas-Gracia ◽  
Alberto García-Martín ◽  
Juan de la Riva-Fernández ◽  
Francisco Escribano-Bernal

2019 ◽  
Vol 11 (3) ◽  
pp. 261 ◽  
Author(s):  
Darío Domingo ◽  
Rafael Alonso ◽  
María Teresa Lamelas ◽  
Antonio Luis Montealegre ◽  
Francisco Rodríguez ◽  
...  

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.


Author(s):  
K. Kiss ◽  
J. Malinen ◽  
T. Tokola

Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.


2020 ◽  
Vol 12 (9) ◽  
pp. 1446 ◽  
Author(s):  
Krystian Kozioł ◽  
Kamil Maciuk

The idea to verify the height of the highest peaks (summits) in the Crown of Polish Mountains arose after analyzing sources regarding the date and method of measuring the height of these mountain peaks. Our investigations revealed that this type of material is not usually available, and the first mention of height values is most often noted in the inter-war period, and occasionally before WWI (when Poland did not exist as an independent state); most of these values are still in use to this day. The problem of accurate measurement of the height of mountain peaks concerns not only the peaks analyzed by the authors, but also almost all mountain peaks worldwide. Therefore, as part of this work, several trips were organized to the highest peaks of several dozen mountain ranges in the territory of Poland. Measurement was made using a precise geodetic GNSS receiver an accuracy of within 10 cm and a DTM model based on ALS (airborne laser scanning). The results showed that commonly published heights can differ by up to several meters from the actual ones. The most important element of this work consists of the establishment of new measurements of the heights of the highest peaks of all mountain ranges in Poland, which may result in an alteration of the officially recorded heights based on this article. Apart from verification of these heights, this work also aimed to address the issue of the heights of all characteristic objects whose heights must be verified by using modern satellite techniques.


2018 ◽  
Vol 10 (10) ◽  
pp. 1660 ◽  
Author(s):  
Rafael Navarro-Cerrillo ◽  
Joaquín Duque-Lazo ◽  
Carlos Rodríguez-Vallejo ◽  
Mª Varo-Martínez ◽  
Guillermo Palacios-Rodríguez

Forest managers are interested in forest-monitoring strategies using low density Airborne Laser Scanning (ALS). However, little research has used ALS to estimate soil organic carbon (SOC) as a criterion for operational thinning. Our objective was to compare three different thinning intensities in terms of the on-site C stock after 13 years (2004–2017) and to develop models of biomass (Wt, Mg ha−1) and SOC (Mg ha−1) in Pinus halepensis forest, based on low density ALS in southern Spain. ALS was performed for the area and stand metrics were measured within 83 plots. Non-parametric kNN models were developed to estimate Wt and SOC. The overall C stock was significantly higher in plots subjected to heavy or moderate thinning (101.17 Mg ha−1 and 100.94 Mg ha−1, respectively) than in the control plots (91.83 Mg ha−1). The best Wt and SOC models provided R2 values of 0.82 (Wt, MSNPP) and 0.82 (SOC-S10, RAW). The study area will be able to stock 134,850 Mg of C under a non-intervention scenario and 157,958 Mg of C under the heavy thinning scenario. High-resolution cartography of the predicted C stock is useful for silvicultural planning and may be used for proper management to increase C sequestration in dry P. halepensis forests.


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