scholarly journals Airborne Laser Scanning Cartography of On-Site Carbon Stocks as a Basis for the Silviculture of Pinus Halepensis Plantations

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.

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

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.


2019 ◽  
Vol 56 (8) ◽  
pp. 1210-1232 ◽  
Author(s):  
Darío Domingo ◽  
Antonio Luis Montealegre ◽  
María Teresa Lamelas ◽  
Alberto García-Martín ◽  
Juan de la Riva ◽  
...  

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.


Author(s):  
Johannes Breidenbach ◽  
Janis Ivanovs ◽  
Annika Kangas ◽  
Thomas Nord-Larsen ◽  
Mats Nilsson ◽  
...  

Policy measures and management decisions aiming at enhancing the role of forests in mitigating climate-change require reliable estimates of C-stock dynamics in greenhouse gas inventories (GHGIs). The aim of this study was to assemble design-based estimators to provide estimates relevant for GHGIs using national forest inventory (NFI) data. We improve basic expansion (BE) estimates of living-biomass C-stock loss using field-data only, by leveraging with remotely-sensed auxiliary data in model-assisted (MA) estimates. Our case studies from Norway, Sweden, Denmark, and Latvia covered an area of >70 Mha. Landsat-based Forest Cover Loss (FCL) and one-time wall-to-wall airborne laser scanning (ALS) data served as auxiliary data. ALS provided information on the C-stock before a potential disturbance indicated by FCL. The use of FCL in MA estimators resulted in considerable efficiency gains which in most cases were further increased by using ALS in addition. A doubling of efficiency was possible for national estimates and even larger efficiencies were observed at the sub-national level. Average annual estimates were considerably more precise than pooled estimates using NFI data from all years at once. The combination of remotely-sensed with NFI field data yields reliable estimates which is not necessarily the case when using remotely-sensed data without reference observations.


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.


Author(s):  
V.F. Kovyazin ◽  
◽  
K.P. Vinogradov ◽  
A.A. Kitcenko ◽  
Е.А. Vasilyeva

Nowadays the latest non-contact methods and technologies for studying the forest fund are being developed for forest monitoring improvement, forest lands assessment and their cadastral registration. It is the use of airborne laser scanning (ALS) in forest inventory that is designed to solve the challenges forest management facing. Laser scanning is the only method of collecting data on the real surface covered with forest vegetation, which allows to obtain data on the shape, location and reflectivity of the studied forest objects. The result of ALS is a 3D array of laser reflections with a density of up to several dozens of points per 1 m2 and accuracy of determining their coordinates of less than 10 cm in plan and height. Various imported scanning systems are used for surveying. The ALS of the Earth’s vegetation cover is superior to all existing technologies for assessing the quantitative and qualitative parameters of forest stands in a set of characteristics. This method of assessment and inventory of forests has no competitors in the field of monitoring and valuation of forest stands. It also has sufficient accuracy in mapping woody vegetation, up to the tree survey of forested lands. The article proposes a method for determining valuation indicators: species composition, density, stock, height and diameter of forest stands according to the results of ALS in the forest area of the Vsevolozhsk district (Leningrad region). The species composition and density were determined by horizontal projections of tree crowns. The heights of the trees were determined using the Global Mapper software, and their average diameter was found using the diameter and height relationship equations known in forest valuation. The planting stock was calculated using the equations of Dementiev, Dentsin and G. Cuvier. It was found that the results of determining the valuation indicators by means of ALS can be used in forest monitoring along with the data of visual valuation, since the obtained information on the forest stand stays within the limits of permissible errors specified in the forest management instruction.


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