Airborne Laser Scanning for Clarification of the Valuation Indicators of Forest Stands

2020 ◽  
pp. 42-54
Author(s):  
V.F. Kovyazin ◽  
◽  
K.P. Vinogradov ◽  
A.A. Kitcenko ◽  
Е.А. Vasilyeva
Keyword(s):  
Forest Management ◽  
Species Composition ◽  
Laser Scanning ◽  
Average Diameter ◽  
Airborne Laser Scanning ◽  
Forest Monitoring ◽  
Real Surface ◽  
Leningrad Region ◽  
Forest Stands ◽  
Airborne Laser

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.

scholarly journals Large-area inventory of species composition using airborne laser scanning and hyperspectral data

Silva Fennica ◽  
2021 ◽  
Vol 55 (4) ◽  
Author(s):  
Hans Ørka ◽  
Endre Hansen ◽  
Michele Dalponte ◽  
Terje Gobakken ◽  
Erik Næsset
Keyword(s):  
Forest Management ◽  
Species Composition ◽  
Laser Scanning ◽  
Hyperspectral Data ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Small Scale ◽  
Large Area ◽  
Airborne Laser ◽  
Species Specific

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m collected over 350 km of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce ( (L.) Karst.), Scots pine ( L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.22Picea abiesPinus sylvestris

scholarly journals Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Forests ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 158 ◽  
Author(s):  
Darío Domingo ◽  
María Lamelas ◽  
Antonio Montealegre ◽  
Alberto García-Martín ◽  
Juan de la Riva
Keyword(s):  
Laser Scanning ◽  
Nonparametric Methods ◽  
Pine Forest ◽  
Airborne Laser Scanning ◽  
Aleppo Pine ◽  
Total Biomass ◽  
Low Density ◽  
Forest Stands ◽  
Airborne Laser

Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data

2007 ◽  
Vol 110 (4) ◽  
pp. 501-508 ◽  
Author(s):  
J WALLERMAN ◽  
J HOLMGREN
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Forest Stands ◽  
Field Plot ◽  
Airborne Laser

Assessing the utility of airborne laser scanning derived indicators for tropical forest management

2020 ◽  
Vol 82 (4) ◽  
pp. 352-358
Author(s):  
Vitor Antunes Martins da Costa ◽  
Adeliton da Fonseca de Oliveira ◽  
Jhonathan Gomes dos Santos ◽  
Alex Augusto Abreu Bovo ◽  
Danilo Roberti Alves de Almeida ◽  
...  
Keyword(s):  
Forest Management ◽  
Tropical Forest ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Tropical Forest Management

scholarly journals Qualifying the Information Detected from Airborne Laser Scanning to Support Tropical Forest Management Operational Planning

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1724
Author(s):  
Cristiano Rodrigues Reis ◽  
Eric Bastos Gorgens ◽  
Danilo Roberti Alves de Almeida ◽  
Carlos Henrique Souza Celes ◽  
Jacqueline Rosette ◽  
...  
Keyword(s):  
Forest Management ◽  
Tropical Forest ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Point Clouds ◽  
Field Work ◽  
Airborne Laser Scanning ◽  
Operational Planning ◽  
Amazon Forest ◽  
Airborne Laser

(1) Background: Forests throughout the world are managed to fulfil a range of commercial and ecosystem services. The same applies to managed areas of the Amazon forest. We explore a method of sustainable forest management (SFM) which anticipates the result of processes of natural mortality of large, mature trees that could fall and damage their neighbors. Collecting all the information required for planning logging in the Brazilian Amazon is, currently, a hard, time-consuming and expensive task. (2) Methods: This information can be obtained more quickly, accurately and objectively by including airborne laser scanning (ALS) products in the operational plan. We used ALS point clouds to isolate emergent crowns from the canopy height model. Then, we performed field work to validate the existence of these trees, and to understand how many commercial trees (tree diameter ≥ 50 cm) we identified by orienting the trees search through the emergent canopy model. (3) Results: We were able to detect 184 (54.4%) trees from 338 field-recorded individuals in 20 plots (totaling 8 ha). Of the detected trees, 66 individuals were classified as having potential for commerce. Furthermore, 58 individuals presented the best stem quality for logging, which represents more than seven high quality commercial trees per hectare. The logistic regression showed that the effects that positively influence the emergent crown formation are strongly presented in the commercial species. (4) Conclusions: Using airborne laser scanning can improve the SFM planning in a structurally complex, dense and mixed composition tropical forest by reducing field work in the initial stages of management. Therefore, we propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census.

scholarly journals Determining basic forest stand characteristics using airborne laser scanning in mixed forest stands of Central Europe

2018 ◽  
Vol 11 (1) ◽  
pp. 181-188 ◽  
Author(s):  
R Smreček ◽  
Z Michnová ◽  
I Sačkov ◽  
Z Danihelová ◽  
M Levická ◽  
...  
Keyword(s):  
Central Europe ◽  
Laser Scanning ◽  
Mixed Forest ◽  
Forest Stand ◽  
Airborne Laser Scanning ◽  
Forest Stands ◽  
Airborne Laser ◽  
Stand Characteristics

scholarly journals Airborne laser scanning and tree crown fragmentation metrics for the assessment of Phytophthora ramorum infected larch forest stands

2017 ◽  
Vol 404 ◽  
pp. 294-305 ◽  
Author(s):  
Chloe Barnes ◽  
Heiko Balzter ◽  
Kirsten Barrett ◽  
James Eddy ◽  
Sam Milner ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Phytophthora Ramorum ◽  
Airborne Laser Scanning ◽  
Larch Forest ◽  
Forest Stands ◽  
Tree Crown ◽  
Airborne Laser

scholarly journals Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

2017 ◽  
Vol 63 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Maroš Sedliak ◽  
Ivan Sačkov ◽  
Ladislav Kulla
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Object Based Image Analysis ◽  
Airborne Laser ◽  
Object Based ◽  
Individual Trees

AbstractRemote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

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