scholarly journals Incorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning

Silva Fennica ◽  
2018 ◽  
Vol 52 (3) ◽  
Author(s):  
Matti Maltamo ◽  
Tomi Karjalainen ◽  
Jaakko Repola ◽  
Jari Vauhkonen
Keyword(s):  
Forest Management ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Level Information ◽  
Crown Base Height

scholarly journals The transferability of airborne laser scanning based tree-level models between different inventory areas

2019 ◽  
Vol 49 (3) ◽  
pp. 228-236 ◽  
Author(s):  
Tomi Karjalainen ◽  
Lauri Korhonen ◽  
Petteri Packalen ◽  
Matti Maltamo
Keyword(s):  
Laser Scanning ◽  
Nearest Neighbor ◽  
Airborne Laser Scanning ◽  
Tree Level ◽  
Stem Volume ◽  
K Nearest Neighbor ◽  
Individual Tree ◽  
Tree Detection ◽  
Airborne Laser ◽  
Crown Base Height

In this paper, we examine the transferability of airborne laser scanning (ALS) based models for individual-tree detection (ITD) from one ALS inventory area (A1) to two other areas (A2 and A3). All areas were located in eastern Finland less than 100 km from each other and were scanned using different ALS devices and parameters. The tree attributes of interest were diameter at breast height (Dbh), height (H), crown base height (Cbh), stem volume (V), and theoretical sawlog volume (Vlog) of Scots pine (Pinus sylvestris L.) with Dbh ≥ 16 cm. All trees were first segmented from the canopy height models, and various ALS metrics were derived for each segment. Then only the segments covering correctly detected pines were chosen for further inspection. The tree attributes were predicted using the k-nearest neighbor (k-NN) imputation. The results showed that the relative root mean square error (RMSE%) values increased for each attribute after the transfers. The RMSE% values were, for A1, A2, and A3, respectively: Dbh, 13.5%, 14.8%, and 18.1%; H, 3.2%, 5.9%, and 6.2%; Cbh, 13.3%, 15.3%, and 18.3%; V, 29.3%, 35.4%, and 39.1%; and Vlog, 38.2%, 54.4% and 51.8%. The observed values indicate that it may be possible to employ ALS-based tree-level k-NN models over different inventory areas without excessive reduction in accuracy, assuming that the tree species are known to be similar.

scholarly journals ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 629-632 ◽  
Author(s):  
E. Hadaś ◽  
A. Borkowski ◽  
J. Estornell
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Field Measurements ◽  
Point Clouds ◽  
Average Density ◽  
Geometric Parameters ◽  
Airborne Laser Scanning ◽  
Maximum Height ◽  
Airborne Laser ◽  
Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter <i>R</i>. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points&thinsp;m<sip>&minus;2</sup>. We noticed, that there was a narrow range of the <i>R</i> parameter, from 0.48&thinsp;m to 0.80&thinsp;m, for which all trees were detected and for which we obtained a high correlation coefficient (>&thinsp;0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8&thinsp;m for the tree height, 0.5&thinsp;m for the crown base height, 0.6&thinsp;m and 0.4&thinsp;m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

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

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 Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines

Silva Fennica ◽  
2019 ◽  
Vol 53 (3) ◽  
Author(s):  
Lauri Korhonen ◽  
Jaakko Repola ◽  
Tomi Karjalainen ◽  
Petteri Packalen ◽  
Matti Maltamo
Keyword(s):  
Mixed Models ◽  
Laser Scanning ◽  
Systematic Errors ◽  
Mixed Effects ◽  
Airborne Laser Scanning ◽  
Mixed Effects Models ◽  
Individual Tree ◽  
Practical Applications ◽  
Airborne Laser ◽  
Crown Base Height

Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines ( L.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications.Pinus sylvestris

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.

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