scholarly journals Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest

2012 ◽  
Vol 4 (4) ◽  
pp. 1004-1023 ◽  
Author(s):  
Eva Lindberg ◽  
Markus Hollaus
Keyword(s):  
Boreal Forest ◽  
Laser Scanning ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Comparison Of Methods ◽  
Stem Number ◽  
Airborne Laser

scholarly journals WIDE-AREA MAPPING OF FOREST WITH NATIONAL AIRBORNE LASER SCANNING AND FIELD INVENTORY DATASETS

2016 ◽  
Vol XLI-B8 ◽  
pp. 727-731 ◽  
Author(s):  
J.-M. Monnet ◽  
C. Ginzler ◽  
J.-C. Clivaz
Keyword(s):  
Laser Scanning ◽  
Prediction Models ◽  
Basal Area ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Stem Density ◽  
Maximum Height ◽  
Airborne Laser

Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R<sup>2</sup> = 0.61 with a root mean square error of 47 %) and basal area (respectively 0.51 and 45 %) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.

scholarly journals WIDE-AREA MAPPING OF FOREST WITH NATIONAL AIRBORNE LASER SCANNING AND FIELD INVENTORY DATASETS

2016 ◽  
Vol XLI-B8 ◽  
pp. 727-731
Author(s):  
J.-M. Monnet ◽  
C. Ginzler ◽  
J.-C. Clivaz
Keyword(s):  
Laser Scanning ◽  
Prediction Models ◽  
Basal Area ◽  
National Forest Inventory ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Stem Density ◽  
Maximum Height ◽  
Airborne Laser

Airborne laser scanning (ALS) remote sensing data are now available for entire countries such as Switzerland. Methods for the estimation of forest parameters from ALS have been intensively investigated in the past years. However, the implementation of a forest mapping workflow based on available data at a regional level still remains challenging. A case study was implemented in the Canton of Valais (Switzerland). The national ALS dataset and field data of the Swiss National Forest Inventory were used to calibrate estimation models for mean and maximum height, basal area, stem density, mean diameter and stem volume. When stratification was performed based on ALS acquisition settings and geographical criteria, satisfactory prediction models were obtained for volume (R<sup>2</sup>&thinsp;=&thinsp;0.61 with a root mean square error of 47&thinsp;%) and basal area (respectively 0.51 and 45&thinsp;%) while height variables had an error lower than 19%. This case study shows that the use of nationwide ALS and field datasets for forest resources mapping is cost efficient, but additional investigations are required to handle the limitations of the input data and optimize the accuracy.

Modelling bird richness and bird species presence in a boreal forest reserve using airborne laser-scanning and aerial images

Bird Study ◽  
2014 ◽  
Vol 61 (2) ◽  
pp. 204-219 ◽  
Author(s):  
Katrine Eldegard ◽  
John Wirkola Dirksen ◽  
Hans Ole Ørka ◽  
Rune Halvorsen ◽  
Erik Næsset ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Laser Scanning ◽  
Bird Species ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Forest Reserve ◽  
Airborne Laser ◽  
Bird Richness

Inoptimality losses in forest management decisions caused by errors in an inventory based on airborne laser scanning and aerial photographs

2010 ◽  
Vol 40 (12) ◽  
pp. 2427-2438 ◽  
Author(s):  
Md. Nurul Islam ◽  
Mikko Kurttila ◽  
Lauri Mehtätalo ◽  
Timo Pukkala
Keyword(s):  
Input Data ◽  
Laser Scanning ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Aerial Photographs ◽  
Stand Age ◽  
Inventory Data ◽  
Airborne Laser ◽  
Mean Diameter ◽  
The Mean

Errors in inventory data may lead to inoptimal decisions that ultimately result in financial losses for forest owners. We estimated the expected monetary losses resulting from data errors that are similar to errors in laser-based forest inventory. The mean loss was estimated for 67 stands by simulating 100 realizations of inventory data for each stand with errors that mimic those in airborne laser scanning (ALS) based inventory. These realizations were used as input data in stand management optimization, which maximized the present value of all future net incomes (NPV). The inoptimality loss was calculated as the difference between the NPV of the optimal solution and the true NPV of the solution obtained with erroneous input data. The results showed that the mean loss exceeded €300·ha–1 (US$425·ha–1) in 84% of the stands. On average, the losses increased with decreasing stand age and mean diameter. Furthermore, increasing errors in the basal area weighted mean diameter and basal area of spruce were found to significantly increase the loss. It has been discussed that improvements in the accuracy of ALS-based inventory could be financially justified.

A comparison of UAV laser scanning, photogrammetry and airborne laser scanning for precision inventory of small-forest properties

2019 ◽  
Vol 93 (1) ◽  
pp. 150-162 ◽  
Author(s):  
Stefano Puliti ◽  
Jonathan P Dash ◽  
Michael S Watt ◽  
Johannes Breidenbach ◽  
Grant D Pearse
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Stem Volume ◽  
Stem Density ◽  
Multiple Sources ◽  
Remotely Sensed Data ◽  
Biophysical Parameters ◽  
Auxiliary Data ◽  
Airborne Laser ◽  
Small Forest

Abstract This study addresses the use of multiple sources of auxiliary data from unmanned aerial vehicles (UAVs) and airborne laser scanning (ALS) data for inference on key biophysical parameters in small forest properties (5–300 ha). We compared the precision of the estimates using plot data alone under a design-based inference with model-based estimates that include plot data and the following four types of auxiliary data: (1) terrain-independent variables from UAV photogrammetric data (UAV-SfM); (2) variables obtained from UAV photogrammetric data normalized using external terrain data (UAV-SfMDTM); (3) UAV-LS and (4) ALS data. The inclusion of remotely sensed data increased the precision of DB estimates by factors of 1.5–2.2. The optimal data sources for top height, stem density, basal area and total stem volume were: UAV-LS, UAV-SfM, UAV-SfMDTM and UAV-SfMDTM. We conclude that the use of UAV data can increase the precision of stand-level estimates even under intensive field sampling conditions.

scholarly journals Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?

2020 ◽  
Vol 12 (11) ◽  
pp. 1854
Author(s):  
Dominik Seidel ◽  
Peter Annighöfer ◽  
Martin Ehbrecht ◽  
Paul Magdon ◽  
Stephan Wöllauer ◽  
...  
Keyword(s):  
Forest Structure ◽  
Laser Scanning ◽  
Structural Complexity ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Stand Age ◽  
Box Dimension ◽  
Forest Types ◽  
3D Point Clouds ◽  
Airborne Laser

The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from fractal analysis, a measure of structural complexity, can be obtained from airborne laser scanning data. Based on 66 plots across different forest types in Germany, each 1 ha in size, we tested the performance of the Db by evaluating it against conventional ground-based measures of forest structure and commonly used stand characteristics. We found that the Db was related (0.34 < R < 0.51) to stand age, management intensity, microclimatic stability, and several measures characterizing the overall stand structural complexity. For the basal area, we could not find a significant relationship, indicating that structural complexity is not tied to the basal area of a forest. We also showed that Db derived from airborne data holds the potential to distinguish forest types, management types, and the developmental phases of forests. We conclude that the box-dimension is a promising measure to describe the structural complexity of forests in an ecologically meaningful way.

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 Potential of Modern Photogrammetry Versus Airborne Laser Scanning for Estimating Forest Variables in a Mountain Environment

2019 ◽  
Vol 11 (6) ◽  
pp. 661 ◽  
Author(s):  
Sami Ullah ◽  
Matthias Dees ◽  
Pawan Datta ◽  
Petra Adler ◽  
Mathias Schardt ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Basal Area ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Photographs ◽  
Data Types ◽  
Viable Option ◽  
Airborne Laser ◽  
Mountain Environment

Digital stereo aerial photographs are periodically updated in many countries and offer a viable option for the regular update of information on forest variables. We compared the potential of image-based point clouds derived from three different sets of aerial photographs with airborne laser scanning (ALS) to assess plot-level forest attributes in a mountain environment. The three data types used were (A) high overlapping pan-sharpened (80/60%); (B) high overlapping panchromatic band (80/60%); and (C) standard overlapping pan-sharpened stereo aerial photographs (60/30%). We used height and density metrics at the plot level derived from image-based and ALS point clouds as the explanatory variables and Lorey’s mean height, timber volume, and mean basal area as the response variables. We obtained a RMSE = 8.83%, 29.24% and 35.12% for Lorey’s mean height, volume, and basal area using ALS data, respectively. Similarly, we obtained a RMSE = 9.96%, 31.13%, and 35.99% and RMSE = 11.28%, 31.01%, and 35.66% for Lorey’s mean height, volume and basal area using image-based point clouds derived from pan-sharpened stereo aerial photographs with 80/60% and 60/30% overlapping, respectively. For image-based point clouds derived from a panchromatic band of stereo aerial photographs (80%/60%), we obtained an RMSE = 10.04%, 31.19% and 35.86% for Lorey’s mean height, volume, and basal area, respectively. The overall findings indicated that the performance of image-based point clouds in all cases were as good as ALS. This highlights that in the presence of a highly accurate digital terrain model (DTM) from ALS, image-based point clouds offer a viable option for operational forest management in all countries where stereo aerial photographs are updated on a routine basis.

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