Next Article in Journal
SAR Target Recognition via Joint Sparse and Dense Representation of Monogenic Signal
Next Article in Special Issue
Mobile Laser Scanning for Estimating Tree Stem Diameter Using Segmentation and Tree Spine Calibration
Previous Article in Journal
Robust, Model-Based External Calibration of Multi-Channel Airborne SAR Sensors Using Range Compressed Raw Data
Previous Article in Special Issue
Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR
Open AccessArticle

Using Tree Detection Based on Airborne Laser Scanning to Improve Forest Inventory Considering Edge Effects and the Co-Registration Factor

1
Forest Research Center, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
2
School of Forest Sciences, University of Eastern Finland, P.O. Box 111, 80101 Joensuu, Finland
Remote Sens. 2019, 11(22), 2675; https://doi.org/10.3390/rs11222675
Received: 2 October 2019 / Revised: 7 November 2019 / Accepted: 13 November 2019 / Published: 15 November 2019
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
The estimation of forest biophysical attributes improves when airborne laser scanning (ALS) is integrated. Individual tree detection methods (ITD) and traditional area-based approaches (ABA) are the two main alternatives in ALS-based forest inventory. This study evaluated the performance of the enhanced area-based approach (EABA), an edge-correction method based on ALS data that combines ITD and ABA, at improving the estimation of forest biophysical attributes, while testing its efficiency when considering co-registration errors that bias remotely sensed predictor variables. The study was developed based on a stone pine forest (Pinus pinea L.) in Central Spain, in which tree spacing and scanning conditions were optimal for the ITD approach. Regression modeling was used to select the optimal predictor variables to estimate forest biophysical attributes. The accuracy of the models improved when using EABA, despite the low-density of the ALS data. The relative mean improvement of EABA in terms of root mean squared error was 15.2%, 17.3%, and 7.2% for growing stock volume, stand basal area, and dominant height, respectively. The impact of co-registration errors in the models was clear in the ABA, while the effect was minor and mitigated under EABA. The implementation of EABA can highly contribute to improve modern forest inventory applications. View Full-Text
Keywords: remote sensing; forest modeling; positioning; precision forestry; 3D point clouds remote sensing; forest modeling; positioning; precision forestry; 3D point clouds
Show Figures

Figure 1

MDPI and ACS Style

Pascual, A. Using Tree Detection Based on Airborne Laser Scanning to Improve Forest Inventory Considering Edge Effects and the Co-Registration Factor. Remote Sens. 2019, 11, 2675.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop