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Applying Laser Scanning in Precision Forestry

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Special Issue Editors

Dr. Guillermo Palacios-Rodríguez

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Guest Editor

Department of Forestry Engineering, Technical School of Agriculture and Forestry Engineering, University of Cordoba, 14071 Córdoba, Spain
Interests: precision forestry; remote sensing; LiDAR; UAV; RPAS; terrestrial laser scanner; forest inventory; adaptative forest management; climate change; spatial modelling; ecophysiology; dendrochronology

Dr. Inmaculada Clavero-Rumbao

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Guest Editor

Department of Graphic Engineering and Geomatics, Technical School of Agriculture and Forestry Engineering, University of Cordoba, 14071 Córdoba, Spain
Interests: remote sensing; UAV; geospatial database; photogrammetry; image analysis; LiDAR; precision forestry; forest inventory; land-cover classification

Special Issue Information

Dear Colleagues,

The sustainable use of forests requires a forest management plan adapted to its characteristics. Any forest management plan requires the quantification of available natural resources. The emergence of new approaches and technologies such as Open Data, Big Data, Artificial Intelligence, Robotics and IOT are boosting a digital transformation of the forestry sector. Among these technologies, Laser Scanning has undergone greater development and implementation in Precision Forestry, often in combination with other Remote Sensing technologies, forest in situ sensors and Big Data analysis, supported by modeling advances. Forest managers demand solutions that integrate these new technologies, supporting decision making in a comprehensive, reliable and user-friendly manner while helping to enhance their efficiency and cost savings. The integration of all the processes that lead to the management of forests represents added value for any customer in need of reliable and up-to-date information.

This Special Issue aims to publish original and innovative research on the application of new technologies and emerging techniques for forest management, and to disseminate their application in a Precision Forestry context.

For this Special Issue, we encourage authors to contribute articles on all applications of Laser Scanners (airborne, UAV-borne, ground-based, etc.) that contribute to precision forest management, including sustainable management of forest stands, management of forest risks such as pests, diseases or fires, improvement forestry operations logistics, new trends in forestry (adaptive, carbon, water), etc. We also welcome articles on other relevant topics, such as sensor calibration, correction procedures, error analysis and control, validation/evaluation of the products obtained and the development of processing algorithms.

Dr. Guillermo Palacios-Rodríguez
Dr. Inmaculada Clavero-Rumbao
Guest Editors

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Keywords

precision forestry
remote sensing
LiDAR
terrestrial Laser Scanner
RPAS
forest management
forest inventory
forest monitoring
biomass
carbon sequestration
modeling/algorithms

Published Papers (2 papers)

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Research

19 pages, 4019 KiB

Open AccessArticle

Comparison of Canopy Height Metrics from Airborne Laser Scanner and Aerial/Satellite Stereo Imagery to Assess the Growing Stock of Hemiboreal Forests

by Grigorijs Goldbergs

Remote Sens. 2023, 15(6), 1688; https://doi.org/10.3390/rs15061688 - 21 Mar 2023

Cited by 1 | Viewed by 1762

Abstract

This study compared the canopy height model (CHM) performance obtained from large-format airborne and very high-resolution satellite stereo imagery (VHRSI), with airborne laser scanning (ALS) data, for growing stock (stand volume) estimation in mature, dense Latvian hemiboreal forests. The study used growing stock data obtained by ALS-based individual tree detection as training/reference data for the image-based and ALS CHM height metrics-based growing stock estimators. The study only compared the growing stock species-specific area-based regression models which are based solely on tree/canopy height as a predictor variable applied to regular rectangular 0.25 and 1 ha plots and irregular forest stands. This study showed that ALS and image-based (IB) height metrics demonstrated comparable effectiveness in growing stock prediction in dense closed-canopy forests. The relative RMSEs did not exceed 20% of the reference mean values for all models. The best relative RMSEs achieved were 13.6% (IB) and 15.7% (ALS) for pine 0.25 ha plots; 10.3% (IB) and 12.1% (ALS) for pine 1 ha plots; 16.4% (IB) and 12.2% (ALS) for spruce 0.25 ha plots; 17.9% (IB) and 14.2% (ALS) for birch 0.25 ha plots; 15.9% (IB) and 18.9% (ALS) for black alder 0.25 ha plots. This research suggests that airborne imagery and, accordingly, image-based CHMs collected regularly can be an efficient solution for forest growing stock calculations/updates, in addition to a traditional visual forest inventory routine. However, VHRSI can be the fastest and cheapest solution for monitoring forest growing stock changes in vast and dense forestland under optimal data collection parameters. Full article

(This article belongs to the Special Issue Applying Laser Scanning in Precision Forestry)

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17 pages, 2882 KiB

Open AccessArticle

Comparison of Errors Produced by ABA and ITC Methods for the Estimation of Forest Inventory Attributes at Stand and Tree Level in Pinus radiata Plantations in Chile

by Miguel Ángel Lara-Gómez, Rafael M. Navarro-Cerrillo, Inmaculada Clavero Rumbao and Guillermo Palacios-Rodríguez

Remote Sens. 2023, 15(6), 1544; https://doi.org/10.3390/rs15061544 - 11 Mar 2023

Cited by 2 | Viewed by 1688

Abstract

Airborne laser scanning (ALS) technology is fully implemented in forest resource assessment processes, providing highly accurate and spatially continuous results throughout the area of interest, thus reducing inventory costs when compared with traditional sampling inventories. Several approaches have been employed to estimate forest parameters using ALS data, such as the Area-Based Approach (ABA) and Individual Tree Crown (ITC). These two methodologies use different information processing and field data collection approaches; thus, it is important to have a selection criterion for the method to be used based on the expected results and admissible errors. The objective of this study was to compare the prediction errors of forest inventory attributes in the functioning of ABA and ITC approaches. A plantation of 500 ha of Pinus radiata (400–600 trees ha⁻¹) in Chile was selected; a forest inventory was conducted using the ABA and ITC methods and the accuracy of both methods was analyzed. The ITC models performed better than the ABA models at low tree densities for all forest inventory attributes (15% MAPE in tree density—N—and 11% in volume—V). There was no significant difference in precision regarding the volume and basal area (G) estimations at medium densities, although ITC obtained better results for density and dominant height (Ho). At high densities, ABA performed better for all the attributes except for height (6.5% MAPE in N, 8.7% in G, and 8.9% in V). Our results showed that the precision of forest inventories based on ALS data can be adjusted depending on tree density to optimize the selected approach (ABA and ITC), thus reducing the inventory costs. Hence, field efforts can be greatly decreased while achieving better prediction accuracies. Full article

(This article belongs to the Special Issue Applying Laser Scanning in Precision Forestry)

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