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Keywords = image point cloud (IPC)

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23 pages, 3085 KiB  
Article
Remote Sensing of Forest Gap Dynamics in the Białowieża Forest: Comparison of Multitemporal Airborne Laser Scanning and High-Resolution Aerial Imagery Point Clouds
by Miłosz Mielcarek, Sylwia Kurpiewska, Kacper Guderski, Dorota Dobrowolska, Ewa Zin, Łukasz Kuberski, Yousef Erfanifard and Krzysztof Stereńczak
Remote Sens. 2025, 17(7), 1149; https://doi.org/10.3390/rs17071149 - 24 Mar 2025
Viewed by 736
Abstract
Remote sensing technologies like airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have emerged as efficient tools for detecting and analysing canopy gaps (CGs). Comparing these technologies is essential to determine their functionality and applicability in various environments. Thus, this study aimed [...] Read more.
Remote sensing technologies like airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have emerged as efficient tools for detecting and analysing canopy gaps (CGs). Comparing these technologies is essential to determine their functionality and applicability in various environments. Thus, this study aimed to assess CG dynamics in the temperate European Białowieża Forest between 2015 and 2022 by comparing ALS data and image-derived point clouds (IPC) from DAP, to evaluate their respective capabilities in describing and analysing forest CG dynamics. Our results demonstrated that ALS-based point clouds provided more detailed and precise spatial information about both the vertical and horizontal structure of forest CGs compared to IPC. ALS detected 27,754 (54%) new CGs between 2015 and 2022, while IPC identified 23,502 (75%) new CGs. Both the average gap area and the total gap area significantly increased over time in both methods. ALS data not only identified a greater number of CGs, particularly smaller ones (below 500 m2), but also produced a more precise representation of CG shape and structure. In conclusion, precise, multi-temporal remote sensing data on the distribution and size of canopy gaps enable effective monitoring of structural changes and disturbances in forest stands, which in turn supports more efficient forest management, e.g., planning of forest regeneration. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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15 pages, 3040 KiB  
Article
Predicting Growing Stock Volume of Scots Pine Stands Using Sentinel-2 Satellite Imagery and Airborne Image-Derived Point Clouds
by Paweł Hawryło and Piotr Wężyk
Forests 2018, 9(5), 274; https://doi.org/10.3390/f9050274 - 17 May 2018
Cited by 35 | Viewed by 4986
Abstract
Estimation of forest stand parameters using remotely sensed data has considerable significance for sustainable forest management. Wide and free access to the collection of medium-resolution optical multispectral Sentinel-2 satellite images is very important for the practical application of remote sensing technology in forestry. [...] Read more.
Estimation of forest stand parameters using remotely sensed data has considerable significance for sustainable forest management. Wide and free access to the collection of medium-resolution optical multispectral Sentinel-2 satellite images is very important for the practical application of remote sensing technology in forestry. This study assessed the accuracy of Sentinel-2-based growing stock volume predictive models of single canopy layer Scots pine (Pinus sylvestris L.) stands. We also investigated whether the inclusion of Sentinel-2 data improved the accuracy of models based on airborne image-derived point cloud data (IPC). A multiple linear regression (LM) and random forest (RF) methods were tested for generating predictive models. The measurements from 94 circular field plots (400 m2) were used as reference data. In general, the LM method provided more accurate models than the RF method. Models created using only Sentinel-2A images had low prediction accuracy and were characterized by a high root mean square error (RMSE%) of 35.14% and a low coefficient of determination (R2) of 0.24. Fusion of IPC data with Sentinel-2 reflectance values provided the most accurate model: RMSE% = 16.95% and R2 = 0.82. However, comparable accuracy was obtained using the IPC-based model: RMSE% = 17.26% and R2 = 0.81. The results showed that for single canopy layer Scots pine dominated stands the incorporation of Sentinel-2 satellite images into IPC-based growing stock volume predictive models did not significantly improve the model accuracy. From an operational point of view, the additional utilization of Sentinel-2 data is not justified in this context. Full article
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
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21 pages, 1886 KiB  
Article
A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario
by Margaret Penner, Murray Woods and Douglas G. Pitt
Forests 2015, 6(11), 4034-4054; https://doi.org/10.3390/f6114034 - 9 Nov 2015
Cited by 33 | Viewed by 7038
Abstract
Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within [...] Read more.
Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area- and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m2). Full article
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
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