Articles containing the keyword 'canopy height model'

Forest stands, crucial for inventory, planning, and management, traditionally rely on time-consuming visual analysis by forest managers. To enhance efficiency, there is a growing need for automated methods that take into account essential forest attributes. In response, we propose a novel approach utilizing airborne Light Detection and Ranging (LiDAR) and hyperspectral data for automated forest stand delineation. Our approach initiates with over-segmentation of the Canopy Height Model (CHM), followed by attribute calculation for each segment using both CHM and hyperspectral data. Two rules are applied to merge homogeneous segments and eliminate others based on calculated attributes. The effectiveness of our method was validated using three types of reference forest stands with two indices: the explained variance (R²) and Intersection over Union (IoU). Results from our study demonstrated notable accuracy, with a R² of 97.35% and 97.86% for mean tree height and mean diameter at breast height (DBH), respectively. The R² for mean canopy height is 81.80%, outperforming manual delineation by 7.31% and multi-scale segmentation results by 2.13%. Furthermore, our approach achieved high IoU values, which indicates a strong spatial agreement with manually delineated forest stands and leading to fewer manual adjustments when applied directly to forest management. In conclusion, our forest stand delineation method enhances both internal consistency and spatial accuracy. This method contributes to improving practical performance and forest management efficiency.

• Xiong, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China; School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China https://orcid.org/0000-0003-4432-2485 E-mail: xiongh29@mail2.sysu.edu.cn
• Pang, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China https://orcid.org/0000-0002-9760-6580 E-mail: pangy@ifrit.ac.cn
• Jia, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China E-mail: jiawen@ifrit.ac.cn
• Bai, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China E-mail: baiyu9224@163.com

In this paper we examine the feasibility of data from unmanned aerial vehicle (UAV)-borne aerial imagery in stand-level forest inventory. As airborne sensor platforms, UAVs offer advantages cost and flexibility over traditional manned aircraft in forest remote sensing applications in small areas, but they lack range and endurance in larger areas. On the other hand, advances in the processing of digital stereo photography make it possible to produce three-dimensional (3D) forest canopy data on the basis of images acquired using simple lightweight digital camera sensors. In this study, an aerial image orthomosaic and 3D photogrammetric canopy height data were derived from the images acquired by a UAV-borne camera sensor. Laser-based digital terrain model was applied for estimating ground elevation. Features extracted from orthoimages and 3D canopy height data were used to estimate forest variables of sample plots. K-nearest neighbor method was used in the estimation, and a genetic algorithm was applied for selecting an appropriate set of features for the estimation task. Among the selected features, 3D canopy features were given the greatest weight in the estimation supplemented by textural image features. Spectral aerial photograph features were given very low weight in the selected feature set. The accuracy of the forest estimates based on a combination of photogrammetric 3D data and orthoimagery from UAV-borne aerial imaging was at a similar level to those based on airborne laser scanning data and aerial imagery acquired using purpose-built aerial camera from the same study area.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: andras.balazs@luke.fi
• Saari, VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, FI-02044 VTT, Finland E-mail: Heikki.Saari@vtt.fi
• Pölönen, University of Jyväskylä, Department of Mathematical Information Technology, P.O. Box 35, FI-40014 University of Jyväskylä, Finland E-mail: ilkka.polonen@jyu.fi
• Sarkeala, Mosaicmill Oy, Kultarikontie 1, FI-01300 Vantaa, Finland E-mail: janne.sarkeala@mosaicmill.com
• Viitala, Häme University of Applied Sciences (HAMK), P.O. Box 230, FI-13101 Hämeenlinna, Finland E-mail: Risto.Viitala@hamk.fi