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作者

Ernandes Macedo da Cunha Neto;Franciel Eduardo Rex;Hudson Franklin Pessoa Veras;Marks Melo Moura;Carlos Roberto Sanquetta;Pâmela Suélen Käfer;Mateus Niroh Inoue Sanquetta;Angelica Maria Almeyda Zambrano;Eben North Broadbent;Ana Paula Dalla Corte

来源

URBAN FORESTRY & URBAN GREENING,Vol.63,Issue1,Article 127197

语言

英文

关键字

Forest inventory;GatorEye;Remote sensing;Urban landscape

作者单位

Department of Forest Engineering, Federal University of Paraná (UFPR), Curitiba, PR, Brazil;State Research Center for Remote Sensing and Meteorology, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil;Spatial Ecology and Conservation Lab, Latin American Studies, University of Florida, Gainesville, FL, USA;Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA;Department of Forest Engineering, Federal University of Paraná (UFPR), Curitiba, PR, Brazil;State Research Center for Remote Sensing and Meteorology, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil;Spatial Ecology and Conservation Lab, Latin American Studies, University of Florida, Gainesville, FL, USA;Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA

摘要

Urban forest remnants contribute to climate change mitigation by reducing the amount of carbon dioxide in urban areas. Hence, understanding the dynamics and the potential of urban forests as carbon pools is crucial to propose effective policies addressing the ecosystem services' maintenance. Remote sensing technologies such as Light detection and ranging (Lidar) are alternatives to acquire information on urban forests accurately. In this paper, we evaluate a UAV-Lidar system's potential to derive individual tree heights of Araucaria angustifolia trees in an Urban Atlantic Forest. Additionally, the influence of point density when deriving tree heights was assessed (2500, 1000, 500, 250, 100, 50, 25, 10 and 5 returns.m−2). The UAV-Lidar data was collected with the GatorEye Unmanned Flying Laboratory ‘Generation 2’. The UAV-Lidar-derived and field-based tree heights were compared by statistical analysis. Higher densities of points allowed a better description of tree profiles. Lower densities presented gaps in the Crown Height Model (CHM). The highest agreement between UAV-Lidar-derived and field-based tree heights (r = 0.73) was noticed when using 100 returns.m−2. The lowest rRMSE was observed for 50 returns.m−2 (8.35 %). There are no explicit differences in derived tree heights using 25 to 2500 returns.m−2. UAV-Lidar data presented satisfactory performance when deriving individual tree heights of Araucaria angustifolia trees.