Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds |
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| Institution: | 1. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China;2. Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China;3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Beijing-Tianjin-Hebei Urban Megaregion National Observation and Research Station for Eco-Environmental Change, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;1. Department of Geo-Engineering, Andhra University, Vishakhapatnam, India;2. Water Resources Department, Indian Institute of Remote Sensing, ISRO, Dehradun, India;3. Forest Research Institute Deemed to be University, Dehradun, India;4. Bennett University, CSE Department Greater Noida, India;5. National Remote Sensing Centre, ISRO, Hyderabad, Telangana, India;1. University of Helsinki, Department of Forest Sciences, Finland;2. University of Helsinki, Department of Environmental Sciences, Finland;3. City of Helsinki, Public Works Department, Street and Park Division, Finland;1. Wageningen University & Research, Laboratory of Geo-Information Science and Remote Sensing, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands;2. Helmholtz Center Potsdam GFZ German Research Centre for Geosciences, Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, 14473 Potsdam, Germany;3. CAVElab – Computational & Applied Vegetation Ecology, Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium;4. Botany and Modelling of Plant Architecture and Vegetation (AMAP) Laboratory, French National Research Institute for Sustainable Development (IRD), Center for International Cooperation in Agricultural Research for Development (CIRAD), Scientific Research National Center (CNRS), Institut national de la recherche agronomique (INRA), Montpellier University, Montpellier, France;5. Department of Agriculture, Water and the Environment, Supervising Scientist Branch, Darwin 0820, NT, Australia;6. Department of Geographical Sciences, University of Maryland, College Park, USA;7. CIRAD, UMR EcoFoG (Agroparistech, CNRS, INRAE, Université des Antilles, Université de la Guyane), Kourou, French Guiana, France;8. CSIRO Land and Water, PMB 44, Winnellie 0822, NT, Australia;9. Mathematics, Tampere University, Korkeakoulunkatu 1, 33720 Tampere, Finland;10. Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi’an 710077, China |
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| Abstract: | Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (Rc), which were then input to the newly established allometric equation with tree H and Rc as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R2 and RMSE of extracted Rc were 0.9006 and 0.28 m; the R2 and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and Rc to accurate AGB estimation of urban forest trees. |
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| Keywords: | AGB Allometric equation Canopy radius Point cloud Tree height Urban forest |
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