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Open Access Estimating the Aboveground Biomass of Urban Trees by Combining Optical and Lidar Data: A Case Study of Hengqin, Zhuhai, China

This article is Open Access under the terms of the Creative Commons CC BY-NC-ND licence.

The aboveground biomass (AGB) of trees plays an important role in the urban ecological environment. Unlike forest biomass estimation, the estimation of AGB of urban trees is greatly influenced by human activities and has strong spatial heterogeneity. In this study, taking Hengqin, China, as an example, we extract the tree area accurately and design a collaborative scheme of optical and lidar data. Finally, five evaluation models are used, including two deep learning models (deep belief network and stacked sparse autoencoder), two machine learning models (random forest and support vector regression), and a geographically weighted regression model. The experimental results show that the deep learning model is effective. The result of the stacked sparse autoen - coder, which is the best model, is that R 2 = 0.768 and root mean square error = 18.17 mg/ha. The results show that our method can be applied to estimate the AGB of urban trees, which greatly influences urban ecological construction.

Document Type: Research Article

Affiliations: 1: State Key Laboratory of Surveying, Mapping, and Remote Sensing Information Engineering, Wuhan, China 2: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China 3: Imperial College of Science, Technology, and Medicine, London, United Kingdom

Publication date: February 1, 2022

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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