基于光学和激光雷达数据的城市树木地上生物量估算——以珠海横琴为例

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL Photogrammetric Engineering and Remote Sensing Pub Date : 2022-02-01 DOI:10.14358/pers.21-00045r2
Linze Bai, Q. Cheng, Yuxuan Shu, Sihang Zhang
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引用次数: 0

摘要

树木的地上生物量(AGB)在城市生态环境中具有重要作用。与森林生物量估算不同,城市树木AGB估算受人类活动影响较大,具有较强的空间异质性。本研究以横琴地区为例,精确提取树木面积,设计光学和激光数据协同方案。最后,使用了5种评估模型,包括2种深度学习模型(深度信念网络和堆叠稀疏自编码器)、2种机器学习模型(随机森林和支持向量回归)和1种地理加权回归模型。实验结果表明,深度学习模型是有效的。叠置稀疏自编码器是最佳模型,其结果R2 = 0.768,均方根误差= 18.17 mg/ha。结果表明,该方法可用于估算城市树木的AGB,对城市生态建设有重要影响。
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Estimating the Aboveground Biomass of Urban Trees by Combining Optical and Lidar Data: A Case Study of Hengqin, Zhuhai, China
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 R2 = 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.
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来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
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