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篇目详细内容 |
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【篇名】 |
Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images |
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【刊名】 |
Frontiers of Earth Science |
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【刊名缩写】 |
Front. Earth Sci |
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【ISSN】 |
2095-0195 |
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【EISSN】 |
2095-0209 |
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【DOI】 |
10.1007/s11707-012-0339-6 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2013 |
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【卷期】 |
7
卷1期 |
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【页码】 |
43-54
页,共
12
页 |
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【作者】 |
Yan HUANG;
Bailang YU;
Jianhua ZHOU;
Chunlin HU;
Wenqi TAN;
Zhiming HU;
Jianping WU;
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【关键词】 |
urban green volume; LiDAR; remote sensing image; object-based method; automatic estimation |
【摘要】 |
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Urban green volume is an important indicator for analyzing urban vegetation structure, ecological evaluation, and green-economic estimation. This paper proposes an object-based method for automated estimation of urban green volume combining three-dimensional (3D) information from airborne Light Detection and Ranging (LiDAR) data and vegetation information from high resolution remotely sensed images through a case study of the Lujiazui region, Shanghai, China. High resolution airborne near-infrared photographs are used for identifying the urban vegetation distribution. Airborne LiDAR data offer the possibility to extract individual trees and to measure the attributes of trees, such as tree height and crown diameter. In this study, individual trees and grassland are identified as the independent objects of urban vegetation, and the urban green volume is computed as the sum of two broad portions: individual trees volume and grassland volume. The method consists of following steps: generating and filtering the normalized digital surface model (nDSM), extracting the nDSM of urban vegetation based on the Normalized Difference Vegetation Index (NDVI), locating the local maxima points, segmenting the vegetation objects of individual tree crowns and grassland, and calculating the urban green volume of each vegetation object. The results show the quantity and distribution characteristics of urban green volume in the Lujiazui region, and provide valuable parameters for urban green planning and management. It is also concluded from this paper that the integrated application of LiDAR data and image data presents an effective way to estimate urban green volume. |
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