Toward automatic estimation of urban green volume using airborne LiDAR data and high resolution Remote Sensing images

2012 ◽  
Vol 7 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Yan Huang ◽  
Bailang Yu ◽  
Jianhua Zhou ◽  
Chunlin Hu ◽  
Wenqi Tan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Remote Sensing Images ◽  
Urban Green ◽  
Automatic Estimation ◽  
Airborne Lidar Data ◽  
Urban Green Volume

scholarly journals Topographical changes revealed by high-resolution airborne LiDAR data: The 1999 Tsaoling landslide induced by the Chi–Chi earthquake

2006 ◽  
Vol 88 (3-4) ◽  
pp. 160-172 ◽  
Author(s):  
Rou-Fei Chen ◽  
Kuo-Jen Chang ◽  
Jacques Angelier ◽  
Yu-Chang Chan ◽  
Benoît Deffontaines ◽  
...  
Keyword(s):  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3845
Author(s):  
Zhiyu Xu ◽  
Yi Zhou ◽  
Shixin Wang ◽  
Litao Wang ◽  
Feng Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Green Space ◽  
Urban Green Space ◽  
Classification Method ◽  
Deciduous Trees ◽  
Remote Sensing Images ◽  
Urban Green

The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.

High-resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data

2007 ◽  
Vol 32 (10) ◽  
pp. 1574-1592 ◽  
Author(s):  
Anna F. Jones ◽  
Paul A. Brewer ◽  
Eric Johnstone ◽  
Mark G. Macklin
Keyword(s):  
High Resolution ◽  
River Valley ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Geomorphological Mapping ◽  
Airborne Lidar Data

scholarly journals Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Xuegang Mao ◽  
Yueqing Deng ◽  
Liang Zhu ◽  
Yao Yao
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Vegetation Type ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sensing Data ◽  
Object Based ◽  
Four Levels ◽  
Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

Evaluating the potential of high‐resolution airborne LiDAR data in glaciology

2006 ◽  
Vol 27 (6) ◽  
pp. 1233-1251 ◽  
Author(s):  
N. S. Arnold ◽  
W. G. Rees ◽  
B. J. Devereux ◽  
G. S. Amable
Keyword(s):  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data
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