- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Wheel-Based Lidar Data for Plant Height and Canopy Cover Evaluation to Aid Biomass Prediction
摘要： Biomass estimation is fundamental for a variety of plant ecological studies. Direct measurement of aboveground biomass by clipping and sorting is destructive, time-consuming and laborious, thus reducing the ability of extensive sampling. Various plant traits, such as plant height, canopy cover, and leaf and plant structure contribute towards its biomass. In this study, we focus on exploiting wheel-based LiDAR data over an agricultural field to perform growth monitoring and canopy cover estimation, which would play a crucial role in the future to develop a non-invasive technique for biomass prediction.
关键词： Biomass,plant traits,LiDAR data,plant height,canopy cover
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fusion of Multitemporal LiDAR Data for Individual Tree Crown Parameter Estimation on Low Density Point Clouds
摘要： The increasingly availability of Light Detection and Ranging (LiDAR) data acquired at different times can be used to analyze the forest dynamics at individual tree level. This often requires to deal with LiDAR point clouds having significantly different point densities. To address this issue, this paper presents a method for the fusion of multitemporal LiDAR data which aims at using the information provided by high density LiDAR data (higher than 10 pts/m2) to improve the single tree parameter estimation of low density data (up to 5 pts/m2) acquired over the same forest at different times. The method first accurately characterizes the crown shapes on the high density data. Then, it uses the obtained estimates to drive the tree parameter estimation on the low density LiDAR data. The method has been tested on a multitemporal dataset acquired in coniferous forests located in the Italian Alps. Experimental results confirmed the effectiveness of the method.
关键词： Point Cloud,Tree Crown Parameters,Remote Sensing,Multitemporal LiDAR Data,Data Fusion
更新于2025-09-23 15:21:01
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Optimisation for large-scale photovoltaic arraysa?? placement based on Light Detection And Ranging data
摘要： The availability of high-resolution LiDAR (Light Detection And Ranging) geospatial data has increased immensely, providing new opportunities to solve challenges in the field of spatial energy planning. This paper presents a new method for large-scale placement of photovoltaic arrays over buildings’ rooftops in an optimal manner by using the global optimisation approach. The position, aspect and slope are the key geometrical parameters being optimised for each photovoltaic array. The predicted energy generation (i.e. photovoltaic potential) is simulated by using state-of-the-art hourly shadowing estimation from the surroundings, anisotropic diffuse, reflected, and direct irradiances that are based on a Typical Meteorological Year, and non-linear efficiency characteristics of a considered photovoltaic system configuration. The optimisation performs multiple simulation scenarios throughout an entire year for each photovoltaic array, in order to maximise its photovoltaic potential. The method was tested over three LiDAR datasets with different landscape topographies and urban densities. In comparison to the methods for photovoltaic arrays’ fixed optimal slope estimation, the proposed method is substantially more suitable for application in urban environments.
关键词： LiDAR data,Optimisation,Photovoltaic potential,Environmental simulation
更新于2025-09-23 15:19:57
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Real-Time Visualization Method for Estimating 3D Highway Sight Distance Using LiDAR Data
摘要： Light detection and ranging (LiDAR) data provide a rather precise depiction of the real three-dimensional (3D) road environment and have been used by some researchers to produce more precise available sight distance (ASD) results compared with those obtained based on conventional digital elevation models with low resolution. However, existing methods have some difficulties in creating digital surface models to accurately estimate ASD using LiDAR data. In addition, dynamic visualization of the driver’s visual conditions along the highway throughout ASD assessment (which is important for monitoring the results in real time) has not been achieved by existing studies. To fill these gaps, this paper discusses the development of a new procedure supported by MATLAB for evaluating, in a real-time visualization manner, ASD along an existing highway based on LiDAR data. With an innovative algorithm that combines cylindrical perspective projection and modified Delaunay triangulation, the computation is processed in real time along the vehicle trajectory, which is represented by a set of points, whereas the driver’s successive perspective views and sight distance results are generated simultaneously. A comparative case study is presented to demonstrate that the new method is more accurate than conventional methods and more flexible for evaluating ASD along highways with complicated roadside components.
关键词： Algorithm,Light detection and ranging (LiDAR) data.,Highway safety,Real time,Three-dimensional sight distance
更新于2025-09-19 17:15:36
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Community-scale multi-level post-hurricane damage assessment of residential buildings using multi-temporal airborne LiDAR data
摘要： Building damage assessment is a critical task following major hurricane events. Use of remotely sensed data to support building damage assessment is a logical choice considering the di?culty of gaining ground access to the impacted areas immediately after hurricane events. However, a remote sensing based damage assessment approach is often only capable of detecting severely damaged buildings. In this study, an airborne LiDAR based approach is proposed to assess multi-level hurricane damage at the community scale. In the proposed approach, building clusters are ?rst extracted using a density-based algorithm. A novel cluster matching algorithm is proposed to robustly match post-event and pre-event building clusters. Multiple features including roof area and volume, roof orientation, and roof shape are computed as building damage indicators. A hierarchical determination process is then employed to identify the extent of damage to each building object. The results of this study suggest that our proposed approach is capable of 1) recognizing building objects, 2) extracting damage features, and 3) characterizing the extent of damage to individual building properties.
关键词： Hurricane damage assessment,Point cloud processing,Geometric computing,Airborne LiDAR,Data clustering
更新于2025-09-10 09:29:36
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Multi-scale features fusion from sparse LiDAR data and single image for depth completion
摘要： Recently deep learning-based methods for dense depth completion from sparse depth data have shown superior performance than traditional techniques. However, sparse depth data lose the details of the scenes, for instance, the spatial and texture information. To overcome this problem, additional single image is introduced and a multi-scale features fusion scheme to learn more correlations of the two different data is proposed. Furthermore, sparse convolution operation to improve feature robustness for sparse depth data is exploited. Experiments demonstrate that the approach obviously improves the performance for depth completion and outperforms all the previous published methods. The authors believe their works also have the guidance significance for stereo images depth estimation fused with sparse LiDAR depth data.
关键词： multi-scale features fusion,sparse convolution,depth completion,single image,sparse LiDAR data
更新于2025-09-10 09:29:36
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Flood depth estimation by means of high-resolution SAR images and lidar data
摘要： When floods hit inhabited areas, great losses are usually registered in terms of both impacts on people (i.e., fatalities and injuries) and economic impacts on urban areas, commercial and productive sites, infrastructures, and agriculture. To properly assess these, several parameters are needed, among which flood depth is one of the most important as it governs the models used to compute damages in economic terms. This paper presents a simple yet effective semiautomatic approach for deriving very precise inundation depth. First, precise flood extent is derived employing a change detection approach based on the normalized difference flood index computed from high-resolution synthetic aperture radar imagery. Second, by means of a high-resolution lidar digital elevation model, water surface elevation is estimated through a statistical analysis of terrain elevation along the boundary lines of the identified flooded areas. Experimental results and quality assessment are given for the flood that occurred in the Veneto region, northeastern Italy, in 2010. In particular, the method proved fast and robust and, compared to hydrodynamic models, it requires sensibly less input information.
关键词： flood depth estimation,SAR images,lidar data,high-resolution,normalized difference flood index
更新于2025-09-09 09:28:46