Prediction of forest canopy light interception using three‐dimensional airborne LiDAR data

2008 ◽  
Vol 30 (1) ◽  
pp. 189-207 ◽  
Author(s):  
H. Lee ◽  
K. C. Slatton ◽  
B. E. Roth ◽  
W. P. Cropper
Author(s):  
Peter Potapov ◽  
Xinyuan Li ◽  
Andres Hernandez-Serna ◽  
Svetlana Turubanova ◽  
Alexandra Tyukavina ◽  
...  

2012 ◽  
Vol 500 ◽  
pp. 511-516 ◽  
Author(s):  
Zhi Ming Hu ◽  
Jian Ping Wu ◽  
Bin Wu ◽  
Song Shu ◽  
Bai Lang Yu

This study utilizes high resolution airborne LiDAR data and topographic solar radiation model to quantify the impacts of three-dimensional morphology on the spatio-temporal variations of solar radiation at the Lujiazui Region, Shanghai, China. Monthly direct and non-direct (diffuse and reflection) plus seasonal total solar radiation distributions are simulated and mapped by using a radiation flux model. The results show that the crowded buildings at the Lujiazui Region have severely changed the spatial pattern of solar radiation intensity and duration. The derived monthly and seasonal solar radiation maps would benefit the understanding of the impacts of urban 3D morphology on the environmental factors and be the scientific basic for the further research.


2012 ◽  
Vol 518-523 ◽  
pp. 5648-5655
Author(s):  
Hui Lin ◽  
Ya Zhou Ji ◽  
Liang Liang ◽  
Wei Liu ◽  
Zhao Ling Hu

The research of Three Dimensional City Model (3DCM) has become a hot topic in GIS field in recent years, and it also has played an important role in traffic, land, mining, surveying and mapping, and other fields, especially in urban planning. However, the difficulty to acquire 3D data is the key obstacle to the further development of 3DCM. Airborne LIDAR, integrating GPS, INS and scanning laser rangefinder, can rapidly acquire the 3D position of ground by airplane, which is very economical, efficient and convenient to acquire 3D data. Because traditional three-dimensional data acquisition method can’t meet the need of the city’s fast development, airborne LIDAR technology is regarded as a convenient, swift, high-efficient three-dimensional data acquisition method. Compared with traditional methods, the airborne LIDAR technology has the following characteristics: 1) High efficiency: in 12 hours, the airborne LIDAR can scan 1000 square kilometers, next, with the help of the related post-processing software, LIDAR cloud data can transform them into GIS format or other receivable format in certain automatic or semiautomatic mode. 2) High precision: because the pulse of laser light isn’t easily subject to shadow and solar angle, it greatly improves the data quality. The flight height limit has no influence on its elevation data precision, which is superior to the conventional photogrammetry. The plane precision may achieve 0.15 to 1 meter, the elevation precision may achieve 10 centimeters. 3) All-weather feature: airborne LIDAR is active remote sensing without considering the digital aerial photogrammetry. 4) Rich information: with the aid of airborne LIDAR ,we can obtains not only the three dimensional coordinate of ground point, but also the three dimensional coordinate of terrain details, such as trees, buildings, roads. If it is integrated with CCD, it could gains image information. We acquired the airborne LIDAR data of 20 square kilometers in the central area of Shanghai using ALTM3100 airborne LIDAR system of the Optech company in 2006.This paper introduces the data processing procedure of the airborne LIDAR data, LIDAR system uses random commercial software to process plane GPS tracking data、plane attitude data、 laser ranging data and the swinging angle data of laser scanning mirror, finally, obtaining the three-dimensional coordinates(X,Y,Z) data of various surveying points. Which three-dimensional discrete dot matrix data is without attribute suspending in the air namely LIDAR original data, named “point cloud”. LIDAR data performs pre-processing to obtain digital surface model (DSM), which is classified and extracted, we acquire topography and object related to modeling, preparing for three-dimensional city model. Data pre-processing includes abnormal point deletion, coordinate transformation and flight strip combination. At present, we used famous business software TerraSolid, developed by Company of Finland to realize the classification and extraction from the LIDAR data TerraSolid depends on MicroStation platform, on the basis of classification and extraction algorithms presented by Axelsson, et al. of Swedish, including a lot of module such as TerraScan, TerraModeler and TerraPhoto. TerraScan is used in the field of LIDAR data classification and extraction, TerraModeler is used for producing and dealing with various planes, TerraPhoto is used for dealing with the primitive image, topography model and building model are got by using this software, complicated artificial building (Oriental Pearl, Jin Mao mansion etc.) need artificial repair and disposal, data processing of 20 sq. km. takes more than one month, efficiency has been improved greatly on the premise of guaranteeing the precision. Topography model and building model can be obtained by using TerraSolid and combining a few manual intervention based on DSM, The topography model is expressed with the triangulated irregular network (TIN), the building model is expressed with 3ds format, three dimensional model of non - texture of Lujiazui region of Shanghai was gained by LIDAR data. In order to achieving better visualization effect, the topography model overlaps orthophoto, and stuck true texture to building model, true city landscape of Lujiazui region of Shanghai is established. This paper has introduce post-processing procedure of airborne LIDAR data systematically, has realized the fast reconstruction of three-dimension urban model based on LIDAR data, enable this technology to serve the information construction of the city better.


2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.


2021 ◽  
Vol 13 (9) ◽  
pp. 1722
Author(s):  
Nian-Wei Ku ◽  
Sorin Popescu ◽  
Marian Eriksson

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.


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