Laser scanning as a tool for the analysis of the greenery on the example of Poremba district in Lublin

2015 ◽  
Vol 26 (3-4) ◽  
pp. 132-140
Author(s):  
P. G. Kotsyuba ◽  
I. D. Semko ◽  
I. I. Kozak ◽  
T. V. Parpan ◽  
G. G. Kozak ◽  
...  

World experience shows that the survey of green spaces by traditional methods is very time consuming, costly and does not always get all the information you need to make of adequate management decisions by municipal authorities. The aim of this article was to show the main stages of analysis and prospects of urban green space using aerial lidar data and submit the effect of three-dimensional visualization of the study area. There were presented the possibilities and perspectives of using the data obtained from airborne laser scanning (ALS) for the analysis of greenery on the example of Poremba district in Lublin (Poland). Research conducted in Poremba district in the Polish city of Lublin (district was built from 1988 to 2005 and is located in the western part of the city). Analysis of green space conducted using quantitative analytical methods. By detailed analysis of the study area were used aerial lidar data from the year 2015. To classify aerial lidar data such software were used: LP360, ArcMap 10.3, Toolbox LAStools. The process of analysis begins with the definition of points, belonging to ground (Ground - GR), and the classification was realized using «lasground» with tools LAStools. The article is dedicated to development the method of estimation the tree height based on airborne LiDAR data. Method applies more information about the three-dimensional structure of natural objects derived from the processing of airborne LiDAR data compared with known methods. Furthermore, the method is adapted to determine and calculate characteristics of stand which using for tree inventory in cities. Methodological and algorithmic instructions to determine the tree parameters in city were proposed. These instructions allow automatically calculating the characteristics of the tree parameters, such as the allocation of each tree and tree height. The study area was analyzed in terms of the distribution of vegetation (separately individual growing trees and groups of trees). For that purpose there was applied an available ALS data. Based on the ALS data there were separated the tops of the trees and their height. In order to verify the ALS data there were used the results of field measurements (coordinates for the tree trunks, the diameter at breast height of trees, their height, crown projection). The analysis of the greenery within the Poremba district using the ALS data after verification with the field measurements proved to be an effective tool for the characterization of the greenery areas in particular city. This research may be important in terms of planning the planting of greenery areas and spatial development of the Lublin.

Author(s):  
X. Yang ◽  
X. Xi ◽  
C. Wang ◽  
J. Shi ◽  
Y. Huang

Abstract. Fraction of absorbed Photosynthetically Active Radiation (FPAR) is one of the pivotal parameters in terrestrial ecosystem modelling and crop growth monitoring. Airborne LiDAR is an advanced active remote sensing technology which can acquire fine three-dimensional canopy structural information quickly and accurately. Although some previous studies have shown that LiDAR-derived metrics had strong relationships with canopy FPARs, these estimation models without physical meaning are hard to be extended to various vegetation canopies and different growth periods. This study proposed a physical FPAR inversion method based on airborne LiDAR data and field measurements. The method considered direct and diffuse radiations separately based on the SAIL model and energy budget balance principle. The canopy FPAR was inversed from the structural information provided by LiDAR point cloud data and the spectral information provided by ground measurements. The estimated FPAR was validated with the field-measured FPAR over 39 maize plots. Results showed that the proposed method had a good performance in estimating the total FPAR of maize canopy (R2 = 0.76, RMSE = 0.062, n = 39). This study provides the potential to estimate the total, direct, and diffuse FPARs of vegetation canopy from airborne LiDAR data.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.


2017 ◽  
Vol 07 (02) ◽  
pp. 255-269 ◽  
Author(s):  
Faith Kagwiria Mutwiri ◽  
Patroba Achola Odera ◽  
Mwangi James Kinyanjui

2016 ◽  
Vol 19 (4) ◽  
pp. 1749-1765 ◽  
Author(s):  
Rhiannon J. C. Caynes ◽  
Matthew G. E. Mitchell ◽  
Dan Sabrina Wu ◽  
Kasper Johansen ◽  
Jonathan R. Rhodes

2014 ◽  
Vol 6 (8) ◽  
pp. 7592-7609 ◽  
Author(s):  
Hanieh Saremi ◽  
Lalit Kumar ◽  
Christine Stone ◽  
Gavin Melville ◽  
Russell Turner

Beskydy ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Olga Brovkina ◽  
František Zemek ◽  
Tomáš Fabiánek

The study presents three models for estimation of forest aboveground biomass (AGB) for plot level using different categories of airborne data. The first and the second models estimate AGB from metrics of airborne LiDAR data. The third model estimates AGB from integration of metrics of airborne hyperspectral and LiDAR data. The results are compared with plot level biomass estimated from field measurements. The results show that the best AGB estimate is obtained from the model utilizing a fusion of hyperspectral and LiDAR metrics. Study results expand existing research on the applicability of airborne hyperspectral and LiDAR datasets for AGB assessment. It evidences the efficiency of using a predicting model based on hyperspectral and LiDAR data for study area.


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