Tracking tree mortality across sites with repeat LiDAR data

2021 ◽  
Author(s):  
Toby Jackson ◽  
Matheus Nunes ◽  
Grégoire Vincent ◽  
David Coomes
Keyword(s):  
Forest Structure ◽  
Tree Mortality ◽  
Mortality Rates ◽  
Landscape Scale ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Forest Types ◽  
Large Tree ◽  
Airborne Lidar Data ◽  
Field Plots

<p>Repeat airborne LiDAR data provides a unique opportunity to study tree mortality at the landscape scale. We use maps of canopy height derived from repeat LiDAR (two or more scans collected a few years apart) to detect changes in forest structure. Visually, the most obvious changes are caused by large treefall events, which are difficult to study using field plots due to their rarity. While repeat LiDAR data provides exciting new possibilities, validation is a challenge, since we cannot easily determine how many trees have died and we may miss trees which are dead but still standing. I will discuss our progress so far, studying large-tree mortality rates across multiple countries and forest types.</p>

scholarly journals FOREST STEM VOLUME CALCULATION USING AIRBORNE LIDAR DATA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 265-270
Author(s):  
I. Büyüksalih ◽  
S. Bayburt ◽  
M. Schardt ◽  
G. Büyüksalih
Keyword(s):  
Near Infrared ◽  
Tree Height ◽  
Airborne Lidar ◽  
Stem Volume ◽  
Lidar Data ◽  
Allometric Functions ◽  
Airborne Lidar Data ◽  
The City ◽  
Field Plots

Airborne LiDAR data have been collected for the city of Istanbul using Riegl laser scanner Q680i with 400&amp;thinsp;kHz and an average flight height of 600&amp;thinsp;m. The flight campaign was performed by a helicopter and covers an area of 5400&amp;thinsp;km<sup>2</sup>. According to a flight speed of 80 knot a point density of more than 16 points/m<sup>2</sup> and a laser footprint size of 30&amp;thinsp;cm could be achieved. As a result of bundle adjustment, in total, approximately 17,000 LAS files with the file size of 500&amp;thinsp;m by 700&amp;thinsp;m have been generated for the whole city. The main object classes Ground, Building, Vegetation (medium, high) were derived from these LAS files using the macros in Terrasolid software. The forest area under investigation is located northwest of the city of Istanbul, main tree species occurring in the test site are pine (pinus pinaster), oak (quercus) and beech (fagus). In total, 120 LAS tiles covering the investigation area have been analysed using the software IMPACT of Joanneum Research Forschungsgesellschaft, Graz, Austria. First of all, the digital terrain model (DTM) and the digital surface models (DSM) were imported and converted into a raster file from the original laser point clouds with a spatial resolution of 50&amp;thinsp;cm. Then, a normalized digital surface model (nDSM) was derived as the difference between DSM and the DTM. Tree top detection was performed by multi – resolution filter operations and tree crowns were segmented by a region growing algorithms develop specifically for this purpose. Breast Height Diameter (BHD) was calculated on the base of tree height and crown areas derived from image segmentation applying allometric functions found in literature. The assessment of stem volume was then calculated as a function of tree height and BHD. A comparison of timber volume estimated from the LiDAR data and field plots measured by the Forest Department of Istanbul showed R2 of 0.46. The low correlation might arise either from the low quality of the field plots or from the inadequacy of the allometric functions used for BHD and stem volume modelling. Further investigations therefore will concentrate both on improving the quality of field measurements and the adoption of the allometric functions to the specific site condition of the forests under investigation. Finally stand boundaries of the forest area made available by the forest department of Istanbul were superimposed to the LiDAR data and the single tree measurements aggregated to the stand level. <br><br> Aside from the LiDAR data, a Pleiades multispectral image characterized by four spectral bands (blue, green, red and near infrared) and a GSD of 2.8&amp;thinsp;m has been used for the classification of different tree species. For this purpose the near infrared band covering the spectral range of 0.75&amp;thinsp;μm to 0.90&amp;thinsp;μm has been utilized and the IMPACT software used.

scholarly journals Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow

2021 ◽  
Vol 13 (23) ◽  
pp. 4763
Author(s):  
Joseph St. Peter ◽  
Jason Drake ◽  
Paul Medley ◽  
Victor Ibeanusi
Keyword(s):  
Open Source ◽  
Forest Structure ◽  
Basal Area ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Forested Landscapes ◽  
Quadratic Mean Diameter ◽  
Structure Metrics ◽  
Airborne Lidar Data ◽  
Structural Metrics

Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

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.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

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