Using Airborne Lidar to Discern Age Classes of Cottonwood Trees in a Riparian Area

Abstract Airborne lidar (light detecting and ranging) is a useful tool for probing the structure of forest canopies. Such information is not readily available from other remote sensing methods and is essential for modern forest inventories. In this study, small-footprint lidar data were used to estimate biophysical properties of young, mature, and old cottonwood trees in the San Pedro River basin near Benson, Arizona. The lidar data were acquired in June 2004, using Optech’s 1233 ALTM during flyovers conducted at an altitude of 600 m. Canopy height, crown diameter, stem dbh, canopy cover, and mean intensity of return laser pulses from the canopy surface were estimated for the cottonwood trees from the data. Linear regression models were used to develop equations relating lidar-derived tree characteristics with corresponding field acquired data for each age class of cottonwoods. The lidar estimates show a good degree of correlation with ground-based measurements. This study also shows that other parameters of young, mature, and old cottonwood trees such as height and canopy cover, when derived from lidar, are significantly different (P < 0.05). Additionally, mean crown diameters of mature and young trees are not statistically different at the study site (P = 0.31). The results illustrate the potential of airborne lidar data to differentiate different age classes of cottonwood trees for riparian areas quickly and quantitatively. West. J. Appl. For. 21(3):149–158.

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Estimation of crown and canopy cover from airborne lidar data

Forestry Studies / Metsanduslikud Uurimused ◽

10.2478/v10132-011-0079-5 ◽

2010 ◽

Vol 52 (1) ◽

pp. 5-17 ◽

Cited By ~ 2

Author(s):

Mait Lang

Keyword(s):

Norway Spruce ◽

Scots Pine ◽

Stand Structure ◽

Canopy Cover ◽

Airborne Lidar ◽

Silver Birch ◽

Lidar Data ◽

Crown Cover ◽

Pine Trees ◽

Airborne Lidar Data

Metsa katvuse ja liituse hindamine lennukilt laserskanneriga Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.

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ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM) WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-2-w2-183-2015 ◽

2015 ◽

Vol II-2/W2 ◽

pp. 183-189 ◽

Cited By ~ 8

Author(s):

M. R. M. Salleh ◽

Z. Ismail ◽

M. Z. A. Rahman

Keyword(s):

Tropical Forest ◽

Digital Terrain Model ◽

Canopy Cover ◽

Airborne Lidar ◽

Lidar Data ◽

Terrain Model ◽

Digital Terrain ◽

Terrain Slope ◽

Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

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Estimation of canopy cover in dense mixed-species forests using airborne lidar data

European Journal of Remote Sensing ◽

10.1080/22797254.2017.1411169 ◽

2017 ◽

Vol 51 (1) ◽

pp. 132-141 ◽

Cited By ~ 8

Author(s):

Tauri Arumäe ◽

Mait Lang

Keyword(s):

Canopy Cover ◽

Airborne Lidar ◽

Lidar Data ◽

Mixed Species ◽

Airborne Lidar Data

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Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha.44.1.10155 ◽

2016 ◽

Vol 44 (1) ◽

Author(s):

Bogdan APOSTOL ◽

Adrian LORENT ◽

Marius PETRILA ◽

Vladimir GANCZ ◽

Ovidiu BADEA

Keyword(s):

Picea Abies ◽

Norway Spruce ◽

Test Site ◽

Volume Estimation ◽

Airborne Lidar ◽

Lidar Data ◽

Stand Volume ◽

Airborne Lidar Data

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

Forest Ecosystems ◽

10.1186/s40663-019-0212-0 ◽

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.

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

Applied Geomatics ◽

10.1007/s12518-021-00371-6 ◽

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

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

Remote Sensing ◽

10.3390/rs13040559 ◽

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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