A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods

2006 ◽  
Vol 32 (5) ◽  
pp. 355-366 ◽  
Author(s):  
Hans-Erik Andersen ◽  
Stephen E Reutebuch ◽  
Robert J McGaughey
Keyword(s):  
Tree Height ◽  
Airborne Lidar ◽  
Field Methods ◽  
Conventional Field

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.

scholarly journals Estimation of Tree Height and Forest Biomass Using Airborne LiDAR Data: A Case Study of Londiani Forest Block in the Mau Complex, Kenya

2017 ◽  
Vol 07 (02) ◽  
pp. 255-269 ◽  
Author(s):  
Faith Kagwiria Mutwiri ◽  
Patroba Achola Odera ◽  
Mwangi James Kinyanjui
Keyword(s):  
Forest Biomass ◽  
Tree Height ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Tree height explains mortality risk during an intense drought

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Atticus E. L. Stovall ◽  
Herman Shugart ◽  
Xi Yang
Keyword(s):  
Environmental Gradients ◽  
Tree Height ◽  
Mortality Rates ◽  
Airborne Lidar ◽  
Optical Data ◽  
Social Benefits ◽  
Tree Level ◽  
Large Trees ◽  
Persistent Drought ◽  
Temperature Water

Abstract Forest mortality is accelerating due to climate change and the largest trees may be at the greatest risk, threatening critical ecological, economic, and social benefits. Here, we combine high-resolution airborne LiDAR and optical data to track tree-level mortality rates for ~2 million trees in California over 8 years, showing that tree height is the strongest predictor of mortality during extreme drought. Large trees die at twice the rate of small trees and environmental gradients of temperature, water, and competition control the intensity of the height-mortality relationship. These findings suggest that future persistent drought may cause widespread mortality of the largest trees on Earth.

scholarly journals LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand

Forests ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 702 ◽  
Author(s):  
Jan Zörner ◽  
John Dymond ◽  
James Shepherd ◽  
Susan Wiser ◽  
Ben Jolly
Keyword(s):  
New Zealand ◽  
High Performance ◽  
Tree Height ◽  
Airborne Lidar ◽  
Aerial Photographs ◽  
Fast Method ◽  
Height Distribution ◽  
Detection Scheme ◽  
Crown Delineation ◽  
Indigenous Forest

Indigenous forests cover 23.9% of New Zealand’s land area and provide highly valued ecosystem services, including climate regulation, habitat for native biota, regulation of soil erosion and recreation. Despite their importance, information on the number of tall trees and the tree height distribution across different forest classes is scarce. We present the first region-wide spatial inventory of tall trees (>30 m) based on airborne LiDAR (Light Detection and Ranging) measurements in New Zealand—covering the Greater Wellington region. This region has 159,000 ha of indigenous forest, primarily on steep mountainous land. We implement a high-performance tree mapping algorithm that uses local maxima in a canopy height model (CHM) as initial tree locations and accurately identifies the tree top positions by combining a raster-based tree crown delineation approach with information from the digital surface and terrain models. Our algorithm includes a check and correction for over-estimated heights of trees on very steep terrain such as on cliff edges. The number of tall trees (>30 m) occurring in indigenous forest in the Wellington Region is estimated to be 286,041 (±1%) and the number of giant trees (>40 m tall) is estimated to be 7340 (±1%). Stereo-analysis of aerial photographs was used to determine the accuracy of the automated tree mapping. The giant trees are mainly in the beech-broadleaved-podocarp and broadleaved-podocarp forests, with density being 0.04 and 0.12 (trees per hectare) respectively. The inventory of tall trees in the Wellington Region established here improves the characterization of indigenous forests for management and provides a useful baseline for long-term monitoring of forest conditions. Our tree top detection scheme provides a simple and fast method to accurately map overstory trees in flat as well as mountainous areas and can be directly applied to improve existing and build new tree inventories in regions where LiDAR data is available.

scholarly journals Democratic Republic of the Congo Tropical Forest Canopy Height and Aboveground Biomass Estimation with Landsat-8 Operational Land Imager (OLI) and Airborne LiDAR Data: The Effect of Seasonal Landsat Image Selection

2020 ◽  
Vol 12 (9) ◽  
pp. 1360
Author(s):  
Herve B. Kashongwe ◽  
David P. Roy ◽  
Jean Robert B. Bwangoy
Keyword(s):  
Tropical Forest ◽  
Aboveground Biomass ◽  
Democratic Republic ◽  
Tree Height ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Landsat Image ◽  
Landsat 8 ◽  
Republic Of The Congo ◽  
The Mean

Inventories of tropical forest aboveground biomass (AGB) are often imprecise and sparse. Increasingly, airborne Light Detection And Ranging (LiDAR) and satellite optical wavelength sensor data are used to map tree height and to estimate AGB. In the tropics, cloud cover is particularly prevalent and so several years of satellite observations must be considered. This may reduce mapping accuracy because of seasonal and inter-annual changes in the forest reflectance. In this paper, the sensitivity of airborne LiDAR and Landsat-8 Operational Land Imager (OLI) based dominant canopy height and AGB 30 m mapping is assessed with respect to the season of Landsat acquisition for a ~10,000 Km2 tropical forest area in the Democratic Republic of the Congo. A random forest regression estimator is used to predict and assess the 30 m dominant canopy height using LiDAR derived test and training data. The AGB is mapped using an allometric model parameterized with the dominant canopy height and is assessed by comparison with field based 30 m AGB estimates. Experiments are undertaken independently using (i) only a wet season Landsat-8 image, (ii) only a dry season Landsat-8 image, and (iii) both Landsat-8 images. At the study area level there is little reported sensitivity to the season of Landsat image used. The mean dominant canopy height and AGB values are similar between seasons, within 0.19 m and 5 Mg ha−1, respectively. The mapping results are improved when both Landsat-8 images are used with Root Mean Square Error (RMSE) values that correspond to 18.8% of the mean study area mapped tree height (20.4 m) and to 41% of the mean study area mapped AGB (204 Mg ha−1). The mean study area mapped AGB is similar to that reported in other Congo Basin forest studies. The results of this detailed study are illustrated and the implications for tropical forest tree height and AGB mapping are discussed.

Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

2011 ◽  
Vol 16 (6) ◽  
pp. 425-431 ◽  
Author(s):  
Kazukiyo Yamamoto ◽  
Tomoaki Takahashi ◽  
Yousuke Miyachi ◽  
Naoto Kondo ◽  
Shinichi Morita ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Tree Height ◽  
Airborne Lidar ◽  
Terrain Model ◽  
Digital Terrain ◽  
Small Footprint

scholarly journals Reliable Estimates of Merchantable Timber Volume from Terrestrial Laser Scanning

2021 ◽  
Vol 13 (18) ◽  
pp. 3610
Author(s):  
Dimitrios Panagiotidis ◽  
Azadeh Abdollahnejad
Keyword(s):  
Laser Scanning ◽  
Accurate Determination ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Tree Height ◽  
Tree Level ◽  
Consensus Method ◽  
Field Methods ◽  
Forest Inventories ◽  
Random Sampling Consensus

Simple and accurate determination of merchantable tree height is needed for accurate estimations of merchantable volume. Conventional field methods of forest inventory can lead to biased estimates of tree height and diameter, especially in complex forest structures. Terrestrial laser scanner (TLS) data can be used to determine merchantable height and diameter at different heights with high accuracy and detail. This study focuses on the use of the random sampling consensus method (RANSAC) for generating the length and diameter of logs to estimate merchantable volume at the tree level using Huber’s formula. For this study, we used two plots; plot A contained deciduous trees and plot B consisted of conifers. Our results demonstrated that the TLS-based outputs for stem modelling using the RANSAC method performed very well with low bias (0.02 for deciduous and 0.01 for conifers) and a high degree of accuracy (97.73% for deciduous and 96.14% for conifers). We also found a high correlation between the proposed method and log length (−0.814 for plot A and −0.698 for plot B), which is an important finding because this information can be used to determine the optimum log properties required for analyzing stem curvature changes at different heights. Furthermore, the results of this study provide insight into the applicability and ergonomics during data collection from forest inventories solely from terrestrial laser scanning, thus reducing the need for field reference data.

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