scholarly journals Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling

2019 ◽  
Vol 11 (1) ◽  
pp. 92 ◽  
Author(s):  
Danilo Roberti Alves de Almeida ◽  
Scott C. Stark ◽  
Gang Shao ◽  
Juliana Schietti ◽  
Bruce Walker Nelson ◽  
...  
Keyword(s):  
Grain Size ◽  
Leaf Area ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Primary Source ◽  
Airborne Lidar ◽  
Area Index ◽  
Pulse Density ◽  
Optimal Values ◽  
And Function

Airborne Laser Scanning (ALS) has been considered as a primary source to model the structure and function of a forest canopy through the indicators leaf area index (LAI) and vertical canopy profiles of leaf area density (LAD). However, little is known about the effects of the laser pulse density and the grain size (horizontal binning resolution) of the laser point cloud on the estimation of LAD profiles and their associated LAIs. Our objective was to determine the optimal values for reliable and stable estimates of LAD profiles from ALS data obtained over a dense tropical forest. Profiles were compared using three methods: Destructive field sampling, Portable Canopy profiling Lidar (PCL) and ALS. Stable LAD profiles from ALS, concordant with the other two analytical methods, were obtained when the grain size was less than 10 m and pulse density was high (>15 pulses m−2). Lower pulse densities also provided stable and reliable LAD profiles when using an appropriate adjustment (coefficient K). We also discuss how LAD profiles might be corrected throughout the landscape when using ALS surveys of lower density, by calibrating with LAI measurements in the field or from PCL. Appropriate choices of grain size, pulse density and K provide reliable estimates of LAD and associated tree plot demography and biomass in dense forest ecosystems.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

Derivation, Validation, and Sensitivity Analysis of Terrestrial Laser Scanning-Based Leaf Area Index

2016 ◽  
Vol 42 (6) ◽  
pp. 719-729 ◽  
Author(s):  
Yumei Li ◽  
Qinghua Guo ◽  
Shengli Tao ◽  
Guang Zheng ◽  
Kaiguang Zhao ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Area Index

scholarly journals Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations

2017 ◽  
Vol 9 (2) ◽  
pp. 163 ◽  
Author(s):  
Haotian You ◽  
Tiejun Wang ◽  
Andrew Skidmore ◽  
Yanqiu Xing
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Coniferous Forest ◽  
Airborne Lidar ◽  
Area Index

Optimising prediction of forest leaf area index from discrete airborne lidar

2017 ◽  
Vol 200 ◽  
pp. 220-239 ◽  
Author(s):  
Grant D. Pearse ◽  
Justin Morgenroth ◽  
Michael S. Watt ◽  
Jonathan P. Dash
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Airborne Lidar ◽  
Area Index

scholarly journals Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

2019 ◽  
Vol 11 (6) ◽  
pp. 709 ◽  
Author(s):  
Ekena Rangel Pinagé ◽  
Michael Keller ◽  
Paul Duffy ◽  
Marcos Longo ◽  
Maiza dos-Santos ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Binary Classification ◽  
Forest Degradation ◽  
Selective Logging ◽  
Ecological Impacts ◽  
Airborne Lidar ◽  
Classification Model ◽  
Amazon Forest ◽  
Gap Formation

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests

2015 ◽  
Vol 36 (10) ◽  
pp. 2569-2583 ◽  
Author(s):  
Janne Heiskanen ◽  
Lauri Korhonen ◽  
Jesse Hietanen ◽  
Petri K.E. Pellikka
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Gap ◽  
Airborne Lidar ◽  
Montane Forests ◽  
Area Index ◽  
Tropical Montane Forests ◽  
Gap Fraction

scholarly journals Comparison of Modeling Algorithms for Forest Canopy Structures Based on UAV-LiDAR: A Case Study in Tropical China

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1324
Author(s):  
Xi Peng ◽  
Anjiu Zhao ◽  
Yongfu Chen ◽  
Qiao Chen ◽  
Haodong Liu ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Sustainable Forest Management ◽  
Mean Squared Error ◽  
Structural Parameters ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Lidar Data ◽  
Area Index

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey’s mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics’ screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.

scholarly journals Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

2019 ◽  
Vol 12 (1) ◽  
pp. 4
Author(s):  
Tiangang Yin ◽  
Jianbo Qi ◽  
Bruce D. Cook ◽  
Douglas C. Morton ◽  
Shanshan Wei ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Transfer Model ◽  
Area Index ◽  
Small Footprint ◽  
Gap Probability

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

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