Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Zhao, Kaiguang; Garcı́a, Mariano; Liu, Shu; Guo, Qinghua; Chen, Gang; Zhang, Xuesong; Zhou, Yuyu; Meng, Xuelian

doi:10.1016/j.agrformet.2015.03.008

Cited by 82 publications

(56 citation statements)

References 58 publications

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“…The application of LiDAR for the retrieval of forest inventory parameters and structural characteristics has been extensively reviewed in many studies (Bergen et al, ; Dassot et al, ; Hall et al, ; van Leeuwen & Nieuwenhuis, ; K. G. Zhao, et al, ). LAI is mainly estimated from LiDAR data by means of correlation with the gap fraction (equation ; Griebel et al, ; Moorthy et al, ; J. J. Richardson et al, ; F. Zhao, Strahler, et al, ; K. Zhao et al, ). The gap fraction is not directly measured by laser scanning but derived from various laser‐based metrics, such as the laser penetration index (S.‐Z.…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

515

252

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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

515

252

View full text Add to dashboard Cite

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“…The integration for MILLER in Equation 4 was done via Gaussian quadrature. The optimization for other algorithms was performed via a Polak–Ribière conjugate gradient optimizer (Zhao et al, ). All the algorithms were coded through the mixed use of C and Fortran and were further implemented as a Matlab library and an R package named “hemiphoto2LAI” (available at github.com/zhaokg/hemiphoto2LAI ).…”

Section: Methodsmentioning

confidence: 99%

How to better estimate leaf area index and leaf angle distribution from digital hemispherical photography? Switching to a binary nonlinear regression paradigm

Zhao

Ryu

et al. 2019

Methods Ecol Evol

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Probabilistic modelling of gaps for light–canopy interactions has long served as a theoretical basis to estimate vegetation structural parameters—leaf area index (LAI) and leaf angle distribution (LAD)—from optical measurements such as hemispherical photos. Direct inversion of such probabilistic models provides a reliable statistical algorithm for parameter estimation, but this inferential paradigm has been seldom explored. Even worse, many classical LAI algorithms implicitly assume “wrong” statistical models inconsistent with the underlying probabilistic gap models—a subtle issue not articulated before but known to cause practical issues. Here, we clarified how to improve LAI and LAD estimation by directly inverting binary gap/non‐gap data of hemispherical photos via binary nonlinear regression (BNR). We implemented the new BNR method and some classical algorithms in an R package “hemiphoto2LAI”, comprising a total of 135 models for LAI estimation. Compared to classical algorithms, BNR features many theoretical advantages and allows estimating LAI and LAD simultaneously. BNR can address questions difficult to answer by classical algorithms (e.g. how better is one LAD than another?). We demonstrated the utility of the BNR paradigm based on both synthetic and real data. Overall, BNR is statistically more justifiable but its potential has been under‐appreciated. We encourage the community to embrace this new paradigm for reliable analyses of hemispherical photos or other gap data for canopy research.

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“…Small elevation changes can alter sediment stability, nutrient, organic matters, tides, salinity, and vegetation growth and therefore may cause significant vegetation transition in relatively flat wetlands [2,3]. Furthermore, topographic information is a prerequisite for extracting vegetation structural characteristics such as canopy height, vegetation cover, and biomass from remote sensing data [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…However, accurate topographic mapping in coastal areas remains challenging due to complication of hydrodynamics, ever-changing landscapes, low elevation, and dense vegetation covers [2,3]. As a common tool for topographic mapping, airborne LiDAR has typical sensor measurement accuracies between 0.10 m to 0.20 m [3,7]. Previous studies based on airborne LiDAR commonly reported mean terrain errors from 0.07 m to 0.17 m in marshes [3] and as high as 0.31 m in areas with relatively tall vegetation [2,16].…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

et al. 2017

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Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.

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Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution

Cited by 82 publications

References 58 publications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

How to better estimate leaf area index and leaf angle distribution from digital hemispherical photography? Switching to a binary nonlinear regression paradigm

Photogrammetric UAV Mapping of Terrain under Dense Coastal Vegetation: An Object-Oriented Classification Ensemble Algorithm for Classification and Terrain Correction

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