Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

Varvia, Petri; Rautiainen, Miina; Seppänen, Aku

doi:10.1016/j.jqsrt.2018.01.008

Cited by 21 publications

(5 citation statements)

References 26 publications

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“…In LLEVAP the evaporation of an organic compound i is controlled by the difference in its gas-phase concentration C i and the equilibrium concentration C eq,i (Vesala et al, 1997;Lehtinen and Kulmala, 2003), equivalent to the gas-phase diffusional gradient between the infinite distance and particle surface, respectively. Therefore, the mass transport between the gas and the particle phases is assumed to be the limiting phenomenon and the diffusion timescales within the particle are assumed to be negligible.…”

Section: Process Modelsmentioning

confidence: 99%

Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data

et al. 2019

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The composition of organic aerosol under different ambient conditions as well as their phase state have been a subject of intense study in recent years. One way to study particle properties is to measure the particle size shrinkage in a diluted environment at isothermal conditions. From these measurements it is possible to separate the fraction of lowvolatility compounds from high-volatility compounds. In this work, we analyse and evaluate a method for obtaining particle composition and viscosity from measurements using process models coupled with input optimization algorithms. Two optimization methods, the Monte Carlo genetic algorithm and Bayesian inference, are used together with process models describing the dynamics of particle evaporation. The process model optimization scheme in inferring particle composition in a volatility-basis-set sense and compositiondependent particle viscosity is tested with artificially generated data sets and real experimental data. Optimizing model input so that the output matches these data yields a good match for the estimated quantities. Both optimization methods give equally good results when they are used to estimate particle composition to artificially test data. The timescale of the experiments and the initial particle size are found to be important in defining the range of values that can be identified for the properties from the optimization.

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Section: Process Modelsmentioning

confidence: 99%

Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data

et al. 2019

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“…where f b is the fraction of direct (non-diffuse) radiation; τ is the canopy transmittance (transmitted PAR/incident PAR); K is the extinction coefficient; χ is the leaf angle distribution, which is defined as the ratio of the horizontal to vertical axes of the ellipsoidal leaf distribution [30]; and θ is the solar zenith angle. Because the obtained parameters are inclusive of the non-photosynthetic component and aggregation effects, the obtained LAI is calculated as the LAI e [33][34][35].…”

Section: Plot-level Data Estimatesmentioning

confidence: 99%

Examining the Angular Effects of UAV-LS on Vegetation Metrics Using a Framework for Mediating Effects

Liu

Hong

et al. 2022

Forests

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Discrete point cloud data from unmanned aerial vehicle laser scanning (UAV-LS) can provide information on the three-dimensional structure of a forest, the leaf area index (LAI) at the landscape or sample plot scales, the distribution of the vertical forest structure at a fine resolution, and other information. The retrieved parameters, however, may be affected in a non-negligible way by the inclusion of scan angle information. In this study, we introduced a relational model that encompasses the angular effect, predicted the mechanism of this effect, and extracted the vegetation structure indices that the angular effect might influence. Second, we quantified the direct and indirect effects, particularly the magnitude of the angular effect in broadleaf forests, and used mediated effects to investigate the components and processes that influence the angular effect. The findings demonstrate that some of the differences between the LAIe extracted by UAV-LS and the Decagon LAIe considering the angular effect of UAV-LS can be explained by adjusting physical LiDAR parameters (aerial height, laser divergence fraction, and scanning angle) and vertical forest structure variables. Along continuous and closed forest vertical gradients, the indirect angle impact is negative for the upper canopy and positive for the understory. Three-dimensional vegetation measurements were created using multiangle LiDAR data. In conclusion, this article (1) addresses the angular effect in UAV-LS; and (2) discusses how the angular effect affects 3D vegetation parameters such as LAIe, demonstrates the nonlinear trend of the angular effect, and demonstrates how multiangle LiDAR data can be used to obtain 3D vegetation parameters. This study serves as a reference for reducing the uncertainty in simulations of the angular effect and vegetation light transmission, in addition to the uncertainty in analyses of the vegetation characteristics determined by UAV-LS (e.g., the uncertainty of LAIe).

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“…While physical uncertainty is likely more of interest to the user community, exploration of theoretical uncertainty can help understand sources of physical uncertainty and improve LAI estimation. Bayesian approaches could provide theoretical uncertainty measures from posterior distribution and concurrent estimation of LAI based on uncertainty of input parameters [96,177,216,217]. Han and Qu [177] and Qu et al [217] used Bayesian approaches to conclude that the addition of high-resolution remote sensing observation data improved LAI estimation models and increase model reliability.…”

Section: The Uncertainty Of Lai Estimationmentioning

confidence: 99%

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Quackenbush

Volk

et al. 2020

Remote Sensing

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Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

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Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

Cited by 21 publications

References 26 publications

Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data

Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data

Examining the Angular Effects of UAV-LS on Vegetation Metrics Using a Framework for Mediating Effects

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

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