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

Yin, Tiangang; Qi, Jianbo; Cook, Bruce D.; Morton, Douglas C.; Wei, Shanshan; Gastellu-Etchegorry, Jean-Philippe

doi:10.3390/rs12010004

Cited by 22 publications

(8 citation statements)

References 78 publications

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“…Most studies suggested that PNB LPIs are superior to IB LPIs for DALS PCD [33,51]. The use of IB LPIs remains a controversial issue due to the unclear physical meaning of intensity and difficulty in performing intensity correction [1,21,42,53,54].…”

Section: Lpis Formmentioning

confidence: 99%

“…The empirical method was also highly sensitive to tree age, time, stand density, the accuracy of field measurement data of LAI, etc. [53,55,57,82]. Overall, all regression models are calibrated and validated against local field data; however, reliable models that can be transferred to other locations, times, and different LiDAR systems are unfortunately lacking [21,53].…”

Section: Empirical Regression Based On Proxy Variablesmentioning

confidence: 99%

“…[53,55,57,82]. Overall, all regression models are calibrated and validated against local field data; however, reliable models that can be transferred to other locations, times, and different LiDAR systems are unfortunately lacking [21,53].…”

Section: Empirical Regression Based On Proxy Variablesmentioning

confidence: 99%

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Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

Luo

2021

Remote Sensing

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The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research.

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Section: Lpis Formmentioning

confidence: 99%

Section: Empirical Regression Based On Proxy Variablesmentioning

confidence: 99%

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Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

Luo

2021

Remote Sensing

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“…Leaves with a square shape and an area of 0.05×0.05 m 2 (used in [42]) were randomly distributed within cylindrical, spherical, and conical tree crowns. We generated six different values of Leaf Area Density (ρ, unit: m 2 /m 3 ), the one-sided leaf area per volumetric unit [43], ranging from 0.25 to 1.50 (the ρ used in previous studies ranges from 0.25 to 1.59 [35], [42], [44], [45]) with an increment of 0.25. Both sides of the leaf surface and the ground were all set to bi-Lambertian.…”

Section: ) Abstract Discontinuous Canopiesmentioning

confidence: 99%

Correcting Crown-Level Clumping Effect for Improving Leaf Area Index Retrieval From Large-Footprint LiDAR: A Study Based on the Simulated Waveform and GLAS Data

Jiang¹,

Yan²,

Tong³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The demand for Leaf Area Index (LAI) retrieval from spaceborne full-waveform LiDAR increases due to its direct sampling of the three-dimensional forest structure at a near-global scale. However, the nonrandomness (i.e., clumping effect) of canopy composition limits the reliability of LAI derived from two common methods. They either assume a homogeneous scene in the footprint or just correct for the large gaps-induced between-crown clumping. The clumping in the crown is still an unaddressed issue. We proposed a method to compensate occlusion (i.e., lower canopy layers are occluded by the upper canopy in the process of LiDAR measurement), through which the vertical canopy profile can be resolved from the waveform. Further, we developed a method of deriving relative path length distribution that can reflect the heterogeneity of the canopy from the occlusion-corrected waveform. In addition to correcting the between-crown clumping, we corrected the within-crown clumping further using the derived relative path length distribution, based on path length distribution (PATH) theory. We used simulated waveform data with known LAI and GLAS data with corresponding field-measured LAI to test the performance of our and the other two common LAI retrieval methods. Results show that the errors of our approach are the lowest (with an error generally below 10% and the maximum error below 20%, compared with up to 69% and 47% for the other two methods), and it is relatively stable in various scenes. This study demonstrated the potential of improving LAI retrieval through full utilization of full-waveform data.

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“…Techniques utilizing remote sensing to retrieve LAI can be divided into three categories according to the platform: 1) ground-based measurement, 2) airborne observation, and 3) satellite observation [5][6][7][8]. As the most accurate measurement of forest LAI, ground-based measurement is usually used to verify the LAI inversion of higher platforms (e.g., airborne or spaceborne) [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Exploring Photon-Counting Laser Altimeter ICESat-2 in Retrieving LAI and Correcting Clumping Effect

Guo

Yan

Song

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) employs a unique multi-beam photon counting approach to acquire a near-continuously sampled profile and provides more precise technology for mapping the leaf area index (LAI) at the global scale. The inversion accuracy of LAI is affected by the clumping effect, which has been an open question for spaceborne laser scanning. Here, we present a segmented method based on the path length distribution model to calculate the clumping-corrected LAI independently using ICESat-2 data. The results showed that the LAI derived by the proposed method with a 200 m segment was consistent with the airborne laser scanningderived LAI, with an RMSE of 0.37. A satisfactory agreement (RMSE = 1.03) was also shown between MODIS LAI and ICESat-2 LAI. Moreover, the LAI derived by the proposed method was on average 31.72 % higher than the LAIe derived by Beer's law, which indicated that the proposed method achieved the purpose of correcting the clumping effect. The gap probability calculated by the 200 m moving window and path length distribution obtained by the 1 m moving window as the model input had the highest accuracy. In addition, the limitation of the point cloud data and the time lag of ICESat-2 acquisitions and ALS observations may affect the inversion accuracy of LAI. This study proposed a feasible way to correct the clumping effect and invert LAI independently using ICESat-2 data, which has the potential to characterize vegetation structure precisely at regional and global scales.

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

Cited by 22 publications

References 78 publications

Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review

Correcting Crown-Level Clumping Effect for Improving Leaf Area Index Retrieval From Large-Footprint LiDAR: A Study Based on the Simulated Waveform and GLAS Data

Exploring Photon-Counting Laser Altimeter ICESat-2 in Retrieving LAI and Correcting Clumping Effect

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