Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review
Abstract
:1. Introduction
2. An Overview of the DALS System
3. LAI Retrieval Methods Using DALS PCD
3.1. Physical Method
3.1.1. Estimating LPIs from DALS PCD
- For PNB LPIs, the , derived from the single return, typically underestimates GF due to the lack of sensitivity of a single echo to the small gap within the crown [41,47,55,56], resulting in 20–30% overestimation of LAI [51]. Conversely, , which is calculated by single and last return, typically overestimates GF, thus underestimates LAI [33,55]. Weighted LPIs (e.g., , ) are typically plagued with the suitability of the weights for different return types [20,57]. Voxel-based LPIs, however, may be biased due to a wrong cell size selected subjectively [23,36,58].
- For IB LPIs, the reflectance of background and vegetation is an important factor that affects the energy from either the background or vegetation [40,59]. Therefore, it is necessary to calibrate IB LPIs for better performance [41,47,60] by introducing the backscattering coefficient of background and vegetation, or at least their ratio ().
3.1.2. Estimating G from DALS PCD
3.2. Empirical Regression Based on Proxy Variables
4. The Validation of DALS PCD-Based LAI
5. Challenges of Estimation of Forest LAI Based on DALS PCD
5.1. Model Scalability
5.2. Calibration of LPIs with a Target Spectral Property
5.3. Correcting Impact from the Clumping Effect and Woody Material
5.4. Saturation Effect
6. Conclusions and Future Direction
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Classification | NRs | RN | Example in Figure 1 |
---|---|---|---|
Single Return (SR) | =1 | =1 | # 1A, # 2A |
First Return (FR) | >1 | =1 | # 3A |
Intermediate Return (IR) | >2 | <NRs & >1 | # 3B |
Last Return (LR) | >1 | =NRs | # 3C |
LPIs Form | Reference | LPIs Form | Reference |
---|---|---|---|
[40] | [51] | ||
[33] | [42] | ||
[20] | [52] |
Type of Proxies | Proxies |
---|---|
Height-related metrics | Max height [83], mean height [84], median height [85], percentiles of height [24], base height, kurtosis of height, thickness of crown [33], height variance [86], standard deviation and coefficient of variation of height [60], interquartile distance [80], etc. |
LPIs | cf. Table 2 |
Canopy cover-related metrics | Canopy cover index [77,86], crown surface area [55], crown diameter [83], density of returns from canopy [81], canopy closure at different height [85], etc. |
Others | Canopy volume [77], number of lidar pulses per return class, and the proportion of 1st, 2nd, 3rd, and 4th returns [81], etc. |
Dominate Species | LiDAR Metrics | Type of Model | Saturate at | References | ||
---|---|---|---|---|---|---|
Picea excelsa Lam. | Gap fraction | Physical | 4 | 35.68 | [124] | |
Pseudotsuga menziesii, Tsuga heterophylla, etc. | Mean height, canopy volume, canopy cover | Empirical | ~4.5 | - | Figure 4a–c in [77] | |
Lecythis idatimon Aubl., Pouteria gongrijpii Eyma, etc. | Height percentiles, canopy cover | Empirical | ~7.5 | 4 | Figure 11 in [24] | |
Pinus taeda | IB LPIs | Physical | 4 | ~10 | [21] | |
Pinus radiata D. Don | Height related, gap fraction-like, and some complex metrics | Empirical | 5 | 11.5 | [39] | |
Pinus taeda L. | Height, LPIs, and Intensity related metric | Empirical | ~4.6 | >20 | Figure 5 in [81] | |
Populus spp., Salix spp., Sabina przewalskii kom., etc. | Height metrics | Empirical | ~4 | 5.49 | Figure 4 in [60] | |
Intensity-based metrics | ~3.8 | |||||
Combination model | ~4.5 | |||||
Eucalyptus grandis, Pinus | Point density related metric | Empirical | ~1.4 | 8 | Figures 4a and 7a in [123] | |
Eucalyptus saligna, Pinus spp., Cupressus lusita-nica, etc. | PNB and IB LPIs, canopy cover | Physical | ~4 | ~8.6 | Figure 2 in [125] | |
Pinus banksiana Lamb., Picea glauca Moench Voss, etc. | Height, cover related metric | Empirical | ~3.5 | 0.81 | Figure 13 in [85] |
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Tian, L.; Qu, Y.; Qi, J. Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review. Remote Sens. 2021, 13, 2408. https://doi.org/10.3390/rs13122408
Tian L, Qu Y, Qi J. Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review. Remote Sensing. 2021; 13(12):2408. https://doi.org/10.3390/rs13122408
Chicago/Turabian StyleTian, Luo, Yonghua Qu, and Jianbo Qi. 2021. "Estimation of Forest LAI Using Discrete Airborne LiDAR: A Review" Remote Sensing 13, no. 12: 2408. https://doi.org/10.3390/rs13122408