Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. General Framework
2.3. LiDAR Data
2.4. Field Data
2.5. Methods
2.5.1. Preprocessing and Validation of Elevation Extracted from the Bare Soil LiDAR Point Cloud
2.5.2. Indicators of Riparian Functions
2.5.3. Direct Indicators
Indirect Indicators
2.5.4. Longitudinal Characterization of the River
3. Results
3.1. Using an Indirect Approach to Map Riparian Functions
3.1.1. Metric Selection
3.1.2. Classification Results
3.2. Longitudinal Characterization of the Sélune River
4. Discussion
4.1. Contribution of LiDAR Data to Riparian Vegetation Assessment
4.1.1. 3D LiDAR Data and Indicator Production
Characteristics of Titan LiDAR: Bispectrum and Intensity
4.1.2. Indirect Attributes: Specific Classification Issues
4.2. Longitudinal Characterization of Riparian Vegetation along a Dammed River
4.2.1. Two Compartments: The Reservoir and Downstream of the Reservoir
4.2.2. High Local Variation in Indicators
4.3. Perspective for Restoration
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Configuration | |
---|---|---|
Channel | 532 nm visible | 1064 nm near infra-red |
Aircraft altitude | 400 m | |
Point density | >45 points/m2 | |
Scan angle | 20° | |
Scan frequency | 0–210 kHz | |
Pulse repetition frequency | 50–300 kHz | |
Beam divergence | 0.7 mrad (1/e) | 0.35 mrad (1/e) |
Laser range precision | 1σ5; <0.008 m | |
Accuracy (z) | 1σ2.3; <5–10 cm |
Indicator | Attribute | Class Number | Number of Trees |
---|---|---|---|
species composition | oak | 1 | 20 |
ash | 2 | 19 | |
lime | 3 | 15 | |
alder | 4 | 28 | |
willow | 5 | 14 | |
poplar | 6 | 31 | |
chestnut | 7 | 21 | |
beech | 8 | 21 | |
number of trunks | One or two | 1 | 147 |
more than 2 | 2 | 23 | |
density of shrub stratum | low | 1 | 116 |
moderate | 2 | 30 | |
high | 3 | 24 | |
density of herbaceous stratum | low | 1 | 80 |
moderate | 2 | 69 | |
high | 3 | 21 |
Indicator | Metric | Function | Validation Method |
---|---|---|---|
Mean canopy height | Canopy height model | Spatial heterogeneity, related to species richness [1] | Elevation validation using tacheometer data |
Standard deviation of canopy height | Canopy height model | ||
Vertical canopy structure | Standard deviation of point elevation | Vertical heterogeneity, related to habitat [3] | |
Area of vegetation overhanging the river | Area of vegetation | Shading effect, water temperature regulation [28] | |
Volume of vegetation overhanging the river | Area × height of vegetation | ||
Tree species composition | Supervised classification | Biodiversity [1] | Direct: field data |
Density of the herbaceous stratum | Supervised classification | <1 m: Flood regulation, bank stabilization [29] | |
Density of the shrub stratum | Supervised classification | 1–3 m: Flood regulation, habitat [30] | |
Number of trunks | Supervised classification | Flood regulation [31] |
Type of Metric | Metric | Abbreviation | Reference |
---|---|---|---|
Vertical canopy structure | Maximum elevation | Max_elev | [12,15,21] |
Mean elevation | Mean_elev | [12,15,21] | |
Standard deviation of elevation | SD_elev | [12,15,21] | |
Strata densities | Density of the herbaceous stratum: percentage of points below 1 m | Perc_herbaceous | -- |
Density of the shrub stratum: percentage of points from 1–3 m | Perc_shrub | -- | |
Signal penetration | Leaf area index | LAI | [34] |
Signal penetration | LPI | [35] | |
Signal penetration | P | [36] | |
Mean number of returns per point | Mean_N | [37] | |
Standard deviation of the number of returns per point | SD_N | [37,38] | |
Intensity | Mean of the ratio of NIR and green | Mean_ratio | [23] |
Maximum of the ratio of NIR and green | Max_ratio | [23] | |
Variance of the ratio of NIR and green | Var_ratio | [23] | |
Mean of the GNDVI | Mean_GNDVI | [23] | |
Maximum of the GNDVI | Max_GNDVI | [23] | |
Variance of the GNDVI | Var_GNDVI | [23] |
Indicator | All Metrics | Without Intensity Metrics | Difference Using Intensity Metrics | |||
---|---|---|---|---|---|---|
Overall Accuracy (%) | Kappa (%) | Overall Accuracy (%) | Kappa (%) | Overall Accuracy (Percentage Points) | Kappa (Percentage Points) | |
Species composition | 67 | 61 | 62 | 55 | +5 | +6 |
Density of the herbaceous stratum | 73 | 55 | 72 | 53 | +1 | +2 |
Density of the shrub stratum | 78 | 34 | 78 | 37 | +0 | −3 |
Number of trunks | 88 | 30 | 88 | 31 | +0 | −1 |
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Laslier, M.; Hubert-Moy, L.; Dufour, S. Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data. Water 2019, 11, 483. https://doi.org/10.3390/w11030483
Laslier M, Hubert-Moy L, Dufour S. Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data. Water. 2019; 11(3):483. https://doi.org/10.3390/w11030483
Chicago/Turabian StyleLaslier, Marianne, Laurence Hubert-Moy, and Simon Dufour. 2019. "Mapping Riparian Vegetation Functions Using 3D Bispectral LiDAR Data" Water 11, no. 3: 483. https://doi.org/10.3390/w11030483