Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data
Abstract
:1. Introduction
- Do off-nadir MISR-HR products significantly improve the estimation of structural parameters compared to those obtained with the nadir-pointing camera only?
- Which data channels or combinations thereof, have the greatest potential to improve the canopy structure models performance?
- Does the performance of predicting structural parameters improve whilst using the MISR-HR RPV model parameters (ρ0, k, Θ) compared to the full MISR-HR BRF multi-angle multi-spectral dataset (i.e., 4 spectral bands at nine view angles)?
2. Materials and Methods
2.1. South African Forested Landscapes and Study Area
2.2. LiDAR Data
2.3. LiDAR Processing and Derived Structural Parameters
- Tree height is defined as the vertical distance from the base of the tree to its treetop [71]. It plays a key role in forest ecosystem studies, for instance for predicting species richness and species distribution models [72,73] or for assessing fire severity and modelling fire escape mechanisms [74,75]. It contributes to estimate variables such as canopy volume and biomass [76,77]. The mean tree height (Hmean) parameter was calculated as the average of the CHM pixels excluding the non-tree pixels (<1 m) within each MISR-HR 275 m pixel. This is a useful measure especially in even-aged forests. There was a strong relationship between tree height estimated by the CAO CHMs and field measurements (R2 = 0.93, p-value < 0.001 and standard error of 0.73 m), as described in Wessels et al. [78].
- Canopy cover (CC) is defined as the percentage area of a MISR-HR 275 m pixel covered by the vertical projection of tree crowns. As a simple 2D structural measure, CC is a key descriptor of ecosystems and is useful for monitoring vegetation changes, for instance habitat connectivity and fragmentation [79,80,81]. This parameter was estimated by calculating the percentage of CHM pixels with a height above 1 m relative to the total number of LiDAR pixels included in a 275 m MISR pixel. A strong relationship between CAO LiDAR-derived CC and field measurements was previously demonstrated for the KNP dataset (R2 = 0.79, Root Mean Square Error (RMSE) of 12.4%) [17].
2.4. MISR Data and Processing
- The MISR L1B2 Terrain-projected Global Mode data, which contains top of atmosphere (TOA) radiance measurements, resampled at surface level and topographically corrected.
- The MISR L2 Terrain–projected bottom of atmosphere (BOA) bidirectional reflectance factors (BRFs), generated by NASA’s standard processing system at 1.1 km spatial resolution.
- Geometric Parameters Product (GPP).
- Ancillary Geographic Product (AGP), which are the reference datasets containing the full latitude/longitude information.
2.5. Data Analysis
- Scenario 0: The baseline reference scenario was set up to establish the expected performance of a RF model using a traditional approach based on the red and NIR spectral bands similar to that provided by the nadir-viewing instruments such as the MODIS instrument. These two MISR-HR datasets bands (at 275 m) have a similar pixel size to that of MODIS dataset (i.e., 250 m).
- Scenario 1: The first scenario considered all four spectral bands from the An (nadir) camera. This scenario was carried out to establish whether adding more spectral bands from the nadir-pointing cameras improves the predictive capacity of the RF model.
- Scenario 2: This second scenario sought to evaluate if angular data is superior to spectral data for the retrieval of the forest parameters, or vice versa. We developed a set of four models; each including all view angles for a single spectral band (i.e., one RF model for each of the MISR bands and involving all 9 cameras).
- Scenario 3: The third scenario assessed the RF model performance constrained by data in each view angle separately. It consisted of eight models including all spectral bands for each individual forward and aft viewing angle (i.e., off-nadir camera: Af, Bf, Cf, Df, Aa, Ba, Ca and Da).
- Scenario 4: The fourth scenario was carried out to ascertain whether the combined use of all MISR angular and spectral data (i.e., the 36 MISR-HR BRF data channels) improves the forest structural parameter retrievals compared to any of the previous scenarios and if it does by how much.
- Scenario 5: The fifth scenario assessed if the anisotropic BRF parameters provide additional benefits compared to the raw MISR-HR BRF data (scenario 4). Here, we tested four models, three models which assessed the performance of each individual RPV model parameter (ρ0, k, Θ) considering all spectral bands, and one additional model combining all three RPV parameters for all spectral bands.
3. Results
3.1. LiDAR Based Strucutural Variability
3.2. Retriaval Performance from Nadir Reflectance
3.3. Comparison of Spectral Information Versus Angular Performance
3.4. Comparison of Single Angular MISR Cameras
3.5. Contribution of All 36 MISR Data Channels
3.6. Contribution of MISR-HR RPV Model Paramters
3.7. Model Performance across Vegetation Types
4. Discussion
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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Lowveld-Savanna n = 6150 | iSimangaliso St-Lucia Indigenous Forest n = 439 | iSimangaliso St-Lucia Forest Plantation n = 755 | Combined Samples n = 7344 | |||||
---|---|---|---|---|---|---|---|---|
Parameters | Hmean | CC | Hmean | CC | Hmean | CC | Hmean | CC |
(m) | (%) | (m) | (%) | (m) | (%) | (m) | (%) | |
Mean | 3.6 | 24.3 | 6.19 | 48.9 | 11.7 | 50 | 4.6 | 29.6 |
SD | 0.8 | 14.2 | 2.51 | 23.8 | 3.9 | 16.4 | 2.9 | 19.5 |
Min | 1.2 | 0 | 1.02 | 0.9 | 1 | 1.6 | 1 | 0 |
Max | 8.4 | 71.9 | 14.2 | 90.6 | 21.4 | 90.9 | 21.4 | 90.9 |
CV | 0.2 | 0.6 | 0.4 | 0.5 | 0.3 | 0.3 | 0.6 | 0.7 |
Target Variable | Hmean (m) | CC (%) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
View-Angle a (Spectral Bands b) | No. of Inputs | R2 | RMSE (m) | rRMSE (%) | Bias | rBias (%) | No of Inputs | R2 | RMSE (%) | rRMSE (%) | Bias | rBias (%) |
Scenario 0 Nadir (red and NIR) | 2 | 0.56 | 1.97 | 42.80 | 0.06 | 1.19 | 2 | 0.37 | 15.28 | 51.12 | 0.83 | 2.76 |
Scenario 1 Nadir(All) | 4 | 0.66 | 1.73 | 37.36 | 0.07 | 1.43 | 4 | 0.51 | 13.36 | 44.69 | 0.21 | 0.71 |
Scenario 2 All(blue) | 9 | 0.65 | 1.76 | 38.03 | 0.04 | 0.84 | 9 | 0.50 | 13.42 | 44.89 | 0.41 | 1.36 |
Scenario 2 All(green) | 9 | 0.67 | 1.69 | 36.70 | 0.03 | 0.67 | 9 | 0.55 | 12.75 | 42.66 | 0.42 | 1.41 |
Scenario 2 All(red) | 9 | 0.66 | 1.71 | 37.07 | 0.05 | 0.97 | 9 | 0.55 | 12.82 | 42.87 | 0.21 | 0.69 |
Scenario2 All(NIR) | 9 | 0.66 | 1.72 | 37.27 | 0.02 | 0.39 | 9 | 0.54 | 13.16 | 44.22 | 0.32 | 1.06 |
Scenario 3 Off_Nadir_Df(All) | 4 | 0.65 | 1.75 | 37.90 | 0.04 | 0.82 | 4 | 0.44 | 14.57 | 48.95 | 0.18 | 0.59 |
Scenario 3 Off_Nadir_Cf(All) | 4 | 0.62 | 1.81 | 39.15 | 0.03 | 0.69 | 4 | 0.44 | 14.32 | 47.90 | 0.25 | 0.83 |
Scenario 3 Off_Nadir_Bf(All) | 4 | 0.64 | 1.77 | 38.27 | 0.05 | 1.13 | 4 | 0.47 | 13.99 | 46.80 | 0.54 | 1.80 |
Scenario 3 Off_Nadir_Af(All) | 4 | 0.66 | 1.72 | 37.29 | 0.06 | 1.30 | 4 | 0.49 | 13.58 | 45.43 | 0.32 | 1.07 |
Scenario 3 Off_Nadir_Aa(All) | 4 | 0.66 | 1.73 | 37.44 | 0.04 | 0.87 | 4 | 0.49 | 13.59 | 45.45 | 0.45 | 1.50 |
Scenario 3 Off_Nadir_Ba(All) | 4 | 0.66 | 1.74 | 37.68 | 0.02 | 0.35 | 4 | 0.47 | 13.88 | 46.43 | 0.47 | 1.56 |
Scenario 3 Off_Nadir_Ca(All) | 4 | 0.66 | 1.75 | 37.83 | 0.04 | 0.93 | 4 | 0.49 | 13.89 | 46.67 | 0.04 | 0.14 |
Scenario 3 Off_Nadir_Da(All) | 4 | 0.61 | 1.86 | 40.35 | 0.03 | 0.65 | 4 | 0.47 | 14.15 | 47.54 | 0.28 | 0.94 |
Scenario 4 All(All) | 36 | 0.73 | 1.53 | 33.14 | 0.01 | 0.30 | 36 | 0.64 | 11.54 | 38.58 | 0.27 | 0.91 |
Target Variable | Hmean (m) | CC (%) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
RPV Parameters a | No of Bands | R2 | RMSE (m) | rRMSE (%) | Bias | rBias (%) | No of Bands | R2 | RMSE (%) | rRMSE (%) | Bias | rBias (%) |
Scenario 5 ρ0 (All) | 4 | 0.65 | 1.76 | 38.22 | 0.00 | 0.00 | 4 | 0.49 | 13.90 | 46.69 | −0.05 | −0.15 |
Scenario 5 k (All) | 4 | 0.62 | 1.83 | 39.59 | 0.01 | 0.24 | 4 | 0.48 | 13.94 | 46.84 | 0.00 | 0.01 |
Scenario 5 Θ (All) | 4 | 0.48 | 2.14 | 46.32 | −0.01 | −0.11 | 4 | 0.43 | 14.71 | 49.42 | 0.14 | 0.47 |
Scenario 5All (All) | 12 | 0.71 | 1.61 | 34.84 | 0.01 | 0.10 | 12 | 0.60 | 12.19 | 40.96 | 0.04 | 0.13 |
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Mahlangu, P.; Mathieu, R.; Wessels, K.; Naidoo, L.; Verstraete, M.; Asner, G.; Main, R. Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data. Remote Sens. 2018, 10, 1537. https://doi.org/10.3390/rs10101537
Mahlangu P, Mathieu R, Wessels K, Naidoo L, Verstraete M, Asner G, Main R. Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data. Remote Sensing. 2018; 10(10):1537. https://doi.org/10.3390/rs10101537
Chicago/Turabian StyleMahlangu, Precious, Renaud Mathieu, Konrad Wessels, Laven Naidoo, Michel Verstraete, Gregory Asner, and Russell Main. 2018. "Indirect Estimation of Structural Parameters in South African Forests Using MISR-HR and LiDAR Remote Sensing Data" Remote Sensing 10, no. 10: 1537. https://doi.org/10.3390/rs10101537