Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods
<p>(<b>a</b>) Location of the study area: Ulan Buh Desert, Deng Kou in the NeiMonggol, China; (<b>b</b>) positions of permanent plots in Ulan Buh Desert; (<b>c</b>) distribution location of sample plots of 30 m × 30 m.</p> "> Figure 2
<p>LiDAR point cloud pretreatment process ((<b>a</b>) refers to the normalized point cloud of sample plot; (<b>b</b>) refers to the intensity of point cloud).</p> "> Figure 3
<p>Correlation coefficients of the variables and screening index ((<b>a</b>) the variables with the top 20 correlation coefficients; (<b>b</b>) fifteen significant variables in a stepwise regression analysis, <span class="html-italic">y</span>-axis is the p value; (<b>c</b>) <span class="html-italic">x</span>-axis is the VIF value).</p> "> Figure 4
<p>Forty-two combinations of the random effects for the basic model 4. (1~6 refer to the parameters needs to be included as random effects; + refer to the combinations of the random effects).</p> "> Figure 5
<p>Predicted ground estimated AGB residuals distribution of six models (<b>A</b>–<b>F</b>) refer to the NLME model, Random Forest model, Support vector machine model, K-Nearest Neighbor, MARS model and the GBM model.</p> "> Figure 6
<p>AGB map based on the SVM model at a sample plot as an example.</p> "> Figure 7
<p>Lollipop plot with the importance of DSA (Data-based Sensitivity Analysis) variables for the SVM-Model.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Preprocessing
2.2.1. Field Survey
2.2.2. LiDAR Data and Preprocessing
2.3. Parametric Regression Models
2.3.1. Base Model
2.3.2. Nonlinear Mixed-Effects Model
2.4. Machine Learning Algorithms
2.4.1. Random Forest
2.4.2. Support Vector Machine
2.4.3. K-Nearest-Neighbor
2.4.4. Gradient Boosting Machine
2.4.5. Multivariate Adaptive Regression Splines Model
2.5. Model Evaluation
3. Results
3.1. Parametric Regression Models
3.1.1. Base Models
3.1.2. NLME Models
3.1.3. Parameter Estimates
3.2. Machine Learning
3.3. Model Evaluation
3.4. Variables Importance
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Plant Species | Formula | Allometric Exponent |
---|---|---|
Artemisia desertorum | 1.135 | |
Nitraria tangutorum | 0.945 | |
Haloxylon ammodendron | 1.007 | |
Caragana korshinskii | 1.014 | |
Hedysarum scoparium | 0.816 | |
Tamarix ramosissima | 0.869 | |
Elaeagnus angustifolia | 1.071 |
Variables | Description | Variables | Description |
---|---|---|---|
CEP1, CEP5, CEP10, …, CEP90, CEP95, CEP99 | Cumulative elevation percentile of 15 different statistical units | EMax, EMean, EMed, EStd, EVar, IMax, IMin, IMean, IMed, IStd, IVar | Maximum, Minimum, Mean, Median, Standard Deviation and Variance of Elevation and Intensity |
CIP1, CIP5, CIP10, …, CIP90, CIP95, CIP99 | Cumulative intensity percentile of 15 different statistical units | EP1, EP5, EP10…, EP95, EP99 | Elevation percentile of 15 different statistical units |
IP1, IP5, IP10, …, IP90, IP95, IP99 | Intensity percentile of 15 different statistical units | DM1, DM2…, DM10 | Point cloud density of 10 different statistical units |
CEPIq, EIq, IIq | Percentile quartile spacing of Cumulative Elevation, Elevation and Intensity | EMAD, IMAD | Median absolute deviation of the median Elevation and Intensity |
ECM, ESM | Mean elevation of Cube and Sqrt | HAd, IAd | Average absolute deviation of Elevation and Intensity |
ECv, ICv | Coefficient of variation of Elevation and Intensity | CFd | Canopy fluctuation rate |
EKu, IKu | Elevation kurtosis and intensity kurtosis | ESk, ISk | Elevation Skewness and Intensity Skewness |
Model | Number of Parameters | Training Data | Validation Data | ||||||
---|---|---|---|---|---|---|---|---|---|
RMSE | R2 | TRE | AIC | BIC | RMSE | R2 | TRE | ||
Linear | 6 | 60.9266 | 0.6362 | 20.2317 | 1771.09 | 1792.58 | 79.8046 | 0.5467 | 25.7699 |
Richards | 6 | 57.2373 | 0.6789 | 17.4414 | 1751.23 | 1772.71 | 85.4208 | 0.4807 | 25.1191 |
Logistic | 7 | 55.9541 | 0.6931 | 16.5402 | 1746.02 | 1770.57 | 74.8961 | 0.6006 | 23.2180 |
Exponential | 6 | 56.5880 | 0.6861 | 16.9811 | 1747.60 | 1769.09 | 73.3495 | 0.6169 | 22.7290 |
Parameter | Variables | Liner | Logistic | Richards | Exponential | NLME |
---|---|---|---|---|---|---|
β1 | / | −60.340 | −182.121 | −20.556 | 26.834 | 24.436 |
β2 | CEP30 | −29.370 | −3.922 | −0.336 | −0.361 | −0.106 |
β3 | EP40 | 270.500 | 0.188 | −1.059 | −0.843 | −0.928 |
β4 | Eske | 86.700 | 0.206 | −0.834 | −0.247 | −0.462 |
β5 | CIP30 | −144.790 | −0.102 | 1.901 | 2.555 | 2.197 |
β6 | CIP95 | 192.47 | −1.572 | −2.327 | −2.434 | −2.272 |
β7 | / | / | 1.117 | / | / | / |
μ5i | / | / | / | / | / | 1.062 |
μ6i | / | / | / | / | / | 0.586 |
Model | Training Data | Validation Data | ||||
---|---|---|---|---|---|---|
RMSE | R2 | TRE | RMSE | R2 | TRE (g m−2) | |
RF | 49.7021 | 0.7579 | 13.0055 | 62.8252 | 0.7191 | 14.4489 |
SVM | 26.8635 | 0.9293 | 3.5050 | 38.1919 | 0.8962 | 5.4063 |
KNN | 46.4816 | 0.7882 | 11.3961 | 63.5297 | 0.7128 | 16.5406 |
MARS | 32.0995 | 0.8990 | 4.8997 | 60.8154 | 0.7367 | 10.4576 |
GBM | 48.8357 | 0.7662 | 13.5585 | 74.0015 | 0.6103 | 23.2776 |
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Xie, D.; Huang, H.; Feng, L.; Sharma, R.P.; Chen, Q.; Liu, Q.; Fu, L. Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods. Remote Sens. 2023, 15, 3344. https://doi.org/10.3390/rs15133344
Xie D, Huang H, Feng L, Sharma RP, Chen Q, Liu Q, Fu L. Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods. Remote Sensing. 2023; 15(13):3344. https://doi.org/10.3390/rs15133344
Chicago/Turabian StyleXie, Dongbo, Hongchao Huang, Linyan Feng, Ram P. Sharma, Qiao Chen, Qingwang Liu, and Liyong Fu. 2023. "Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods" Remote Sensing 15, no. 13: 3344. https://doi.org/10.3390/rs15133344