Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges
Abstract
:1. Introduction
2. Methods
2.1. Study Area and Lidar Data Used
2.2. Forest Inventory Data
2.3. Lidar Plot Size
- (1)
- The size of the lidar plot should be as close to that needed for the FIA plot (87.8 m).
- (2)
- Given the uncertainty in the spatial location of the FIA plot, the lidar plot should have a good probability of encompassing the entire FIA plot.
2.4. Lidar Data Processing and Metrics Computed
- (1)
- For each FIA plot location, we checked whether there was a spatially corresponding lidar acquisition within ±2 years of plot measurement. If so, lidar data corresponding to a plot encompassing the FIA plot was extracted. This was a north-south oriented square plot of length 120 m, centered at the given location of the FIA plot center. These square plots will be henceforth called the buffered FIA plots (see Figure 3). A total of 3337 such plots (corresponding to FIA plots) were cut out from the lidar point cloud data.
- (2)
- Ground classification on the buffered FIA plots was done using the method of progressive TIN (triangulated irregular network) densification [26], as implemented in lasground, which is part of the open-source toolset lastools (see http://lastools.org).
- (3)
- Understory removal: Several height thresholds have been used in the literature to remove possible ground and understory points; these range from 0.9 m [27] to 2.0 m [10,28]. We decided on the threshold of 3.0 m for the following two reasons: (a) After manual inspection of several plots for understory height, we decided that a threshold of 3.0 m was better; (b) We looked at the FIA phase 3 plots for the region, where understory heights were also measured. There, we noticed that a good proportion of shrub heights were recorded above 3 m. Hence, all points in the height bin of 0.0–3.0 m (above ground) were considered non-canopy points, and were not considered for calculation of canopy height metrics.
- (1)
- Lidar distributional metrics for canopy height: Height percentiles have been shown to be significant canopy height predictors in previous related efforts [10,27]. Hence, the major percentiles (5th, 10th, 15th, 20th …) of the heights (above ground) of canopy first returns (i.e., greater than 3.0 m from the ground) over the buffered FIA plots were calculated. These are denoted as h5, h10, h15, etc. We also calculated the coefficient of variation of the heights of canopy first return points, as it has shown to be significant in such models [27]. This metric is denoted as cv_canPts.
- (2)
- Plot homogeneity: This quantifies the homogeneity of distribution of vegetation height in the area around the FIA plots. The importance of this metric will be explained in the next section. For computing it, we divide the buffered FIA plot into 144 square units, each of area 100 m2. This was done by using a regular grid pattern. Then, we calculate the 85th percentile of heights of all returns (including understory) for each of those 144 square units. Finally, we calculate the coefficient of variation of these 144 values (henceforth CV), which is a normalized quantification of the amount of dispersion of vegetation heights over the plot. Hence, given that (h85)i is the 85th percentile height of all lidar returns for the ith square unit, the CV at the plot level is calculated as:
- (1)
- Scan angle: The average scan angle, in degrees, as recorded in the lidar metadata. Additionally, we flagged plots that had lidar data with either no scan angle recorded or had scan angles improperly recorded. Also, we flagged plots where the lidar data were from multiple flight lines (hence, averaged scan angles are not representative). These were marked with a “no good scan angle information” flag.
- (2)
- Slope of the buffered FIA lidar plot: This is the average slope, expressed in degrees, measured in the field by the FIA crew.
- (3)
- The dominant tree height of all the trees measured by FIA on the subplots. The dominant tree height (henceforth “dominant height”) is the average height of the five tallest trees in the four FIA subplots. Note that this definition differs from the more common definition used in forestry. We used this definition to maintain consistency across all plots.
- (4)
- We also estimated the effect of broad species grouping (softwood vs. hardwood) on the models. Softwood trees include evergreen conifers such as pines, spruces and cedars, while hardwoods include deciduous trees such as oaks, maples and birch. The FIA field crew also records species data for all trees measured. We used that information to estimate the percentage (by basal area) of softwoods in the four FIA subplots. That is, we defined and calculated percent_softwood using the following formula:
2.5. Accounting for Plots with Multiple Land Use Conditions
2.6. Main Model Specification
- (1)
- A height percentile from lidar canopy first returns (from the set of h5, h10, h15, h20, etc). The percentile chosen was the one most correlated to the dominant height.
- (2)
- cv_canPts, the coefficient of variation of the heights of canopy first returns.
2.7. Factors Affecting Efficacy of Prediction
- (a)
- Point density of lidar returns (all returns), over the buffered FIA plot. This is expressed as (number of returns)/m2.
- (b)
- Plot homogeneity, estimated from lidar returns (quantified by CV).
- (c)
- Percent softwood (estimated from FIA field data).
- (d)
- Average height of trees (estimated from FIA field data).
- (e)
- Slope of the plot terrain (estimated from FIA field data).
- (f)
- Average lidar pulse scan angle (estimated from lidar metadata).
2.8. Generation of Sample Canopy Height Map
3. Results
3.1. Main Model
3.2. Variable Importance for Goodness of Fit
3.3. Variable Importance from the Random Forest Model
Variable | Increase in Error (%) | Brown-Forsythe Test Statistic |
---|---|---|
h85 | 113.2 | NA |
Percent softwood | 22.3 | 7.28 |
CV (measure of plot homogeneity) | 15.0 | 55.05 |
cv_canPts | 14.3 | NA |
Slope | 9.0 | 0.67 |
Point density (PD) | 4.2 | 2.17 |
Scan angle | 3.3 | 0.41 |
3.4. Sample Canopy Height Map
4. Discussion
4.1. The Influence of Co-Registration Issues
- Etotal is the total error, which is the difference between the actual dominant height of the forest patches on that lidar plot, and that predicted by our models.
- Emodel is the error in modelling the dominant height of the trees on the FIA subplots from the lidar data, on the lidar plot. This is captured by the RMSEs of our models, and may be due to several factors, as discussed in Section 2.7.
- Enon-representative-subplots is the error caused by the fact that the dominant height of the trees on the FIA subplots (that we model) is not the same as the dominant height of the trees over the entire lidar plot. The difference can sometimes be large, may be due to the fact that the FIA subplots may be sampling a patch of forest with very different characteristics (see above) or that most of the area of the subplots could be outside the lidar plot (due to very high co-registration errors). But in general, this error term is expected to be lesser in areas where vegetation is more homogeneous. There have been recent reports that LiDAR metrics are less sensitive to co-registration errors in dense, spatially homogeneous stands than sparse, heterogeneous stands [40]. However, further work is needed to quantify this term.
- EFIA-measurements is the error in the FIA measurement, recording and processing of tree heights. We assume this to be relatively so small as to be negligible.
4.2. Lidar-Based Grid Cell Homogeneity
CV Threshold | Number of Plots | % of Original Num. Plots | R2 | RMSE (m) |
---|---|---|---|---|
CV < 0.5 | 1573 | 89.6 | 0.81 | 2.60 |
CV < 0.2 | 729 | 41.5 | 0.84 | 2.44 |
4.3. The Advantage of Using National-Level Forest Inventory Plots
4.4. Uses of Large-Area Canopy Height Maps
4.5. Possible Improvements to Lidar Data
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
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Gopalakrishnan, R.; Thomas, V.A.; Coulston, J.W.; Wynne, R.H. Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges. Remote Sens. 2015, 7, 11036-11060. https://doi.org/10.3390/rs70911036
Gopalakrishnan R, Thomas VA, Coulston JW, Wynne RH. Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges. Remote Sensing. 2015; 7(9):11036-11060. https://doi.org/10.3390/rs70911036
Chicago/Turabian StyleGopalakrishnan, Ranjith, Valerie A. Thomas, John W. Coulston, and Randolph H. Wynne. 2015. "Prediction of Canopy Heights over a Large Region Using Heterogeneous Lidar Datasets: Efficacy and Challenges" Remote Sensing 7, no. 9: 11036-11060. https://doi.org/10.3390/rs70911036