# Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems

^{1}

^{2}

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## Abstract

**:**

^{2}= 0.89, RMSE = 0.10). This methodology shows potential for successfully refining estimates of GF at large scales and identifies the most suitable attributes for deriving appropriate scaling factors. Large-scale active sensor estimates of GF can establish a baseline from which future monitoring investigations can be initiated via upcoming Earth Observation missions.

## 1. Introduction

^{2}) to global scales [2,3,4]. However, the passive nature and/or low resolution of satellite sensors have restricted our ability to simultaneously map canopy structural attributes over large areas and at the tree crown level [5,6].

## 2. Materials and Methods

#### 2.1. Discrete Return ALS Data

^{2}). Watts Creek (37.692°S, 145.684°E), located 70 km east of Melbourne, is an old-regrowth open forest dominated by Eucalypts over complex terrain. ALS data were acquired with a mean point density of 5–6 ppm

^{2}. Warra (43.106°S, 146.657°E) approximately 60 km southwest of Hobart, Tasmania, is a cool, temperate wet forest biome over moderately complex terrain. The site consists of moorland, temperate rainforest, riparian and montane conifer forest and shrubs. ALS data were acquired during May 2014 with a mean point density of up to 25 ppm

^{2}. Tumbarumba (35.686°S, 148.234°E) is approximately 120 km southwest of Canberra. It is a Eucalypt-dominated, moderately open forest with complex terrain [23,24]; ALS data were acquired during November 2009 at a mean point density of 5 ppm

^{2}. This site is not to be confused with the TERN site at the Tumbarumba research station, the site employed here is located approximately 7 km east of the research station, but the vegetation and terrain characteristics observed at the research station are still applicable. This location was strategically selected as a study site due to overlap with numerous GLAS footprints.

#### 2.2. GLAS Data

#### 2.3. ALS Gap Fraction

^{2}= 0.92) between this ALS product and field measured GF via Digital Hemispherical Photographs (DHPs) in mature and regenerating mixed wood plots [13]. This method was up scaled and applied to 7 Canadian boreal sites where similar high correlation was also demonstrated (R

^{2}= 0.77) [1].

#### 2.4. GLAS Gap Fraction

#### 2.5. Gap Fraction Scaling

#### 2.6. Predictor Attributes

#### 2.7. Gap Fraction Comparisons

^{2}), fraction of predictions within a factor of 2 of observations (F

_{2}), fractional bias (F

_{B}) and mean prediction bias ($\overline{{\mathrm{P}}_{\mathrm{B}}}$). The difference in model summary statistics is noted between ALS and unscaled GLAS estimates of GF for all available GLAS data and the optimal subset. This comparison was also performed for two forms of scaled GLAS estimates of GF. The first set of refined GLAS GF estimates was modified based on predictions of f made from an RF model trained via all available GLAS data, where the second predictions of f were made via an RF model trained using the optimal GLAS subset only. The percentage difference of each summary statistic is calculated between unscaled and scaled GLAS GF estimates for all and the optimal datasets. Assessing the greatest accuracy gains between all and optimal datasets helps to identify if the restrictions of the latter are worth imposing for predicting large-scale estimates of GF across Australia.

## 3. Results

#### 3.1. Consistency Assessment

#### 3.2. Scaling Factor Sensitivity

#### 3.3. Gap Fraction Comparisons

## 4. Discussion

## 5. Conclusions

^{2}= 0.89, RMSE = 0.10). This represented the greatest percent change (all model summary statistics considered) in the results with respect to unscaled GF equivalents, suggesting that restricting GLAS data to what is deemed optimal holds value for GF purposes, not only vegetation height.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

ALS | Airborne Laser Scanning |

ASRIS | Australian Soil Resource Information System |

DLCD | Dynamic Land Cover Dataset |

FC | Fractional Cover |

GEDI | Global Ecosystem Dynamics Investigation |

GF | Gap Fraction |

GLAS | Geoscience Laser Altimeter System |

ICESat | Ice, Cloud and land Elevation Satellite |

LAI | Leaf Area Index |

LiDAR | Light Detection And Ranging |

LVIS | Land, Vegetation, and Ice Sensor |

MODIS | Moderate Resolution Imaging Spectroradiometer |

NASA | National Aeronautic Space Administration |

NVIS | National Vegetation Inventory System |

RMSE | Root Mean Squared Error |

SLICER | Scanning Lidar Imager of Canopies by Echo Recovery |

SRTM | Shuttle Radar Topography Mission |

VBF | Valley Bottom Flatness |

VCF | Vegetation Continuous Fields |

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**Figure 1.**Location of study sites. ALS extents are shown in red, and GLAS footprint centres are indicated by an orange point.

**Figure 2.**Example waveform indicating (

**a**) the locations of the canopy top, ground elevation, and an example height threshold above the ground; and (

**b**) the same waveform with fitted Gaussians.

**Figure 3.**Methodological workflow for obtaining Squared Sum of fitted model Residuals (SSR) for independent predictor ranking from ALS and GLAS GF.

**Figure 4.**Boxplots illustrating how GLAS scaling factors vary as a function of (

**a**) footprint eccentricity and (

**b**) laser campaign (Lc). Boxes indicate the median (black line), first (Q1) and third (Q3) quartiles (lower and upper box edges, respectively), up to 1.5-times the Interquartile Range (IQR) beyond the box (whiskers) and outliers greater than 1.5-times the IQR beyond the box (triangles). Note that whiskers extend to a point, which is no more than 1.5-times the IQR from the box; if no outliers exist beyond this range, the whiskers are truncated and do not always appear symmetrical as a result.

**Figure 5.**Comparison of ALS and GLAS unscaled (grey) and scaled (black) estimates of GF where scaling factors were predicted via an RF model trained with the best suited predictor attributes using (

**a**) all available training data and (

**b**) an optimized subset.

**Table 1.**Summary of predictor attributes used in testing the sensitivity of GLAS GF scaling factors.

Data | Units | Resolution | Description | Reference |
---|---|---|---|---|

Aspect | ° | 30 m | Derived from Shuttle Radar Topography Mission (SRTM) elevation product. Aspect is the direction of the maximum rate of change in the z-value from each cell in a raster surface. | [38] |

Slope | ° | 30 m | Derived from SRTM elevation product. Slope is the rate of maximum change in z-value from each cell. | [38] |

Elevation | m | 30 m | National SRTM Digital Elevation Model (DEM), Version 1.0. | [39] |

Valley Bottom Flatness (VBF) | - | 30 m | Derived from SRTM DEM. VBF is a topographic index that identifies areas of deposited material at a range of scales based on the observations that valley bottoms are low and flat relative to their surroundings and that large valley bottoms are flatter than smaller ones. | [40] |

Vegetation Continuous Fields (VCF) | % | 250 m | Derived from Moderate Resolution Imaging Spectroradiometer (MODIS) MOD44B product. The VCF collection contains proportional estimates for vegetative cover types: woody vegetation, herbaceous vegetation and bare ground. | [41] |

Vegetation classification | - | 100 m | The National Vegetation Information System (NVIS) is a comprehensive data system that provides information on the extent and distribution of vegetation types in Australian landscapes based on extensive field data acquisition. | [42] |

Vegetation height | m | 250 m | Derived product based on the integration of GLAS data with other Australian inventory products. | [43] |

Land cover classification | - | 250 m | The National Dynamic Land Cover Dataset of Australia is the first nationally-consistent and thematically-comprehensive land cover reference for Australia based on MODIS data. | [31] |

Soil type | - | 250 m | Based on extensive field acquisitions, the Atlas of Australian Soils was compiled in the 1960s to provide a consistent national description of Australia’s soils. | [44] |

Soil depth | m | 90 m | Derived from extensive field acquisitions and spectroscopic measurements, soil depth profile (A and B horizons) | [45] |

Soil nitrogen | % | 90 m | Derived from extensive field acquisitions and spectroscopic measurements, a mass fraction of total nitrogen in the soil by weight. | [45] |

Soil phosphorus | % | 90 m | Derived from extensive field acquisitions and spectroscopic measurements, a mass fraction of total phosphorus in the soil by weight. | [45] |

Soil pH | - | 90 m | Derived from extensive field acquisitions and spectroscopic measurements, a pH of 1:5 soil/0.01M calcium chloride (CaCl_{2}) extract. | [45] |

**Table 2.**Independent predictor attribute importance as determined by a measure of the Sum of Squared Residuals (SSR). Note, residuals are expressed as a percentage form the true value. The fit type ‘L’ represents a linear fit, whereas ‘NL’ represents a non-linear fit, as determined from the Harvey-Collier (HC) test.

Attribute | SSR | Fit Type |
---|---|---|

Phosphorus | 0.01 | L |

Height | 0.01 | L |

Nitrogen | 0.06 | L |

VCF | 0.15 | L |

Slope | 0.16 | NL |

Aspect | 0.20 | L |

Elevation | 0.22 | L |

pH | 0.44 | L |

NVIS | 0.70 | L |

Soil | 1.53 | L |

Depth | 2.00 | L |

VBF | - | L |

Land cover | - | L |

**Table 3.**Summary statistics (and percent change) for comparisons of ALS and GLAS estimates of GF for unscaled and scaled results per RF predicted scaling factors made across all available GLAS footprints and an optimized subset of GLAS footprints. Note: N is the population size, R

^{2}the coefficient of determination, RMSE the root mean squared error, F

_{2}the fraction of predictions within a factor of 2 of observations, F

_{B}the fractional bias and $\overline{{\mathrm{P}}_{\mathrm{B}}}$ the mean prediction bias.

GLAS GF | Dataset | N | R^{2} | RMSE | F_{2} | F_{B} | $\overline{{\mathbf{P}}_{\mathbf{B}}}$ |
---|---|---|---|---|---|---|---|

Unscaled | All Data | 309 | 0.77 | 0.18 | 0.32 | 0.20 | −0.09 |

Scaled | 0.88 | 0.11 | 0.59 | −0.01 | 0.01 | ||

% difference | 14 | −39 | 84 | −105 | 111 | ||

Unscaled | Optimized Data | 102 | 0.83 | 0.16 | 0.34 | 0.21 | −0.10 |

Scaled | 0.89 | 0.09 | 0.67 | −0.01 | 0.01 | ||

% difference | 7 | −43 | 97 | −105 | 90 |

© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Mahoney, C.; Hopkinson, C.; Kljun, N.; Van Gorsel, E.
Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems. *Remote Sens.* **2017**, *9*, 59.
https://doi.org/10.3390/rs9010059

**AMA Style**

Mahoney C, Hopkinson C, Kljun N, Van Gorsel E.
Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems. *Remote Sensing*. 2017; 9(1):59.
https://doi.org/10.3390/rs9010059

**Chicago/Turabian Style**

Mahoney, Craig, Chris Hopkinson, Natascha Kljun, and Eva Van Gorsel.
2017. "Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems" *Remote Sensing* 9, no. 1: 59.
https://doi.org/10.3390/rs9010059