# Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

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## Abstract

**:**

^{2}and 2.0 kg/m

^{2}for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively. We would suggest implementing the transects method for field sampling and establishing end points of these transects with a highly accurate positioning system. The study demonstrated the possibility of assessing biophysical variations of mangroves using remotely-sensed data.

## 1. Introduction

## 2. Data and Methods

#### 2.1. Study Area

#### 2.2. Field Sampling, Satellite Data and Predictor Variables

#### 2.2.1. Remotely-Sensed Data and Predictor Variables

#### 2.2.2. Estimating the Leaf Area Index

_{L}) between tree crowns and total gaps (g

_{T}), considering the relation to neighbouring features. For example, if the area of the gap is less than 50 pixels and it is surrounded by vegetation, it is classified as a small gap within a tree. Finally, a total number of pixels for large gaps and small gaps was summed to obtain g

_{T}(see Heenkenda et al. [32] for the detailed description).

_{f}) that is the proportion of the ground area covered by the vertical projection of foliage and branches [30,31,34] and the crown cover (f

_{c}) were calculated as Equations (2) and (3).

_{L}is the total number of pixels of large gaps (gaps between tree crowns), g

_{T}is the total number of pixels of gaps, f

_{c}is the crown cover, f

_{f}is the fraction of the foliage cover and $\sum}\mathrm{Pixels$ is the total number of pixels of the photograph.

_{f}is the fraction of the foliage coverage and f

_{c}is the crown cover.

_{t}) includes the contribution from woody elements to the total plant cover. Hence, it provides an overestimation for leaf area index [30]. The effective plant area index was estimated using the modified version of the Beer–Lambert law as specified in Equation (5).

_{c}is the crown cover; ф is the crown porosity; and k is the canopy extinction coefficient.

_{t}is the effective plant area index.

#### 2.2.3. Estimating the Above Ground Biomass

_{10}(Biomass). Finally, the calculated above ground biomass values were converted to AGB per square meter (unit = kg/m

^{2}) considering the extent of each plot.

#### 2.3. Predicting LAI and AGB

#### 2.4. Accuracy Assessment

## 3. Results

^{2}). The R

^{2}of LAI equals 0.96, and the log-transformed AGB showed 0.9. Once outliers were removed, the remaining field samples were normally distributed.

#### 3.1. Predicting AGB and LAI

^{2}; however, once predicted AGB over the study area, the RMSE was 2.2 kg/m

^{2}. The correlation coefficient obtained with respect to validation samples was 0.4 (Table 4). Hence, it can be concluded that the model was over fitted. The RMSEP for a 5-m spatial resolution was 1.7 kg/m

^{2}, and the RMSE with respect to validation data was 2.0 kg/m

^{2}with strong linear correlation between predicted and sampled AGB values.

^{2}to 425.5 kg/m

^{2}with a mean value of 22.5 kg/m

^{2}and a standard deviation of 8.1 kg/m

^{2}(Figure 5C). AGB values with a 5-m spatial resolution ranged from 0.12 kg/m

^{2}to 423.2 kg/m

^{2}with mean value of 18.4 kg/m

^{2}and a standard deviation of 7.2 kg/m

^{2}(Figure 5D). At both instances, the AGB values showed a skewed distribution rather than a normal distribution. The majority of the data (more than 70%) were in between 30 kg/m

^{2}and 267 kg/m

^{2}. The main reason should be the extremely large, multi-stemmed Avicenna marina and Rhizophora stylosa mangrove trees in this forest. They were spread over a large area without secondary forest underneath. The highest measured, as well as highest predicted AGB values represent these areas (423.2 kg/m

^{2}and 425.5 kg/m

^{2}).

#### 3.2. Accuracy Assessment

## 4. Discussion

#### 4.1. Predicting LAI

_{t}. Although the used value (0.5) represents the average of already published k values for mangroves around the world, this might not be the correct value for this area. Further, LAI values vary with the spatial resolutions of remote sensing images used for data processing. Kamal et al. [15] recently confirmed that the accuracy of LAI estimation was site specific and depends on the pixel size of the remotely-sensed images, and their study even indicated two different LAI distribution patterns for homogeneous and heterogeneous mangrove forests.

#### 4.2. Predicating AGB

^{2}to 40.66 kg/m

^{2}with the average value of 24.77 kg/m

^{2}, and artificially-restored Sonneratia apetala mangroves ranged from 3.4 kg/m

^{2}to 23.42 kg/m

^{2}with the average value of 11.38 kg/m

^{2}[16]. However, in this study, we did not find Kandelia candel and Sonneratia apetala mangroves, and thus, this limits the comparison possibilities.

## 5. Conclusions and Recommendations

^{2}and 2.0 kg/m

^{2}for a 2-m and a 5-m spatial resolution, and the correlation coefficients were 0.4 and 0.8, respectively.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Rapid Creek coastal mangrove forest, Darwin, Northern Territory, Australia. The distribution of field sampling plots (5 m × 5 m) is shown in the map (the sizes of magenta squares are not to the scale). Aerial photographs © Northern Territory Government.

**Figure 2.**Mangrove trees inside the Rapid Creek forest; (

**A**–

**C**) small, newly-regenerated mangrove trees; (

**D**,

**E**) large, multi-stemmed mangrove trees; (

**F**,

**G**) densely-clustered areas.

**Figure 3.**Normal score plots of field estimates of (

**A**) leaf area index and (

**B**) log-transformed above ground biomass.

**Figure 4.**Cross-validated predictions for the normalized leaf area index (LAI) and above ground biomass (AGB): (

**A**) predicted versus field measured normalized LAI with 2-m spatial resolution predictor variables; (

**B**) predicted versus field measured normalized LAI with 5-m spatial resolution predictor variables; (

**C**) predicted versus field measured normalized LogAGB with 2-m spatial resolution predictor variables; (

**D**) predicted versus field measured normalized LogAGB with 5-m spatial resolution predictor variables. (RMSEP, root mean square error of prediction).

**Figure 5.**Predicted maps: (

**A**) leaf area index with a 2-m spatial resolution; (

**B**) leaf area index with a 5-m spatial resolution; (

**C**) above ground biomass with a 2-m spatial resolution; and (

**D**) above ground biomass with a 5-m spatial resolution; using the partial least squares regression algorithm. The distribution of field sampling plots is shown in the maps (the sizes of the black squares are not to scale).

Band | Spectral Range (nm) | Spatial Resolution (m) |
---|---|---|

Panchromatic | 447–808 | 0.5 |

Coastal | 396–458 | 2 |

Blue | 442–515 | |

Green | 506–586 | |

Yellow | 584–632 | |

Red | 624–694 | |

Red-edge | 699–749 | |

NIR1 | 765–901 | |

NIR2 | 856–1043 |

**Table 2.**Predictor variables generated from the WorldView-2 multispectral image for estimating above ground biomass and leaf area index. NIR1, NIR2, red edge, red and green: surface reflectance of Near Infrared 1, Near Infrared 2, red-edge, red and green wavelength regions, respectively.

Vegetation Index | Band Relationship | Source |
---|---|---|

Normalized Difference Vegetation Index (NDVI) | $\left(\mathrm{NIR}1-\mathrm{red}\right)/\left(\mathrm{NIR}1+\mathrm{red}\right)$ | Rouse et al. [24] and Ahamed et al. [10] |

Normalized Difference Red Edge index (NDRE) | $\left(\mathrm{NIR}1-\mathrm{Red}\text{}\mathrm{Edge}\right)/\left(\mathrm{NIR}1+\mathrm{Red}\text{}\mathrm{Edge}\right)$ | Ahamed et al. [10] and Barnes et al. [25] |

Green Normalized Difference Vegetation Index (GNDVI) | $\left(\mathrm{NIR}1-\mathrm{green}\right)/\left(\mathrm{NIR}1+\mathrm{green}\right)$ | Ahamed et al. [10], Li et al. [26] and Gitelson et al. [27] |

Green Normalized Difference Vegetation Index 2 (GNDVI2) | $\left(\mathrm{NIR}2-\mathrm{green}\right)/\left(\mathrm{NIR}2+\mathrm{green}\right)$ | Mutanga et al. [28] |

Normalized Difference Vegetation Index 2 (NDVI2) | $\left(\mathrm{NIR}2-\mathrm{red}\right)/\left(\mathrm{NIR}2+\mathrm{red}\right)$ | Mutanga et al. [28] |

Normalized Difference Red Edge index 2 (NDRE2) | $\left(\mathrm{NIR}2-\mathrm{Red}\text{}\mathrm{Edge}\right)/\left(\mathrm{NIR}2+\mathrm{Red}\text{}\mathrm{Edge}\right)$ | Mutanga et al. [28] |

Renormalized Vegetation Index (RDVI) | $\left(\mathrm{NIR}1-\mathrm{red}\right)/\sqrt{\mathrm{NIR}1+\mathrm{red}}$ | Li et al. [26] |

Ratio Vegetation Index (RVI) | $\mathrm{NIR}1/\mathrm{red}$ | Li et al. [26] |

Modified Soil Adjusted Vegetation Index (MSAVI) | $\left(1+0.5\right)\left(\mathrm{NIR}1-\mathrm{red}\right)/\left(\mathrm{NIR}1+\mathrm{red}+0.5\right)$ | Qi et al. [29] |

**Table 3.**Log

_{10}-transformed allometric relationships used for different mangrove species. The equations are in the form of $lo{g}_{10}\left(Biomass\right)={B}_{0}+{B}_{1}*lo{g}_{10}\left(DBH\right)$; where DBH is the diameter at breast height; B

_{0}and B

_{1}are regression coefficients. These equations are specific to Northern Australia [39,40,41], North-eastern Queensland, Australia [41], and Sri Lanka [20] (biomass in kg and DBH in cm).

Mangrove Species | B_{0} | B_{1} | Study |
---|---|---|---|

Avicennia marina | −0.511 | 2.113 | Comley and McGuinness [40] |

Bruguiera exaristata | −0.643 | 2.141 | Comley and McGuinness [40] |

Ceriops tagal | −0.7247 | 2.3379 | Clough and Scott [41] |

Lumnitzera racemosa | 1.788 | 2.529 | Perera and Amarasinghe [20] |

Rhizophora stylosa | −0.696 | 2.465 | Comley and McGuinness [40] |

Sonneratia alba | −0.634 | 2.248 | Bai [39] |

Excoecaria agallocha var. ovalis | −0.634 | 2.248 | Bai [39] |

**Table 4.**Root mean square errors (RMSEs) and correlation coefficients (r) for above ground biomass and leaf area index maps with respect to the validation samples.

Biophysical Variable | RMSE | Correlation Coefficient | ||
---|---|---|---|---|

Spatial resolution | 2 m | 5 m | 2 m | 5 m |

Above ground biomass (AGB) | 2.2 kg/m^{2} | 2.0 kg/m^{2} | 0.4 | 0.8 |

Leaf area index (LAI) | 0.75 | 0.78 | 0.7 | 0.8 |

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**MDPI and ACS Style**

Heenkenda, M.K.; Maier, S.W.; Joyce, K.E.
Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia. *J. Imaging* **2016**, *2*, 24.
https://doi.org/10.3390/jimaging2030024

**AMA Style**

Heenkenda MK, Maier SW, Joyce KE.
Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia. *Journal of Imaging*. 2016; 2(3):24.
https://doi.org/10.3390/jimaging2030024

**Chicago/Turabian Style**

Heenkenda, Muditha K., Stefan W. Maier, and Karen E. Joyce.
2016. "Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia" *Journal of Imaging* 2, no. 3: 24.
https://doi.org/10.3390/jimaging2030024